[ { "path": "README.md", "content": "![book cover](book-cover.png)\n\n[ebook repo](https://github.com/ageron/handson-ml/blob/master/15_autoencoders.ipynb)\n\n# book chapters\n1) Intro to Machine Learning\n2) Example end-to-end Machine Learning project (California Housing dataset)\n3) Basic Classification\n4) Training Techniques\n5) Support Vector Machines\n6) Decision Trees\n7) Ensemble Learning & Random Forests\n8) Dimensionality Reduction\n9) TensorFlow Installation & Checkout\n10) TensorFlow & Neural Nets\n11) TensorFlow Training\n12) TensorFlow on Distributed Hardware\n13) Convolutional Neural Nets\n14) Recurrent Neural Nets\n15) Autoencoders\n16) Reinforcement Learning\n" }, { "path": "_config.yml", "content": "theme: jekyll-theme-tactile" }, { "path": "ch02 - cal housing analysis.html", "content": "\n\n\nch02 - cal housing analysis\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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Get Dataset & Create Workspace

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In [1]:
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import os\nimport tarfile\nfrom six.moves import urllib\n\nimport pandas as pd\nimport numpy  as np\n
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In [2]:
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DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/"\nHOUSING_PATH = "datasets/housing"\nHOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + "/housing.tgz"\n
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In [3]:
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def fetch_housing_data(\n    housing_url=HOUSING_URL, \n    housing_path=HOUSING_PATH):\n    \n    # create datasets/housing directory if needed\n    if not os.path.isdir(housing_path):\n        os.makedirs(housing_path)\n\n    tgz_path = os.path.join(housing_path, "housing.tgz")\n    \n    # retrieve tarfile\n    urllib.request.urlretrieve(housing_url, tgz_path)\n    \n    # extract tarfile & close path\n    housing_tgz = tarfile.open(tgz_path)\n    housing_tgz.extractall(path=housing_path)\n    housing_tgz.close()\n
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In [4]:
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def load_housing_data(\n    housing_path=HOUSING_PATH):\n    \n    csv_path = os.path.join(housing_path, "housing.csv")\n    return pd.read_csv(csv_path)\n
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In [5]:
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# do it\n#fetch_housing_data() -- already downloaded - static dataset\nhousing = load_housing_data()\n
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Data structure - quick peek

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In [6]:
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housing.head()\n
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Out[6]:
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
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So... what's in the dataset?

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In [7]:
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# housing is a Pandas DataFrame.\n# untouched datafile: 20640 records, 10 cols (9 float, 1 text)\nhousing.info()\n
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<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20640 entries, 0 to 20639\nData columns (total 10 columns):\nlongitude             20640 non-null float64\nlatitude              20640 non-null float64\nhousing_median_age    20640 non-null float64\ntotal_rooms           20640 non-null float64\ntotal_bedrooms        20433 non-null float64\npopulation            20640 non-null float64\nhouseholds            20640 non-null float64\nmedian_income         20640 non-null float64\nmedian_house_value    20640 non-null float64\nocean_proximity       20640 non-null object\ndtypes: float64(9), object(1)\nmemory usage: 1.6+ MB\n
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In [8]:
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# let's see if ocean_proximity can be lumped into categories:\nhousing['ocean_proximity'].value_counts()\n
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Out[8]:
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<1H OCEAN     9136\nINLAND        6551\nNEAR OCEAN    2658\nNEAR BAY      2290\nISLAND           5\nName: ocean_proximity, dtype: int64
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In [9]:
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# percentiles analysis of each feature\nhousing.describe()\n
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Out[9]:
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count20640.00000020640.00000020640.00000020640.00000020433.00000020640.00000020640.00000020640.00000020640.000000
mean-119.56970435.63186128.6394862635.763081537.8705531425.476744499.5396803.870671206855.816909
std2.0035322.13595212.5855582181.615252421.3850701132.462122382.3297531.899822115395.615874
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.000000
25%-121.80000033.93000018.0000001447.750000296.000000787.000000280.0000002.563400119600.000000
50%-118.49000034.26000029.0000002127.000000435.0000001166.000000409.0000003.534800179700.000000
75%-118.01000037.71000037.0000003148.000000647.0000001725.000000605.0000004.743250264725.000000
max-114.31000041.95000052.00000039320.0000006445.00000035682.0000006082.00000015.000100500001.000000
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In [10]:
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# feature histograms\n%matplotlib inline\nimport matplotlib.pyplot as plt\n\nhousing.hist(bins=50, figsize=(20,15))\n
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Out[10]:
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array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a792b5438>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75f1c2e8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75f39d68>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75eaf7b8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75e7acc0>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75e40438>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75e0a860>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75dce198>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75d1d1d0>]], dtype=object)
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Create a test set

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In [11]:
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# split dataset into training (80%) and test (20%) subsets\n\nimport numpy as np\n\ndef split_train_test(\n    data, test_ratio):\n\n    shuffled_indices = np.random.permutation(len(data))\n\n    test_set_size = int(len(data) * test_ratio)\n\n    test_indices = shuffled_indices[:test_set_size]\n    train_indices = shuffled_indices[test_set_size:]\n\n    return data.iloc[train_indices], data.iloc[test_indices]\n
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In [12]:
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train_set, test_set = split_train_test(housing, 0.2)\n
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In [13]:
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print(len(train_set), "train +", len(test_set), "test")\n
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16512 train + 4128 test\n
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In [14]:
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# create method for ensuring consistent test sets across multiple runs \n# (new test sets won't contain instances in previous training sets.)\n\n# example method:\n# compute hash of each instance\n# keep only the last byte\n# include instance in test set if value < 51 (20% of 256)\n\nimport hashlib\n\ndef test_set_check(\n    identifier, test_ratio, hash):\n    \n    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n\ndef split_train_test_by_id(\n    data, test_ratio, id_column, hash=hashlib.md5):\n\n    ids = data[id_column]\n    in_test_set = ids.apply(\n        lambda id_: test_set_check(\n            id_, test_ratio, hash))\n\n    return data.loc[~in_test_set], data.loc[in_test_set]\n
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In [15]:
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# housing dataset doesn't have ID attribute,\n# so let's add an index to it.\n\nhousing_with_id = housing.reset_index()\n\ntrain_set, test_set = split_train_test_by_id(\n    housing_with_id, 0.2, "index")\n
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In [16]:
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train_set.head()\n
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Out[16]:
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indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
00-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
11-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
22-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
33-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
66-122.2537.8452.02535.0489.01094.0514.03.6591299200.0NEAR BAY
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In [17]:
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test_set.head()\n
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Out[17]:
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indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
44-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
55-122.2537.8552.0919.0213.0413.0193.04.0368269700.0NEAR BAY
1111-122.2637.8552.03503.0752.01504.0734.03.2705241800.0NEAR BAY
2020-122.2737.8540.0751.0184.0409.0166.01.3578147500.0NEAR BAY
2323-122.2737.8452.01688.0337.0853.0325.02.180699700.0NEAR BAY
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In [18]:
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# a better index:\n# let's use longitude & latitude to build stable identifier\n\nhousing_with_id["id"] = housing["longitude"] * 1000 + housing["latitude"]\n\ntrain_set, test_set = split_train_test_by_id(\n    housing_with_id, 0.2, "id")\n
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In [19]:
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train_set.head()\n
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Out[19]:
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indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximityid
00-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY-122192.12
11-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY-122182.14
22-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY-122202.15
33-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY-122212.15
44-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY-122212.15
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In [20]:
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test_set.head()\n
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Out[20]:
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indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximityid
88-122.2637.8442.02555.0665.01206.0595.02.0804226700.0NEAR BAY-122222.16
1010-122.2637.8552.02202.0434.0910.0402.03.2031281500.0NEAR BAY-122222.15
1111-122.2637.8552.03503.0752.01504.0734.03.2705241800.0NEAR BAY-122222.15
1212-122.2637.8552.02491.0474.01098.0468.03.0750213500.0NEAR BAY-122222.15
1313-122.2637.8452.0696.0191.0345.0174.02.6736191300.0NEAR BAY-122222.16
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In [21]:
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# another option: scikit-learn splitters\n\nfrom sklearn.model_selection import train_test_split\n\ntrain_set, test_set = train_test_split(\n    housing, test_size=0.2, random_state=42)\n
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In [22]:
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test_set.head()\n
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Out[22]:
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
20046-119.0136.0625.01505.0NaN1392.0359.01.681247700.0INLAND
3024-119.4635.1430.02943.0NaN1565.0584.02.531345800.0INLAND
15663-122.4437.8052.03830.0NaN1310.0963.03.4801500001.0NEAR BAY
20484-118.7234.2817.03051.0NaN1705.0495.05.7376218600.0<1H OCEAN
9814-121.9336.6234.02351.0NaN1063.0428.03.7250278000.0NEAR OCEAN
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In [23]:
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train_set.head()\n
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Out[23]:
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
14196-117.0332.7133.03126.0627.02300.0623.03.2596103000.0NEAR OCEAN
8267-118.1633.7749.03382.0787.01314.0756.03.8125382100.0NEAR OCEAN
17445-120.4834.664.01897.0331.0915.0336.04.1563172600.0NEAR OCEAN
14265-117.1132.6936.01421.0367.01418.0355.01.942593400.0NEAR OCEAN
2271-119.8036.7843.02382.0431.0874.0380.03.554296500.0INLAND
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In [24]:
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# does sampling plan have a sampling bias?\n# each strata in test dataset should mimic reality\n\nhousing['median_income'].hist(bins=5)\n
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Out[24]:
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<matplotlib.axes._subplots.AxesSubplot at 0x7f15f7250588>
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In [25]:
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housing.describe()\n
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Out[25]:
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count20640.00000020640.00000020640.00000020640.00000020433.00000020640.00000020640.00000020640.00000020640.000000
mean-119.56970435.63186128.6394862635.763081537.8705531425.476744499.5396803.870671206855.816909
std2.0035322.13595212.5855582181.615252421.3850701132.462122382.3297531.899822115395.615874
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.000000
25%-121.80000033.93000018.0000001447.750000296.000000787.000000280.0000002.563400119600.000000
50%-118.49000034.26000029.0000002127.000000435.0000001166.000000409.0000003.534800179700.000000
75%-118.01000037.71000037.0000003148.000000647.0000001725.000000605.0000004.743250264725.000000
max-114.31000041.95000052.00000039320.0000006445.00000035682.0000006082.00000015.000100500001.000000
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In [26]:
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housing["income_cat"]=np.ceil(housing["median_income"]/1.5)\n\nhousing["income_cat"].where(housing["income_cat"]<5, 5.0, inplace=True)\n
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In [27]:
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housing.describe()\n
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Out[27]:
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueincome_cat
count20640.00000020640.00000020640.00000020640.00000020433.00000020640.00000020640.00000020640.00000020640.00000020640.000000
mean-119.56970435.63186128.6394862635.763081537.8705531425.476744499.5396803.870671206855.8169093.006686
std2.0035322.13595212.5855582181.615252421.3850701132.462122382.3297531.899822115395.6158741.054618
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.0000001.000000
25%-121.80000033.93000018.0000001447.750000296.000000787.000000280.0000002.563400119600.0000002.000000
50%-118.49000034.26000029.0000002127.000000435.0000001166.000000409.0000003.534800179700.0000003.000000
75%-118.01000037.71000037.0000003148.000000647.0000001725.000000605.0000004.743250264725.0000004.000000
max-114.31000041.95000052.00000039320.0000006445.00000035682.0000006082.00000015.000100500001.0000005.000000
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In [28]:
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from sklearn.model_selection import StratifiedShuffleSplit\n\nsplit = StratifiedShuffleSplit(\n    n_splits=1, test_size=0.2, random_state=42)\n\nfor train_index, test_index in split.split(housing, housing["income_cat"]):\n    strat_train_set = housing.loc[train_index]\n    strat_test_set  = housing.loc[test_index]\n
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In [29]:
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# review income category proportions\nhousing["income_cat"].value_counts() / len(housing)\n
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Out[29]:
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3.0    0.350581\n2.0    0.318847\n4.0    0.176308\n5.0    0.114438\n1.0    0.039826\nName: income_cat, dtype: float64
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In [30]:
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# remove income_cat attribute (return dataset to original state)\n\nfor set in (strat_train_set, strat_test_set):\n    set.drop(["income_cat"], axis=1, inplace=True)\n
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Visualization

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In [31]:
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housing = strat_train_set.copy()\n\n# first: basic geographic distribution\n\nhousing.plot(kind="scatter", x="longitude", y="latitude", alpha=0.1)\n
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Out[31]:
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<matplotlib.axes._subplots.AxesSubplot at 0x7f15f71c4668>
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In [32]:
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# next: housing prices.\n# color = price \n# radius = population\n# use predefined "jet" color map \n\nhousing.plot(\n    kind="scatter", \n    x="longitude", \n    y="latitude", \n    alpha=0.4,\n    #s=housing["population"].apply(lambda n: n/100), \n    s=housing["population"]/100,\n    label="population",\n    c="median_house_value", \n    cmap=plt.get_cmap("jet"), \n    colorbar=True,\n)\nplt.legend()\n
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Out[32]:
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<matplotlib.legend.Legend at 0x7f15f5eff1d0>
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Correlations

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In [33]:
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# next: look for correlatons to median house value.\n\ncorr_matrix = housing.corr()\n\ncorr_matrix['median_house_value'].sort_values(ascending=False)\n
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Out[33]:
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median_house_value    1.000000\nmedian_income         0.687160\ntotal_rooms           0.135097\nhousing_median_age    0.114110\nhouseholds            0.064506\ntotal_bedrooms        0.047689\npopulation           -0.026920\nlongitude            -0.047432\nlatitude             -0.142724\nName: median_house_value, dtype: float64
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In [34]:
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# another way of looking for correlations: scatter_matrix\n# focus on top 3 factors from above\n\nfrom pandas.tools.plotting import scatter_matrix\n\nattributes = ["median_house_value", "median_income", "total_rooms",\n"housing_median_age"]\n\nscatter_matrix(housing[attributes], figsize=(12, 8))\n
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Out[34]:
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array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5fc20f0>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5eda860>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f602f898>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5ff2860>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5f7dd68>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5e836d8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f460e710>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f45d50b8>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15f45224e0>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f454ab38>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f449ec88>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f44e5898>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15f44380b8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f4450be0>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f43c2da0>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f43960f0>]], dtype=object)
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In [35]:
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# median house value to median income seems to be the most promising.\n# let's zoom in.\n\nhousing.plot(\n    kind="scatter", x="median_income", y="median_house_value",\n    alpha=0.1)\n
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Out[35]:
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<matplotlib.axes._subplots.AxesSubplot at 0x7f15f41b1a90>
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In [36]:
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# combine some attributes to create more useful ones\n# then rebuild the correlation matrix.\n\nhousing["rooms_per_household"] = housing["total_rooms"]/housing["households"]\nhousing["bedrooms_per_room"] = housing["total_bedrooms"]/housing["total_rooms"]\nhousing["population_per_household"]=housing["population"]/housing["households"]\n\ncorr_matrix = housing.corr()\ncorr_matrix['median_house_value'].sort_values(ascending=False)\n\n# *** NOTE: rooms_per_household corr (in book) show more improvement, ~0.199\n# compared to our 0.146. Not sure of root cause yet. ***\n
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Out[36]:
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median_house_value          1.000000\nmedian_income               0.687160\nrooms_per_household         0.146285\ntotal_rooms                 0.135097\nhousing_median_age          0.114110\nhouseholds                  0.064506\ntotal_bedrooms              0.047689\npopulation_per_household   -0.021985\npopulation                 -0.026920\nlongitude                  -0.047432\nlatitude                   -0.142724\nbedrooms_per_room          -0.259984\nName: median_house_value, dtype: float64
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Data Cleanup

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In [37]:
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# revert to clean copy of stratified training dataset\n# separate predictors from labels\n\nhousing = strat_train_set.drop("median_house_value", axis=1)\n\nhousing_labels = strat_train_set["median_house_value"].copy()\n
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In [38]:
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# 'total bedrooms' has some missing values - fix\n# can use DataFrame dropna(), drop(), fillna()\n\n# use Scikit-Learn class to handle missing values\nfrom sklearn.preprocessing import Imputer\nimputer = Imputer(strategy="median")\n
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In [39]:
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# drop ocean_proximity attribute, since it's non-numeric.\n# then fit to training data.\n\nhousing_num = housing.drop("ocean_proximity", axis=1)\nimputer.fit(housing_num)\n
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Out[39]:
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Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)
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In [40]:
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# now what do we have?\nimputer.statistics_\n
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Out[40]:
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array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n        1164.    ,   408.    ,     3.5409])
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In [41]:
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housing_num.median().values\n
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Out[41]:
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array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n        1164.    ,   408.    ,     3.5409])
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In [42]:
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# update training set by replacing missing values with learned medians\nX = imputer.transform(housing_num)\n
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In [43]:
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pd.DataFrame(X, columns=housing_num.columns).info()\n
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<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 16512 entries, 0 to 16511\nData columns (total 8 columns):\nlongitude             16512 non-null float64\nlatitude              16512 non-null float64\nhousing_median_age    16512 non-null float64\ntotal_rooms           16512 non-null float64\ntotal_bedrooms        16512 non-null float64\npopulation            16512 non-null float64\nhouseholds            16512 non-null float64\nmedian_income         16512 non-null float64\ndtypes: float64(8)\nmemory usage: 1.0 MB\n
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In [44]:
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# convert ocean_proximity feature to numbers using LabelEncoder.\n\nfrom sklearn.preprocessing import LabelEncoder\nencoder = LabelEncoder()\n\nhousing_cat = housing['ocean_proximity']\nhousing_cat_encoded = encoder.fit_transform(housing_cat)\nhousing_cat_encoded\n
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Out[44]:
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array([0, 0, 4, ..., 1, 0, 3])
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In [45]:
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# how is 'ocean_proximity' mapped?\nprint(encoder.classes_)\n
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['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n
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In [46]:
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# a better solution for categorical data: one-hot encoding\n\nfrom sklearn.preprocessing import OneHotEncoder\nencoder = OneHotEncoder()\n\n# output = SciPy sparse matrix, better for memory usage\n# if you need a dense NumPy array, call toarray()\n\nhousing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\nhousing_cat_1hot\n
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Out[46]:
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<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n\twith 16512 stored elements in Compressed Sparse Row format>
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Label Binarization:

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  • A shortcut (text categories => integer categories => one-hot vectors)
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In [47]:
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from sklearn.preprocessing import LabelBinarizer\nencoder = LabelBinarizer()\n\nhousing_cat_1hot = encoder.fit_transform(housing_cat)\nhousing_cat_1hot\n
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Out[47]:
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array([[1, 0, 0, 0, 0],\n       [1, 0, 0, 0, 0],\n       [0, 0, 0, 0, 1],\n       ..., \n       [0, 1, 0, 0, 0],\n       [1, 0, 0, 0, 0],\n       [0, 0, 0, 1, 0]])
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Custom Transformers:

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  • Create your own using SciKit-Learn classes
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  • implement fit(), transform() and fit_transform() methods
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  • (fit_transform comes for free by using TransformerMixin as a base class.)
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In [48]:
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from sklearn.base import BaseEstimator, TransformerMixin\n\nrooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n\nclass CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n    \n    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n    \n        self.add_bedrooms_per_room = add_bedrooms_per_room\n\n    def fit(self, X, y=None):\n        return self # nothing else to do\n\n    def transform(self, X, y=None):\n        rooms_per_household      = X[:, rooms_ix] / X[:, household_ix]\n        population_per_household = X[:, population_ix] / X[:, household_ix]\n\n        if self.add_bedrooms_per_room:\n            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n            return np.c_[X, \n                         rooms_per_household, \n                         population_per_household,\n                         bedrooms_per_room]\n        else:\n            return np.c_[X, \n                         rooms_per_household, \n                         population_per_household]\n\nattr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n\nhousing_extra_attribs = attr_adder.transform(housing.values)\n
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Feature Scaling

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  • Min-max scaling (normalization) = shift & rescale to [0,1]
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  • SciKit MinMaxScaler will do this for you.
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  • Standardization subtracts mean & divides by variance - result has unit variance
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  • SciKit StandardScaler does this for you.
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Pipelining

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  • SciKit Pipeline class helps to standardize the sequence of transforms\nyou need for your project.
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  • Pipelines = list of estimator steps. All but the last must be transformers\n(they must have fit_transform() method.)
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In [49]:
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# "DataFrameSelector" is a custom transformer class.\n# grabs the specified feature, drops the rest, converts the DF into a NumPy array.\n\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nclass DataFrameSelector(BaseEstimator, TransformerMixin):\n    def __init__ (self, attribute_names):\n        self.attribute_names = attribute_names\n    \n    def fit (self, X, y=None):\n        return self\n    \n    def transform (self, X):\n        return X[self.attribute_names].values\n    \n
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In [50]:
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from sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.preprocessing import StandardScaler\n\nnum_attribs = list(housing_num)\ncat_attribs = ['ocean_proximity']\n\nnum_pipeline = Pipeline([\n    ('selector',      DataFrameSelector(num_attribs)),\n    ('imputer',       Imputer(strategy="median")),\n    ('attribs_adder', CombinedAttributesAdder()),\n    ('std_scaler',    StandardScaler()),\n    ])\n\ncat_pipeline = Pipeline([\n    ('selector',      DataFrameSelector(cat_attribs)),\n    ('label_binarizer', LabelBinarizer()),\n])\n\nfull_pipeline = FeatureUnion(transformer_list =[\n    ('num_pipeline', num_pipeline),\n    ('cat_pipeline', cat_pipeline)\n])\n
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In [51]:
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# let's try it out:\n\nhousing_prepared = full_pipeline.fit_transform(housing)\nhousing_prepared\n
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Out[51]:
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array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n         0.        ,  0.        ],\n       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n         0.        ,  0.        ],\n       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n         0.        ,  1.        ],\n       ..., \n       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n         0.        ,  0.        ],\n       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n         0.        ,  0.        ],\n       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n         1.        ,  0.        ]])
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In [52]:
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housing_prepared.shape\n
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Out[52]:
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(16512, 16)
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Model Selection & Training

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In [53]:
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# let's start with a linear regression\n\nfrom sklearn.linear_model import LinearRegression\n\nlin_reg = LinearRegression()\nlin_reg.fit(housing_prepared, housing_labels)\n
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LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
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In [54]:
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# first try. NOT very accurate.\n\nsome_data          = housing.iloc[:5]\nsome_labels        = housing_labels.iloc[:5]\nsome_data_prepared = full_pipeline.transform(some_data)\n\nprint ("predictions:\\t", lin_reg.predict(some_data_prepared))\nprint ("labels:\\t", list(some_labels))\n
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predictions:\t [ 210644.60459286  317768.80697211  210956.43331178   59218.98886849\n  189747.55849879]\nlabels:\t [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n
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In [55]:
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# why? look at RMSE on whole training set.\n\nfrom sklearn.metrics import mean_squared_error\n\nhousing_predictions = lin_reg.predict(housing_prepared)\nlin_mse             = mean_squared_error(housing_labels, housing_predictions)\nlin_rmse            = np.sqrt(lin_mse)\n\nprint ("typical prediction error:\\t", lin_rmse)\n
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typical prediction error:\t 68628.1981985\n
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In [56]:
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# Hmmm. Not good. Underfit situation. \n# Let's try a more powerful model, like a Decision Tree.\n\nfrom sklearn.tree import DecisionTreeRegressor\n\ntree_reg = DecisionTreeRegressor()\ntree_reg.fit(housing_prepared, housing_labels)\n
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DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n           max_leaf_nodes=None, min_impurity_split=1e-07,\n           min_samples_leaf=1, min_samples_split=2,\n           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n           splitter='best')
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In [57]:
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# Zero error? No way...\n\nhousing_predictions = tree_reg.predict(housing_prepared)\ntree_mse = mean_squared_error(housing_labels, housing_predictions)\ntree_rmse = np.sqrt(tree_mse)\n\nprint ("typical prediction error:\\t", tree_rmse)\n
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typical prediction error:\t 0.0\n
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In [58]:
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# Use K-fold cross-validation\n# Train & eval Decision Tree model against 10 splits of training dataset\n# Returns 10 evaluation scores.\n\nfrom sklearn.model_selection import cross_val_score\n\nscores = cross_val_score(\n    tree_reg, \n    housing_prepared, \n    housing_labels,\n    scoring="neg_mean_squared_error", \n    cv=10)\n\nrmse_scores = np.sqrt(-scores)\n\ndef display_scores(scores):\n    print("Scores:", scores)\n    print("Mean:", scores.mean())\n    print("Standard deviation:", scores.std())\n    \ndisplay_scores(rmse_scores)\n
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Scores: [ 69368.62190153  66248.56520386  72284.6557095   68417.57732406\n  70049.44916939  74941.75765797  70236.59348749  69466.63688954\n  76140.22952307  70217.59755116]\nMean: 70737.1684418\nStandard deviation: 2815.58298405\n
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In [59]:
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# So, Decision Tree RMSE: mean ~71097, stdev 2165 (still sucks.)\n# compare to earlier Linear Regression:\n\nlin_scores = cross_val_score(\n    lin_reg,\n    housing_prepared,\n    housing_labels,\n    scoring="neg_mean_squared_error",\n    cv=10)\n\nlin_rmse_scores = np.sqrt(-lin_scores)\n\ndisplay_scores(lin_rmse_scores)\n
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Scores: [ 66782.73843989  66960.118071    70347.95244419  74739.57052552\n  68031.13388938  71193.84183426  64969.63056405  68281.61137997\n  71552.91566558  67665.10082067]\nMean: 69052.4613635\nStandard deviation: 2731.6740018\n
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In [60]:
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# Yep, DT overfit is just about as bad. (RMSE mean 69052, stdev 2731)\n# Let's try a RandomForest.\n\nfrom sklearn.ensemble import RandomForestRegressor\n\nforest_reg = RandomForestRegressor()\nforest_reg.fit(housing_prepared, housing_labels)\n\nforest_scores = cross_val_score(\n    forest_reg,\n    housing_prepared,\n    housing_labels,\n    scoring="neg_mean_squared_error",\n    cv=10)\n\nforest_rmse_scores = np.sqrt(-forest_scores)\n\ndisplay_scores(forest_rmse_scores)\n
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Scores: [ 52480.82629458  50035.41358467  53747.69332484  55053.95194112\n  51800.65152945  55919.01705209  52226.75176017  50912.82366116\n  55708.47271341  51931.81080304]\nMean: 52981.7412665\nStandard deviation: 1929.32402243\n
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In [61]:
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# OK, RandomForest is a little better.\n# RMSE mean ~52495, stdev ~1569\n
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Fine-Tuning Model with Grid Search of Hyperparameters

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In [62]:
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from sklearn.model_selection import GridSearchCV\n\nparam_grid = [\n    {'n_estimators': [3, 10, 30], \n     'max_features': [2, 4, 6, 8]},\n    {'bootstrap': [False], # bootstrap = True = default setting\n     'n_estimators': [3, 10], \n     'max_features': [2, 3, 4]},\n]\n\nforest_reg  = RandomForestRegressor()\n\ngrid_search = GridSearchCV(\n    forest_reg, \n    param_grid, \n    cv=5,\n    scoring = 'neg_mean_squared_error')\n\ngrid_search.fit(housing_prepared, housing_labels)\n
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Out[62]:
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GridSearchCV(cv=5, error_score='raise',\n       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n           max_features='auto', max_leaf_nodes=None,\n           min_impurity_split=1e-07, min_samples_leaf=1,\n           min_samples_split=2, min_weight_fraction_leaf=0.0,\n           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n           verbose=0, warm_start=False),\n       fit_params={}, iid=True, n_jobs=1,\n       param_grid=[{'max_features': [2, 4, 6, 8], 'n_estimators': [3, 10, 30]}, {'bootstrap': [False], 'max_features': [2, 3, 4], 'n_estimators': [3, 10]}],\n       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n       scoring='neg_mean_squared_error', verbose=0)
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In [63]:
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# Best combination of parameters?\ngrid_search.best_params_\n
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Out[63]:
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{'max_features': 6, 'n_estimators': 30}
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In [64]:
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# Best estimator?\ngrid_search.best_estimator_\n
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Out[64]:
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RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n           max_features=6, max_leaf_nodes=None, min_impurity_split=1e-07,\n           min_samples_leaf=1, min_samples_split=2,\n           min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=1,\n           oob_score=False, random_state=None, verbose=0, warm_start=False)
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In [65]:
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# Evaluation scores:\n\ncvres = grid_search.cv_results_\n\nfor mean_score, params in zip(cvres["mean_test_score"],\n                              cvres["params"]):\n    print(np.sqrt(-mean_score), params)\n
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63492.9975584 {'max_features': 2, 'n_estimators': 3}\n55677.1037862 {'max_features': 2, 'n_estimators': 10}\n52917.801725 {'max_features': 2, 'n_estimators': 30}\n60442.2787178 {'max_features': 4, 'n_estimators': 3}\n53209.7111283 {'max_features': 4, 'n_estimators': 10}\n50621.1191846 {'max_features': 4, 'n_estimators': 30}\n58591.8196313 {'max_features': 6, 'n_estimators': 3}\n52353.3606044 {'max_features': 6, 'n_estimators': 10}\n49838.3807 {'max_features': 6, 'n_estimators': 30}\n58615.6100561 {'max_features': 8, 'n_estimators': 3}\n51726.2593734 {'max_features': 8, 'n_estimators': 10}\n50074.3050139 {'max_features': 8, 'n_estimators': 30}\n62010.5215854 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n54852.7770725 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n60246.2164711 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n52752.4109521 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n58355.1846204 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n51724.6800894 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n
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In [66]:
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# best solution:\n# max_features = 6, n_estimators = 30 (RMSE ~49,960)\n
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In [67]:
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feature_importances = grid_search.best_estimator_.feature_importances_\nfeature_importances\n
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Out[67]:
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array([  8.00229340e-02,   7.13499357e-02,   4.21346911e-02,\n         1.73340009e-02,   1.55694906e-02,   1.76527489e-02,\n         1.56813711e-02,   3.21068169e-01,   7.54675530e-02,\n         1.07645094e-01,   5.74608930e-02,   1.47327045e-02,\n         1.57310792e-01,   9.20951468e-05,   2.63317542e-03,\n         3.84435167e-03])
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In [68]:
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# display feature "importance" scores next to their names:\n\nextra_attribs       = ["rooms_per_hhold", "pop_per_hhold", "bedrooms_per_room"]\ncat_one_hot_attribs = list(encoder.classes_)\nattributes          = num_attribs + extra_attribs + cat_one_hot_attribs\n\nsorted(zip(feature_importances, attributes), reverse=True)\n
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Out[68]:
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[(0.32106816893273865, 'median_income'),\n (0.15731079177984286, 'INLAND'),\n (0.10764509417315272, 'pop_per_hhold'),\n (0.080022934000105003, 'longitude'),\n (0.075467553036607335, 'rooms_per_hhold'),\n (0.071349935674308126, 'latitude'),\n (0.057460893036370447, 'bedrooms_per_room'),\n (0.04213469106714228, 'housing_median_age'),\n (0.017652748894983483, 'population'),\n (0.017334000890698829, 'total_rooms'),\n (0.015681371107232313, 'households'),\n (0.015569490624941605, 'total_bedrooms'),\n (0.014732704544371122, '<1H OCEAN'),\n (0.0038443516681782959, 'NEAR OCEAN'),\n (0.00263317542255579, 'NEAR BAY'),\n (9.2095146771177451e-05, 'ISLAND')]
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Time to Eval System on Test dataset

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In [69]:
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final_model = grid_search.best_estimator_\n\nX_test          = strat_test_set.drop("median_house_value", axis=1)\ny_test          = strat_test_set["median_house_value"].copy()\nX_test_prepared = full_pipeline.transform(X_test)\n\nfinal_predictions = final_model.predict(X_test_prepared)\nfinal_mse = mean_squared_error(y_test, final_predictions)\nfinal_rmse = np.sqrt(final_mse)\nfinal_rmse\n
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Out[69]:
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47574.62166586089
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In [ ]:
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\n\n\n" }, { "path": "ch02 - cal housing analysis.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Get Dataset & Create Workspace\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import tarfile\\n\",\n \"from six.moves import urllib\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"DOWNLOAD_ROOT = \\\"https://raw.githubusercontent.com/ageron/handson-ml/master/\\\"\\n\",\n \"HOUSING_PATH = \\\"datasets/housing\\\"\\n\",\n \"HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + \\\"/housing.tgz\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def fetch_housing_data(\\n\",\n \" housing_url=HOUSING_URL, \\n\",\n \" housing_path=HOUSING_PATH):\\n\",\n \" \\n\",\n \" # create datasets/housing directory if needed\\n\",\n \" if not os.path.isdir(housing_path):\\n\",\n \" os.makedirs(housing_path)\\n\",\n \"\\n\",\n \" tgz_path = os.path.join(housing_path, \\\"housing.tgz\\\")\\n\",\n \" \\n\",\n \" # retrieve tarfile\\n\",\n \" urllib.request.urlretrieve(housing_url, tgz_path)\\n\",\n \" \\n\",\n \" # extract tarfile & close path\\n\",\n \" housing_tgz = tarfile.open(tgz_path)\\n\",\n \" housing_tgz.extractall(path=housing_path)\\n\",\n \" housing_tgz.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def load_housing_data(\\n\",\n \" housing_path=HOUSING_PATH):\\n\",\n \" \\n\",\n \" csv_path = os.path.join(housing_path, \\\"housing.csv\\\")\\n\",\n \" return pd.read_csv(csv_path)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# do it\\n\",\n \"#fetch_housing_data() -- already downloaded - static dataset\\n\",\n \"housing = load_housing_data()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Data structure - quick peek\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" longitude latitude housing_median_age total_rooms total_bedrooms \\\\\\n\",\n \"0 -122.23 37.88 41.0 880.0 129.0 \\n\",\n \"1 -122.22 37.86 21.0 7099.0 1106.0 \\n\",\n \"2 -122.24 37.85 52.0 1467.0 190.0 \\n\",\n \"3 -122.25 37.85 52.0 1274.0 235.0 \\n\",\n \"4 -122.25 37.85 52.0 1627.0 280.0 \\n\",\n \"\\n\",\n \" population households median_income median_house_value ocean_proximity \\n\",\n \"0 322.0 126.0 8.3252 452600.0 NEAR BAY \\n\",\n \"1 2401.0 1138.0 8.3014 358500.0 NEAR BAY \\n\",\n \"2 496.0 177.0 7.2574 352100.0 NEAR BAY \\n\",\n \"3 558.0 219.0 5.6431 341300.0 NEAR BAY \\n\",\n \"4 565.0 259.0 3.8462 342200.0 NEAR BAY \"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"housing.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### So... what's in the dataset?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 20640 entries, 0 to 20639\\n\",\n \"Data columns (total 10 columns):\\n\",\n \"longitude 20640 non-null float64\\n\",\n \"latitude 20640 non-null float64\\n\",\n \"housing_median_age 20640 non-null float64\\n\",\n \"total_rooms 20640 non-null float64\\n\",\n \"total_bedrooms 20433 non-null float64\\n\",\n \"population 20640 non-null float64\\n\",\n \"households 20640 non-null float64\\n\",\n \"median_income 20640 non-null float64\\n\",\n \"median_house_value 20640 non-null float64\\n\",\n \"ocean_proximity 20640 non-null object\\n\",\n \"dtypes: float64(9), object(1)\\n\",\n \"memory usage: 1.6+ MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"# housing is a Pandas DataFrame.\\n\",\n \"# untouched datafile: 20640 records, 10 cols (9 float, 1 text)\\n\",\n \"housing.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"<1H OCEAN 9136\\n\",\n \"INLAND 6551\\n\",\n \"NEAR OCEAN 2658\\n\",\n \"NEAR BAY 2290\\n\",\n \"ISLAND 5\\n\",\n \"Name: ocean_proximity, dtype: int64\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# let's see if ocean_proximity can be lumped into categories:\\n\",\n \"housing['ocean_proximity'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count20640.00000020640.00000020640.00000020640.00000020433.00000020640.00000020640.00000020640.00000020640.000000
mean-119.56970435.63186128.6394862635.763081537.8705531425.476744499.5396803.870671206855.816909
std2.0035322.13595212.5855582181.615252421.3850701132.462122382.3297531.899822115395.615874
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.000000
25%-121.80000033.93000018.0000001447.750000296.000000787.000000280.0000002.563400119600.000000
50%-118.49000034.26000029.0000002127.000000435.0000001166.000000409.0000003.534800179700.000000
75%-118.01000037.71000037.0000003148.000000647.0000001725.000000605.0000004.743250264725.000000
max-114.31000041.95000052.00000039320.0000006445.00000035682.0000006082.00000015.000100500001.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" longitude latitude housing_median_age total_rooms \\\\\\n\",\n \"count 20640.000000 20640.000000 20640.000000 20640.000000 \\n\",\n \"mean -119.569704 35.631861 28.639486 2635.763081 \\n\",\n \"std 2.003532 2.135952 12.585558 2181.615252 \\n\",\n \"min -124.350000 32.540000 1.000000 2.000000 \\n\",\n \"25% -121.800000 33.930000 18.000000 1447.750000 \\n\",\n \"50% -118.490000 34.260000 29.000000 2127.000000 \\n\",\n \"75% -118.010000 37.710000 37.000000 3148.000000 \\n\",\n \"max -114.310000 41.950000 52.000000 39320.000000 \\n\",\n \"\\n\",\n \" total_bedrooms population households median_income \\\\\\n\",\n \"count 20433.000000 20640.000000 20640.000000 20640.000000 \\n\",\n \"mean 537.870553 1425.476744 499.539680 3.870671 \\n\",\n \"std 421.385070 1132.462122 382.329753 1.899822 \\n\",\n \"min 1.000000 3.000000 1.000000 0.499900 \\n\",\n \"25% 296.000000 787.000000 280.000000 2.563400 \\n\",\n \"50% 435.000000 1166.000000 409.000000 3.534800 \\n\",\n \"75% 647.000000 1725.000000 605.000000 4.743250 \\n\",\n \"max 6445.000000 35682.000000 6082.000000 15.000100 \\n\",\n \"\\n\",\n \" median_house_value \\n\",\n \"count 20640.000000 \\n\",\n \"mean 206855.816909 \\n\",\n \"std 115395.615874 \\n\",\n \"min 14999.000000 \\n\",\n \"25% 119600.000000 \\n\",\n \"50% 179700.000000 \\n\",\n \"75% 264725.000000 \\n\",\n \"max 500001.000000 \"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# percentiles analysis of each feature\\n\",\n \"housing.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[,\\n\",\n \" ,\\n\",\n \" ],\\n\",\n \" [,\\n\",\n \" ,\\n\",\n \" ],\\n\",\n \" [,\\n\",\n \" ,\\n\",\n \" ]], dtype=object)\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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SfbsOprdzZJkiRJY6WqvgZ8lM5YRg+3Lmq0n/vaZnuAU7p2W9nK9rTl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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# feature histograms\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"housing.hist(bins=50, figsize=(20,15))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Create a test set\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# split dataset into training (80%) and test (20%) subsets\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"def split_train_test(\\n\",\n \" data, test_ratio):\\n\",\n \"\\n\",\n \" shuffled_indices = np.random.permutation(len(data))\\n\",\n \"\\n\",\n \" test_set_size = int(len(data) * test_ratio)\\n\",\n \"\\n\",\n \" test_indices = shuffled_indices[:test_set_size]\\n\",\n \" train_indices = shuffled_indices[test_set_size:]\\n\",\n \"\\n\",\n \" return data.iloc[train_indices], data.iloc[test_indices]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"train_set, test_set = split_train_test(housing, 0.2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"16512 train + 4128 test\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(len(train_set), \\\"train +\\\", len(test_set), \\\"test\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create method for ensuring consistent test sets across multiple runs \\n\",\n \"# (new test sets won't contain instances in previous training sets.)\\n\",\n \"\\n\",\n \"# example method:\\n\",\n \"# compute hash of each instance\\n\",\n \"# keep only the last byte\\n\",\n \"# include instance in test set if value < 51 (20% of 256)\\n\",\n \"\\n\",\n \"import hashlib\\n\",\n \"\\n\",\n \"def test_set_check(\\n\",\n \" identifier, test_ratio, hash):\\n\",\n \" \\n\",\n \" return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\\n\",\n \"\\n\",\n \"def split_train_test_by_id(\\n\",\n \" data, test_ratio, id_column, hash=hashlib.md5):\\n\",\n \"\\n\",\n \" ids = data[id_column]\\n\",\n \" in_test_set = ids.apply(\\n\",\n \" lambda id_: test_set_check(\\n\",\n \" id_, test_ratio, hash))\\n\",\n \"\\n\",\n \" return data.loc[~in_test_set], data.loc[in_test_set]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# housing dataset doesn't have ID attribute,\\n\",\n \"# so let's add an index to it.\\n\",\n \"\\n\",\n \"housing_with_id = housing.reset_index()\\n\",\n \"\\n\",\n \"train_set, test_set = split_train_test_by_id(\\n\",\n \" housing_with_id, 0.2, \\\"index\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
00-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
11-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
22-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
33-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
66-122.2537.8452.02535.0489.01094.0514.03.6591299200.0NEAR BAY
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" index longitude latitude housing_median_age total_rooms \\\\\\n\",\n \"0 0 -122.23 37.88 41.0 880.0 \\n\",\n \"1 1 -122.22 37.86 21.0 7099.0 \\n\",\n \"2 2 -122.24 37.85 52.0 1467.0 \\n\",\n \"3 3 -122.25 37.85 52.0 1274.0 \\n\",\n \"6 6 -122.25 37.84 52.0 2535.0 \\n\",\n \"\\n\",\n \" total_bedrooms population households median_income median_house_value \\\\\\n\",\n \"0 129.0 322.0 126.0 8.3252 452600.0 \\n\",\n \"1 1106.0 2401.0 1138.0 8.3014 358500.0 \\n\",\n \"2 190.0 496.0 177.0 7.2574 352100.0 \\n\",\n \"3 235.0 558.0 219.0 5.6431 341300.0 \\n\",\n \"6 489.0 1094.0 514.0 3.6591 299200.0 \\n\",\n \"\\n\",\n \" ocean_proximity \\n\",\n \"0 NEAR BAY \\n\",\n \"1 NEAR BAY \\n\",\n \"2 NEAR BAY \\n\",\n \"3 NEAR BAY \\n\",\n \"6 NEAR BAY \"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_set.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
44-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
55-122.2537.8552.0919.0213.0413.0193.04.0368269700.0NEAR BAY
1111-122.2637.8552.03503.0752.01504.0734.03.2705241800.0NEAR BAY
2020-122.2737.8540.0751.0184.0409.0166.01.3578147500.0NEAR BAY
2323-122.2737.8452.01688.0337.0853.0325.02.180699700.0NEAR BAY
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" index longitude latitude housing_median_age total_rooms \\\\\\n\",\n \"4 4 -122.25 37.85 52.0 1627.0 \\n\",\n \"5 5 -122.25 37.85 52.0 919.0 \\n\",\n \"11 11 -122.26 37.85 52.0 3503.0 \\n\",\n \"20 20 -122.27 37.85 40.0 751.0 \\n\",\n \"23 23 -122.27 37.84 52.0 1688.0 \\n\",\n \"\\n\",\n \" total_bedrooms population households median_income median_house_value \\\\\\n\",\n \"4 280.0 565.0 259.0 3.8462 342200.0 \\n\",\n \"5 213.0 413.0 193.0 4.0368 269700.0 \\n\",\n \"11 752.0 1504.0 734.0 3.2705 241800.0 \\n\",\n \"20 184.0 409.0 166.0 1.3578 147500.0 \\n\",\n \"23 337.0 853.0 325.0 2.1806 99700.0 \\n\",\n \"\\n\",\n \" ocean_proximity \\n\",\n \"4 NEAR BAY \\n\",\n \"5 NEAR BAY \\n\",\n \"11 NEAR BAY \\n\",\n \"20 NEAR BAY \\n\",\n \"23 NEAR BAY \"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test_set.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# a better index:\\n\",\n \"# let's use longitude & latitude to build stable identifier\\n\",\n \"\\n\",\n \"housing_with_id[\\\"id\\\"] = housing[\\\"longitude\\\"] * 1000 + housing[\\\"latitude\\\"]\\n\",\n \"\\n\",\n \"train_set, test_set = split_train_test_by_id(\\n\",\n \" housing_with_id, 0.2, \\\"id\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximityid
00-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY-122192.12
11-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY-122182.14
22-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY-122202.15
33-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY-122212.15
44-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY-122212.15
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" index longitude latitude housing_median_age total_rooms \\\\\\n\",\n \"0 0 -122.23 37.88 41.0 880.0 \\n\",\n \"1 1 -122.22 37.86 21.0 7099.0 \\n\",\n \"2 2 -122.24 37.85 52.0 1467.0 \\n\",\n \"3 3 -122.25 37.85 52.0 1274.0 \\n\",\n \"4 4 -122.25 37.85 52.0 1627.0 \\n\",\n \"\\n\",\n \" total_bedrooms population households median_income median_house_value \\\\\\n\",\n \"0 129.0 322.0 126.0 8.3252 452600.0 \\n\",\n \"1 1106.0 2401.0 1138.0 8.3014 358500.0 \\n\",\n \"2 190.0 496.0 177.0 7.2574 352100.0 \\n\",\n \"3 235.0 558.0 219.0 5.6431 341300.0 \\n\",\n \"4 280.0 565.0 259.0 3.8462 342200.0 \\n\",\n \"\\n\",\n \" ocean_proximity id \\n\",\n \"0 NEAR BAY -122192.12 \\n\",\n \"1 NEAR BAY -122182.14 \\n\",\n \"2 NEAR BAY -122202.15 \\n\",\n \"3 NEAR BAY -122212.15 \\n\",\n \"4 NEAR BAY -122212.15 \"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_set.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximityid
88-122.2637.8442.02555.0665.01206.0595.02.0804226700.0NEAR BAY-122222.16
1010-122.2637.8552.02202.0434.0910.0402.03.2031281500.0NEAR BAY-122222.15
1111-122.2637.8552.03503.0752.01504.0734.03.2705241800.0NEAR BAY-122222.15
1212-122.2637.8552.02491.0474.01098.0468.03.0750213500.0NEAR BAY-122222.15
1313-122.2637.8452.0696.0191.0345.0174.02.6736191300.0NEAR BAY-122222.16
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" index longitude latitude housing_median_age total_rooms \\\\\\n\",\n \"8 8 -122.26 37.84 42.0 2555.0 \\n\",\n \"10 10 -122.26 37.85 52.0 2202.0 \\n\",\n \"11 11 -122.26 37.85 52.0 3503.0 \\n\",\n \"12 12 -122.26 37.85 52.0 2491.0 \\n\",\n \"13 13 -122.26 37.84 52.0 696.0 \\n\",\n \"\\n\",\n \" total_bedrooms population households median_income median_house_value \\\\\\n\",\n \"8 665.0 1206.0 595.0 2.0804 226700.0 \\n\",\n \"10 434.0 910.0 402.0 3.2031 281500.0 \\n\",\n \"11 752.0 1504.0 734.0 3.2705 241800.0 \\n\",\n \"12 474.0 1098.0 468.0 3.0750 213500.0 \\n\",\n \"13 191.0 345.0 174.0 2.6736 191300.0 \\n\",\n \"\\n\",\n \" ocean_proximity id \\n\",\n \"8 NEAR BAY -122222.16 \\n\",\n \"10 NEAR BAY -122222.15 \\n\",\n \"11 NEAR BAY -122222.15 \\n\",\n \"12 NEAR BAY -122222.15 \\n\",\n \"13 NEAR BAY -122222.16 \"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test_set.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# another option: scikit-learn splitters\\n\",\n \"\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"train_set, test_set = train_test_split(\\n\",\n \" housing, test_size=0.2, random_state=42)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
20046-119.0136.0625.01505.0NaN1392.0359.01.681247700.0INLAND
3024-119.4635.1430.02943.0NaN1565.0584.02.531345800.0INLAND
15663-122.4437.8052.03830.0NaN1310.0963.03.4801500001.0NEAR BAY
20484-118.7234.2817.03051.0NaN1705.0495.05.7376218600.0<1H OCEAN
9814-121.9336.6234.02351.0NaN1063.0428.03.7250278000.0NEAR OCEAN
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" longitude latitude housing_median_age total_rooms total_bedrooms \\\\\\n\",\n \"20046 -119.01 36.06 25.0 1505.0 NaN \\n\",\n \"3024 -119.46 35.14 30.0 2943.0 NaN \\n\",\n \"15663 -122.44 37.80 52.0 3830.0 NaN \\n\",\n \"20484 -118.72 34.28 17.0 3051.0 NaN \\n\",\n \"9814 -121.93 36.62 34.0 2351.0 NaN \\n\",\n \"\\n\",\n \" population households median_income median_house_value \\\\\\n\",\n \"20046 1392.0 359.0 1.6812 47700.0 \\n\",\n \"3024 1565.0 584.0 2.5313 45800.0 \\n\",\n \"15663 1310.0 963.0 3.4801 500001.0 \\n\",\n \"20484 1705.0 495.0 5.7376 218600.0 \\n\",\n \"9814 1063.0 428.0 3.7250 278000.0 \\n\",\n \"\\n\",\n \" ocean_proximity \\n\",\n \"20046 INLAND \\n\",\n \"3024 INLAND \\n\",\n \"15663 NEAR BAY \\n\",\n \"20484 <1H OCEAN \\n\",\n \"9814 NEAR OCEAN \"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test_set.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
14196-117.0332.7133.03126.0627.02300.0623.03.2596103000.0NEAR OCEAN
8267-118.1633.7749.03382.0787.01314.0756.03.8125382100.0NEAR OCEAN
17445-120.4834.664.01897.0331.0915.0336.04.1563172600.0NEAR OCEAN
14265-117.1132.6936.01421.0367.01418.0355.01.942593400.0NEAR OCEAN
2271-119.8036.7843.02382.0431.0874.0380.03.554296500.0INLAND
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" longitude latitude housing_median_age total_rooms total_bedrooms \\\\\\n\",\n \"14196 -117.03 32.71 33.0 3126.0 627.0 \\n\",\n \"8267 -118.16 33.77 49.0 3382.0 787.0 \\n\",\n \"17445 -120.48 34.66 4.0 1897.0 331.0 \\n\",\n \"14265 -117.11 32.69 36.0 1421.0 367.0 \\n\",\n \"2271 -119.80 36.78 43.0 2382.0 431.0 \\n\",\n \"\\n\",\n \" population households median_income median_house_value \\\\\\n\",\n \"14196 2300.0 623.0 3.2596 103000.0 \\n\",\n \"8267 1314.0 756.0 3.8125 382100.0 \\n\",\n \"17445 915.0 336.0 4.1563 172600.0 \\n\",\n \"14265 1418.0 355.0 1.9425 93400.0 \\n\",\n \"2271 874.0 380.0 3.5542 96500.0 \\n\",\n \"\\n\",\n \" ocean_proximity \\n\",\n \"14196 NEAR OCEAN \\n\",\n \"8267 NEAR OCEAN \\n\",\n \"17445 NEAR OCEAN \\n\",\n \"14265 NEAR OCEAN \\n\",\n \"2271 INLAND \"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_set.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# does sampling plan have a sampling bias?\\n\",\n \"# each strata in test dataset should mimic reality\\n\",\n \"\\n\",\n \"housing['median_income'].hist(bins=5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count20640.00000020640.00000020640.00000020640.00000020433.00000020640.00000020640.00000020640.00000020640.000000
mean-119.56970435.63186128.6394862635.763081537.8705531425.476744499.5396803.870671206855.816909
std2.0035322.13595212.5855582181.615252421.3850701132.462122382.3297531.899822115395.615874
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.000000
25%-121.80000033.93000018.0000001447.750000296.000000787.000000280.0000002.563400119600.000000
50%-118.49000034.26000029.0000002127.000000435.0000001166.000000409.0000003.534800179700.000000
75%-118.01000037.71000037.0000003148.000000647.0000001725.000000605.0000004.743250264725.000000
max-114.31000041.95000052.00000039320.0000006445.00000035682.0000006082.00000015.000100500001.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" longitude latitude housing_median_age total_rooms \\\\\\n\",\n \"count 20640.000000 20640.000000 20640.000000 20640.000000 \\n\",\n \"mean -119.569704 35.631861 28.639486 2635.763081 \\n\",\n \"std 2.003532 2.135952 12.585558 2181.615252 \\n\",\n \"min -124.350000 32.540000 1.000000 2.000000 \\n\",\n \"25% -121.800000 33.930000 18.000000 1447.750000 \\n\",\n \"50% -118.490000 34.260000 29.000000 2127.000000 \\n\",\n \"75% -118.010000 37.710000 37.000000 3148.000000 \\n\",\n \"max -114.310000 41.950000 52.000000 39320.000000 \\n\",\n \"\\n\",\n \" total_bedrooms population households median_income \\\\\\n\",\n \"count 20433.000000 20640.000000 20640.000000 20640.000000 \\n\",\n \"mean 537.870553 1425.476744 499.539680 3.870671 \\n\",\n \"std 421.385070 1132.462122 382.329753 1.899822 \\n\",\n \"min 1.000000 3.000000 1.000000 0.499900 \\n\",\n \"25% 296.000000 787.000000 280.000000 2.563400 \\n\",\n \"50% 435.000000 1166.000000 409.000000 3.534800 \\n\",\n \"75% 647.000000 1725.000000 605.000000 4.743250 \\n\",\n \"max 6445.000000 35682.000000 6082.000000 15.000100 \\n\",\n \"\\n\",\n \" median_house_value \\n\",\n \"count 20640.000000 \\n\",\n \"mean 206855.816909 \\n\",\n \"std 115395.615874 \\n\",\n \"min 14999.000000 \\n\",\n \"25% 119600.000000 \\n\",\n \"50% 179700.000000 \\n\",\n \"75% 264725.000000 \\n\",\n \"max 500001.000000 \"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"housing.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"housing[\\\"income_cat\\\"]=np.ceil(housing[\\\"median_income\\\"]/1.5)\\n\",\n \"\\n\",\n \"housing[\\\"income_cat\\\"].where(housing[\\\"income_cat\\\"]<5, 5.0, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueincome_cat
count20640.00000020640.00000020640.00000020640.00000020433.00000020640.00000020640.00000020640.00000020640.00000020640.000000
mean-119.56970435.63186128.6394862635.763081537.8705531425.476744499.5396803.870671206855.8169093.006686
std2.0035322.13595212.5855582181.615252421.3850701132.462122382.3297531.899822115395.6158741.054618
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.0000001.000000
25%-121.80000033.93000018.0000001447.750000296.000000787.000000280.0000002.563400119600.0000002.000000
50%-118.49000034.26000029.0000002127.000000435.0000001166.000000409.0000003.534800179700.0000003.000000
75%-118.01000037.71000037.0000003148.000000647.0000001725.000000605.0000004.743250264725.0000004.000000
max-114.31000041.95000052.00000039320.0000006445.00000035682.0000006082.00000015.000100500001.0000005.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" longitude latitude housing_median_age total_rooms \\\\\\n\",\n \"count 20640.000000 20640.000000 20640.000000 20640.000000 \\n\",\n \"mean -119.569704 35.631861 28.639486 2635.763081 \\n\",\n \"std 2.003532 2.135952 12.585558 2181.615252 \\n\",\n \"min -124.350000 32.540000 1.000000 2.000000 \\n\",\n \"25% -121.800000 33.930000 18.000000 1447.750000 \\n\",\n \"50% -118.490000 34.260000 29.000000 2127.000000 \\n\",\n \"75% -118.010000 37.710000 37.000000 3148.000000 \\n\",\n \"max -114.310000 41.950000 52.000000 39320.000000 \\n\",\n \"\\n\",\n \" total_bedrooms population households median_income \\\\\\n\",\n \"count 20433.000000 20640.000000 20640.000000 20640.000000 \\n\",\n \"mean 537.870553 1425.476744 499.539680 3.870671 \\n\",\n \"std 421.385070 1132.462122 382.329753 1.899822 \\n\",\n \"min 1.000000 3.000000 1.000000 0.499900 \\n\",\n \"25% 296.000000 787.000000 280.000000 2.563400 \\n\",\n \"50% 435.000000 1166.000000 409.000000 3.534800 \\n\",\n \"75% 647.000000 1725.000000 605.000000 4.743250 \\n\",\n \"max 6445.000000 35682.000000 6082.000000 15.000100 \\n\",\n \"\\n\",\n \" median_house_value income_cat \\n\",\n \"count 20640.000000 20640.000000 \\n\",\n \"mean 206855.816909 3.006686 \\n\",\n \"std 115395.615874 1.054618 \\n\",\n \"min 14999.000000 1.000000 \\n\",\n \"25% 119600.000000 2.000000 \\n\",\n \"50% 179700.000000 3.000000 \\n\",\n \"75% 264725.000000 4.000000 \\n\",\n \"max 500001.000000 5.000000 \"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"housing.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import StratifiedShuffleSplit\\n\",\n \"\\n\",\n \"split = StratifiedShuffleSplit(\\n\",\n \" n_splits=1, test_size=0.2, random_state=42)\\n\",\n \"\\n\",\n \"for train_index, test_index in split.split(housing, housing[\\\"income_cat\\\"]):\\n\",\n \" strat_train_set = housing.loc[train_index]\\n\",\n \" strat_test_set = housing.loc[test_index]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3.0 0.350581\\n\",\n \"2.0 0.318847\\n\",\n \"4.0 0.176308\\n\",\n \"5.0 0.114438\\n\",\n \"1.0 0.039826\\n\",\n \"Name: income_cat, dtype: float64\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# review income category proportions\\n\",\n \"housing[\\\"income_cat\\\"].value_counts() / len(housing)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# remove income_cat attribute (return dataset to original state)\\n\",\n \"\\n\",\n \"for set in (strat_train_set, strat_test_set):\\n\",\n \" set.drop([\\\"income_cat\\\"], axis=1, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Visualization\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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eZOh7d3OzQrfm64WyU2F0oXPh/XkjMjb1uSVGn2uiGrVR/PtmYhqn6Y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\\n9Ao4MtL/XQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"housing = strat_train_set.copy()\\n\",\n \"\\n\",\n \"# first: basic geographic distribution\\n\",\n \"\\n\",\n \"housing.plot(kind=\\\"scatter\\\", x=\\\"longitude\\\", y=\\\"latitude\\\", alpha=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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qeTV199FYDFixdz2WWXUVRUxIUXXkhU1JGmtnvuuYcrr7ySF154occ2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\\nOCq/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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# next: housing prices.\\n\",\n \"# color = price \\n\",\n \"# radius = population\\n\",\n \"# use predefined \\\"jet\\\" color map \\n\",\n \"\\n\",\n \"housing.plot(\\n\",\n \" kind=\\\"scatter\\\", \\n\",\n \" x=\\\"longitude\\\", \\n\",\n \" y=\\\"latitude\\\", \\n\",\n \" alpha=0.4,\\n\",\n \" #s=housing[\\\"population\\\"].apply(lambda n: n/100), \\n\",\n \" s=housing[\\\"population\\\"]/100,\\n\",\n \" label=\\\"population\\\",\\n\",\n \" c=\\\"median_house_value\\\", \\n\",\n \" cmap=plt.get_cmap(\\\"jet\\\"), \\n\",\n \" colorbar=True,\\n\",\n \")\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Correlations\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"median_house_value 1.000000\\n\",\n \"median_income 0.687160\\n\",\n \"total_rooms 0.135097\\n\",\n \"housing_median_age 0.114110\\n\",\n \"households 0.064506\\n\",\n \"total_bedrooms 0.047689\\n\",\n \"population -0.026920\\n\",\n \"longitude -0.047432\\n\",\n \"latitude -0.142724\\n\",\n \"Name: median_house_value, dtype: float64\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# next: look for correlatons to median house value.\\n\",\n \"\\n\",\n \"corr_matrix = housing.corr()\\n\",\n \"\\n\",\n \"corr_matrix['median_house_value'].sort_values(ascending=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ],\\n\",\n \" [,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ],\\n\",\n \" [,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ],\\n\",\n \" [,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ]], dtype=object)\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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+enV/mbZ2f4y2dmCMOIbcMFPnj7JF8+tEjGFLiB5D++Zzeff2aag7MN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\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# another way of looking for correlations: scatter_matrix\\n\",\n \"# focus on top 3 factors from above\\n\",\n \"\\n\",\n \"from pandas.tools.plotting import scatter_matrix\\n\",\n \"\\n\",\n \"attributes = [\\\"median_house_value\\\", \\\"median_income\\\", \\\"total_rooms\\\",\\n\",\n \"\\\"housing_median_age\\\"]\\n\",\n \"\\n\",\n \"scatter_matrix(housing[attributes], figsize=(12, 8))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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K5aQa69ocYEE9g4i8cznhUgPI214H14zsbRSRU6EmgJQnqMdxjbvlQE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\\nWCwWi2VXsIJjsVgsll3h/wNrxcnzKrmQVAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# median house value to median income seems to be the most promising.\\n\",\n \"# let's zoom in.\\n\",\n \"\\n\",\n \"housing.plot(\\n\",\n \" kind=\\\"scatter\\\", x=\\\"median_income\\\", y=\\\"median_house_value\\\",\\n\",\n \" alpha=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"median_house_value 1.000000\\n\",\n \"median_income 0.687160\\n\",\n \"rooms_per_household 0.146285\\n\",\n \"total_rooms 0.135097\\n\",\n \"housing_median_age 0.114110\\n\",\n \"households 0.064506\\n\",\n \"total_bedrooms 0.047689\\n\",\n \"population_per_household -0.021985\\n\",\n \"population -0.026920\\n\",\n \"longitude -0.047432\\n\",\n \"latitude -0.142724\\n\",\n \"bedrooms_per_room -0.259984\\n\",\n \"Name: median_house_value, dtype: float64\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# combine some attributes to create more useful ones\\n\",\n \"# then rebuild the correlation matrix.\\n\",\n \"\\n\",\n \"housing[\\\"rooms_per_household\\\"] = housing[\\\"total_rooms\\\"]/housing[\\\"households\\\"]\\n\",\n \"housing[\\\"bedrooms_per_room\\\"] = housing[\\\"total_bedrooms\\\"]/housing[\\\"total_rooms\\\"]\\n\",\n \"housing[\\\"population_per_household\\\"]=housing[\\\"population\\\"]/housing[\\\"households\\\"]\\n\",\n \"\\n\",\n \"corr_matrix = housing.corr()\\n\",\n \"corr_matrix['median_house_value'].sort_values(ascending=False)\\n\",\n \"\\n\",\n \"# *** NOTE: rooms_per_household corr (in book) show more improvement, ~0.199\\n\",\n \"# compared to our 0.146. Not sure of root cause yet. ***\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"### Data Cleanup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# revert to clean copy of stratified training dataset\\n\",\n \"# separate predictors from labels\\n\",\n \"\\n\",\n \"housing = strat_train_set.drop(\\\"median_house_value\\\", axis=1)\\n\",\n \"\\n\",\n \"housing_labels = strat_train_set[\\\"median_house_value\\\"].copy()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# 'total bedrooms' has some missing values - fix\\n\",\n \"# can use DataFrame dropna(), drop(), fillna()\\n\",\n \"\\n\",\n \"# use Scikit-Learn class to handle missing values\\n\",\n \"from sklearn.preprocessing import Imputer\\n\",\n \"imputer = Imputer(strategy=\\\"median\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# drop ocean_proximity attribute, since it's non-numeric.\\n\",\n \"# then fit to training data.\\n\",\n \"\\n\",\n \"housing_num = housing.drop(\\\"ocean_proximity\\\", axis=1)\\n\",\n \"imputer.fit(housing_num)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ -118.51 , 34.26 , 29. , 2119.5 , 433. ,\\n\",\n \" 1164. , 408. , 3.5409])\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# now what do we have?\\n\",\n \"imputer.statistics_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ -118.51 , 34.26 , 29. , 2119.5 , 433. ,\\n\",\n \" 1164. , 408. , 3.5409])\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"housing_num.median().values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# update training set by replacing missing values with learned medians\\n\",\n \"X = imputer.transform(housing_num)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 16512 entries, 0 to 16511\\n\",\n \"Data columns (total 8 columns):\\n\",\n \"longitude 16512 non-null float64\\n\",\n \"latitude 16512 non-null float64\\n\",\n \"housing_median_age 16512 non-null float64\\n\",\n \"total_rooms 16512 non-null float64\\n\",\n \"total_bedrooms 16512 non-null float64\\n\",\n \"population 16512 non-null float64\\n\",\n \"households 16512 non-null float64\\n\",\n \"median_income 16512 non-null float64\\n\",\n \"dtypes: float64(8)\\n\",\n \"memory usage: 1.0 MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"pd.DataFrame(X, columns=housing_num.columns).info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0, 0, 4, ..., 1, 0, 3])\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# convert ocean_proximity feature to numbers using LabelEncoder.\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import LabelEncoder\\n\",\n \"encoder = LabelEncoder()\\n\",\n \"\\n\",\n \"housing_cat = housing['ocean_proximity']\\n\",\n \"housing_cat_encoded = encoder.fit_transform(housing_cat)\\n\",\n \"housing_cat_encoded\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\\n\"\n ]\n }\n ],\n \"source\": [\n \"# how is 'ocean_proximity' mapped?\\n\",\n \"print(encoder.classes_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"<16512x5 sparse matrix of type ''\\n\",\n \"\\twith 16512 stored elements in Compressed Sparse Row format>\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# a better solution for categorical data: one-hot encoding\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import OneHotEncoder\\n\",\n \"encoder = OneHotEncoder()\\n\",\n \"\\n\",\n \"# output = SciPy sparse matrix, better for memory usage\\n\",\n \"# if you need a dense NumPy array, call toarray()\\n\",\n \"\\n\",\n \"housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\\n\",\n \"housing_cat_1hot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### [Label Binarization:](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html)\\n\",\n \"- A shortcut (text categories => integer categories => one-hot vectors)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 0, 0, 0, 0],\\n\",\n \" [1, 0, 0, 0, 0],\\n\",\n \" [0, 0, 0, 0, 1],\\n\",\n \" ..., \\n\",\n \" [0, 1, 0, 0, 0],\\n\",\n \" [1, 0, 0, 0, 0],\\n\",\n \" [0, 0, 0, 1, 0]])\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.preprocessing import LabelBinarizer\\n\",\n \"encoder = LabelBinarizer()\\n\",\n \"\\n\",\n \"housing_cat_1hot = encoder.fit_transform(housing_cat)\\n\",\n \"housing_cat_1hot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Custom Transformers:\\n\",\n \"\\n\",\n \"* Create your own using SciKit-Learn classes\\n\",\n \"* implement fit(), transform() and fit_transform() methods\\n\",\n \"* (fit_transform comes for free by using TransformerMixin as a base class.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.base import BaseEstimator, TransformerMixin\\n\",\n \"\\n\",\n \"rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\\n\",\n \"\\n\",\n \"class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\\n\",\n \" \\n\",\n \" def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\\n\",\n \" \\n\",\n \" self.add_bedrooms_per_room = add_bedrooms_per_room\\n\",\n \"\\n\",\n \" def fit(self, X, y=None):\\n\",\n \" return self # nothing else to do\\n\",\n \"\\n\",\n \" def transform(self, X, y=None):\\n\",\n \" rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\\n\",\n \" population_per_household = X[:, population_ix] / X[:, household_ix]\\n\",\n \"\\n\",\n \" if self.add_bedrooms_per_room:\\n\",\n \" bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\\n\",\n \" return np.c_[X, \\n\",\n \" rooms_per_household, \\n\",\n \" population_per_household,\\n\",\n \" bedrooms_per_room]\\n\",\n \" else:\\n\",\n \" return np.c_[X, \\n\",\n \" rooms_per_household, \\n\",\n \" population_per_household]\\n\",\n \"\\n\",\n \"attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\\n\",\n \"\\n\",\n \"housing_extra_attribs = attr_adder.transform(housing.values)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Feature Scaling\\n\",\n \"\\n\",\n \"* Min-max scaling (normalization) = shift & rescale to [0,1]\\n\",\n \"* SciKit MinMaxScaler will do this for you.\\n\",\n \"* Standardization subtracts mean & divides by variance - result has unit variance\\n\",\n \"* SciKit StandardScaler does this for you.\\n\",\n \"\\n\",\n \"### Pipelining\\n\",\n \"\\n\",\n \"* SciKit Pipeline class helps to standardize the sequence of transforms\\n\",\n \" you need for your project.\\n\",\n \"* Pipelines = list of estimator steps. All but the last must be transformers\\n\",\n \" (they must have fit_transform() method.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# \\\"DataFrameSelector\\\" is a custom transformer class.\\n\",\n \"# grabs the specified feature, drops the rest, converts the DF into a NumPy array.\\n\",\n \"\\n\",\n \"from sklearn.base import BaseEstimator, TransformerMixin\\n\",\n \"\\n\",\n \"class DataFrameSelector(BaseEstimator, TransformerMixin):\\n\",\n \" def __init__ (self, attribute_names):\\n\",\n \" self.attribute_names = attribute_names\\n\",\n \" \\n\",\n \" def fit (self, X, y=None):\\n\",\n \" return self\\n\",\n \" \\n\",\n \" def transform (self, X):\\n\",\n \" return X[self.attribute_names].values\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.pipeline import Pipeline, FeatureUnion\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"\\n\",\n \"num_attribs = list(housing_num)\\n\",\n \"cat_attribs = ['ocean_proximity']\\n\",\n \"\\n\",\n \"num_pipeline = Pipeline([\\n\",\n \" ('selector', DataFrameSelector(num_attribs)),\\n\",\n \" ('imputer', Imputer(strategy=\\\"median\\\")),\\n\",\n \" ('attribs_adder', CombinedAttributesAdder()),\\n\",\n \" ('std_scaler', StandardScaler()),\\n\",\n \" ])\\n\",\n \"\\n\",\n \"cat_pipeline = Pipeline([\\n\",\n \" ('selector', DataFrameSelector(cat_attribs)),\\n\",\n \" ('label_binarizer', LabelBinarizer()),\\n\",\n \"])\\n\",\n \"\\n\",\n \"full_pipeline = FeatureUnion(transformer_list =[\\n\",\n \" ('num_pipeline', num_pipeline),\\n\",\n \" ('cat_pipeline', cat_pipeline)\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[-1.15604281, 0.77194962, 0.74333089, ..., 0. ,\\n\",\n \" 0. , 0. ],\\n\",\n \" [-1.17602483, 0.6596948 , -1.1653172 , ..., 0. ,\\n\",\n \" 0. , 0. ],\\n\",\n \" [ 1.18684903, -1.34218285, 0.18664186, ..., 0. ,\\n\",\n \" 0. , 1. ],\\n\",\n \" ..., \\n\",\n \" [ 1.58648943, -0.72478134, -1.56295222, ..., 0. ,\\n\",\n \" 0. , 0. ],\\n\",\n \" [ 0.78221312, -0.85106801, 0.18664186, ..., 0. ,\\n\",\n \" 0. , 0. ],\\n\",\n \" [-1.43579109, 0.99645926, 1.85670895, ..., 0. ,\\n\",\n \" 1. , 0. ]])\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# let's try it out:\\n\",\n \"\\n\",\n \"housing_prepared = full_pipeline.fit_transform(housing)\\n\",\n \"housing_prepared\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(16512, 16)\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"housing_prepared.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Note: \\n\",\n \" pip3 install sklearn-pandas => gets a DataFrameMapper class\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Model Selection & Training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# let's start with a linear regression\\n\",\n \"\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"\\n\",\n \"lin_reg = LinearRegression()\\n\",\n \"lin_reg.fit(housing_prepared, housing_labels)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"predictions:\\t [ 210644.60459286 317768.80697211 210956.43331178 59218.98886849\\n\",\n \" 189747.55849879]\\n\",\n \"labels:\\t [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# first try. NOT very accurate.\\n\",\n \"\\n\",\n \"some_data = housing.iloc[:5]\\n\",\n \"some_labels = housing_labels.iloc[:5]\\n\",\n \"some_data_prepared = full_pipeline.transform(some_data)\\n\",\n \"\\n\",\n \"print (\\\"predictions:\\\\t\\\", lin_reg.predict(some_data_prepared))\\n\",\n \"print (\\\"labels:\\\\t\\\", list(some_labels))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"typical prediction error:\\t 68628.1981985\\n\"\n ]\n }\n ],\n \"source\": [\n \"# why? look at RMSE on whole training set.\\n\",\n \"\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"\\n\",\n \"housing_predictions = lin_reg.predict(housing_prepared)\\n\",\n \"lin_mse = mean_squared_error(housing_labels, housing_predictions)\\n\",\n \"lin_rmse = np.sqrt(lin_mse)\\n\",\n \"\\n\",\n \"print (\\\"typical prediction error:\\\\t\\\", lin_rmse)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\\n\",\n \" max_leaf_nodes=None, min_impurity_split=1e-07,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, presort=False, random_state=None,\\n\",\n \" splitter='best')\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Hmmm. Not good. Underfit situation. \\n\",\n \"# Let's try a more powerful model, like a Decision Tree.\\n\",\n \"\\n\",\n \"from sklearn.tree import DecisionTreeRegressor\\n\",\n \"\\n\",\n \"tree_reg = DecisionTreeRegressor()\\n\",\n \"tree_reg.fit(housing_prepared, housing_labels)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"typical prediction error:\\t 0.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Zero error? No way...\\n\",\n \"\\n\",\n \"housing_predictions = tree_reg.predict(housing_prepared)\\n\",\n \"tree_mse = mean_squared_error(housing_labels, housing_predictions)\\n\",\n \"tree_rmse = np.sqrt(tree_mse)\\n\",\n \"\\n\",\n \"print (\\\"typical prediction error:\\\\t\\\", tree_rmse)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Scores: [ 69368.62190153 66248.56520386 72284.6557095 68417.57732406\\n\",\n \" 70049.44916939 74941.75765797 70236.59348749 69466.63688954\\n\",\n \" 76140.22952307 70217.59755116]\\n\",\n \"Mean: 70737.1684418\\n\",\n \"Standard deviation: 2815.58298405\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Use K-fold cross-validation\\n\",\n \"# Train & eval Decision Tree model against 10 splits of training dataset\\n\",\n \"# Returns 10 evaluation scores.\\n\",\n \"\\n\",\n \"from sklearn.model_selection import cross_val_score\\n\",\n \"\\n\",\n \"scores = cross_val_score(\\n\",\n \" tree_reg, \\n\",\n \" housing_prepared, \\n\",\n \" housing_labels,\\n\",\n \" scoring=\\\"neg_mean_squared_error\\\", \\n\",\n \" cv=10)\\n\",\n \"\\n\",\n \"rmse_scores = np.sqrt(-scores)\\n\",\n \"\\n\",\n \"def display_scores(scores):\\n\",\n \" print(\\\"Scores:\\\", scores)\\n\",\n \" print(\\\"Mean:\\\", scores.mean())\\n\",\n \" print(\\\"Standard deviation:\\\", scores.std())\\n\",\n \" \\n\",\n \"display_scores(rmse_scores)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Scores: [ 66782.73843989 66960.118071 70347.95244419 74739.57052552\\n\",\n \" 68031.13388938 71193.84183426 64969.63056405 68281.61137997\\n\",\n \" 71552.91566558 67665.10082067]\\n\",\n \"Mean: 69052.4613635\\n\",\n \"Standard deviation: 2731.6740018\\n\"\n ]\n }\n ],\n \"source\": [\n \"# So, Decision Tree RMSE: mean ~71097, stdev 2165 (still sucks.)\\n\",\n \"# compare to earlier Linear Regression:\\n\",\n \"\\n\",\n \"lin_scores = cross_val_score(\\n\",\n \" lin_reg,\\n\",\n \" housing_prepared,\\n\",\n \" housing_labels,\\n\",\n \" scoring=\\\"neg_mean_squared_error\\\",\\n\",\n \" cv=10)\\n\",\n \"\\n\",\n \"lin_rmse_scores = np.sqrt(-lin_scores)\\n\",\n \"\\n\",\n \"display_scores(lin_rmse_scores)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Scores: [ 52480.82629458 50035.41358467 53747.69332484 55053.95194112\\n\",\n \" 51800.65152945 55919.01705209 52226.75176017 50912.82366116\\n\",\n \" 55708.47271341 51931.81080304]\\n\",\n \"Mean: 52981.7412665\\n\",\n \"Standard deviation: 1929.32402243\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Yep, DT overfit is just about as bad. (RMSE mean 69052, stdev 2731)\\n\",\n \"# Let's try a RandomForest.\\n\",\n \"\\n\",\n \"from sklearn.ensemble import RandomForestRegressor\\n\",\n \"\\n\",\n \"forest_reg = RandomForestRegressor()\\n\",\n \"forest_reg.fit(housing_prepared, housing_labels)\\n\",\n \"\\n\",\n \"forest_scores = cross_val_score(\\n\",\n \" forest_reg,\\n\",\n \" housing_prepared,\\n\",\n \" housing_labels,\\n\",\n \" scoring=\\\"neg_mean_squared_error\\\",\\n\",\n \" cv=10)\\n\",\n \"\\n\",\n \"forest_rmse_scores = np.sqrt(-forest_scores)\\n\",\n \"\\n\",\n \"display_scores(forest_rmse_scores)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# OK, RandomForest is a little better.\\n\",\n \"# RMSE mean ~52495, stdev ~1569\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Fine-Tuning Model with Grid Search of Hyperparameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"GridSearchCV(cv=5, error_score='raise',\\n\",\n \" estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n \" max_features='auto', max_leaf_nodes=None,\\n\",\n \" min_impurity_split=1e-07, min_samples_leaf=1,\\n\",\n \" min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n \" n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\\n\",\n \" verbose=0, warm_start=False),\\n\",\n \" fit_params={}, iid=True, n_jobs=1,\\n\",\n \" param_grid=[{'max_features': [2, 4, 6, 8], 'n_estimators': [3, 10, 30]}, {'bootstrap': [False], 'max_features': [2, 3, 4], 'n_estimators': [3, 10]}],\\n\",\n \" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\\n\",\n \" scoring='neg_mean_squared_error', verbose=0)\"\n ]\n },\n \"execution_count\": 62,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import GridSearchCV\\n\",\n \"\\n\",\n \"param_grid = [\\n\",\n \" {'n_estimators': [3, 10, 30], \\n\",\n \" 'max_features': [2, 4, 6, 8]},\\n\",\n \" {'bootstrap': [False], # bootstrap = True = default setting\\n\",\n \" 'n_estimators': [3, 10], \\n\",\n \" 'max_features': [2, 3, 4]},\\n\",\n \"]\\n\",\n \"\\n\",\n \"forest_reg = RandomForestRegressor()\\n\",\n \"\\n\",\n \"grid_search = GridSearchCV(\\n\",\n \" forest_reg, \\n\",\n \" param_grid, \\n\",\n \" cv=5,\\n\",\n \" scoring = 'neg_mean_squared_error')\\n\",\n \"\\n\",\n \"grid_search.fit(housing_prepared, housing_labels)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'max_features': 6, 'n_estimators': 30}\"\n ]\n },\n \"execution_count\": 63,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Best combination of parameters?\\n\",\n \"grid_search.best_params_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n \" max_features=6, max_leaf_nodes=None, min_impurity_split=1e-07,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=1,\\n\",\n \" oob_score=False, random_state=None, verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 64,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Best estimator?\\n\",\n \"grid_search.best_estimator_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"63492.9975584 {'max_features': 2, 'n_estimators': 3}\\n\",\n \"55677.1037862 {'max_features': 2, 'n_estimators': 10}\\n\",\n \"52917.801725 {'max_features': 2, 'n_estimators': 30}\\n\",\n \"60442.2787178 {'max_features': 4, 'n_estimators': 3}\\n\",\n \"53209.7111283 {'max_features': 4, 'n_estimators': 10}\\n\",\n \"50621.1191846 {'max_features': 4, 'n_estimators': 30}\\n\",\n \"58591.8196313 {'max_features': 6, 'n_estimators': 3}\\n\",\n \"52353.3606044 {'max_features': 6, 'n_estimators': 10}\\n\",\n \"49838.3807 {'max_features': 6, 'n_estimators': 30}\\n\",\n \"58615.6100561 {'max_features': 8, 'n_estimators': 3}\\n\",\n \"51726.2593734 {'max_features': 8, 'n_estimators': 10}\\n\",\n \"50074.3050139 {'max_features': 8, 'n_estimators': 30}\\n\",\n \"62010.5215854 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\\n\",\n \"54852.7770725 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\\n\",\n \"60246.2164711 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\\n\",\n \"52752.4109521 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\\n\",\n \"58355.1846204 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\\n\",\n \"51724.6800894 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Evaluation scores:\\n\",\n \"\\n\",\n \"cvres = grid_search.cv_results_\\n\",\n \"\\n\",\n \"for mean_score, params in zip(cvres[\\\"mean_test_score\\\"],\\n\",\n \" cvres[\\\"params\\\"]):\\n\",\n \" print(np.sqrt(-mean_score), params)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# best solution:\\n\",\n \"# max_features = 6, n_estimators = 30 (RMSE ~49,960)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 8.00229340e-02, 7.13499357e-02, 4.21346911e-02,\\n\",\n \" 1.73340009e-02, 1.55694906e-02, 1.76527489e-02,\\n\",\n \" 1.56813711e-02, 3.21068169e-01, 7.54675530e-02,\\n\",\n \" 1.07645094e-01, 5.74608930e-02, 1.47327045e-02,\\n\",\n \" 1.57310792e-01, 9.20951468e-05, 2.63317542e-03,\\n\",\n \" 3.84435167e-03])\"\n ]\n },\n \"execution_count\": 67,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"feature_importances = grid_search.best_estimator_.feature_importances_\\n\",\n \"feature_importances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[(0.32106816893273865, 'median_income'),\\n\",\n \" (0.15731079177984286, 'INLAND'),\\n\",\n \" (0.10764509417315272, 'pop_per_hhold'),\\n\",\n \" (0.080022934000105003, 'longitude'),\\n\",\n \" (0.075467553036607335, 'rooms_per_hhold'),\\n\",\n \" (0.071349935674308126, 'latitude'),\\n\",\n \" (0.057460893036370447, 'bedrooms_per_room'),\\n\",\n \" (0.04213469106714228, 'housing_median_age'),\\n\",\n \" (0.017652748894983483, 'population'),\\n\",\n \" (0.017334000890698829, 'total_rooms'),\\n\",\n \" (0.015681371107232313, 'households'),\\n\",\n \" (0.015569490624941605, 'total_bedrooms'),\\n\",\n \" (0.014732704544371122, '<1H OCEAN'),\\n\",\n \" (0.0038443516681782959, 'NEAR OCEAN'),\\n\",\n \" (0.00263317542255579, 'NEAR BAY'),\\n\",\n \" (9.2095146771177451e-05, 'ISLAND')]\"\n ]\n },\n \"execution_count\": 68,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# display feature \\\"importance\\\" scores next to their names:\\n\",\n \"\\n\",\n \"extra_attribs = [\\\"rooms_per_hhold\\\", \\\"pop_per_hhold\\\", \\\"bedrooms_per_room\\\"]\\n\",\n \"cat_one_hot_attribs = list(encoder.classes_)\\n\",\n \"attributes = num_attribs + extra_attribs + cat_one_hot_attribs\\n\",\n \"\\n\",\n \"sorted(zip(feature_importances, attributes), reverse=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Time to Eval System on Test dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"47574.62166586089\"\n ]\n },\n \"execution_count\": 69,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"final_model = grid_search.best_estimator_\\n\",\n \"\\n\",\n \"X_test = strat_test_set.drop(\\\"median_house_value\\\", axis=1)\\n\",\n \"y_test = strat_test_set[\\\"median_house_value\\\"].copy()\\n\",\n \"X_test_prepared = full_pipeline.transform(X_test)\\n\",\n \"\\n\",\n \"final_predictions = final_model.predict(X_test_prepared)\\n\",\n \"final_mse = mean_squared_error(y_test, final_predictions)\\n\",\n \"final_rmse = np.sqrt(final_mse)\\n\",\n \"final_rmse\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "ch03-classification.html", "content": "\n\n\nch03-classification\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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MNIST: the \"Hello World\" of Machine Learning

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# Alternative local file loader (due to mldata.org being down)\n\nfrom scipy.io import loadmat\nmnist_raw = loadmat("mnist-original.mat")\nmnist = {\n    "data": mnist_raw["data"].T,\n    "target": mnist_raw["label"][0],\n    "COL_NAMES": ["label", "data"],\n    "DESCR": "mldata.org dataset: mnist-original",\n    }\n
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# 70K images, 28x28 pixels/image, each pixel = 0 (white) to 255 (black)\nmnist # a dict object\n
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{'COL_NAMES': ['label', 'data'],\n 'DESCR': 'mldata.org dataset: mnist-original',\n 'data': array([[0, 0, 0, ..., 0, 0, 0],\n        [0, 0, 0, ..., 0, 0, 0],\n        [0, 0, 0, ..., 0, 0, 0],\n        ..., \n        [0, 0, 0, ..., 0, 0, 0],\n        [0, 0, 0, ..., 0, 0, 0],\n        [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n 'target': array([ 0.,  0.,  0., ...,  9.,  9.,  9.])}
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# take a peek\nX,y = mnist['data'], mnist['target']\n\nX.shape, y.shape\n
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((70000, 784), (70000,))
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# display example image\n\n%matplotlib inline\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nsome_digit = X[36000]\nsome_digit_image = some_digit.reshape(28, 28)\n\nplt.imshow(\n    some_digit_image, \n    cmap = matplotlib.cm.binary,\n    interpolation="nearest")\n\nplt.axis("off")\nplt.show()\n
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# looks like a "five". What's the corresponding label?\ny[36000]\n
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5.0
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# dataset already split into training (1st 60K) & test (last 10K) images.\n# shuffle training set for cross-validation quality\n\nX_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\n\nimport numpy as np\n\nshuffle_index = np.random.permutation(60000)\nX_train, y_train = X_train[shuffle_index], y_train[shuffle_index]\n\nprint(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n
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(60000, 784) (10000, 784) (60000,) (10000,)\n
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Binary classifier training - distinguish between 2 classes

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  • Using Stochastic Descent
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# Start by only trying to ID "five" digits.\n\ny_train_5 = (y_train == 5) # create target vectors\ny_test_5  = (y_test == 5)\n\nprint(y_train_5.shape, y_train_5)\nprint(y_test_5.shape, y_test_5)\n\n# SGD classifier: good at handling large DBs\n#                 also good at handling one-at-a-time learning\n\nfrom sklearn.linear_model import SGDClassifier\nsgd_clf = SGDClassifier(random_state=42)\nsgd_clf.fit(X_train, y_train_5)\n\n# did it correctly predict the "five" found above?\nprint(sgd_clf.predict([some_digit]))\n
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(60000,) [False False False ..., False False False]\n(10000,) [False False False ..., False False False]\n[ True]\n
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Performance Measures

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# measure accuracy using K-fold (n=3) cross-validation scores\n\nfrom sklearn.model_selection import cross_val_score\n\nprint(cross_val_score(\n        sgd_clf, \n        X_train, \n        y_train_5, \n        cv=3, \n        scoring="accuracy"))\n\n# 90% accuracy = pretty easy when 90% of digits aren't fives to begin with ... :-|\n
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[ 0.96795  0.96975  0.96855]\n
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# rolling your own cross-validation. Results should be similar-ish to above.\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.base import clone\n\nskfolds = StratifiedKFold(n_splits=3, random_state=42)\n\nfor train_index, test_index in skfolds.split(X_train, y_train_5):\n    \n    clone_clf = clone(sgd_clf)\n    \n    X_train_folds =  X_train[train_index]\n    y_train_folds = (y_train_5[train_index])\n    X_test_fold =    X_train[test_index]\n    y_test_fold =   (y_train_5[test_index])\n\n    clone_clf.fit(X_train_folds, y_train_folds)\n    \n    y_pred = clone_clf.predict(X_test_fold)\n    \n    n_correct = sum(y_pred == y_test_fold)\n    print(n_correct / len(y_pred)) \n
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0.96795\n0.96975\n0.96855\n
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# 95% accuracy sounds too good to be true. How about not-fives?\n\nfrom sklearn.base import BaseEstimator\n\nclass Never5Classifier(BaseEstimator):\n    def fit(self, X, y=None):\n        pass\n\n    def predict(self, X):\n        return np.zeros((len(X), 1), dtype=bool)\n\nnever_5_clf = Never5Classifier()\n\nprint(cross_val_score(\n    never_5_clf,\n    X_train,\n    y_train_5,\n    cv=3,\n    scoring="accuracy"))\n
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[ 0.9096   0.9124   0.90695]\n
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# only ~10% of images are "five", so ~90% of images are "not five". \n# You SHOULD be right about 90% of the time. :-)\n\n# Lesson Learned:\n# Accuracy not a good metric for classifiers - esp those with skewed datasets.\n
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Confusion Matrix - a better way of evaluating a classifier

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# general idea: count #times instances of A are classified as B.\n# first, need a set of predictions.\n\nfrom sklearn.model_selection import cross_val_predict\n\n# Generate cross-val'd predictions for each datapoint\ny_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)\n\n# ROWS = actual classes\n# COLS = predicted classes\n\nfrom sklearn.metrics import confusion_matrix\n\nprint(confusion_matrix(y_train_5, y_train_pred))\n
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[[54044   535]\n [ 1340  4081]]\n
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Classifier metrics: precision = TP/(TP+FP); recall (sensitivity) = TP/(TP+FN)

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print(3841 / (3841+1515), 3841/(3841+1580))\n
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0.7171396564600448 0.7085408596199964\n
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# precision, recall, f1 metrics\n# precision/recall tradeoff: increasing one reduces the other.\n\nfrom sklearn.metrics import precision_score, recall_score, f1_score\nprint("precision:\\n",precision_score(y_train_5, y_train_pred))\nprint("recall:\\n",recall_score(y_train_5, y_train_pred))\n\n# F1 score favors classifiers with similar precision & recall.\nprint("f1:\\n",f1_score(y_train_5, y_train_pred))\n
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precision:\n 0.884098786828\nrecall:\n 0.752813134108\nf1:\n 0.813191192587\n
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Precision/Recall Tradeoffs

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# Scikit doesn't let you directly set threshold values (which drive the decision\n# function for precision/recall.) But you can use the decision function itself.\n\ny_scores = sgd_clf.decision_function([some_digit])\nprint(y_scores)\n\nthreshold = 0\ny_some_digit_pred = (y_scores > threshold) \nprint(y_some_digit_pred)\n
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[ 57844.42736708]\n[ True]\n
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# raising the threshold reduces recall...\n\nthreshold = 200000\ny_some_digit_pred = (y_scores > threshold)\nprint(y_some_digit_pred)\n
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[False]\n
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\"tradeoff\"

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# how to find the right threshold?\n# start with getting decision scores instead of predictions.\n\ny_scores = cross_val_predict(\n    sgd_clf, \n    X_train, \n    y_train_5, \n    cv=3,\n    method="decision_function")\n\n# use results to build a precision/recall curve\n\nfrom sklearn.metrics import precision_recall_curve\nprecisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)\n\n# plot the result\n\ndef plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n    plt.plot(thresholds, \n             precisions[:-1], \n             "b--", \n             label="Precision")\n    plt.plot(thresholds, \n             recalls[:-1], \n             "g-", \n             label="Recall")\n    plt.xlabel("Threshold")\n    plt.legend(loc="upper left")\n    plt.ylim([0, 1])\n\nplot_precision_recall_vs_threshold(precisions, recalls, thresholds)\nplt.show()\n
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# plot precision vs recall to look for knee of the curve\n\ndef plot_precision_vs_recall(precisions, recalls):\n    plt.plot(recalls, precisions, "b-", linewidth=2)\n    plt.xlabel("Recall", fontsize=16)\n    plt.ylabel("Precision", fontsize=16)\n    plt.axis([0, 1, 0, 1])\n\nplt.figure(figsize=(10, 4))\nplot_precision_vs_recall(precisions, recalls)\nplt.show()\n
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# assume you're targeting 90% precision:\n# guesswork from precision-recall curve suggests setting threshold ~50000\n\ny_train_pred_90 = (y_scores > 50000)\nprint(y_train_pred_90.shape, y_train_pred_90)\n\nprint("precision:\\n",precision_score(y_train_5, y_train_pred_90))\nprint("recall:\\n",recall_score(y_train_5, y_train_pred_90))\n
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(60000,) [False False False ..., False False False]\nprecision:\n 0.924948770492\nrecall:\n 0.666113263236\n
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ROC (Receiver Operating Characteristic) curve

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# ROC plots TRUE POSITIVE rate (TP = recall) vs FALSE POSITIVE rate. (FP = 1-specificity)\n\nfrom sklearn.metrics import roc_curve\nfpr, tpr, thresholds = roc_curve(y_train_5, y_scores)\n\ndef plot_roc_curve(fpr, tpr, label=None):\n    plt.plot(fpr, tpr, linewidth=2, label=label)\n    plt.plot([0, 1], [0, 1], 'k--')\n    plt.axis([0, 1, 0, 1])\n    plt.xlabel('False Positive Rate')\n    plt.ylabel('True Positive Rate')\n\nplot_roc_curve(fpr, tpr)\nplt.show()\n\n# tradeoff: higher recall (TP) => more false positives produced.\n# dotted line = purely random classifier results.\n
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# area under curve (AUC) metric:\n# perfect score = ROC AUC = 1.0\n# random score = ROC AUC = 0.5\n\nfrom sklearn.metrics import roc_auc_score\nprint(roc_auc_score(y_train_5, y_scores))\n
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0.964880839199\n
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# train Random Forest classifier\n# compare its ROC curve & AUC to SGD classifier\n\nfrom sklearn.ensemble import RandomForestClassifier\n\nforest_clf = RandomForestClassifier(random_state=42)\n\n# Random Forest doesn't have decision_function(); use predict_proba() instead.\n# returns array (row per instance, column per class)\n\ny_probas_forest = cross_val_predict(\n    forest_clf, \n    X_train, \n    y_train_5, \n    cv=3,\n    method="predict_proba")\n\n# To plot ROC curve, you need scores - not probabilities.\n# use positive class probability as the score.\n\ny_scores_forest = y_probas_forest[:, 1]\nfpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)\n\n# plot ROC curve\nplt.plot(fpr, tpr, "b:", label="SGD")\nplot_roc_curve(fpr_forest, tpr_forest, "Random Forest")\nplt.legend(loc="lower right")\nplt.show()\n
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# Random Forest curve looks much steeper (better). How's the ROC AUC score?\nprint(roc_auc_score(y_train_5, y_scores_forest))\n\n# How's the precision & recall?\ny_train_pred_forest = cross_val_predict(\n    forest_clf, \n    X_train, \n    y_train_5, \n    cv=3)\n\nprint(precision_score(y_train_5, y_train_pred_forest))\nprint(recall_score(y_train_5, y_train_pred_forest))\n
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0.992589481683\n0.985567461185\n0.831396421324\n
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Multiclass Classification

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# some algorithms (RF, Bayes, ..) can handle multiple classes\n# others (SVMs, linear, ...) cannot\n\n# one-vs-all (OVA) strategy for 0-9 digit classication:\n# 10 binary classifiers, one for each digit -- select class with highest score\n\n# one-vs-one (OVO) strategy:\n# train classifiers for every PAIR of digits -- N*(N-1)/2 classifiers needed!\n\n# Scikit detects using binary classifier when multi-class problem is present,\n# auto-selects OVA.\n\nsgd_clf.fit(X_train, y_train)\nprint(sgd_clf.predict([some_digit])) # can SGD correctly predict the "five"?\n
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# let's see 10 scores, one per class. \n# highest score corresponds to "five".\n\nsome_digit_scores = sgd_clf.decision_function([some_digit])\nprint(some_digit_scores)\nprint(sgd_clf.classes_)\n
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[[-177277.32782496 -561668.18573184 -385895.43788059 -114677.95360751\n  -410210.58824666   57844.42736708 -654717.63929413 -200777.6510135\n  -772154.70175904 -614737.18986655]]\n[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n
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# to force Scikit to use OVO (in this case) or OVA: use corresponding classifier.\n\nfrom sklearn.multiclass import OneVsOneClassifier\n\novo_clf = OneVsOneClassifier(SGDClassifier(random_state=42))\novo_clf.fit(X_train, y_train)\nprint("prediction:\\n",ovo_clf.predict([some_digit]))\n\n# same thing for Random Forest (RF can directly handle multiple classifications)\nforest_clf.fit(X_train, y_train)\nprint("prediction via Random Forest:\\n",forest_clf.predict([some_digit]))\nprint("probability via Random Forest:\\n",forest_clf.predict_proba([some_digit]))\n
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prediction:\n [ 5.]\nprediction via Random Forest:\n [ 5.]\nprobability via Random Forest:\n [[ 0.1  0.   0.   0.1  0.   0.8  0.   0.   0.   0. ]]\n
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In [27]:
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# let's check these classifiers via CV. SGD first.\n\nprint("CV score:\\n",cross_val_score(\n    sgd_clf, \n    X_train, \n    y_train, \n    cv=3, \n    scoring="accuracy"))\n
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CV score:\n [ 0.84843031  0.85419271  0.81062159]\n
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In [28]:
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# scaling the inputs should help improve the scores.\n\nfrom sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\n\nX_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n\nprint("CV score, scaled inputs:\\n",cross_val_score(\n    sgd_clf, \n    X_train_scaled, \n    y_train, \n    cv=3, \n    scoring="accuracy"))\n
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CV score, scaled inputs:\n [ 0.91011798  0.91089554  0.90908636]\n
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Error Analysis

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In [29]:
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# as earlier: a confusion matrix from the SGD classificer\n\ny_train_pred = cross_val_predict(\n    sgd_clf, \n    X_train_scaled, \n    y_train, \n    cv=3)\n\nconf_mx = confusion_matrix(y_train, y_train_pred)\nprint("confusion matrix:\\n",conf_mx)\n\n# image equivalent\nplt.matshow(conf_mx, cmap=plt.cm.gray)\nplt.show()\n
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confusion matrix:\n [[5735    4   24   11   13   45   43    8   37    3]\n [   1 6489   43   24    6   35    8    8  116   12]\n [  57   38 5329   88   79   27   92   60  174   14]\n [  53   41  140 5333    2  234   35   60  142   91]\n [  17   26   36   10 5371    8   48   30   77  219]\n [  69   38   39  185   76 4600  114   28  175   97]\n [  34   24   42    2   41   95 5625    7   48    0]\n [  22   21   64   31   49    9    8 5792   14  255]\n [  54  157   70  148   14  158   58   28 5029  135]\n [  42   37   25   85  155   36    2  193   75 5299]]\n
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In [30]:
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# focus on errors.\n# 1st: divide each value in confusion matrix by #images in corresponding class\n# (compares error rates instead of #errors)\n\nrow_sums = conf_mx.sum(axis=1, keepdims=True)\nnorm_conf_mx = conf_mx / row_sums\n\n# fill diagonals with zeroes to keep only the errors, and plot.\n# brighter colors = more misclassifications\n\nnp.fill_diagonal(norm_conf_mx, 0)\nplt.matshow(norm_conf_mx, cmap=plt.cm.gray)\nplt.show()\n\n# rows = actual classes\n# cols = predicted classes\n# 8s & 9s are a problem.\n
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In [31]:
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# more on analyzing individual errors\n\n# EXTRA\ndef plot_digits(instances, images_per_row=10, **options):\n    size = 28\n    images_per_row = min(len(instances), images_per_row)\n    images = [instance.reshape(size,size) for instance in instances]\n    n_rows = (len(instances) - 1) // images_per_row + 1\n    row_images = []\n    n_empty = n_rows * images_per_row - len(instances)\n    images.append(np.zeros((size, size * n_empty)))\n    for row in range(n_rows):\n        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n        row_images.append(np.concatenate(rimages, axis=1))\n    image = np.concatenate(row_images, axis=0)\n    plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n    plt.axis("off")\n\ncl_a, cl_b = 3, 5\n\nX_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\nX_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\nX_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\nX_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n\nplt.figure(figsize=(8,8))\nplt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\nplt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\nplt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\nplt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\nplt.show()\n\n# shows difficulty in seeing difference between threes and fives.\n# We used SGDclassifier, which is sensitive to image shifts/rotates.\n
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MultiLabel Classification

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In [32]:
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# use case: returning multiple classes for each instance\n# (example: multiple people's faces in one picture.)\n\n# create y_multilabel array with 2 target labels for each digit image:\n# first = large digit (7,8,9)?; second = odd (1,3,5,7,9)?\n\n\nfrom sklearn.neighbors import KNeighborsClassifier\n\ny_train_large = (y_train >= 7)\ny_train_odd = (y_train % 2 == 1)\n\nprint("large nums?\\n",y_train_large)\nprint("odd nums?\\n",y_train_odd)\n\ny_multilabel = np.c_[y_train_large, y_train_odd]\n\nprint("combined (multilabel)?\\n",y_multilabel)\n\n# KNeighbors classifier supports multilabeling\n\nknn_clf = KNeighborsClassifier()\nknn_clf.fit(X_train, y_multilabel)\n\n# make example prediction using "some_digit" from above\n# >= 7 = false (correct); odd digit = true (correct)\n\nprint("KNN prediction of some_digit: (>=7? odd?)\\n",knn_clf.predict([some_digit]))\n
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large nums?\n [False False  True ..., False False False]\nodd nums?\n [False False  True ..., False False False]\ncombined (multilabel)?\n [[False False]\n [False False]\n [ True  True]\n ..., \n [False False]\n [False False]\n [False False]]\nKNN prediction of some_digit: (>=7? odd?)\n [[False  True]]\n
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In [33]:
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# another example: find avg F1 score across all labels\n\ny_train_knn_pred = cross_val_predict(knn_clf, X_train, y_train, cv=3)\n\nprint(f1_score(\n    y_train, \n    y_train_knn_pred, \n    average="macro")) # use "weighted" if more weight to be given to more common labels.\n
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0.968186511757\n
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MultiOutput Classification

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In [36]:
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# generalization of multilabel, where each label can have multiple values.\n# example: build image noise removal system\n\n# start by adding noise to MNIST dataset\n\nimport numpy.random as rnd\n\nnoise = rnd.randint(0, 100, (len(X_train), 784))\nX_train_mod = X_train + noise\nnoise = rnd.randint(0, 100, (len(X_test), 784))\nX_test_mod = X_test + noise\n\ny_train_mod = X_train\ny_test_mod = X_test\n\nsome_index = 5500\n
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In [40]:
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def plot_digit(data):\n    image = data.reshape(28, 28)\n    plt.imshow(image, cmap = matplotlib.cm.binary,\n               interpolation="nearest")\n    plt.axis("off")\n
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In [41]:
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# train classifier, and clean up the image\nknn_clf.fit(X_train_mod, y_train_mod)\nclean_digit = knn_clf.predict([X_test_mod[some_index]])\n\nplot_digit(clean_digit)\n
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In [42]:
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some_index = 5500\n\nplt.subplot(121); plot_digit(X_test_mod[some_index])\nplt.subplot(122); plot_digit(y_test_mod[some_index])\n#save_fig("noisy_digit_example_plot")\nplt.show()\n\n# left: noisy image; right: cleaned up\n
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In [ ]:
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\n\n\n" }, { "path": "ch03-classification.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### MNIST: the \\\"Hello World\\\" of Machine Learning\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Alternative local file loader (due to mldata.org being down)\\n\",\n \"\\n\",\n \"from scipy.io import loadmat\\n\",\n \"mnist_raw = loadmat(\\\"mnist-original.mat\\\")\\n\",\n \"mnist = {\\n\",\n \" \\\"data\\\": mnist_raw[\\\"data\\\"].T,\\n\",\n \" \\\"target\\\": mnist_raw[\\\"label\\\"][0],\\n\",\n \" \\\"COL_NAMES\\\": [\\\"label\\\", \\\"data\\\"],\\n\",\n \" \\\"DESCR\\\": \\\"mldata.org dataset: mnist-original\\\",\\n\",\n \" }\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'COL_NAMES': ['label', 'data'],\\n\",\n \" 'DESCR': 'mldata.org dataset: mnist-original',\\n\",\n \" 'data': array([[0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" ..., \\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\\n\",\n \" 'target': array([ 0., 0., 0., ..., 9., 9., 9.])}\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 70K images, 28x28 pixels/image, each pixel = 0 (white) to 255 (black)\\n\",\n \"mnist # a dict object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"((70000, 784), (70000,))\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# take a peek\\n\",\n \"X,y = mnist['data'], mnist['target']\\n\",\n \"\\n\",\n \"X.shape, y.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n+CGU+CGU+CGU+CGU+CGU+CHUf5Zt+b+OQHReAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# display example image\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"some_digit = X[36000]\\n\",\n \"some_digit_image = some_digit.reshape(28, 28)\\n\",\n \"\\n\",\n \"plt.imshow(\\n\",\n \" some_digit_image, \\n\",\n \" cmap = matplotlib.cm.binary,\\n\",\n \" interpolation=\\\"nearest\\\")\\n\",\n \"\\n\",\n \"plt.axis(\\\"off\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5.0\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# looks like a \\\"five\\\". What's the corresponding label?\\n\",\n \"y[36000]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000, 784) (10000, 784) (60000,) (10000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# dataset already split into training (1st 60K) & test (last 10K) images.\\n\",\n \"# shuffle training set for cross-validation quality\\n\",\n \"\\n\",\n \"X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"shuffle_index = np.random.permutation(60000)\\n\",\n \"X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]\\n\",\n \"\\n\",\n \"print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Binary classifier training - distinguish between 2 classes\\n\",\n \"* Using Stochastic Descent\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000,) [False False False ..., False False False]\\n\",\n \"(10000,) [False False False ..., False False False]\\n\",\n \"[ True]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Start by only trying to ID \\\"five\\\" digits.\\n\",\n \"\\n\",\n \"y_train_5 = (y_train == 5) # create target vectors\\n\",\n \"y_test_5 = (y_test == 5)\\n\",\n \"\\n\",\n \"print(y_train_5.shape, y_train_5)\\n\",\n \"print(y_test_5.shape, y_test_5)\\n\",\n \"\\n\",\n \"# SGD classifier: good at handling large DBs\\n\",\n \"# also good at handling one-at-a-time learning\\n\",\n \"\\n\",\n \"from sklearn.linear_model import SGDClassifier\\n\",\n \"sgd_clf = SGDClassifier(random_state=42)\\n\",\n \"sgd_clf.fit(X_train, y_train_5)\\n\",\n \"\\n\",\n \"# did it correctly predict the \\\"five\\\" found above?\\n\",\n \"print(sgd_clf.predict([some_digit]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Performance Measures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.96795 0.96975 0.96855]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# measure accuracy using K-fold (n=3) cross-validation scores\\n\",\n \"\\n\",\n \"from sklearn.model_selection import cross_val_score\\n\",\n \"\\n\",\n \"print(cross_val_score(\\n\",\n \" sgd_clf, \\n\",\n \" X_train, \\n\",\n \" y_train_5, \\n\",\n \" cv=3, \\n\",\n \" scoring=\\\"accuracy\\\"))\\n\",\n \"\\n\",\n \"# 90% accuracy = pretty easy when 90% of digits aren't fives to begin with ... :-|\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.96795\\n\",\n \"0.96975\\n\",\n \"0.96855\\n\"\n ]\n }\n ],\n \"source\": [\n \"# rolling your own cross-validation. Results should be similar-ish to above.\\n\",\n \"\\n\",\n \"from sklearn.model_selection import StratifiedKFold\\n\",\n \"from sklearn.base import clone\\n\",\n \"\\n\",\n \"skfolds = StratifiedKFold(n_splits=3, random_state=42)\\n\",\n \"\\n\",\n \"for train_index, test_index in skfolds.split(X_train, y_train_5):\\n\",\n \" \\n\",\n \" clone_clf = clone(sgd_clf)\\n\",\n \" \\n\",\n \" X_train_folds = X_train[train_index]\\n\",\n \" y_train_folds = (y_train_5[train_index])\\n\",\n \" X_test_fold = X_train[test_index]\\n\",\n \" y_test_fold = (y_train_5[test_index])\\n\",\n \"\\n\",\n \" clone_clf.fit(X_train_folds, y_train_folds)\\n\",\n \" \\n\",\n \" y_pred = clone_clf.predict(X_test_fold)\\n\",\n \" \\n\",\n \" n_correct = sum(y_pred == y_test_fold)\\n\",\n \" print(n_correct / len(y_pred)) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.9096 0.9124 0.90695]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 95% accuracy sounds too good to be true. How about not-fives?\\n\",\n \"\\n\",\n \"from sklearn.base import BaseEstimator\\n\",\n \"\\n\",\n \"class Never5Classifier(BaseEstimator):\\n\",\n \" def fit(self, X, y=None):\\n\",\n \" pass\\n\",\n \"\\n\",\n \" def predict(self, X):\\n\",\n \" return np.zeros((len(X), 1), dtype=bool)\\n\",\n \"\\n\",\n \"never_5_clf = Never5Classifier()\\n\",\n \"\\n\",\n \"print(cross_val_score(\\n\",\n \" never_5_clf,\\n\",\n \" X_train,\\n\",\n \" y_train_5,\\n\",\n \" cv=3,\\n\",\n \" scoring=\\\"accuracy\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# only ~10% of images are \\\"five\\\", so ~90% of images are \\\"not five\\\". \\n\",\n \"# You SHOULD be right about 90% of the time. :-)\\n\",\n \"\\n\",\n \"# Lesson Learned:\\n\",\n \"# Accuracy not a good metric for classifiers - esp those with skewed datasets.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Confusion Matrix - a better way of evaluating a classifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[54044 535]\\n\",\n \" [ 1340 4081]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# general idea: count #times instances of A are classified as B.\\n\",\n \"# first, need a set of predictions.\\n\",\n \"\\n\",\n \"from sklearn.model_selection import cross_val_predict\\n\",\n \"\\n\",\n \"# Generate cross-val'd predictions for each datapoint\\n\",\n \"y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)\\n\",\n \"\\n\",\n \"# ROWS = actual classes\\n\",\n \"# COLS = predicted classes\\n\",\n \"\\n\",\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"\\n\",\n \"print(confusion_matrix(y_train_5, y_train_pred))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Classifier metrics: precision = TP/(TP+FP); recall (sensitivity) = TP/(TP+FN)\\n\",\n \"![alt text](pics/precision-recall-50pct.png \\\"Logo Title Text 1\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7171396564600448 0.7085408596199964\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(3841 / (3841+1515), 3841/(3841+1580))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"precision:\\n\",\n \" 0.884098786828\\n\",\n \"recall:\\n\",\n \" 0.752813134108\\n\",\n \"f1:\\n\",\n \" 0.813191192587\\n\"\n ]\n }\n ],\n \"source\": [\n \"# precision, recall, f1 metrics\\n\",\n \"# precision/recall tradeoff: increasing one reduces the other.\\n\",\n \"\\n\",\n \"from sklearn.metrics import precision_score, recall_score, f1_score\\n\",\n \"print(\\\"precision:\\\\n\\\",precision_score(y_train_5, y_train_pred))\\n\",\n \"print(\\\"recall:\\\\n\\\",recall_score(y_train_5, y_train_pred))\\n\",\n \"\\n\",\n \"# F1 score favors classifiers with similar precision & recall.\\n\",\n \"print(\\\"f1:\\\\n\\\",f1_score(y_train_5, y_train_pred))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Precision/Recall Tradeoffs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 57844.42736708]\\n\",\n \"[ True]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Scikit doesn't let you directly set threshold values (which drive the decision\\n\",\n \"# function for precision/recall.) But you can use the decision function itself.\\n\",\n \"\\n\",\n \"y_scores = sgd_clf.decision_function([some_digit])\\n\",\n \"print(y_scores)\\n\",\n \"\\n\",\n \"threshold = 0\\n\",\n \"y_some_digit_pred = (y_scores > threshold) \\n\",\n \"print(y_some_digit_pred)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[False]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# raising the threshold reduces recall...\\n\",\n \"\\n\",\n \"threshold = 200000\\n\",\n \"y_some_digit_pred = (y_scores > threshold)\\n\",\n \"print(y_some_digit_pred)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![tradeoff](pics/decision-threshold-and-precision-vs-recall.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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tCyZUv69u0LWAVhwYIFtG/fHoDTp0+TlJTE+vXrGT58ODVq1ACgevXq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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# how to find the right threshold?\\n\",\n \"# start with getting decision scores instead of predictions.\\n\",\n \"\\n\",\n \"y_scores = cross_val_predict(\\n\",\n \" sgd_clf, \\n\",\n \" X_train, \\n\",\n \" y_train_5, \\n\",\n \" cv=3,\\n\",\n \" method=\\\"decision_function\\\")\\n\",\n \"\\n\",\n \"# use results to build a precision/recall curve\\n\",\n \"\\n\",\n \"from sklearn.metrics import precision_recall_curve\\n\",\n \"precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)\\n\",\n \"\\n\",\n \"# plot the result\\n\",\n \"\\n\",\n \"def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\\n\",\n \" plt.plot(thresholds, \\n\",\n \" precisions[:-1], \\n\",\n \" \\\"b--\\\", \\n\",\n \" label=\\\"Precision\\\")\\n\",\n \" plt.plot(thresholds, \\n\",\n \" recalls[:-1], \\n\",\n \" \\\"g-\\\", \\n\",\n \" label=\\\"Recall\\\")\\n\",\n \" plt.xlabel(\\\"Threshold\\\")\\n\",\n \" plt.legend(loc=\\\"upper left\\\")\\n\",\n \" plt.ylim([0, 1])\\n\",\n \"\\n\",\n \"plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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5/+VGxd6likAdAn2tLSkubPn190GZIkNaz16+HQQ6Hyr9OpU+GGG/ID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\\nkhqEwU2SJKlBGNwkSZIaxP8HTIDkZZrWvHwAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot precision vs recall to look for knee of the curve\\n\",\n \"\\n\",\n \"def plot_precision_vs_recall(precisions, recalls):\\n\",\n \" plt.plot(recalls, precisions, \\\"b-\\\", linewidth=2)\\n\",\n \" plt.xlabel(\\\"Recall\\\", fontsize=16)\\n\",\n \" plt.ylabel(\\\"Precision\\\", fontsize=16)\\n\",\n \" plt.axis([0, 1, 0, 1])\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 4))\\n\",\n \"plot_precision_vs_recall(precisions, recalls)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000,) [False False False ..., False False False]\\n\",\n \"precision:\\n\",\n \" 0.924948770492\\n\",\n \"recall:\\n\",\n \" 0.666113263236\\n\"\n ]\n }\n ],\n \"source\": [\n \"# assume you're targeting 90% precision:\\n\",\n \"# guesswork from precision-recall curve suggests setting threshold ~50000\\n\",\n \"\\n\",\n \"y_train_pred_90 = (y_scores > 50000)\\n\",\n \"print(y_train_pred_90.shape, y_train_pred_90)\\n\",\n \"\\n\",\n \"print(\\\"precision:\\\\n\\\",precision_score(y_train_5, y_train_pred_90))\\n\",\n \"print(\\\"recall:\\\\n\\\",recall_score(y_train_5, y_train_pred_90))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ROC (Receiver Operating Characteristic) curve\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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mmnRWyl13CIikSxaV8a3285iNslXH9mY6fDMcZEgIyMDO699166devG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\\nMSYoSxTGGGOCskRhjDEmKEsUxhhjgvp/Ce3C08mJJ3sAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# ROC plots TRUE POSITIVE rate (TP = recall) vs FALSE POSITIVE rate. (FP = 1-specificity)\\n\",\n \"\\n\",\n \"from sklearn.metrics import roc_curve\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)\\n\",\n \"\\n\",\n \"def plot_roc_curve(fpr, tpr, label=None):\\n\",\n \" plt.plot(fpr, tpr, linewidth=2, label=label)\\n\",\n \" plt.plot([0, 1], [0, 1], 'k--')\\n\",\n \" plt.axis([0, 1, 0, 1])\\n\",\n \" plt.xlabel('False Positive Rate')\\n\",\n \" plt.ylabel('True Positive Rate')\\n\",\n \"\\n\",\n \"plot_roc_curve(fpr, tpr)\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# tradeoff: higher recall (TP) => more false positives produced.\\n\",\n \"# dotted line = purely random classifier results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.964880839199\\n\"\n ]\n }\n ],\n \"source\": [\n \"# area under curve (AUC) metric:\\n\",\n \"# perfect score = ROC AUC = 1.0\\n\",\n \"# random score = ROC AUC = 0.5\\n\",\n \"\\n\",\n \"from sklearn.metrics import roc_auc_score\\n\",\n \"print(roc_auc_score(y_train_5, y_scores))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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ijKFMqVzJbui4iAadPsZC1gB6b7v/+zE6hoM5JS6nYdPnyYli1b8swz\\nz3DkyBEuX77sdEhu87pEER1ruBweTR7/bBTKnf22ywsLs39/9BE884wdxuLUKXvb6owZdn4CpZRK\\nrZiYGEaOHEnVqlVZt24dY8eOZeXKleTN6/n+1bTidYkiwjVceNlCuW5rQiBjbAd1QIAdyvqdd+xQ\\n1Vu32vGMlFIqLZw5c4a3336bxo0bs3PnTvr164ePj3eder0rWiAy2j68cDvNTmFhEB1tn3koUMAO\\nqgd2ZFQv+/dTSmVAUVFRTJ8+ndjYWIoWLcqmTZv4+eefKVWqlNOhpYrXnRYjXIkiNR3Zhw/b5xm6\\ndbM1ihUr7PSP1auncZBKqSxr06ZN1KlThx49erB06VIAypYt69VTInttokhNjeLoUfssRESEHWYj\\nA96urJTyUmFhYQwZMoR69eoRGhrK3LlzadmypdNhpQmve44iMiaWXMBdBQOS3TbOmDFQv77toL5y\\nxU6/qZRSacUYw8MPP8yqVavo1asX//nPf8iXiea19boaRWysfeItr797ExK98w4MHGjvajJGk4RS\\nKu1cvnyZqKgoRITXX3+dZcuWMWnSpEyVJMALE0WM69HoXDncqwz16GHnd/j6a30eQimVdhYvXkyV\\nKlX47LPPAHj44Ydp3ry5w1F5htclirgaRe5kEoUxdsC+u+6CZ5+1U3cqpdTtOnPmDF27dqV169bk\\nyZOHJk2aOB2Sx3ldojCAj4C/X9Khv/ceFCtmZ4VTSqm0sGjRIipXrsysWbN466232Lx5M/Xr13c6\\nLI/zus5ssM1OSd1qFh0NW7bYcZoefTQdA1NKZWo5cuSgdOnSLFu2jOpZ6L56r0wUSTU7GWNvfZ09\\n294Gq/0SSqnUMsYwZcoUTp8+zdChQ3nwwQdp3ry51z1Zfbu88tsm1ZE9cCC8/779WZ+TUEql1sGD\\nB2nRogXPPvssK1asICbGDh+U1ZIEZLJEceGCHQF2xQqtSSilUicmJobPP/+cqlWrsnHjRiZMmMCS\\nJUvw9fV1OjTHeGnTU+L/YHnzwsiRULVqOgeklMo0tm/fzksvvUTr1q0ZN24cJUqUcDokx3llosiV\\n/Z9hG2MH9OvVy4GAlFJeLTIykqVLl9K6dWuCgoLYtGkTQUFBXj0+U1ryyqanxDqzP//cDtNx/rwD\\nASmlvNbGjRupU6cO//rXv9i9ezcANWvW1CQRj1cmihwJnqEwBkaPhnPnIJM9Oa+U8pBr167xyiuv\\nUL9+fc6dO8eCBQuoVKmS02FlSF7Z9OTrc3OmF4Hff7ed2XoRoJRKTlRUFHXq1GH37t307t2bTz/9\\nlMDAQKfDyrC8M1Ekkg1KlrQvpZS6lbCwMHLmzImfnx+DBg2iYsWKNG3a1OmwMjyvbHrySVCjGDgQ\\n+vRxKBillFdYuHAh5cqV4+effwagX79+miTc5NFEISIPi8geEdkvIq8l8n6giCwUka0islNEerhT\\nbvwaRXQ0fPPNjelMlVIqvtDQUDp37kzbtm3Jnz8/RYsWdTokr+OxpicR8QXGAA8Cx4CNIrLAGLMr\\n3mYDgF3GmDYiUhjYIyIzjTGRSZUdv48iJgamTIGyZdP+OyilvNuPP/5I//79uXjxIu+++y6vvfYa\\n2XVSmhTzZB9FXWC/MeYggIjMAtoB8ROFAfKIvQ8tN3AOiE6u4PhNTzlyQIcOaRi1UirTOH78OHff\\nfTdTpkyhSpUqTofjtTzZ9FQcOBpv+ZhrXXyjgUrA/4DtwPPGmNiEBYlIbxEJEZEQuLnpacoUOzGR\\nUkrFxsYyceJEfvzxRwAGDRrEmjVrNEncJqc7s1sCW4A7gSBgtIjkTbiRMWaiMaaOMaYO3Fyj6NXL\\njhSrlMra9u/fT/PmzenTpw8//fQTAL6+vll6jKa04slEcRyIf8NqCde6+HoAc4y1HzgEVEyu4Lga\\nRWysvdupZs20CVgp5X2io6MZPnw41apVY/PmzUyaNIlZs2Y5HVam4sk+io1AOREpg00QnYDOCbb5\\nG2gOrBaRokAF4GByBWfztYnCxwfGjdOH7JTKyhYsWMArr7xC27ZtGTt2LMWLJ2zhVrfLYzUKY0w0\\nMBBYAuwGfjDG7BSRviLS17XZe0BDEdkOLAeGGGPOJBu0KzPs2gVr19o7n5RSWUdERAQbNmwA4LHH\\nHmPZsmXMmzdPk4SHePTJbGPMYmBxgnXj4/38P+ChlJbr60pvn30GP/1kh+5QSmUN69evp2fPnhw9\\nepTDhw9ToEABmjdv7nRYmZrTndmpElejOH0aqlTRpielsoKrV6/y4osv0rBhQy5fvsz3339PgQIF\\nnA4rS/DOsZ5cdz0tWABnkm2oUkp5u7Nnz1K3bl0OHjxI//79+eijj8ib9x83SCoP8dpEERsLERFQ\\nuLDT0SilPCUmJgZfX18KFizIY489Rtu2bWncuLHTYWU5Xtv0tGsX5MkD8+Y5HY1SyhPmz59P+fLl\\n2bNnDwDDhw/XJOEQr00U+/bZu51KlXI6GqVUWjp16hQdO3bk0UcfJXfu3ERGJjn0m0oHXpoooHVr\\nWLcOypd3OhqlVFqZOXMmlStXZt68ebz//vuEhIRQrVo1p8PK8ryyj0IEsmeHevX0jielMpPffvuN\\nChUqMGXKFJ2WNAPx0kQhvPkmhIXZZymUUt4pNjaWCRMmUK9ePWrVqsWoUaPIkSOHjs+UwXhp05Ow\\ncCHocC5Kea+9e/fSpEkT+vfvz4wZMwAICAjQJJEBeWeNAujbF44dczoSpVRKRUdHM2LECN5++238\\n/f2ZNm0a3bp1czoslQSvTBQ+PtCvn9NRKKVS48svv2TIkCE89thjjBkzhmLFijkdkkqGVzY9YYQJ\\nE+DQIacDUUq5IyIigv379wPQr18/5s2bx5w5czRJeAmvTBSRkbbpafRopyNRSiVn7dq1BAUF8cgj\\njxAVFUVAQADt2rVzOiyVAl6ZKMKuCXfeqRMWKZWRXblyheeff55GjRpx7do1Ro0ahZ+fn9NhqVTw\\nyj6KQoXgeMK58pRSGca+fft46KGHOHz4MAMHDuTDDz8kT548ToelUsmtRCEi2YFSrulKHSfoU3ZK\\nZUTGGESEu+66i1q1avH111/TqFEjp8NStynZpicRaQ1sB5a6loNEZK6nA0vK0l/tgIDama1UxjFn\\nzhzq1q3LxYsXyZ49O7Nnz9YkkUm400cxDKgHXAAwxmwB7vFkUMkxBq5cAX9/J6NQSgGcPHmSxx9/\\nnA4dOhAdHc0ZnSQm03EnUUQZYxJONmo8EYy76tYVFi3SuSiUcpIxhhkzZlC5cmUWLVrEhx9+yIYN\\nG7j77rudDk2lMXf6KHaLyJOAj4iUAZ4D1ns2rKQVLgQPPeBkBEqp2NhYxo8fT+XKlZk8eTIVK1Z0\\nOiTlIe7UKAYCtYFYYA4QATzvyaCSE7xa+M9/nIxAqawpNjaWcePGERoaiq+vLwsXLmTVqlWaJDI5\\ndxJFS2PMEGNMTdfrNaCVpwNLyto1MH26kxEolfXs2bOHxo0b079/f6ZOnQpAoUKF8PHxysexVAq4\\n8y/8ZiLr3kjrQFKiQgXhX/9yMgKlso6oqCg++ugjatSowa5du5gxYwavvvqq02GpdHTLPgoRaQk8\\nDBQXkRHx3sqLbYZyzBNPQJMKTkagVNbx0ksvMWrUKB5//HFGjx5N0aJFnQ5JpbOkOrNPAzuAcGBn\\nvPWXgdc8GVRyjh8XzhaCggWdjEKpzCs8PJzLly9TuHBhXnzxRZo0aUL79u2dDks5RIxJ+k5XEfE3\\nxoSnUzzJylGsnMlXZC3tGxZm3Dino1Eq8wkODqZnz57cfffdLF682OlwVBoRkU3GmDqp+aw7fRTF\\nRWSWiGwTkb1xr9TsLK1cvSLky+dkBEplPpcvX2bgwIHcf//9REZG8uKLLzodksog3HmOYjrwPjAc\\ne7dTDxx+4G7hQqhX2skIlMpcQkJC6NChA0ePHuX555/n/fffJ3fu3E6HpTIId2oUAcaYJQDGmAPG\\nmDdx+PbYa1chIMDJCJTKXEqUKEHJkiUJDg7miy++0CShbuJOoogQER/ggIj0FZE2gKPjBb/8Cnz/\\nvZMRKOXdjDH89NNPPP7448TGxnLHHXcQHBxMw4YNnQ5NZUDuJIrBQC7s0B33Ac8Cz3gyqOQcOgSi\\nI40rlSonTpygQ4cOPPHEExw+fFgH8VPJSjZRGGP+MMZcNsb8bYzpaoxpCxz2fGi3NmgQ1KrlZARK\\neR9jDNOmTaNy5cr88ssvfPLJJ6xfv54iRYo4HZrK4JK8PVZE7gWKA8HGmDMiUgUYAjQzxpRIpxhv\\nkqNYObMieB0N7y7kxO6V8lqXLl2iYsWK3HPPPUyePJny5cs7HZJKRx65PVZEPgJmAk8D/xWRd4AV\\nwFbA0f9hS/4rnD7tZARKeYeYmBhmzJhBVFQUefPmJTg4mJUrV2qSUCmS1O2x7YAaxpgwESkAHAWq\\nGWMOulu4iDwMfAn4ApONMR8nsk0T4AvADzhjjEl2APFPPoFWtUFrzErd2q5du+jVqxfr1q3Dz8+P\\nzp07U7ZsWafDUl4oqT6KcGNMGIAx5hywN4VJwhcYg72VtjLwlIhUTrBNPmAs0NYYUwV4wp2yy5SB\\nvHndjUSprCUqKor333+fmjVrsnfvXr755hueeuopp8NSXiypGkVZEZnj+lmAMvGWMcYkN/BLXWB/\\nXHIRkVnYWsqueNt0BuYYY/52lelWg9K0aVBZL4yUStSTTz7JvHnz6NSpE19++aV2VqvbllSi6JBg\\neXQKyy6Oba6Kcww793Z85QE/EVmJfTbjS2PMVwkLEpHeQG+A7HfcQzLDUymV5YSFhSEi+Pv7M3jw\\nYHr06EHbtm2dDktlErdMFMaY5em0/9pAcyAnsE5E1htjbhpLyhgzEZgI9q6nFs3hf1shMDAdIlQq\\ng1u1ahW9evXiscce45NPPqFx48ZOh6QyGU9OTXUcKBlvuYRrXXzHgCXGmKvGmDPAKqBGcgXHxICO\\nMKCyukuXLtG/f38eeOABoqOjeeihh5wOSWVSnkwUG4FyIlJGRLIDnYAFCbaZDzQSkWwiEoBtmtqd\\nXMHjxwu+vmker1Je4/fff6dq1aqMHz+ewYMHs337dpo3b+50WCqTcmf0WABEJIcxJsLd7Y0x0SIy\\nEFiCvT12qjFmp4j0db0/3hizW0T+C2zDzpo32RizI7myK1dObgulMrecOXOSP39+fvjhB+rXr+90\\nOCqTc2fiorrAFCDQGFNKRGoAvYwxg9IjwIRyFCtnnuryB9P/U8CJ3SvlCGMMP/zwA1u3buXDDz8E\\nIDY2Fh8fTzYKqMzE0xMXjQT+BZwFMMZsBZqmZmdpZdZ3Tu5dqfR1/PhxHn30UTp16sTy5csJD7cT\\nTmqSUOnFnf9pPsaYIwnWxXgiGHc92dHJvSuVPowxTJo0icqVK7N06VKGDx/OmjVr8Pf3dzo0lcW4\\n00dx1NX8ZFxPWw8CHJ0KdZAjjV5Kpa9Dhw4xcOBAGjZsyKRJk7jnnnucDkllUe7UKPoBLwKlgFNA\\nfdc6xxxJWL9RKpOIiYlh0aJFAJQtW5b169ezfPlyTRLKUe4kimhjTCdjTCHXq5PrmQfHvP2Wk3tX\\nyjN27tzJfffdR5s2bVi7di0ANWvW1L4I5Th3/gduFJHFItJNRBydAjVONrdv6lUq44uMjGTYsGHU\\nrFmTAwcO8O2339KgQQOnw1LqumRPucaYu0WkIfaBuXdFZAswyxgzy+PR3cLkyU7tWam0ZYyhSZMm\\nrFu3js6dO/PFF19QuHBhp8NS6iZu1WmNMWuNMc8BtYBL2AmNHKM1ceXtwsLCMMYgIvTp04cFCxYw\\nc+ZMTRIqQ0r2lCsiuUXkaRFZCGwAQoGGHo8sCcOHi5O7V+q2rFixgqpVqzJzpr3e6tatG23atHE4\\nKqVuzZ1r8x3YO50+NcbcY4x5yRjzh4fjStL//ufk3pVKnYsXL9KnTx+aNWuGj48PpUqVcjokpdzi\\nTrdwWWNMrMcjSYEnn3Q6AqVS5pdffqFXr16cPHmSV155hXfeeYeAgACnw1LKLbdMFCLymTHmJWC2\\niPxjQCg3ZrjzGL0hRHmb0NBQChYsyPz586lTJ1XD7SjlmFsOCigidY0xG0Qk0bGL02lio3/IUayc\\nGTV6A7075Hdi90q5xRjDrFmzCA8Pp0ePHhhjiI6Oxs/Pz+nQVBblkUEBjTEbXD9WMsYsj/8CKqVm\\nZ2nl55/A/4LDAAAgAElEQVSd3LtSSTt27Bht27alc+fOzJw58/rdTZoklLdypzP7mUTW9UzrQFKi\\nRAkn965U4mJjY5kwYQKVK1dm+fLljBgxgiVLliCid+kp75ZUH0VH7EN2ZURkTry38gAXPB1YUp59\\nVn/xVMazatUq+vbtS7NmzZg0aRJly5Z1OiSl0kRSdz1twM5BUQIYE2/9ZeBPTwaVnGTmWlIq3URH\\nRxMSEkL9+vVp0qQJS5cupXnz5lqLUJlKsjPcZTQ5ipUzHTps5NvR+ZwORWVx27Zto2fPnmzbto19\\n+/bpcxEqQ/NIZ7aI/O76+7yInIv3Oi8i51IbbFrw1UEBlYMiIiJ46623qF27Nn///TfffPMNJUuW\\ndDospTwmqVNu3HSnhdIjkJTo2sXpCFRWde3aNerWrcvOnTvp2rUrn3/+OQULFnQ6LKU8KqnbY+Oe\\nxi4J+BpjYoAGQB8gVzrEdktFizq5d5UVxcTY2X8DAgJo3749P//8M1999ZUmCZUluHN77DzsNKh3\\nA9OAcsC3Ho0qGStWOLl3ldUsX76cSpUqERISAsCwYcN45JFHHI5KqfTjTqKINcZEAe2BUcaYwUBx\\nz4aVtD17nNy7yiouXLjAs88+S4sWLYAbtQqlshq3pkIVkSeArsAi1zpHHzGtUsXJvausYMGCBVSu\\nXJmpU6fy6quvsnXrVurVq+d0WEo5wp37h54B+mOHGT8oImWA7zwbVtLuv9/JvausYNWqVRQuXJgF\\nCxboIH4qy3PrOQoRyQbc41rcb4yJ9mhUSchRrJxZvHgjzWvqcxQq7Rhj+OabbyhVqhQPPPAA4eHh\\n+Pr66vhMKtPwyHMU8Qq/H9gPTAGmAntF5L7U7CytLFni5N5VZvP333/TunVr/u///o9JkyYB4O/v\\nr0lCKRd3mp4+Bx4xxuwCEJFKwNeAY/VxX1+n9qwyk9jYWMaPH8+QIUMwxjBy5Ej69+/vdFhKZTju\\nJIrscUkCwBizW0SyezCmZHXq5OTeVWYxY8YMBgwYwIMPPsjEiRMpXbq00yEplSG5kyg2i8h44BvX\\n8tM4PCigUqkVHR3NwYMHKV++PF26dCF37tw8/vjjOoifUklw5/bYvsBB4FXX6yD26WzH/Pabk3tX\\n3iruFtcmTZpw5coV/Pz8eOKJJzRJKJWMJGsUIlINuBuYa4z5NH1CSt6Vq05HoLxJeHg477//Pp98\\n8gkFCxZkzJgx5M6d2+mwlPIaSU1cNBQ7k91m4F4RGWaMmZpukSUhqIbTEShvcfz4cVq0aMFff/1F\\nt27dGDFiBAUKFHA6LKW8SlI1iqeB6saYqyJSGFiMvT3WcTrsv0pO3DzVd9xxB0FBQXzxxRe0bNnS\\n6bCU8kpJ9VFEGGOuAhhjQpPZNl0dPep0BCoj+/XXX6lTpw6nTp3C19eX7777TpOEUrchqZN/WRGZ\\n43rNBe6Otzwnic9dJyIPi8geEdkvIq8lsd29IhItIo+7U64OCqgSc/78eXr06EHLli25evUqp0+f\\ndjokpTKFpJqeOiRYHp2SgkXEFzvX9oPAMWCjiCyI/0xGvO0+AX51t+zAwJREorKCOXPmMGDAAEJD\\nQxk6dCj//ve/8ff3dzospTKFWyYKY8zy2yy7LnZcqIMAIjILaAfsSrDdIGA2cK+7Bd/r9pYqKzDG\\nMGnSJIoVK8Yvv/xCUFCQ0yEplal4st+hOBC/N+EYCeaxEJHiwGPAuKQKEpHeIhIiIiEAscmPY6gy\\nOWMMM2bM4MiRI4gIM2fO5I8//tAkoZQHON1B/QUwJN60q4kyxkw0xtSJG/lw3dp0iU1lUIcPH+bh\\nhx+me/fujBkzBoACBQroIH5KeYg7Q3gAICI5jDERKSj7OHa+7TglXOviqwPMcj0ZWwh4RESijTHz\\nko4lBVGoTCM2NpYxY8bw+uuvIyKMHj2afv36OR2WUpmeO8OM1xWR7cA+13INERnlRtkbgXIiUsY1\\niGAnYEH8DYwxZYwxpY0xpYGfgP7JJQkAbV3ImoYNG8Zzzz1Ho0aN2LFjBwMGDMDHx+lKsVKZnzs1\\nipHAv4B5AMaYrSLSNLkPGWOiRWQgsATwBaYaY3aKSF/X++NTG3RAQGo/qbxNVFQUZ8+e5Y477qBf\\nv37cfffddOnSRcdnUioduZMofIwxRxL8Yro1y7wxZjH2ie746xJNEMaY7u6UCXDoENQomfx2yrtt\\n3ryZnj17kjNnToKDgylatChdu3Z1Oiylshx36u1HRaQuYETEV0ReAPZ6OK4knT3r5N6Vp4WFhfH6\\n669Tt25dTp48ySuvvKJNTEo5yJ0aRT9s81Mp4BSwzLXOMUWKOLl35Um7d+/m0UcfZe/evTzzzDMM\\nHz6c/PnzOx2WUllasonCGHMa2xGdYeiggJnXnXfeSZEiRRgzZgwtWrRwOhylFG4kChGZBPzjETdj\\nTG+PROSGqzofRaby3//+lzFjxjB79mwCAwNZvXq10yEppeJxp+F3GbDc9VoDFAFS8jxFmjtwwMm9\\nq7Ry9uxZunXrRqtWrThw4AAnTpxwOiSlVCLcaXr6Pv6yiHwNBHssIjdov6Z3M8Ywe/ZsBgwYwLlz\\n53jzzTd58803yZEjh9OhKaUS4faT2fGUAYqmdSApUbWqk3tXtysyMpLXXnuNkiVL8uuvv1Kjhk5Z\\nqFRG5k4fxXlu9FH4AOeAW84toVRijDF8++23PPbYYwQEBLBs2TJKlChBtmypuVZRSqWnJBtxxD5l\\nVwMo7HrlN8aUNcb8kB7B3cqhQ07uXaXUoUOHeOihh+jSpQtTp9rZdEuXLq1JQikvkWSiMMYYYLEx\\nJsb1yhADfEdGOh2BckdMTAxffvklVatW5Y8//mDcuHH079/f6bCUUinkziXdFhGpaYz50+PRuKlY\\nMacjUO7o06cPU6ZMoVWrVkyYMIGSJXXcFaW80S0ThYhkM8ZEAzWx05geAK4Cgq1s1EqnGP8hb16n\\n9qySExkZSWRkJLlz56Z///40bdqUzp076yB+SnmxpGoUG4BaQNt0isVtFy9y80wXKkMICQmhZ8+e\\n1KtXj4kTJ1KrVi1q1XLsekIplUaS6qMQAGPMgcRe6RRfonRQwIzl2rVrvPrqq9SrV48zZ87QunVr\\np0NSSqWhpGoUhUXkxVu9aYwZ4YF43OLv79SeVUIbN26kc+fO7N+/n2effZZPP/2UfPnyOR2WUioN\\nJZUofIHcuGoWGcmddzodgYqTJ08e/Pz8WL58Oc2aNXM6HKWUBySVKE4YY4alWyQpkDFu0s26fv75\\nZ3799Ve+/PJLKlasyI4dO3S+CKUysWT7KDKio0edjiBrOnPmDF26dOFf//oXy5cv58KFCwCaJJTK\\n5JL6DW+eblGoDM0Yw6xZs6hUqRI//PADb7/9Nps3b9a+CKWyiFs2PRljzqVnICmhD9ylr9OnT/Ps\\ns89SqVIlpkyZQrVq1ZwOSSmVjryyzSB7dqcjyPyMMSxatAhjDEWLFmX16tWsW7dOk4RSWZBXJopz\\nGbaukzkcOHCA5s2b06ZNGxYvXgxAUFAQvr6+DkemlHKCVyaK8HCnI8icYmJiGDFiBNWqVWPTpk1M\\nnDiRVq1aOR2WUsphXjnOc65cTkeQObVt25bFixfTpk0bxo0bR/HixZ0OSSmVAXhloggMdDqCzCMy\\nMhJfX198fX155pln6Nq1Kx07dtRB/JRS13ll05POR5E2NmzYQO3atRk9ejQAHTp0oFOnTpoklFI3\\n8cpEcf680xF4t2vXrvHyyy/ToEEDzp8/T7ly5ZwOSSmVgXll05Ne76be6tWr6d69OwcPHqRv3758\\n/PHHBGpbnlIqCV6ZKIoUdToC73XhwgV8fHxYuXIlDzzwgNPhKKW8gFc2PamUWbhw4fV+iDZt2rBz\\n505NEkopt3llotA+CveEhobSuXNn2rZty4wZM4iOjgYguz7arpRKAa9MFLGxTkeQsRlj+Pbbb6lU\\nqRI//fQTw4YNY82aNWTL5pUtjUoph3nlmSN3bqcjyNi2bdvG008/Tf369Zk8eTJVqlRxOiSllBfz\\nyhpFjhxOR5DxxMbGsm7dOgBq1KjBsmXLCA4O1iShlLptHk0UIvKwiOwRkf0i8loi7z8tIttEZLuI\\nrBWRGu6UGxGR9rF6s3379tGsWTMaNWrEjh07AGjevLkO4qeUShMeSxQi4guMAVoBlYGnRKRygs0O\\nAQ8YY6oB7wET3Sk7LCwtI/Ve0dHR/Oc//6F69eps2bKFSZMmaQ1CKZXmPNlHURfYb4w5CCAis4B2\\nwK64DYwxa+Ntvx4o4U7BeqFsk8T999/P+vXradeuHWPHjuXOO+90OiylVCbkyaan4kD82a2Pudbd\\nSk/gl8TeEJHeIhIiIiEAefKkWYxeJyYmBoBs2bLRrl07fvjhB+bOnatJQinlMRmiM1tEmmITxZDE\\n3jfGTDTG1DHG1LHL6RldxrF+/Xpq1KjB8uXLAXjttdd44okndBA/pZRHeTJRHAdKxlsu4Vp3ExGp\\nDkwG2hljzrpT8LVraRKf17h69SqDBw+mYcOGXLp0SRODUipdeTJRbATKiUgZEckOdAIWxN9AREoB\\nc4Cuxpi9HozFay1fvpxq1arxxRdf0K9fP3bs2EGzZs2cDksplYV4rDPbGBMtIgOBJYAvMNUYs1NE\\n+rreHw+8BRQExrqukqPjmpeS4u/vqagzng0bNpAtWzZWrVrF/fff73Q4SqksSIyXNfjnKFbObNy4\\nkeol8jkdisfMmzeP7Nmz88gjjxAVFUV0dDQ5c+Z0OiyllBcTkU3uXIgnxiuH8MisD9ydOnWKQYMG\\n8eOPP9KqVSseeeQR/Pz88PPzczo0lcVERUVx7NgxwsPDnQ5FpZC/vz8lSpRI0/OGVyaKzDYooDGG\\nb775hhdeeIErV67wwQcf8MorrzgdlsrCjh07Rp48eShdurTePOFFjDGcPXuWY8eOUaZMmTQrN0Pc\\nHptSmW0Q1AULFvB///d/VKxYka1btzJ06FCtRShHhYeHU7BgQU0SXkZEKFiwYJrXBL0yUWSGc2hs\\nbCx79uwB7GRC3333HatWraJixYoOR6aUpUnCO3ni380rE4W3Nz3t3buXJk2a0KBBA86cOYOPjw+d\\nOnXSQfyUUhmSVyaKqCinI0id6OhoPvnkE6pXr8727dsZMWIEBQsWdDospTKsDz74gCpVqlC9enWC\\ngoL4448/iI6OZujQoZQrV46goCCCgoL44IMPrn/G19eXoKAgqlSpQo0aNfjss8+I9farS4d5ZWu/\\nN9aIz58/T4sWLdi8eTPt27dnzJgx3HHHHU6HpVSGtW7dOhYtWsTmzZvJkSMHZ86cITIykjfffJOT\\nJ0+yfft2/P39uXz5Mp999tn1z+XMmZMtW7YAcPr0aTp37sylS5d49913nfoqXs8raxQ5sntPpoh7\\nTiVfvnwEBQXx008/MXv2bE0Syqs0aWJfrm41hg+3y8OH2+U9e25sE6d3b7u8cKFdXrjQLvfu7d4+\\nT5w4QaFChcjhmqmsUKFC5MuXj0mTJjFq1Cj8XU/e5smTh3feeSfRMooUKcLEiRMZPXo03vbMWEbi\\nlYnCW6xZs4Z7772XQ4cOISJMmTKFDh06OB2WUl7hoYce4ujRo5QvX57+/fvz+++/s3//fkqVKkWe\\nFAwhXbZsWWJiYjh9+rQHo83cvLLpyTXSdoZ15coVhg4dyujRoylVqhSnT59O03ualUpvK1fevPzy\\ny/YVp0KFf24zMcE0ZG3a2Je7cufOzaZNm1i9ejUrVqygY8eODB069KZtpk2bxpdffsnZs2dZu3Yt\\nJUuWvEVp6nZojSKN/frrr1StWpXRo0czcOBAduzYQb169ZwOSymv5OvrS5MmTXj33XcZPXo0Cxcu\\n5O+//+by5csA9OjRgy1bthAYGHh9rpaEDh48iK+vL0WKFEnP0DMVr6xRSAZOb9OnT8ff35/Vq1dz\\n3333OR2OUl5rz549+Pj4UK5cOQC2bNlChQoVqFmzJgMHDmTChAn4+/sTExNDZGRkomWEhobSt29f\\nBg4cqM+F3AavTBQ+Gezfe86cOVSoUIEqVaowduxY/P39r3e0KaVS58qVKwwaNIgLFy6QLVs27rnn\\nHiZOnEhgYCD//ve/qVq1Knny5CFnzpx069bt+iyPYWFhBAUFERUVRbZs2ejatSsvvviiw9/Gu3np\\n6LEhVC8R6HQonDx5koEDBzJ79mx69uzJ5MmTnQ5JqTSxe/duKlWq5HQYKpUS+/e7ndFjM3AjTsZl\\njGH69OlUqlSJRYsW8dFHHzFu3Dinw1JKKY/wyqYnp40aNYrnn3+eRo0aMXnyZCpUqOB0SEop5TFe\\nmSic6KKIjY3l1KlTFCtWjO7duxMQEMAzzzyDj49WypRSmZue5dywe/du7r//fh588EEiIyPJmzcv\\nvXr10iShlMoS9EyXhKioKD788EOCgoL466+/GDJkiM4ToZTKcryy6Sk9HDlyhEcffZQtW7bw5JNP\\nMnLkSIoWLep0WEople60RnELRYoUITAwkLlz5/L9999rklAqncUNF161alXatGnDhQsX0qTcw4cP\\nU7Vq1TQpK7533nmH4sWLXx/6/LXXXkvzfcTZsmULixcv9lj5CWmiiGf16tU8/PDDXL16lZw5c7Jy\\n5UoeffRRp8NSKkuKGy58x44dFChQgDFjxjgdUrIGDx7Mli1b2LJlCx9//LHbn7vV8CO3kt6JQpue\\ngEuXLvH6668zduxYSpcuzZEjR6hcubLTYSmVIZR+7WePlHv449Zub9ugQQO2bdsG2Ce227Vrx/nz\\n54mKiuL999+nXbt2HD58mFatWtGoUSPWrl1L8eLFmT9/Pjlz5mTTpk0888wzgB2VNk54eDj9+vUj\\nJCSEbNmyMWLECJo2bcr06dOZN28eV69eZd++fbz88stERkby9ddfkyNHDhYvXkyBAgXcin358uW8\\n/PLLREdHc++99zJu3Dhy5MhB6dKl6dixI0uXLuXVV1/l3nvvZcCAAYSGhhIQEMCkSZOoWLEiP/74\\nI++++y6+vr4EBgaybNky3nrrLcLCwggODub111+nY8eOKTjyKZflaxS//PILVatWZdy4cbzwwgts\\n375dk4RSGUhMTAzLly+nbdu2APj7+zN37lw2b97MihUreOmll67PNbFv3z4GDBjAzp07yZcvH7Nn\\nzwbs4IGjRo1i69atN5U9ZswYRITt27fz3Xff0a1bN8LDwwHYsWMHc+bMYePGjbzxxhsEBATw559/\\n0qBBA7766qtEY/3888+vNz0tWbKE8PBwunfvzvfff8/27duJjo6+6eHcggULsnnzZjp16kTv3r0Z\\nNWoUmzZtYvjw4fTv3x+AYcOGsWTJErZu3cqCBQvInj07w4YNo2PHjmzZssXjSQKyeI0iNjaWN954\\ngzx58rBmzRoaNGjgdEhKZTgpufJPS3FjNh0/fpxKlSrx4IMPAnZkhKFDh7Jq1Sp8fHw4fvw4p06d\\nAqBMmTIEBQUBULt2bQ4fPsyFCxe4cOECjRs3BqBr16788ssvAAQHBzNo0CAAKlasyF133cXevXsB\\naNq0KXny5CFPnjwEBgbSxjVGerVq1a7XbhIaPHgwL8cbf33r1q2UKVOG8uXLA9CtWzfGjBnDCy+8\\nAHD9JH/lyhXWrl3LE088cf2zERERANx33310796dJ598kvbt29/WMU2tLJcojDHMmTOHZs2akT9/\\nfubPn0+RIkWuz6KllMoY4voorl27RsuWLRkzZgzPPfccM2fOJDQ0lE2bNuHn50fp0qWv1wLi/x77\\n+voSFhaW6v3HL8vHx+f6so+PD9HR0akuN75cuXIB9qI1X75816dwjW/8+PH88ccf/Pzzz9SuXZtN\\nmzalyb5TIks1PZ04cYL27dvz+OOPM2rUKABKliypSUKpDCwgIICRI0fy2WefER0dzcWLFylSpAh+\\nfn6sWLGCI0eOJPn5fPnykS9fPoKDgwGYOXPm9ffuv//+68t79+7l77//TtMheSpUqMDhw4fZv38/\\nAF9//TUPPPDAP7bLmzcvZcqU4ccffwTsBW1cM9mBAweoV68ew4YNo3Dhwhw9epQ8efJcn5MjPWSJ\\nRGGMYerUqVSqVIn//ve/fPrpp/+YKUsplXHVrFmT6tWr89133/H0008TEhJCtWrV+Oqrr6hYsWKy\\nn582bRoDBgwgKCjoprmz+/fvT2xsLNWqVaNjx45Mnz49TS8c/f39mTZtGk888QTVqlXDx8eHvn37\\nJrrtzJkzmTJlCjVq1KBKlSrMnz8fgFdeeYVq1apRtWpVGjZsSI0aNWjatCm7du0iKCiI77//Ps3i\\nvRWvHGY8ZGMI1VIwzPiQIUP49NNPady4MZMnT74+EYpSKnE6zLh3S+thxjNtH0VMTAxXr14lb968\\n9OzZkzJlytC7d28dn0kppVIoUyaKnTt30rNnT4oXL87s2bMpX7789bsOlFJKpUymuryOjIzkvffe\\no2bNmuzfv58OHTrgbU1rSmUU+rvjnTzx75ZpahQ7d+7kqaeeYvv27XTq1ImRI0dSuHBhp8NSyiv5\\n+/tz9uxZChYsiEgGm6Re3ZIxhrNnz+Lv75+m5WaaRJE3b16io6OZP3/+9Sc4lVKpU6JECY4dO0Zo\\naKjToagU8vf3p0SJEmlaplcnit9//52ZM2cyYcIESpYsyY4dO7SzWqk04OfnR5kyZZwOQ2UQHj2r\\nisjDIrJHRPaLyD/G3BVrpOv9bSJSy51yr1y+RL9+/WjSpAnLly/nxIkTAJoklFLKAzxWoxARX2AM\\n8CBwDNgoIguMMbvibdYKKOd61QPGuf6+pdiIqzzWvAGhp07w4osv8t577xEQEOCZL6GUUsqjTU91\\ngf3GmIMAIjILaAfETxTtgK+M7aZfLyL5RKSYMebErQqNvnCK3OUqMH/ubOrVSzKnKKWUSgOeTBTF\\ngaPxlo/xz9pCYtsUB25KFCLSG+jtWow4sHf3jvr166dttN6pEHDG6SAyCD0WN+ixuEGPxQ2pHsTK\\nKzqzjTETgYkAIhKS2sfQMxs9FjfosbhBj8UNeixuEJGQ1H7Wk72/x4GS8ZZLuNaldBullFIO8mSi\\n2AiUE5EyIpId6AQsSLDNAuD/XHc/1QcuJtU/oZRSKv15rOnJGBMtIgOBJYAvMNUYs1NE+rreHw8s\\nBh4B9gPXgB5uFD3RQyF7Iz0WN+ixuEGPxQ16LG5I9bHwumHGlVJKpS99Qk0ppVSSNFEopZRKUoZN\\nFJ4a/sMbuXEsnnYdg+0islZEajgRZ3pI7ljE2+5eEYkWkcfTM7705M6xEJEmIrJFRHaKyO/pHWN6\\nceN3JFBEForIVtexcKc/1OuIyFQROS0iO27xfurOm8aYDPfCdn4fAMoC2YGtQOUE2zwC/AIIUB/4\\nw+m4HTwWDYH8rp9bZeVjEW+737A3SzzudNwO/r/Ihx0JoZRruYjTcTt4LIYCn7h+LgycA7I7HbsH\\njkVjoBaw4xbvp+q8mVFrFNeH/zDGRAJxw3/Ed334D2PMeiCfiBRL70DTQbLHwhiz1hhz3rW4Hvs8\\nSmbkzv8LgEHAbOB0egaXztw5Fp2BOcaYvwGMMZn1eLhzLAyQR+zkGrmxiSI6fcP0PGPMKux3u5VU\\nnTczaqK41dAeKd0mM0jp9+yJvWLIjJI9FiJSHHgMO8BkZubO/4vyQH4RWSkim0Tk/9ItuvTlzrEY\\nDVQC/gdsB543xsSmT3gZSqrOm14xhIdyj4g0xSaKRk7H4qAvgCHGmFidmY1sQG2gOZATWCci640x\\ne50NyxEtgS1AM+BuYKmIrDbGXHI2LO+QUROFDv9xg1vfU0SqA5OBVsaYs+kUW3pz51jUAWa5kkQh\\n4BERiTbGzEufENONO8fiGHDWGHMVuCoiq4AaQGZLFO4cix7Ax8Y21O8XkUNARWBD+oSYYaTqvJlR\\nm550+I8bkj0WIlIKmAN0zeRXi8keC2NMGWNMaWNMaeAnoH8mTBLg3u/IfKCRiGQTkQDs6M270znO\\n9ODOsfgbW7NCRIpiR1I9mK5RZgypOm9myBqF8dzwH17HzWPxFlAQGOu6ko42mXDETDePRZbgzrEw\\nxuwWkf8C24BYYLIxJtHbJr2Zm/8v3gOmi8h27B0/Q4wxmW74cRH5DmgCFBKRY8DbgB/c3nlTh/BQ\\nSimVpIza9KSUUiqD0EShlFIqSZoolFJKJUkThVJKqSRpolBKKZUkTRQqwxGRGNeIp3Gv0klsW/pW\\nI2WmcJ8rXaOPbhWRNSJSIRVl9I0bJkNEuovInfHemywildM4zo0iEuTGZ15wPUehVKpoolAZUZgx\\nJije63A67fdpY0wNYAbwn5R+2PXswleuxe7AnfHe62WM2ZUmUd6IcyzuxfkCoIlCpZomCuUVXDWH\\n1SKy2fVqmMg2VURkg6sWsk1EyrnWd4m3foKI+Cazu1XAPa7PNheRP8XO9TFVRHK41n8sIrtc+xnu\\nWveOiLwsdg6MOsBM1z5zumoCdVy1jusnd1fNY3Qq41xHvAHdRGSciISInW/hXde657AJa4WIrHCt\\ne0hE1rmO448ikjuZ/agsThOFyohyxmt2mutadxp40BhTC+gIjEzkc32BL40xQdgT9TERqeTa/j7X\\n+hjg6WT23wbYLiL+wHSgozGmGnYkg34iUhA7Qm0VY0x14P34HzbG/ASEYK/8g4wxYfHenu36bJyO\\n2LGpUhPnw0D84UnecD2RXx14QESqG2NGYkdMbWqMaSoihYA3gRauYxkCvJjMflQWlyGH8FBZXpjr\\nZBmfHzDa1SYfgx1CO6F1wBsiUgI7D8M+EWmOHUF1o2t4k5zcep6KmSISBhzGzmlRATgUb/ysGcAA\\n7JDV4cAUEVkELHL3ixljQkXkoGucnX3YgenWuMpNSZzZsfMqxD9OT4pIb+zvdTGgMnb4jvjqu9av\\nce0nO/a4KXVLmiiUtxgMnMKOfuqDPVHfxBjzrYj8AbQGFotIH+y4PjOMMa+7sY+njTEhcQsiUiCx\\njSosidwAAAF0SURBVFxjC9XFDjL3ODAQO3y1u2YBTwJ/AXONMUbsWdvtOIFN2P6JUUB7ESkDvAzc\\na4w5LyLTAf9EPivAUmPMUymIV2Vx2vSkvEUgcMI12UxX7OBvNxGRssBBV3PLfGwTzHLgcREp4tqm\\ngIjc5eY+9wClReQe13JX4HdXm36gMWYxNoElNkf5ZSDPLcqdi51p7Cls0iClcbqGy/43UF9EKgJ5\\ngavARbGjo7a6RSzrgfvivpOI5BKRxGpnSl2niUJ5i7FANxHZim2uuZrINk8CO0RkC1AVO+XjLmyb\\n/K8isg1Yim2WSZYxJhw7uuaPrlFHY4Hx2JPuIld5wSTexj8dGB/XmZ2g3PPY4b7vMsZscK1LcZyu\\nvo/PgFeMMVuBP7G1lG+xzVlxJgL/FZEVxphQ7B1Z37n2sw57PJW6JR09VimlVJK0RqGUUipJmiiU\\nUkolSROFUkqpJGmiUEoplSRNFEoppZKkiUIppVSSNFEopZRK0v8D7OqRhMOIFqUAAAAASUVORK5C\\nYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# train Random Forest classifier\\n\",\n \"# compare its ROC curve & AUC to SGD classifier\\n\",\n \"\\n\",\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"\\n\",\n \"forest_clf = RandomForestClassifier(random_state=42)\\n\",\n \"\\n\",\n \"# Random Forest doesn't have decision_function(); use predict_proba() instead.\\n\",\n \"# returns array (row per instance, column per class)\\n\",\n \"\\n\",\n \"y_probas_forest = cross_val_predict(\\n\",\n \" forest_clf, \\n\",\n \" X_train, \\n\",\n \" y_train_5, \\n\",\n \" cv=3,\\n\",\n \" method=\\\"predict_proba\\\")\\n\",\n \"\\n\",\n \"# To plot ROC curve, you need scores - not probabilities.\\n\",\n \"# use positive class probability as the score.\\n\",\n \"\\n\",\n \"y_scores_forest = y_probas_forest[:, 1]\\n\",\n \"fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)\\n\",\n \"\\n\",\n \"# plot ROC curve\\n\",\n \"plt.plot(fpr, tpr, \\\"b:\\\", label=\\\"SGD\\\")\\n\",\n \"plot_roc_curve(fpr_forest, tpr_forest, \\\"Random Forest\\\")\\n\",\n \"plt.legend(loc=\\\"lower right\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.992589481683\\n\",\n \"0.985567461185\\n\",\n \"0.831396421324\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Random Forest curve looks much steeper (better). How's the ROC AUC score?\\n\",\n \"print(roc_auc_score(y_train_5, y_scores_forest))\\n\",\n \"\\n\",\n \"# How's the precision & recall?\\n\",\n \"y_train_pred_forest = cross_val_predict(\\n\",\n \" forest_clf, \\n\",\n \" X_train, \\n\",\n \" y_train_5, \\n\",\n \" cv=3)\\n\",\n \"\\n\",\n \"print(precision_score(y_train_5, y_train_pred_forest))\\n\",\n \"print(recall_score(y_train_5, y_train_pred_forest))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Multiclass Classification\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 5.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# some algorithms (RF, Bayes, ..) can handle multiple classes\\n\",\n \"# others (SVMs, linear, ...) cannot\\n\",\n \"\\n\",\n \"# one-vs-all (OVA) strategy for 0-9 digit classication:\\n\",\n \"# 10 binary classifiers, one for each digit -- select class with highest score\\n\",\n \"\\n\",\n \"# one-vs-one (OVO) strategy:\\n\",\n \"# train classifiers for every PAIR of digits -- N*(N-1)/2 classifiers needed!\\n\",\n \"\\n\",\n \"# Scikit detects using binary classifier when multi-class problem is present,\\n\",\n \"# auto-selects OVA.\\n\",\n \"\\n\",\n \"sgd_clf.fit(X_train, y_train)\\n\",\n \"print(sgd_clf.predict([some_digit])) # can SGD correctly predict the \\\"five\\\"?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[-177277.32782496 -561668.18573184 -385895.43788059 -114677.95360751\\n\",\n \" -410210.58824666 57844.42736708 -654717.63929413 -200777.6510135\\n\",\n \" -772154.70175904 -614737.18986655]]\\n\",\n \"[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# let's see 10 scores, one per class. \\n\",\n \"# highest score corresponds to \\\"five\\\".\\n\",\n \"\\n\",\n \"some_digit_scores = sgd_clf.decision_function([some_digit])\\n\",\n \"print(some_digit_scores)\\n\",\n \"print(sgd_clf.classes_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"prediction:\\n\",\n \" [ 5.]\\n\",\n \"prediction via Random Forest:\\n\",\n \" [ 5.]\\n\",\n \"probability via Random Forest:\\n\",\n \" [[ 0.1 0. 0. 0.1 0. 0.8 0. 0. 0. 0. ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# to force Scikit to use OVO (in this case) or OVA: use corresponding classifier.\\n\",\n \"\\n\",\n \"from sklearn.multiclass import OneVsOneClassifier\\n\",\n \"\\n\",\n \"ovo_clf = OneVsOneClassifier(SGDClassifier(random_state=42))\\n\",\n \"ovo_clf.fit(X_train, y_train)\\n\",\n \"print(\\\"prediction:\\\\n\\\",ovo_clf.predict([some_digit]))\\n\",\n \"\\n\",\n \"# same thing for Random Forest (RF can directly handle multiple classifications)\\n\",\n \"forest_clf.fit(X_train, y_train)\\n\",\n \"print(\\\"prediction via Random Forest:\\\\n\\\",forest_clf.predict([some_digit]))\\n\",\n \"print(\\\"probability via Random Forest:\\\\n\\\",forest_clf.predict_proba([some_digit]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CV score:\\n\",\n \" [ 0.84843031 0.85419271 0.81062159]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# let's check these classifiers via CV. SGD first.\\n\",\n \"\\n\",\n \"print(\\\"CV score:\\\\n\\\",cross_val_score(\\n\",\n \" sgd_clf, \\n\",\n \" X_train, \\n\",\n \" y_train, \\n\",\n \" cv=3, \\n\",\n \" scoring=\\\"accuracy\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CV score, scaled inputs:\\n\",\n \" [ 0.91011798 0.91089554 0.90908636]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# scaling the inputs should help improve the scores.\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"scaler = StandardScaler()\\n\",\n \"\\n\",\n \"X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\\n\",\n \"\\n\",\n \"print(\\\"CV score, scaled inputs:\\\\n\\\",cross_val_score(\\n\",\n \" sgd_clf, \\n\",\n \" X_train_scaled, \\n\",\n \" y_train, \\n\",\n \" cv=3, \\n\",\n \" scoring=\\\"accuracy\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Error Analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"confusion matrix:\\n\",\n \" [[5735 4 24 11 13 45 43 8 37 3]\\n\",\n \" [ 1 6489 43 24 6 35 8 8 116 12]\\n\",\n \" [ 57 38 5329 88 79 27 92 60 174 14]\\n\",\n \" [ 53 41 140 5333 2 234 35 60 142 91]\\n\",\n \" [ 17 26 36 10 5371 8 48 30 77 219]\\n\",\n \" [ 69 38 39 185 76 4600 114 28 175 97]\\n\",\n \" [ 34 24 42 2 41 95 5625 7 48 0]\\n\",\n \" [ 22 21 64 31 49 9 8 5792 14 255]\\n\",\n \" [ 54 157 70 148 14 158 58 28 5029 135]\\n\",\n \" [ 42 37 25 85 155 36 2 193 75 5299]]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# as earlier: a confusion matrix from the SGD classificer\\n\",\n \"\\n\",\n \"y_train_pred = cross_val_predict(\\n\",\n \" sgd_clf, \\n\",\n \" X_train_scaled, \\n\",\n \" y_train, \\n\",\n \" cv=3)\\n\",\n \"\\n\",\n \"conf_mx = confusion_matrix(y_train, y_train_pred)\\n\",\n \"print(\\\"confusion matrix:\\\\n\\\",conf_mx)\\n\",\n \"\\n\",\n \"# image equivalent\\n\",\n \"plt.matshow(conf_mx, cmap=plt.cm.gray)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# focus on errors.\\n\",\n \"# 1st: divide each value in confusion matrix by #images in corresponding class\\n\",\n \"# (compares error rates instead of #errors)\\n\",\n \"\\n\",\n \"row_sums = conf_mx.sum(axis=1, keepdims=True)\\n\",\n \"norm_conf_mx = conf_mx / row_sums\\n\",\n \"\\n\",\n \"# fill diagonals with zeroes to keep only the errors, and plot.\\n\",\n \"# brighter colors = more misclassifications\\n\",\n \"\\n\",\n \"np.fill_diagonal(norm_conf_mx, 0)\\n\",\n \"plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# rows = actual classes\\n\",\n \"# cols = predicted classes\\n\",\n \"# 8s & 9s are a problem.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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qRIkSKAkQ5yx44dKuWdIyZlaxg1ahRDhgxR/zf/kdoDef2jR4/y7rvv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\\n/5oj8GDDTAv3AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# more on analyzing individual errors\\n\",\n \"\\n\",\n \"# EXTRA\\n\",\n \"def plot_digits(instances, images_per_row=10, **options):\\n\",\n \" size = 28\\n\",\n \" images_per_row = min(len(instances), images_per_row)\\n\",\n \" images = [instance.reshape(size,size) for instance in instances]\\n\",\n \" n_rows = (len(instances) - 1) // images_per_row + 1\\n\",\n \" row_images = []\\n\",\n \" n_empty = n_rows * images_per_row - len(instances)\\n\",\n \" images.append(np.zeros((size, size * n_empty)))\\n\",\n \" for row in range(n_rows):\\n\",\n \" rimages = images[row * images_per_row : (row + 1) * images_per_row]\\n\",\n \" row_images.append(np.concatenate(rimages, axis=1))\\n\",\n \" image = np.concatenate(row_images, axis=0)\\n\",\n \" plt.imshow(image, cmap = matplotlib.cm.binary, **options)\\n\",\n \" plt.axis(\\\"off\\\")\\n\",\n \"\\n\",\n \"cl_a, cl_b = 3, 5\\n\",\n \"\\n\",\n \"X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\\n\",\n \"X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\\n\",\n \"X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\\n\",\n \"X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\\n\",\n \"\\n\",\n \"plt.figure(figsize=(8,8))\\n\",\n \"plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\\n\",\n \"plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\\n\",\n \"plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\\n\",\n \"plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# shows difficulty in seeing difference between threes and fives.\\n\",\n \"# We used SGDclassifier, which is sensitive to image shifts/rotates.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### MultiLabel Classification\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"large nums?\\n\",\n \" [False False True ..., False False False]\\n\",\n \"odd nums?\\n\",\n \" [False False True ..., False False False]\\n\",\n \"combined (multilabel)?\\n\",\n \" [[False False]\\n\",\n \" [False False]\\n\",\n \" [ True True]\\n\",\n \" ..., \\n\",\n \" [False False]\\n\",\n \" [False False]\\n\",\n \" [False False]]\\n\",\n \"KNN prediction of some_digit: (>=7? odd?)\\n\",\n \" [[False True]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# use case: returning multiple classes for each instance\\n\",\n \"# (example: multiple people's faces in one picture.)\\n\",\n \"\\n\",\n \"# create y_multilabel array with 2 target labels for each digit image:\\n\",\n \"# first = large digit (7,8,9)?; second = odd (1,3,5,7,9)?\\n\",\n \"\\n\",\n \"\\n\",\n \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n \"\\n\",\n \"y_train_large = (y_train >= 7)\\n\",\n \"y_train_odd = (y_train % 2 == 1)\\n\",\n \"\\n\",\n \"print(\\\"large nums?\\\\n\\\",y_train_large)\\n\",\n \"print(\\\"odd nums?\\\\n\\\",y_train_odd)\\n\",\n \"\\n\",\n \"y_multilabel = np.c_[y_train_large, y_train_odd]\\n\",\n \"\\n\",\n \"print(\\\"combined (multilabel)?\\\\n\\\",y_multilabel)\\n\",\n \"\\n\",\n \"# KNeighbors classifier supports multilabeling\\n\",\n \"\\n\",\n \"knn_clf = KNeighborsClassifier()\\n\",\n \"knn_clf.fit(X_train, y_multilabel)\\n\",\n \"\\n\",\n \"# make example prediction using \\\"some_digit\\\" from above\\n\",\n \"# >= 7 = false (correct); odd digit = true (correct)\\n\",\n \"\\n\",\n \"print(\\\"KNN prediction of some_digit: (>=7? odd?)\\\\n\\\",knn_clf.predict([some_digit]))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.968186511757\\n\"\n ]\n }\n ],\n \"source\": [\n \"# another example: find avg F1 score across all labels\\n\",\n \"\\n\",\n \"y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_train, cv=3)\\n\",\n \"\\n\",\n \"print(f1_score(\\n\",\n \" y_train, \\n\",\n \" y_train_knn_pred, \\n\",\n \" average=\\\"macro\\\")) # use \\\"weighted\\\" if more weight to be given to more common labels.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### MultiOutput Classification\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# generalization of multilabel, where each label can have multiple values.\\n\",\n \"# example: build image noise removal system\\n\",\n \"\\n\",\n \"# start by adding noise to MNIST dataset\\n\",\n \"\\n\",\n \"import numpy.random as rnd\\n\",\n \"\\n\",\n \"noise = rnd.randint(0, 100, (len(X_train), 784))\\n\",\n \"X_train_mod = X_train + noise\\n\",\n \"noise = rnd.randint(0, 100, (len(X_test), 784))\\n\",\n \"X_test_mod = X_test + noise\\n\",\n \"\\n\",\n \"y_train_mod = X_train\\n\",\n \"y_test_mod = X_test\\n\",\n \"\\n\",\n \"some_index = 5500\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_digit(data):\\n\",\n \" image = data.reshape(28, 28)\\n\",\n \" plt.imshow(image, cmap = matplotlib.cm.binary,\\n\",\n \" interpolation=\\\"nearest\\\")\\n\",\n \" plt.axis(\\\"off\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nECV+iPob3byufrwOPwwAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# train classifier, and clean up the image\\n\",\n \"knn_clf.fit(X_train_mod, y_train_mod)\\n\",\n \"clean_digit = knn_clf.predict([X_test_mod[some_index]])\\n\",\n \"\\n\",\n \"plot_digit(clean_digit)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXQAAAC7CAYAAAB1qmWGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nPpy3wDHB6AAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"some_index = 5500\\n\",\n \"\\n\",\n \"plt.subplot(121); plot_digit(X_test_mod[some_index])\\n\",\n \"plt.subplot(122); plot_digit(y_test_mod[some_index])\\n\",\n \"#save_fig(\\\"noisy_digit_example_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# left: noisy image; right: cleaned up\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n 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Training Models - Intro

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Linear Regression

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  • y = theta0 + (theta1 x1) + (theta2 x2) + ...
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  • = theta^T (dot) x --- theta^T = theta vector, transposed (row instead of col)

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  • Training a model = finding theta that minimizes error function (ex: MSE)

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Normal Equation: finds theta that minimizes cost function

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In [1]:
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# generate some data\nimport numpy as np\nX = 2 * np.random.rand(100, 1)\ny = 4 + 3 * X + np.random.randn(100, 1)\n\n%matplotlib inline\nimport matplotlib.pyplot as plt\n\nplt.scatter(X,y)\nplt.show()\n
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In [2]:
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# find theta. \n# 1) use NumPy's matrix inverse function.\n# 2) use dot method for matrix multiply.\n\nX_b = np.c_[np.ones((100, 1)), X] # add x0 = 1 to each instance\ntheta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)\n\n# results:\nprint(theta_best) # compare to generated data: y = 4 + 3x + noise\n
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[[ 3.58859665]\n [ 3.41876053]]\n
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In [3]:
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# make some predictions\n\nX_new     = np.array([[0],[1],[2]])\nX_new_b   = np.c_[np.ones((3, 1)), X_new] # add x0 = 1 to each instance\ny_predict = X_new_b.dot(theta_best)\nprint(y_predict)\n\n# then plot\n\nplt.plot(X_new, y_predict, "r-")\nplt.plot(X, y, "b.")\nplt.axis([0, 2, 0, 15])\nplt.show()\n
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[[  3.58859665]\n [  7.00735719]\n [ 10.42611772]]\n
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In [4]:
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# Scikit equivalent\nfrom sklearn.linear_model import LinearRegression\nlin_reg = LinearRegression()\n\nlin_reg.fit(X,y)\nprint("intercept & coefficient:\\n", lin_reg.intercept_, lin_reg.coef_)\nprint("predictions:\\n", lin_reg.predict(X_new))\n
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intercept & coefficient:\n [ 3.58859665] [[ 3.41876053]]\npredictions:\n [[  3.58859665]\n [  7.00735719]\n [ 10.42611772]]\n
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Gradient Descent

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# Gradient Descent - Batch\n# (Batch: math includes full training set X.)\n\n# need to find partial derivative (slope) of the cost function\n# for each model parameter (theta).\n\ntheta_path_bgd = []\n\neta = 0.1 # learning rate\nn_iterations = 1000\nm = 100\n\ntheta = np.random.randn(2,1) # random initialization\n\nfor iteration in range(n_iterations):\n    gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y)\n    theta = theta - eta * gradients\n    theta_path_bgd.append(theta)\n    \nprint(theta)\n
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[[ 3.58859665]\n [ 3.41876053]]\n
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In [6]:
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# Gradient Descent - Stochastic\n# Stochastic: finds gradients based on random instances\n# adv: better for huge datasets\n# dis: much more erratic than batch GD \n#      -- good for avoiding local minima\n#      -- bad b/c may not find optimum sol'n\n\n# simulated annealing helps. (gradually reduces learning rate)\n\ntheta_path_sgd = []\n\nn_epochs, t0, t1 = 50, 5, 50 # learning schedule hyperparameters\n\ndef learning_schedule(t):\n    return t0 / (t + t1)\n\ntheta = np.random.randn(2,1) # random initialization\n\nfor epoch in range(n_epochs):\n    for i in range(m):\n        random_index = np.random.randint(m)\n        xi = X_b[random_index:random_index+1]\n        yi =   y[random_index:random_index+1]\n\n        gradients = 2 * xi.T.dot(xi.dot(theta) - yi)\n\n        eta = learning_schedule(epoch * m + i)\n        theta = theta - eta * gradients\n        theta_path_sgd.append(theta)\n        \nprint(theta)\n
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[[ 3.6036273 ]\n [ 3.44079196]]\n
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In [7]:
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# SGD Regression using Scikit:\n\nfrom sklearn.linear_model import SGDRegressor\n\nsgd_reg = SGDRegressor(n_iter=50, penalty=None, eta0=0.1)\nsgd_reg.fit(X, y.ravel())\n\nprint(sgd_reg.intercept_, sgd_reg.coef_)\n
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[ 3.57214013] [ 3.39609675]\n
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In [8]:
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# Gradient Descent - MiniBatch\n# adv: performance boost via GPUs\n\ntheta_path_mgd = []\n\nn_iterations = 50\nminibatch_size = 20\n\nimport numpy.random as rnd\n\nrnd.seed(42)\ntheta = rnd.randn(2,1)  # random initialization\n\nt0, t1 = 10, 1000\ndef learning_schedule(t):\n    return t0 / (t + t1)\n\nt = 0\nfor epoch in range(n_iterations):\n    shuffled_indices = rnd.permutation(m)\n    X_b_shuffled = X_b[shuffled_indices]\n    y_shuffled = y[shuffled_indices]\n        \n    for i in range(0, m, minibatch_size):\n        t += 1\n        \n        xi = X_b_shuffled[i:i+minibatch_size]\n        yi =   y_shuffled[i:i+minibatch_size]\n        \n        gradients = 2 * xi.T.dot(xi.dot(theta) - yi)\n        eta = learning_schedule(t)\n        theta = theta - eta * gradients\n        theta_path_mgd.append(theta)\n        \nprint(theta)\n
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[[ 3.70412445]\n [ 3.54124923]]\n
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In [9]:
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theta_path_bgd = np.array(theta_path_bgd)\ntheta_path_sgd = np.array(theta_path_sgd)\ntheta_path_mgd = np.array(theta_path_mgd)\n
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In [10]:
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plt.figure(figsize=(10,4))\nplt.plot(theta_path_sgd[:, 0], theta_path_sgd[:, 1], "r-s", linewidth=1, label="Stochastic")\nplt.plot(theta_path_mgd[:, 0], theta_path_mgd[:, 1], "g-+", linewidth=1, label="Mini-batch")\nplt.plot(theta_path_bgd[:, 0], theta_path_bgd[:, 1], "b-o", linewidth=1, label="Batch")\nplt.legend(loc="upper right", fontsize=14)\nplt.xlabel(r"$\\theta_0$", fontsize=20)\nplt.ylabel(r"$\\theta_1$   ", fontsize=20, rotation=0)\nplt.axis([2.5, 4.5, 2.3, 3.9])\n#save_fig("gradient_descent_paths_plot")\nplt.show()\n
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Polynomial Regression

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# example quadratic equation + noise: y = 0.5*X^2 + X + 2 + noise\nm = 100\nX = 6 * np.random.rand(m, 1) - 3\ny = 0.5 * X**2 + X + 2 + np.random.randn(m, 1)\n\nplt.scatter(X,y)\nplt.show()\n
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# fit using Scikit\n\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model  import LinearRegression\n\n# caution: PolynomialFeatures converts array of n features\n# into array of (n+d)!/d!n! features -- combinatorial explosions possible :-)\n\npoly_features = PolynomialFeatures(degree=2, include_bias=False)\nprint(poly_features)\n\n# X_poly: original feature of X, plus its square.\nX_poly = poly_features.fit_transform(X)\n\n#print(X, X_poly)\nprint(X[0], X_poly[0])\n\n# fit it:\nlin_reg = LinearRegression()\nlin_reg.fit(X_poly, y)\nprint(lin_reg.intercept_, lin_reg.coef_)\n\n# result estimate: 0.48x(1)^2 + 0.99x(2) + 2.06\n# original:        0.50x(1)^2 + 1.00x(2) + 2.00 + gaussian noise\n
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PolynomialFeatures(degree=2, include_bias=False, interaction_only=False)\n[ 2.38942838] [ 2.38942838  5.709368  ]\n[ 1.9735233] [[ 0.95038538  0.52577032]]\n
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In [13]:
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X_new      = np.linspace(-3, 3, 100).reshape(100, 1)\nX_new_poly = poly_features.transform(X_new)\ny_new      = lin_reg.predict(X_new_poly)\n\n#testme = np.linspace(-3,3,20)\n#print(testme, testme.reshape(20,1))\n\nplt.plot(X, y, "b.")\nplt.plot(X_new, y_new, "r-", linewidth=2, label="Predictions")\nplt.xlabel("$x_1$", fontsize=14)\nplt.ylabel("$y$", rotation=0, fontsize=14)\nplt.legend(loc="upper left", fontsize=14)\nplt.axis([-3, 3, 0, 10])\n#save_fig("quadratic_predictions_plot")\nplt.show()\n
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Learning Curves

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# another way to check for underfit & overfit:\n# use learning curve plots to see performance vs training set size.\n\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\n\n# train model multiple times on various training subsets (of various sizes)\n\ndef plot_learning_curves(model, X, y):\n    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=10)\n    train_errors, val_errors = [], []\n    for m in range(1, len(X_train)):\n        model.fit(X_train[:m], y_train[:m])\n        y_train_predict = model.predict(X_train[:m])\n        y_val_predict = model.predict(X_val)\n        train_errors.append(mean_squared_error(y_train_predict, y_train[:m]))\n        val_errors.append(mean_squared_error(y_val_predict, y_val))\n\n    plt.plot(np.sqrt(train_errors), "r-+", linewidth=2, label="Training set")\n    plt.plot(np.sqrt(val_errors), "b-", linewidth=3, label="Validation set")\n    plt.legend(loc="upper right", fontsize=14)\n    plt.xlabel("Training set size", fontsize=14)\n    plt.ylabel("RMSE", fontsize=14)\n\nlin_reg = LinearRegression()\nplot_learning_curves(lin_reg, X, y)\nplt.axis([0, 80, 0, 3])\n#save_fig("underfitting_learning_curves_plot")\nplt.show()\n
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In [15]:
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# repeat exercise for 10th-degree polynomial\n\nfrom sklearn.pipeline import Pipeline\n\npolynomial_regression = Pipeline((\n    ("poly_features", PolynomialFeatures(degree=10, include_bias=False)),\n    ("sgd_reg", LinearRegression()),\n))\n\nplot_learning_curves(polynomial_regression, X, y)\nplt.axis([0,80,0,3])\nplt.show()\n\n# note: training error rate much lower than on Linear Regression\n# note: training/validation gap closes to zero. good fit?\n
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Bias/Variance Tradeoff

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  • Irreducibility: due to data noise.

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  • Rule of thumb: increasing model complexity increases variance & reduces bias (and vice versa.)

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Regularization

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  • Used to reduce overfit by constraining the model (ex: reducing the # of degrees in a polynomial).
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In [16]:
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# Ridge -- regularization term added to cost function.\n# alpha param -- forces model weights to minimal values. higher alpha = "flatter" function (converge to mean)\n
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  • Cost function:\n\"ridge-regression-cost-function\"
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In [17]:
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# build dataset\n\nimport numpy.random as rnd\n\nrnd.seed(42)\nm = 20\nX = 3 * rnd.rand(m, 1)\ny = 1 + 0.5 * X + rnd.randn(m, 1) / 1.5\nX_new = np.linspace(0, 3, 100).reshape(100, 1)\n\n# plot it\nplt.plot(X, y, "b.")\nplt.xlabel("$x_1$", fontsize=18)\nplt.ylabel("$y$", rotation=0, fontsize=18)\nplt.axis([0, 3, 0, 4])\n\n# apply Ridge regression\nfrom sklearn.linear_model import Ridge\n\nridge_reg = Ridge(alpha=1, solver="cholesky")\nridge_reg.fit(X,y)\nridge_reg.predict([[0.0],[1.5],[2.0],[3.0]])\n
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Out[17]:
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array([[ 1.00650911],\n       [ 1.55071465],\n       [ 1.73211649],\n       [ 2.09492018]])
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In [18]:
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# Ridge using SGD:\nsgd_reg = SGDRegressor(penalty="l2") \nsgd_reg.fit(X,y.ravel())\nridge_reg.predict([[0.0],[1.5],[2.0],[3.0]])\n
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Out[18]:
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array([[ 1.00650911],\n       [ 1.55071465],\n       [ 1.73211649],\n       [ 2.09492018]])
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In [19]:
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# Lasso -- similar to Ridge, also adds regularization term\n# uses L1 norm (instead of 1/2 square of L2 norm, as in Ridge.)\n# -- tends to force least important features to zero.\n\nfrom sklearn.linear_model import Lasso\n\nlasso_reg = Lasso(alpha=0.1)\nlasso_reg.fit(X,y)\nlasso_reg.predict([[0.0],[1.5],[2.0],[3.0]])\n
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Out[19]:
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array([ 1.14537356,  1.53788174,  1.66871781,  1.93038993])
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In [20]:
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# Elastic Net -- midddle ground.\n# regularization = mix of Ridge & Lasso (mix ratio "r")\n\nfrom sklearn.linear_model import ElasticNet\n\nelastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)\nelastic_net.fit(X,y)\nelastic_net.predict([[0.0],[1.5],[2.0],[3.0]])\n
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Out[20]:
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array([ 1.08639303,  1.54333232,  1.69564542,  2.00027161])
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In [21]:
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# Early Stopping -- stop training when minimum validation error reached\n\n# build dataset\nrnd.seed(42)\nm = 100\nX = 6 * rnd.rand(m, 1) - 3\ny = 2 + X + 0.5 * X**2 + rnd.randn(m, 1)\n\nX_train, X_val, y_train, y_val = train_test_split(X[:50], y[:50].ravel(), test_size=0.5, random_state=10)\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import Pipeline\n\npoly_scaler = Pipeline((\n    ("poly_features", PolynomialFeatures(\n        degree=90, \n        include_bias=False)),\n    ("std_scaler", StandardScaler()),\n    ))\n\nX_train_poly_scaled = poly_scaler.fit_transform(X_train)\nX_val_poly_scaled   = poly_scaler.transform(X_val)\n\nsgd_reg = SGDRegressor(n_iter=1,\n                       penalty=None,\n                       eta0=0.0005,\n                       warm_start=True,\n                       learning_rate="constant",\n                       random_state=42)\n\nn_epochs = 500\ntrain_errors, val_errors = [], []\n\nfor epoch in range(n_epochs):\n    sgd_reg.fit(X_train_poly_scaled, y_train)\n\n    y_train_predict = sgd_reg.predict(X_train_poly_scaled)\n    y_val_predict   = sgd_reg.predict(X_val_poly_scaled)\n\n    train_errors.append(mean_squared_error(y_train_predict, y_train))\n    val_errors.append(mean_squared_error(y_val_predict, y_val))\n\nbest_epoch    = np.argmin(val_errors)\nbest_val_rmse = np.sqrt(val_errors[best_epoch])\n\nplt.annotate('Best model',\n             xy=(best_epoch, best_val_rmse),\n             xytext=(best_epoch, best_val_rmse + 1),\n             ha="center",\n             arrowprops=dict(facecolor='black', shrink=0.05),\n             fontsize=16,\n            )\n\nbest_val_rmse -= 0.03  # just to make the graph look better\nplt.plot([0, n_epochs], [best_val_rmse, best_val_rmse], "k:", linewidth=2)\nplt.plot(np.sqrt(val_errors), "b-", linewidth=3, label="Validation set")\nplt.plot(np.sqrt(train_errors), "r--", linewidth=2, label="Training set")\nplt.legend(loc="upper right", fontsize=14)\nplt.xlabel("Epoch", fontsize=14)\nplt.ylabel("RMSE", fontsize=14)\n#save_fig("early_stopping_plot")\nplt.show()\n
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Logistic Regression

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  • commonly used to est probability of instance belonging to specified class. positive if >50% (labeled \"1\"), otherwise labeled \"0\".
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\"Logistic

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  • logistic is a sigmoid function, outputs 0<n<1.
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\"Logistic

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  • cost function = average over all training data. It is convex, so gradient descent will find global minimum.
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In [22]:
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#from sklearn import datasets\n#iris = datasets.load_iris()\n\nimport numpy as np\n\nfrom sklearn import datasets\niris = datasets.load_iris()\nprint(iris.keys())\n\nX = iris["data"][:, 3:] # petal width\ny = (iris["target"] == 2).astype(np.int) # 1 if Iris-Virginica, else 0\n
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dict_keys(['target_names', 'DESCR', 'data', 'target', 'feature_names'])\n
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In [23]:
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# train a LR model\n\nfrom sklearn.linear_model import LogisticRegression\nlog_reg=LogisticRegression()\nlog_reg.fit(X,y)\n\n# predict probability of flowers with petal widths = 0-3cm \nX_new             = np.linspace(0, 3, 1000).reshape(-1, 1)\ny_proba           = log_reg.predict_proba(X_new)\nprint(y_proba)\ndecision_boundary = X_new[y_proba[:, 1] >= 0.5][0]\n\nplt.plot(X_new, y_proba[:, 1], "g-", label="Iris-Virginica")\nplt.plot(X_new, y_proba[:, 0], "b--", label="Not Iris-Virginica")\nplt.text(decision_boundary+0.02, 0.15, "Decision  boundary", fontsize=14, color="k", ha="center")\nplt.xlabel("Petal width (cm)", fontsize=14)\nplt.ylabel("Probability", fontsize=14)\nplt.legend(loc="center left", fontsize=14)\nplt.show()\n
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[[ 0.98552764  0.01447236]\n [ 0.98541511  0.01458489]\n [ 0.98530171  0.01469829]\n ..., \n [ 0.02620686  0.97379314]\n [ 0.02600703  0.97399297]\n [ 0.02580868  0.97419132]]\n
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In [24]:
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# what's the prediction for petal length = 1.5 or 1.7cm?\nprint(log_reg.predict([[1.5], [1.7]]))\n
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In [25]:
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# Logistic Regressin contour plot\n# with multiple decision boundaries (not just 50%)\n\nfrom sklearn.linear_model import LogisticRegression\n\nX = iris["data"][:, (2, 3)]  # petal length, petal width\ny = (iris["target"] == 2).astype(np.int)\n\nlog_reg = LogisticRegression(C=10**10)\nlog_reg.fit(X, y)\n\nx0, x1 = np.meshgrid(\n         np.linspace(2.9, 7, 500).reshape(-1, 1),\n         np.linspace(0.8, 2.7, 200).reshape(-1, 1),\n    )\n\n# ravel(): return contiguous flattened array\nX_new = np.c_[x0.ravel(), x1.ravel()]\n\ny_proba = log_reg.predict_proba(X_new)\n\nplt.figure(figsize=(10, 4))\nplt.plot(X[y==0, 0], X[y==0, 1], "bs")\nplt.plot(X[y==1, 0], X[y==1, 1], "g^")\n\nzz = y_proba[:, 1].reshape(x0.shape)\ncontour = plt.contour(x0, x1, zz, cmap=plt.cm.brg)\n\n\nleft_right = np.array([2.9, 7])\nboundary = -(log_reg.coef_[0][0] * left_right + log_reg.intercept_[0]) / log_reg.coef_[0][1]\n\nplt.clabel(contour, inline=1, fontsize=12)\nplt.plot(left_right, boundary, "k--", linewidth=3)\nplt.text(3.5, 1.5, "Not Iris-Virginica", fontsize=14, color="b", ha="center")\nplt.text(6.5, 2.3, "Iris-Virginica", fontsize=14, color="g", ha="center")\nplt.xlabel("Petal length", fontsize=14)\nplt.ylabel("Petal width", fontsize=14)\nplt.axis([2.9, 7, 0.8, 2.7])\n#save_fig("logistic_regression_contour_plot")\nplt.show()\n
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Softmax Regression (Multinomial Logistic Regression)

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  • Predicts one class at a time (multiclass, not multioutput). Use only for mutually exclusive classes.
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  • Scoring for K classes: \"softmax-score-for-class
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  • Softmax function (aka normalized exponential): \"softmax-function\"
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  • Prediction: \"softmax
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  • Uses cross entropy to minimize cost function. (Same as log loss, used for Logistic Regression, when k=2.)\n\"cross
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In [26]:
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# use Softmax to classify iris flowers\n\nX = iris["data"][:, (2, 3)] # petal length, width\ny = iris["target"]\n\n# Scikit LR can be switched to Softmax with "multinomial" setting.\n# also defaults to L2 regularization (control with C parameter)\n\nsoftmax_reg = LogisticRegression(multi_class="multinomial",solver="lbfgs", C=10)\nsoftmax_reg.fit(X, y)\n\n# predict iris 5cm long, 2cm wide:\n\nsoftmax_reg.predict([[5, 2]])\nsoftmax_reg.predict_proba([[5,2]])\n
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Out[26]:
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array([[  6.33134078e-07,   5.75276067e-02,   9.42471760e-01]])
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In [27]:
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# softmax contour plot\n\nx0, x1 = np.meshgrid(\n        np.linspace(0, 8, 500).reshape(-1, 1),\n        np.linspace(0, 3.5, 200).reshape(-1, 1),\n    )\nX_new = np.c_[x0.ravel(), x1.ravel()]\n\n\ny_proba = softmax_reg.predict_proba(X_new)\ny_predict = softmax_reg.predict(X_new)\n\nzz1 = y_proba[:, 1].reshape(x0.shape)\nzz = y_predict.reshape(x0.shape)\n\nplt.figure(figsize=(10, 4))\nplt.plot(X[y==2, 0], X[y==2, 1], "g^", label="Iris-Virginica")\nplt.plot(X[y==1, 0], X[y==1, 1], "bs", label="Iris-Versicolor")\nplt.plot(X[y==0, 0], X[y==0, 1], "yo", label="Iris-Setosa")\n\nfrom matplotlib.colors import ListedColormap\ncustom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n\nplt.contourf(x0, x1, zz, cmap=custom_cmap, linewidth=5)\ncontour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg)\nplt.clabel(contour, inline=1, fontsize=12)\nplt.xlabel("Petal length", fontsize=14)\nplt.ylabel("Petal width", fontsize=14)\nplt.legend(loc="center left", fontsize=14)\nplt.axis([0, 7, 0, 3.5])\n#save_fig("softmax_regression_contour_plot")\nplt.show()\n
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\n\n\n" }, { "path": "ch04-training-models.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Training Models - Intro\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Linear Regression\\n\",\n \"\\n\",\n \"* y = theta0 + (theta1 * x1) + (theta2 * x2) + ...\\n\",\n \"* = h(theta)(x) \\n\",\n \"* = theta^T (dot) x --- theta^T = theta vector, transposed (row instead of col)\\n\",\n \"\\n\",\n \"* Training a model = finding theta that minimizes error function (ex: MSE)\\n\",\n \"\\n\",\n \"![alt text](MSE-cost-function-linear-regression.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Normal Equation: finds theta that minimizes cost function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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3sHhCZXQjezKTO72cweMbOHzey3igqsam1dlFFV3MN6nNv63gGhyTtC\\n/6Sk/3T30yWdJenh/CHVo62LMqqKe9gkX1vfOyA0mRO6mR0v6S2SbpAkd3/B3ReKCqxqbe1KqCru\\nYR0nUTF0jjEdeuHwSB0x7BQI5JNnUvQ0SQck/auZnSVpj6Sr3P35QiKrWFu7EqqMO26SbzCG4yc7\\nev6Fwzp4aElSuknStk5KA01i7p7tiWazku6WtMnd7zGzT0p6zt3/ZuBxWyVtlaSZmZlznnjiiZwh\\no+k27dgduahmempSd23fXNhzgHFhZnvcfTbpcXlG6E9Jesrd7+l+f7Ok7YMPcvedknZK0uzsbLZP\\nD1Qqb095lklSJlaB/DLX0N39x5J+aGa9Yu1bJT1USFSoTRG7BGaZJGViFcgvb5fLn0m60czul3S2\\npL/LHxLqVERPeZaJ2rZOSgNNkmulqLvvk5RY10F7FFH6yDJR29ZJaaBJWPqfQ1v3fhmmqF0Csyx7\\nZ6k8kA9L/zMK9UBeSh9Ae5HQMwp1/5I2b1kLjDtKLhmF3GaXp/QRYhkKaAtG6BnRZrdaqGUooC0Y\\noWfU5uPRijI4Gn/+V4djy1CM0oHyMULPaNxrzVGj8YXFpcjHzi8sstkWUAFG6DmMc5td1KTwMGy2\\nBZSPhN5ydU1CZpn8pfwClIuSS4vVOQkZN/l7wtqOpodMDIfQBQQ0FQk9oyYcxlBnL3zcAqRrLztD\\nd23fHJvUx7kLCCgbCT2DskbGo35I1NkLnzQpzIpToHpjXUPPWn8eNjLOsyBn1BN7itp3Jathk8Js\\ntgVUb2wT+igJdDDxRyVRKd/IOMuHRNN74ce5Cwiow9iWXOIS6NU37VtR7ti1d17bvnjfivJKnDwj\\n4yzlk3HvhQew0tiO0Iclyv7R+nW37dfS0eST8/KOjLOWTxgFA+gZ2xF6UqLslTviVj9KKnRkzCQi\\ngLzGNqFHJdBBVfZMh14+aUKbJxA6c08uJxRldnbW5+bmKnu9JL3Jzri6+PTUpA69cFgHD8WP0nsm\\nOxOlJOAQtqMdnICWynu/gBCZ2R53Tzzuc2xH6NLyqPiu7Zv1iSvOji13XHvZGepMWOLPGmVBT9rR\\naijb0YZ6GAjQNGM7KdovTc90UW2Lo7RLltHvXoeQDwMBmmRsEnpS6SJpkUz/fZt27M68oGeUJB1K\\nIqx7ARQwLsai5FJ06SJPR8ooSTqUU5Ho4AGqMRYJPW5U/JEv78/08/J0pMQl46m1nVW3hZIIQ+/g\\nAZpiLLpcTtv+VcX9K9977ow+uuXMymLZtXde226+T0tHVkbUOcZ0/e+flbjtQBu7XADkk7bLZSxq\\n6MMmMm+8+0nNvubEypLklo3Tuu62/asWLC0d9cg6OitBAaQ1FiWXYSUKlypvn3s2ZvVp2yY7ATTL\\nWCT0LRunNTW5ukbdkzaRFrXaMZTJTgDNMhYJXZKue/sZilselCaRFtkpE8pkJ4BmGZuEvmXjtN5z\\n7syqpJ42kRa52pGuDwBlyD0pamYTkuYkzbv7pflDyiZNN8hHt5yp2decmKlrpOhFPkx2AihaEV0u\\nV0l6WNKvFfCzMhllOX3WRMpqRwBNl6vkYmanSrpE0qeKCSebKjZ/ou4NoOnyjtA/IemDko4rIJbM\\nqtjzhEOPATRd5oRuZpdKesbd95jZ+UMet1XSVkmamZnJ+nJDVVUOoe4NoMnylFw2SXq7mT0u6QuS\\nNpvZ5wYf5O473X3W3WfXrVuX4+XiUQ4BgBwJ3d2vcfdT3X29pHdJ2u3u7y0sshHQBggAAe3lQjkE\\nwLgrJKG7+zclfbOIn1WUsnYpZPdDAE0VzAi93yh96U34uQBQhCAS+uCo+dALh0s5izOUMz4BhKn1\\nCT1q1Bwnb196KGd8AghT6xN61Kg5Tt6+9CL73anFAyha63dbTDs6LqIvvah+96IPrQYAKYCEHnvo\\n8mSn8L70ovrdq9h7BsD4aX3J5YLT1+nGu59ccQj0ZGdC1739jFJKGEX0u1OLB1CGVo/Qd+2d1y17\\n5lckc5P0znOavciII+gAlKHVCT2qdOGS7nzkQD0BpXTB6dF72sTdDgBptDqht7V0EfeB0/QPIgDN\\n1uoaelXb5hbdYtjWDyIAzdbqEXoV2+aW0WJIDR1AGVqd0KvYNreMFkP2bwdQhlaXXKToNsIiSyRl\\nlEc4zg5AGVqf0AcVvSNiWXV69m8HULRWl1yiFF0iiSqPmGgxBNA8wSX0okskWzZO653nTMv6bnNJ\\nt+yZZ+8VAI0SXEIvo4PkzkcOrFiNKrH3CoDmCS6hl9FBQt84gDYILqGX0cpI3ziANgiuy0UqvoNk\\n24UbVnTOSPSNA2ieIBN60egbB9AGJPSU6BsH0HTB1dABYFyR0AEgECR0AAgECR0AAkFCB4BAkNAB\\nIBAkdAAIBAkdAAKROaGb2avN7E4ze8jM9pvZVUUGBgAYTZ6Voocl/ZW732tmx0naY2Z3uPtDBcUG\\nABhB5hG6uz/t7vd2v/65pIclsTYeAGpSSA3dzNZL2ijpnoj7tprZnJnNHThwoIiXAwBEyJ3QzeyV\\nkm6RdLW7Pzd4v7vvdPdZd59dt45zOAGgLLl2WzSzjpaT+Y3ufmsxIUXbtXee7WsBYIjMCd3MTNIN\\nkh52938oLqTVdu2dX3HAxPzCoq659QFJIqkDQFeekssmSX8oabOZ7ev+d3FBca1w/e2PrjgtSOKQ\\nZgAYlHmE7u7/LckKjCUWhzQDQLJWrBTlkGYASNaKhL7twg2a7EysuI1DmgFgpVacKcohzQCQrBUJ\\nXeKQZgBI0oqSCwAgGQkdAAJBQgeAQJDQASAQJHQACIS5e3UvZnZA0hMjPu0kST8pIZwiNDW2psYl\\nNTe2psYlNTe2psYlNTe2rHG9xt0Tt6utNKFnYWZz7j5bdxxRmhpbU+OSmhtbU+OSmhtbU+OSmhtb\\n2XFRcgGAQJDQASAQbUjoO+sOYIimxtbUuKTmxtbUuKTmxtbUuKTmxlZqXI2voQMA0mnDCB0AkEJt\\nCd3MLjKzR83se2a2PeJ+M7N/7N5/v5m9Me1zK4jtPd2YHjCzb5vZWX33Pd69fZ+ZzdUQ2/lm9mzf\\nKVIfTvvckuPa1hfTg2Z2xMxO7N5X2ntmZp82s2fM7MGY++u8zpJiq+U6SxFXLddYytjqus5ebWZ3\\nmtlDZrbfzK6KeEz515q7V/6fpAlJ35f0Wkkvk3SfpN8ceMzFkr6m5VORzpV0T9rnVhDbeZJO6H79\\ne73Yut8/LumkGt+38yV9Jctzy4xr4PGXSdpd0Xv2FklvlPRgzP21XGcpY6vrOkuKq/JrLG1sNV5n\\nJ0t6Y/fr4yR9t46cVtcI/U2SvufuP3D3FyR9QdLlA4+5XNJnfdndkqbM7OSUzy01Nnf/trsf7H57\\nt6RTC3z9XLGV9Nyif/a7JX2+oNceyt2/JelnQx5S13WWGFtd11mK9yxO7e/ZgCqvs6fd/d7u1z+X\\n9LCkwf2+S7/W6kro05J+2Pf9U1r9j497TJrnlh1bvyu1/Knb45L+y8z2mNnWAuMaJbbzun/Sfc3M\\nzhjxuWXGJTNbK+kiSbf03Vzme5akrutsVFVeZ2lUfY2NpM7rzMzWS9oo6Z6Bu0q/1lpzwEUTmdkF\\nWv4/2pv7bn6zu8+b2a9LusPMHumOKqpyr6QZd/+FmV0saZek11X4+kkuk3SXu/ePsup+zxqtgddZ\\n068xqabrzMxeqeUPkavd/bkif3YadY3Q5yW9uu/7U7u3pXlMmueWHZvM7A2SPiXpcnf/ae92d5/v\\n/u8zkr6k5T+nKovN3Z9z9190v/4PSR0zOynNc8uMq8+7NPBncMnvWZK6rrNUarrOhqrpGhtV5deZ\\nmXW0nMxvdPdbIx5S/rVWxgRBigmENZJ+IOk0vTQJcMbAYy7RygmE/0n73Apim5H0PUnnDdx+rKTj\\n+r7+tqSLKo7tN/TS+oI3SXqy+x6W9r6l/dmSjtdy/fPYqt6z7s9dr/gJvlqus5Sx1XKdpYir8mss\\nbWx1XWfdf/9nJX1iyGNKv9YKfaNHfAMu1vJM8Pclfah72wckfaDvDfrn7v0PSJod9tyKY/uUpIOS\\n9nX/m+ve/truL+M+Sftriu1Pu699n5Yn0s4b9tyq4up+/35JXxh4XqnvmZZHaU9LWtJybfLKBl1n\\nSbHVcp2liKuWayxNbDVeZ2/Wco3+/r7f18VVX2usFAWAQLBSFAACQUIHgECQ0AEgECR0AAgECR0A\\nAkFCB4BAkNABIBAkdAAIxP8DyrKqeAevNQgAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# generate some data\\n\",\n \"import numpy as np\\n\",\n \"X = 2 * np.random.rand(100, 1)\\n\",\n \"y = 4 + 3 * X + np.random.randn(100, 1)\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"plt.scatter(X,y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 3.58859665]\\n\",\n \" [ 3.41876053]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# find theta. \\n\",\n \"# 1) use NumPy's matrix inverse function.\\n\",\n \"# 2) use dot method for matrix multiply.\\n\",\n \"\\n\",\n \"X_b = np.c_[np.ones((100, 1)), X] # add x0 = 1 to each instance\\n\",\n \"theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)\\n\",\n \"\\n\",\n \"# results:\\n\",\n \"print(theta_best) # compare to generated data: y = 4 + 3x + noise\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 3.58859665]\\n\",\n \" [ 7.00735719]\\n\",\n \" [ 10.42611772]]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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1VIjRHkVk4N/3Ezuw9YBXQBT5LpyYvUmjDryjkD77XXfImmrQ0WLYJd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\\nIhITCnwRkZhQ4IuIxIQCX0QkJv4/thaqLyvY4UAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# make some predictions\\n\",\n \"\\n\",\n \"X_new = np.array([[0],[1],[2]])\\n\",\n \"X_new_b = np.c_[np.ones((3, 1)), X_new] # add x0 = 1 to each instance\\n\",\n \"y_predict = X_new_b.dot(theta_best)\\n\",\n \"print(y_predict)\\n\",\n \"\\n\",\n \"# then plot\\n\",\n \"\\n\",\n \"plt.plot(X_new, y_predict, \\\"r-\\\")\\n\",\n \"plt.plot(X, y, \\\"b.\\\")\\n\",\n \"plt.axis([0, 2, 0, 15])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"intercept & coefficient:\\n\",\n \" [ 3.58859665] [[ 3.41876053]]\\n\",\n \"predictions:\\n\",\n \" [[ 3.58859665]\\n\",\n \" [ 7.00735719]\\n\",\n \" [ 10.42611772]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Scikit equivalent\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"lin_reg = LinearRegression()\\n\",\n \"\\n\",\n \"lin_reg.fit(X,y)\\n\",\n \"print(\\\"intercept & coefficient:\\\\n\\\", lin_reg.intercept_, lin_reg.coef_)\\n\",\n \"print(\\\"predictions:\\\\n\\\", lin_reg.predict(X_new))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Gradient Descent\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 3.58859665]\\n\",\n \" [ 3.41876053]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Gradient Descent - Batch\\n\",\n \"# (Batch: math includes full training set X.)\\n\",\n \"\\n\",\n \"# need to find partial derivative (slope) of the cost function\\n\",\n \"# for each model parameter (theta).\\n\",\n \"\\n\",\n \"theta_path_bgd = []\\n\",\n \"\\n\",\n \"eta = 0.1 # learning rate\\n\",\n \"n_iterations = 1000\\n\",\n \"m = 100\\n\",\n \"\\n\",\n \"theta = np.random.randn(2,1) # random initialization\\n\",\n \"\\n\",\n \"for iteration in range(n_iterations):\\n\",\n \" gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y)\\n\",\n \" theta = theta - eta * gradients\\n\",\n \" theta_path_bgd.append(theta)\\n\",\n \" \\n\",\n \"print(theta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 3.6036273 ]\\n\",\n \" [ 3.44079196]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Gradient Descent - Stochastic\\n\",\n \"# Stochastic: finds gradients based on random instances\\n\",\n \"# adv: better for huge datasets\\n\",\n \"# dis: much more erratic than batch GD \\n\",\n \"# -- good for avoiding local minima\\n\",\n \"# -- bad b/c may not find optimum sol'n\\n\",\n \"\\n\",\n \"# simulated annealing helps. (gradually reduces learning rate)\\n\",\n \"\\n\",\n \"theta_path_sgd = []\\n\",\n \"\\n\",\n \"n_epochs, t0, t1 = 50, 5, 50 # learning schedule hyperparameters\\n\",\n \"\\n\",\n \"def learning_schedule(t):\\n\",\n \" return t0 / (t + t1)\\n\",\n \"\\n\",\n \"theta = np.random.randn(2,1) # random initialization\\n\",\n \"\\n\",\n \"for epoch in range(n_epochs):\\n\",\n \" for i in range(m):\\n\",\n \" random_index = np.random.randint(m)\\n\",\n \" xi = X_b[random_index:random_index+1]\\n\",\n \" yi = y[random_index:random_index+1]\\n\",\n \"\\n\",\n \" gradients = 2 * xi.T.dot(xi.dot(theta) - yi)\\n\",\n \"\\n\",\n \" eta = learning_schedule(epoch * m + i)\\n\",\n \" theta = theta - eta * gradients\\n\",\n \" theta_path_sgd.append(theta)\\n\",\n \" \\n\",\n \"print(theta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 3.57214013] [ 3.39609675]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# SGD Regression using Scikit:\\n\",\n \"\\n\",\n \"from sklearn.linear_model import SGDRegressor\\n\",\n \"\\n\",\n \"sgd_reg = SGDRegressor(n_iter=50, penalty=None, eta0=0.1)\\n\",\n \"sgd_reg.fit(X, y.ravel())\\n\",\n \"\\n\",\n \"print(sgd_reg.intercept_, sgd_reg.coef_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 3.70412445]\\n\",\n \" [ 3.54124923]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Gradient Descent - MiniBatch\\n\",\n \"# adv: performance boost via GPUs\\n\",\n \"\\n\",\n \"theta_path_mgd = []\\n\",\n \"\\n\",\n \"n_iterations = 50\\n\",\n \"minibatch_size = 20\\n\",\n \"\\n\",\n \"import numpy.random as rnd\\n\",\n \"\\n\",\n \"rnd.seed(42)\\n\",\n \"theta = rnd.randn(2,1) # random initialization\\n\",\n \"\\n\",\n \"t0, t1 = 10, 1000\\n\",\n \"def learning_schedule(t):\\n\",\n \" return t0 / (t + t1)\\n\",\n \"\\n\",\n \"t = 0\\n\",\n \"for epoch in range(n_iterations):\\n\",\n \" shuffled_indices = rnd.permutation(m)\\n\",\n \" X_b_shuffled = X_b[shuffled_indices]\\n\",\n \" y_shuffled = y[shuffled_indices]\\n\",\n \" \\n\",\n \" for i in range(0, m, minibatch_size):\\n\",\n \" t += 1\\n\",\n \" \\n\",\n \" xi = X_b_shuffled[i:i+minibatch_size]\\n\",\n \" yi = y_shuffled[i:i+minibatch_size]\\n\",\n \" \\n\",\n \" gradients = 2 * xi.T.dot(xi.dot(theta) - yi)\\n\",\n \" eta = learning_schedule(t)\\n\",\n \" theta = theta - eta * gradients\\n\",\n \" theta_path_mgd.append(theta)\\n\",\n \" \\n\",\n \"print(theta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"theta_path_bgd = np.array(theta_path_bgd)\\n\",\n \"theta_path_sgd = np.array(theta_path_sgd)\\n\",\n \"theta_path_mgd = np.array(theta_path_mgd)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Xz5pDUuWzZo3Ro2bIBly6BTJ/2x/v7QoYOc888/cP26tN59SAeJFOyV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\\nIkVOCZ2IiIhIkfv/SCQDYuYTKfEAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,4))\\n\",\n \"plt.plot(theta_path_sgd[:, 0], theta_path_sgd[:, 1], \\\"r-s\\\", linewidth=1, label=\\\"Stochastic\\\")\\n\",\n \"plt.plot(theta_path_mgd[:, 0], theta_path_mgd[:, 1], \\\"g-+\\\", linewidth=1, label=\\\"Mini-batch\\\")\\n\",\n \"plt.plot(theta_path_bgd[:, 0], theta_path_bgd[:, 1], \\\"b-o\\\", linewidth=1, label=\\\"Batch\\\")\\n\",\n \"plt.legend(loc=\\\"upper right\\\", fontsize=14)\\n\",\n \"plt.xlabel(r\\\"$\\\\theta_0$\\\", fontsize=20)\\n\",\n \"plt.ylabel(r\\\"$\\\\theta_1$ \\\", fontsize=20, rotation=0)\\n\",\n \"plt.axis([2.5, 4.5, 2.3, 3.9])\\n\",\n \"#save_fig(\\\"gradient_descent_paths_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Polynomial Regression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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atQr52krvGPcU/T4XkE9BKq+EOqej2RphoUWIvenWQJ1qHufGbnF3TD\\n7ffXEljHOU2Htcihl1DVH9I4lhemzWQ1qfDdSdprdmW588lSQtrtmSfNBJXynw95y1aZBYwueugl\\njGu+uwpJqSaTdM2lFxa+uKW9ppTtzidrCmVYaifP+VAkbZM1TVdllQ+agR56CSHXzx73tcqrqBIp\\n+5pZV6gc1APPez4UWRUzy/tk3fTxQEBPkSXAhgpC/LGtqiLVVOY1s6bU0lIeG80y3QX0nj9pKaJh\\naZth75Plk8cDAT1BngAbIgjxxzZ6WS7YWXPTaXdqWVM6vedZb638oGPmxcDpeCCHnmDUZWCh/9jI\\nlQ6WNU+dNTddtDIpbUGvKpYLYLxnPNBDTzDq3kzIKgXSN8NlvSPKk1IrcqeWdj65Vi8KIcdTqt4v\\nFc1AQE8w6jKwkH9spG+Gy3PBrrKENO08m5qc0NGZXUGPVffSBBgNAnqCUfdmQv6xpQWrhaVlXTTz\\nWf6Q1Zy67TrOs3H+vY8DAnqCOnozRf7Ykgb2BlVK9OaLu8ccR01JP9BrRmjmKbPbqjA9Pe1zc3Mj\\nO16b9efKpdWg9Ks/M7VmH8s0VdzWx2Tc6/7H/f3HxsyOufv0sOdF2UPnZEzPld/99UXdfNUlZz6f\\n0Ate5dHk39M4px8YOG+v6AI6J+OqQQN7vcEqaZ9Jqfp8Mb+n5w27sKV9v6oLIgPn7RVdHfo4LBWa\\npY48a11xXcvxjsPvKYthNe9p3//w7InKluJlklF7FQ7oZnaBmd1tZg+a2QNmdl3IhqVp+8nYlEkv\\nZbXh9xRigtawC1va92+997HKLohMMmqvMimXU5JucPf7zOzFko6Z2efd/cFAbUuUVsWxwUyz8wvR\\n3zI2ZdJLWU0pDSwqVMpo2IUt7fuhluJN0pQqH4RXuIfu7k+6+32df/9Q0kOSKo8aST1TafUPYBS7\\nw1Qt76SXozO79PC+K3R0ZlejLmax77wUKmU0rDec9wIX4oI4rpuojIMgg6Jmtk3SDkn3JnzvWknX\\nStKFF15Y+ljdk+6G2+9f14tpw8BO7D3brthrrEOljIb1hpO+nybkBXGcq3zarHRAN7MXSbpD0vvc\\n/Qf933f3A5IOSKt16GWPJ62ejNffdjzxe7GX47XpdjjmoBHqwjrswjaogyKtLsH7nHt0F0TUo1RA\\nN7NNWg3mB939UJgmZVNXT7bqcrzYe7ZtEfLCOuzCNqiD8py7Ht53Re5jYjwVDuhmZpJukfSQu38s\\nXJOyqasnO4oa3ph7tm0x6gtrW1JtqFeZHvpOSe+UdMLMut2LD7r7XeWbNVxdPdkYyvHKpoSaPMNz\\nlEZ5YW1Tqg31KRzQ3f1LWr8W/0hRjrde2ZQQMzzrQaoNIUQ39b9uRXtSo+r1lk0JxTQtvG13EqTa\\nUBYBPaciPalR9nrLpoRiSClJ1X6mbbtQYHwQ0AvI25MaZa+3bEqo6Smlrqo+U1JOiFl0i3PFaJS9\\n3rIzNGOZ4VnVZ8qiYogZAX0ERrkYUtlp3bFMC6/qM40l5QQkIeUyAk3bO3JYjjiGwbmqPtNYUk5A\\nEnroI9CkXm/W5XmbrqrPNJaUE5CEPUXHTNoORuO+x2ivPFUuVMRgFFq9pyiKy5MjDh2sYgl+WVNO\\nVMSgaUi5jJm0XPDk5k1rvg6dmvnw7Aldf9vx6FM9vaiIQdMQ0AsKsT1ZHfbu3q5NG9ev2PD0j0+t\\neQ9ZglXWz2B2fkEH73lU/cm92IMfFTFoGgJ6ATEPLO7ZMaVzzl6faVt5ztcE12HBKs9nsP/wyXXB\\nfNhxusdo8kUz7W7HpUa2F+1HQC8g9lvt7y+vJD7eG1yH1Xnn+QwGBe2048Rw0UzbDlFqZnvRfgT0\\nAmK/1c4yKWdY+V7ae11YWl7XO007nnWOk6TOi2bWO4Pe0skkMV3k0Q4E9ALKzlKsO5WQFKw3bTA9\\n8+ypM22SNLDOe9B77e+dJh3PJF1z6YWp1SB1XTST7gyuv+24tqX8rrobdaetIx3LRR7tQEAvoMzk\\nkyakEvon5UxObJJM+t4zK2vaJElHZ3bp4X1X6OjMrjXBd1C6QVrbO02aBPRXv/F6/dmeS1J/fpTL\\nJfRKujPo5v8H/a7qai/Qi4BeQJlZik3Jv3d7lg/vu0LnvOAsrZxeO2w5rE3D0g3S2t5p7/H6Lw5J\\n6pqxOaxHnfa5MMMUTcDEooKKrnfSxPx70TZ1P4O02ad5UlBpE45GPREpbS2XXkmfCzsOoQkI6CMW\\nevGnELMvy7apzEJZw2ZbjjogJr2XfmmfSwyLmqHdSLmMWMhb81D5+LJtakMKqqs/ldQ/2EkaBU1G\\nD33EQt6ah9q1p2ibQtwdNDEF1dvTjmX9GUAioNci1K15yGCYt02hFqZq+vrjpFEQE1IuEauzVC5U\\nqoTqECAcAnrE6gyGoe4OmrT5BxA7Ui4Rq7NULmSqhLQGEAYBPXJ1BcNR75MKYDgCOgphIg3QPAR0\\nFEaqBGgWBkUBoCVKBXQzu9zMTprZN81sJlSjAAD5FQ7oZrZR0t9K+kVJF0u62swuDtUwAEA+ZXro\\nb5D0TXf/X3d/VtI/SboyTLMAAHmVCehTkh7r+frxzmNrmNm1ZjZnZnOLi4slDgcAGKTyKhd3PyDp\\ngCSZ2aKZPVLwpc6T9J1gDasX76WZeC/N1Kb3IhV7P6/M8qQyAX1B0gU9X7+i81gqd99S9GBmNufu\\n00V/vkl4L83Ee2mmNr0Xqdr3Uybl8hVJrzKzi8zsbEm/KenOMM0CAORVuIfu7qfM7L2SDkvaKOmT\\n7v5AsJYBAHIplUN397sk3RWoLcMcGNFxRoH30ky8l2Zq03uRKnw/5u7DnwUAaDym/gNAS0QT0M3s\\nT83sq2Z23Mw+Z2Zb625TGWa238y+3nlP/2xmk3W3qSgz+3Uze8DMnjOzKKsR2rKMhZl90syeMrOv\\n1d2WsszsAjO728we7Jxf19XdpqLM7IVm9t9mdn/nvfxxJceJJeViZi9x9x90/v37ki529/fU3KzC\\nzOwXJB3pDC7/hSS5+x/W3KxCzOynJD0n6e8lvd/d52puUi6dZSz+R9JbtTpB7iuSrnb3B2ttWAFm\\n9kZJT0v6R3d/bd3tKcPMzpd0vrvfZ2YvlnRM0p5Ify8m6Rx3f9rMNkn6kqTr3P2ekMeJpofeDeYd\\n50iK40qUwt0/5+6nOl/eo9U6/ii5+0Punm8z0WZpzTIW7v5FSd+tux0huPuT7n5f598/lPSQEmaj\\nx8BXPd35clPnv+AxLJqALklm9lEze0zSNZL+qO72BPRuSf9WdyPGWKZlLFAfM9smaYeke+ttSXFm\\nttHMjkt6StLn3T34e2lUQDez/zCzryX8d6UkufuH3P0CSQclvbfe1g437P10nvMhSae0+p4aK8t7\\nAapgZi+SdIek9/XdqUfF3U+7++u1ejf+BjMLnhJr1I5F7v6WjE89qNX695sqbE5pw96Pmf2WpLdJ\\nuswbPpiR43cTo9zLWGA0OvnmOyQddPdDdbcnBHdfMrO7JV0uKejgdaN66IOY2at6vrxS0tfraksI\\nZna5pA9I+mV3f6bu9ow5lrFooM5A4i2SHnL3j9XdnjLMbEu3ks3MJrQ6AB88hsVU5XKHpO1araZ4\\nRNJ73D3aXpSZfVPSCyT9X+ehe2Kt2jGzX5H0N5K2SFqSdNzdd9fbqnzM7JckfVzPL2Px0ZqbVIiZ\\n3SrpTVpd0e/bkm5y91tqbVRBZvbzkv5L0gmt/t1L0gc7M9SjYmavk/RprZ5fGyTd7u5/Evw4sQR0\\nAMBg0aRcAACDEdABoCUI6ADQEgR0AGgJAjoAtAQBHQBagoAOAC1BQAeAlvh//BULzjgI7hcAAAAA\\nSUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# example quadratic equation + noise: y = 0.5*X^2 + X + 2 + noise\\n\",\n \"m = 100\\n\",\n \"X = 6 * np.random.rand(m, 1) - 3\\n\",\n \"y = 0.5 * X**2 + X + 2 + np.random.randn(m, 1)\\n\",\n \"\\n\",\n \"plt.scatter(X,y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"PolynomialFeatures(degree=2, include_bias=False, interaction_only=False)\\n\",\n \"[ 2.38942838] [ 2.38942838 5.709368 ]\\n\",\n \"[ 1.9735233] [[ 0.95038538 0.52577032]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# fit using Scikit\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import PolynomialFeatures\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"\\n\",\n \"# caution: PolynomialFeatures converts array of n features\\n\",\n \"# into array of (n+d)!/d!n! features -- combinatorial explosions possible :-)\\n\",\n \"\\n\",\n \"poly_features = PolynomialFeatures(degree=2, include_bias=False)\\n\",\n \"print(poly_features)\\n\",\n \"\\n\",\n \"# X_poly: original feature of X, plus its square.\\n\",\n \"X_poly = poly_features.fit_transform(X)\\n\",\n \"\\n\",\n \"#print(X, X_poly)\\n\",\n \"print(X[0], X_poly[0])\\n\",\n \"\\n\",\n \"# fit it:\\n\",\n \"lin_reg = LinearRegression()\\n\",\n \"lin_reg.fit(X_poly, y)\\n\",\n \"print(lin_reg.intercept_, lin_reg.coef_)\\n\",\n \"\\n\",\n \"# result estimate: 0.48x(1)^2 + 0.99x(2) + 2.06\\n\",\n \"# original: 0.50x(1)^2 + 1.00x(2) + 2.00 + gaussian noise\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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NoIhVzufu1a+NOf3ALn1fB8Gca9sPSOMcbUxt13w5w5bjKeqVP3ugqW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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X_new = np.linspace(-3, 3, 100).reshape(100, 1)\\n\",\n \"X_new_poly = poly_features.transform(X_new)\\n\",\n \"y_new = lin_reg.predict(X_new_poly)\\n\",\n \"\\n\",\n \"#testme = np.linspace(-3,3,20)\\n\",\n \"#print(testme, testme.reshape(20,1))\\n\",\n \"\\n\",\n \"plt.plot(X, y, \\\"b.\\\")\\n\",\n \"plt.plot(X_new, y_new, \\\"r-\\\", linewidth=2, label=\\\"Predictions\\\")\\n\",\n \"plt.xlabel(\\\"$x_1$\\\", fontsize=14)\\n\",\n \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=14)\\n\",\n \"plt.legend(loc=\\\"upper left\\\", fontsize=14)\\n\",\n \"plt.axis([-3, 3, 0, 10])\\n\",\n \"#save_fig(\\\"quadratic_predictions_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Learning Curves\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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9OmTTX69z+UBx64kX37ItfRzJz5BCecULia6Omn/8XJJzfk+ONrMnHi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\\nYgnDGGOMJ5YwjDHGePL/cpSTX2c3xnAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# another way to check for underfit & overfit:\\n\",\n \"# use learning curve plots to see performance vs training set size.\\n\",\n \"\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"# train model multiple times on various training subsets (of various sizes)\\n\",\n \"\\n\",\n \"def plot_learning_curves(model, X, y):\\n\",\n \" X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=10)\\n\",\n \" train_errors, val_errors = [], []\\n\",\n \" for m in range(1, len(X_train)):\\n\",\n \" model.fit(X_train[:m], y_train[:m])\\n\",\n \" y_train_predict = model.predict(X_train[:m])\\n\",\n \" y_val_predict = model.predict(X_val)\\n\",\n \" train_errors.append(mean_squared_error(y_train_predict, y_train[:m]))\\n\",\n \" val_errors.append(mean_squared_error(y_val_predict, y_val))\\n\",\n \"\\n\",\n \" plt.plot(np.sqrt(train_errors), \\\"r-+\\\", linewidth=2, label=\\\"Training set\\\")\\n\",\n \" plt.plot(np.sqrt(val_errors), \\\"b-\\\", linewidth=3, label=\\\"Validation set\\\")\\n\",\n \" plt.legend(loc=\\\"upper right\\\", fontsize=14)\\n\",\n \" plt.xlabel(\\\"Training set size\\\", fontsize=14)\\n\",\n \" plt.ylabel(\\\"RMSE\\\", fontsize=14)\\n\",\n \"\\n\",\n \"lin_reg = LinearRegression()\\n\",\n \"plot_learning_curves(lin_reg, X, y)\\n\",\n \"plt.axis([0, 80, 0, 3])\\n\",\n \"#save_fig(\\\"underfitting_learning_curves_plot\\\")\\n\",\n \"plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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U9W1a6quiBe8Zno2BOGadGiBd26dcststm2bRuzZs1i8ODBefZ78803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\\n/w/wE18KcZc4GwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# repeat exercise for 10th-degree polynomial\\n\",\n \"\\n\",\n \"from sklearn.pipeline import Pipeline\\n\",\n \"\\n\",\n \"polynomial_regression = Pipeline((\\n\",\n \" (\\\"poly_features\\\", PolynomialFeatures(degree=10, include_bias=False)),\\n\",\n \" (\\\"sgd_reg\\\", LinearRegression()),\\n\",\n \"))\\n\",\n \"\\n\",\n \"plot_learning_curves(polynomial_regression, X, y)\\n\",\n \"plt.axis([0,80,0,3])\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# note: training error rate much lower than on Linear Regression\\n\",\n \"# note: training/validation gap closes to zero. good fit?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Bias/Variance Tradeoff\\n\",\n \"\\n\",\n \"* Bias: the part of generalization error due to wrong assumptions.\\n\",\n \"* Variance: due to model sensitivity to small training variations. (More common in high-dimensional models.)\\n\",\n \"* Irreducibility: due to data noise.\\n\",\n \"\\n\",\n \"* Rule of thumb: increasing model complexity increases variance & reduces bias (and vice versa.)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Regularization\\n\",\n \"\\n\",\n \"* Used to reduce overfit by constraining the model (ex: reducing the # of degrees in a polynomial).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Ridge -- regularization term added to cost function.\\n\",\n \"# alpha param -- forces model weights to minimal values. higher alpha = \\\"flatter\\\" function (converge to mean)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Cost function:\\n\",\n \"![ridge-regression-cost-function](pics/ridge-regression-cost-function.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1.00650911],\\n\",\n \" [ 1.55071465],\\n\",\n \" [ 1.73211649],\\n\",\n \" [ 2.09492018]])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYYAAAESCAYAAAD5d3KwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nUovBIElqMRgkSS0GgySp5f8DLm3GQEg8GJYAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# build dataset\\n\",\n \"\\n\",\n \"import numpy.random as rnd\\n\",\n \"\\n\",\n \"rnd.seed(42)\\n\",\n \"m = 20\\n\",\n \"X = 3 * rnd.rand(m, 1)\\n\",\n \"y = 1 + 0.5 * X + rnd.randn(m, 1) / 1.5\\n\",\n \"X_new = np.linspace(0, 3, 100).reshape(100, 1)\\n\",\n \"\\n\",\n \"# plot it\\n\",\n \"plt.plot(X, y, \\\"b.\\\")\\n\",\n \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \"plt.ylabel(\\\"$y$\\\", rotation=0, fontsize=18)\\n\",\n \"plt.axis([0, 3, 0, 4])\\n\",\n \"\\n\",\n \"# apply Ridge regression\\n\",\n \"from sklearn.linear_model import Ridge\\n\",\n \"\\n\",\n \"ridge_reg = Ridge(alpha=1, solver=\\\"cholesky\\\")\\n\",\n \"ridge_reg.fit(X,y)\\n\",\n \"ridge_reg.predict([[0.0],[1.5],[2.0],[3.0]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1.00650911],\\n\",\n \" [ 1.55071465],\\n\",\n \" [ 1.73211649],\\n\",\n \" [ 2.09492018]])\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Ridge using SGD:\\n\",\n \"sgd_reg = SGDRegressor(penalty=\\\"l2\\\") \\n\",\n \"sgd_reg.fit(X,y.ravel())\\n\",\n \"ridge_reg.predict([[0.0],[1.5],[2.0],[3.0]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1.14537356, 1.53788174, 1.66871781, 1.93038993])\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Lasso -- similar to Ridge, also adds regularization term\\n\",\n \"# uses L1 norm (instead of 1/2 square of L2 norm, as in Ridge.)\\n\",\n \"# -- tends to force least important features to zero.\\n\",\n \"\\n\",\n \"from sklearn.linear_model import Lasso\\n\",\n \"\\n\",\n \"lasso_reg = Lasso(alpha=0.1)\\n\",\n \"lasso_reg.fit(X,y)\\n\",\n \"lasso_reg.predict([[0.0],[1.5],[2.0],[3.0]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1.08639303, 1.54333232, 1.69564542, 2.00027161])\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Elastic Net -- midddle ground.\\n\",\n \"# regularization = mix of Ridge & Lasso (mix ratio \\\"r\\\")\\n\",\n \"\\n\",\n \"from sklearn.linear_model import ElasticNet\\n\",\n \"\\n\",\n \"elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)\\n\",\n \"elastic_net.fit(X,y)\\n\",\n \"elastic_net.predict([[0.0],[1.5],[2.0],[3.0]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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hhThBIS/JcwSE9P54knnuDdd99l+/btHDt2jKNHj+a5nnO7du38juvW\\nrUtycnKB37Nu3ToaNWpErVq1MvM7dep0ws8TEUaPHs2QIUOYPHkyF154IQMGDKC556HmsmXL+Oab\\nbzKXHc1q48aNOcpTEpXvIOGlymmnCVWqwOHDsH077NgBdeuGumDGZJFX757izi8iUVFRfsdPPPEE\\nL7/8MhMmTMhcj/ruu+/m6NGjJ/yc7A+8RSTPdZ9ze09ezUl5eeqppxg6dCjz5s3js88+Y8yYMUyb\\nNo3BgweTkZHBlVdeyZNPPpnjfXXq1CnUdYOl/D6TANfX9cILoUYNwo8dJmtT4NKloSuWMeXJokWL\\n6NevH9dccw1nnHEGTZo04ddffw16OVq1asXmzZvJOtXPkiVL8vXeli1bMnr0aD755BMGDx7MlClT\\nAOjQoQNr1qyhcePGNGvWzG/z1i4iIyPzDG6hVL6DRIUKrt/rvn3w00+cfbYvK5//NowxhdSiRQvm\\nz5/P4sWLWbt2LTfddBM7duwIejkuvfRSGjZsyPXXX8+qVav49ttveeCBBzJ7HeVm//793HnnnXz1\\n1Vds3ryZ7777jsWLF9O6dWsARo0aRVJSEtdccw1Lly5l06ZNfPbZZwwbNoxjx44B0KhRI1auXMn6\\n9etJSUkhPT09aPecH+U7SIDfeImOHX3JFiSMCY5x48bRrl07LrroIrp3706tWrVOenR1UQgPD+fD\\nDz9k3759dOzYkeHDh/P3v/8dgEqVKuX6noiICJKTk7n22mtp0aIFf/nLX7jgggt45plnAGjYsCHf\\nffcdR48e5aKLLqJt27bceeedREdHE+aZfvqWW26hcePGtG/fnvj4eBITE4Nzw/kkGqR2yOKSkJCg\\nhfqjTpgAo0fDiBH89uBrNGnikmNj4Y8/bOS1Cb4TLV5vguuHH36gc+fOrF69mjZtin3lgiJ3on9L\\nIrJMVRNyzczCHlx7axI//kijRhAX52bs2LcPNmyAFi1CWjpjTBDNmjWL6tWr06xZMzZu3Mhdd93F\\n2WefXSoDRFGx38lnngndukHPnohgTU7GlGP79+/n5ptvplWrVlx33XW0b9+ejz/+ONTFCimrSVSr\\nBl9+mXl49tnwySduf8kSGDIkNMUyxgTf8OHDGT58eKiLUaJYTcLr6FHYu5fOnX1J334buuIYY0xJ\\nYEECYMoUqFoVxo7lnHPA29ttxQo4cCC0RTPGmFCyIAFQvz6kpcGPPxITA96R8hkZUMImZDTlRGnv\\ndWhCr6j+DVmQAGjvWQtpxQrIyKBrV19WHhNRGlPkIiIiSE1NDXUxTCmXmppaJIsuWZAAqFUL6tWD\\nQ4dgwwbOO8+X9c03oSuWKZ9q1arF9u3bOXz4sNUozElTVQ4fPsz27dv9JissKOvd5NWhg5vZ78cf\\nOa+rb3DEDz/AsWMQGRnCsplypVq1agDs2LGDtLS0EJfGlEYRERHUrl07899SYViQ8Bo0CM44A9q0\\noV49aNwYfvsNUlNh+XLIY8ZgY4pUtWrViuR/cGMKy4KE1zXX+B2ed54LEuCGUViQMMaUR/ZMIqtD\\nh2DhQkhP54ILfMlffBG6IhljTChZkMgqIcGtL7FqFT16+JK/+QaOHAldsYwxJlSCFiREpJKILBGR\\nlSKyRkTG5XKOiMhLIrJBRFaJSIdglQ/wtSktXkzDhr7J/Y4cge++C2pJjDGmRAhmTeIocKGqngGc\\nCVwiIp2zndMbaO7ZRgKvBLF8cM457tUTEXr29GV9/nlQS2KMMSVC0IKEOoc8hxGeLXsn8L7Am55z\\nvwdiReSUYJWRc891rxYkjDEGCPIzCREJE5EVQDKwQFV/yHZKPWBrluNtnrTgaNPGzeH0+++wYwcX\\nXOBbdCgxEfbuDVpJjDGmRAhqkFDV46p6JlAfOFtE2hbkc0RkpIgkikhi1kXLCy0sDF5+2VUbatQg\\nNta3voQq/N//Fd2ljDGmNAhJ7yZV3QcsBC7JlrUdaJDluL4nLfv7X1PVBFVNiI+PL9rCXXst9OgB\\nnjVtrcnJGFOeBbN3U7yIxHr2KwMXAeuynfYRcJ2nl1NnYL+qJgWrjIBbV+LTT+G11wD/IPHZZ65G\\nYYwx5UUwR1yfArwhImG44PQ/VZ0rIjcDqOq/gXlAH2ADcBi4IYjlc1JToXdvN1nTdddxzjmViI52\\n4+w2bYJ168DWqDfGlBdBCxKqugpon0v6v7PsK3BbsMqUq9hYaNsWVq+GpUup2LUrF18M77/vsufM\\nsSBhjCk/bMR1brp3d68LFwJw+eW+rLlzg18cY4wJFQsSufFO3PTllwD06eNb0vTbb+GPP0JTLGOM\\nCTYLErk5/3z3+uOPkJ5OrVq+GTsyMuCTT0JXNGOMCSYLErmJi4PFiyEpCcLdY5usTU5z5oSoXMYY\\nE2QWJALp3BkqV848vOwyX9ann4ItGGaMKQ8sSASSnAwPPQS33w7A6adDw4Yu68AB+OqrEJbNGGOC\\nxIJEIOHh8PTTMHkyHDmCCPTt68t+993QFc0YY4LFgkQgNWpAu3ZuBPbixQAMGODLfv99OH48RGUz\\nxpggsSBxIt6usJ7xEl26QJ06Lmn3bvj66xCVyxhjgsSCxIl41zD97DPATRLbv78ve9asEJTJGGOC\\nyILEiXTv7taXiImB9HQA/vIXX7Y1ORljyjoLEicSHQ0pKTB/fuZ4ifPOg9q1XfauXfDNNyEsnzHG\\nFDMLEnmJjHSvR48COZuc/ve/EJTJGGOCxIJEXlJS4JxzoHnzzMUkBg70Zc+aBceOhahsxhhTzCxI\\n5KVmTbfm9datbvpwoGtXaOBZPy8lxY3ANsaYssiCRF5E4OKL3f78+QBUqACDB/tOeeutEJTLGGOC\\nwIJEfvTq5V49QQLcUthec+bA3r1BLpMxxgSBBYn8uOgiV6P4+mu3jinQujWcdZbLPnrUxkwYY8om\\nCxL5ER8Pw4fDmDGZ4yXAvzZhTU7GmLJI1NNjp7RKSEjQxMTEkFw7ORnq1vUNqFu/Hpo1C0lRjDHm\\npIjIMlVNyOs8q0mcjEOHYPbszKhQqxb07u3Lnjw5ROUyxphiYkHiZHTqBP36wQ8/ZCaNGOHLnjbN\\nxkwYY8oWCxInw9sV9qOPMpP69IF69dx+crJfljHGlHpBCxIi0kBEForIzyKyRkRG5XJOdxHZLyIr\\nPNujwSpfvlxxhXvNEgnCw2HYMN8pr74a5DIZY0wxCmZNIh24W1VbA52B20SkdS7nfaOqZ3q2x4JY\\nvryddx7ExsLate4ptcewYW6AHcDnn8PGjSEqnzHGFLF8BQkReVJEqmQ57iMilbMcVxORN0/0Gaqa\\npKo/evYPAmuBegUrdohERLj2JYB58zKTGza0B9jGmLIpvzWJ+4HoLMczgVOyHFcGBpNPItIIaA/8\\nkEv2uSKySkQ+EZE2+f3MoLnnHjeo7o47/JJHjvTtT51qD7CNMWVDfoOE5HGcbyISDbwH3KWqB7Jl\\n/wg0VNV2wERgdoDPGCkiiSKSuHv37oIWpWDat3cz/FXw/9Nlf4D93nvBLZYxxhSHoPZuEpEIXIB4\\nR1Xfz56vqgdU9ZBnfx4QISJxuZz3mqomqGpCfHx8sZc7hzVr4M47XZXBIzwcbrrJd8qLL2bOLG6M\\nMaVWMHs3CTAFWKuqLwQ4p47nPETkbE/5/ghWGfNt7VqYOBFee80v+aaboGJFt790KSxeHIKyGWNM\\nEQo/iXNvFpFDWd43TES8X+BV8/H+LsC1wE8issKT9hDQEEBV/w0MAG4RkXQgFRikJXHekD59oEoV\\nN6huyxb35Bo3AnvIEJgyxZ324otw7rkhLKcxxhRSvuZuEpHfgTxPVNXGRVCmkxKyuZsGDnRTv77w\\nAowenZn800/Qrp3br1ABNm2CU08NfvGMMeZEinTuJlVtpKqN89oKX+xSZMAA95ptkevTT4cePdx+\\nRgZMmhTkchljTBGyaTkK6tJLIS7OVROy9XfNUrHg9dfh4MEgl80YY4pIfgfTnSEiF2RLGywim0Qk\\nWUT+LSKRxVPEEioqCrZvh5kzIdL/1nv3hubN3f7+/TmebxtjTKmR35rE48B53gPPdBrTgPXADNxA\\nuvuLvHQlnTc4rF/v19+1QgW4+27faf/4Bxw5EuSyGWNMEchvkOgAfJbleBDws6r2UtVRwF3A1UVd\\nuFKhb19o0cJv+nCAoUPdgkQAO3fC9OlBL5kxxhRafoNETWBHluPzgTlZjr/E05W13PG2K2Vbv7Ri\\nRTeDh9czz0BaWhDLVYpNnz4dEcncwsLCqFevHgMHDuSXX34plmt++eWXjB07loyMjGL5/KLUqFEj\\nhg4detLvGzt2LJ5hSMbkW36DxG48k/GJSBhwFv7zLkUCJf//ruLgXeh65swcD7BHjoSaNd3+77/D\\njBnBLVppN2vWLBYvXszXX3/NU089xfLly+nRowf79+8v8mt9+eWXjBs3rlQECWOCKb9B4ktgjIg0\\nAbyt7Quz5LcGfi+6YpUiZ5zh+r3u2QOffuqXFRUFd93lO37qKdct1uTPmWeeSefOnenSpQvXXXcd\\nr7zyCtu3b+e7774LddGMKTfyGyT+DjQHNgBPAPep6p9Z8q8FvijispUeQ4a412xNTgC33w7Vqrn9\\ndevc+DtTMNU8f8i0bO12K1eu5IorrqB69epUrlyZLl268M033/ids3TpUi666CJq1qxJ5cqVadKk\\nCbfeeivgmmHGjRsHQERERGYz14mICI888gjPP/88p556KlWqVOHSSy8lOTmZ5ORkBg4cSExMDA0a\\nNOCZZ57J8f4lS5bQs2dPoqOjiYqKokePHixZsiTHef/85z9p1KgRlSpVIiEhIcd9ef32228MHjyY\\n+Ph4KlasyJlnnskHH3xwwnswJl9UNV8bbiqOM4C6ueSdAdTM72cV5XbWWWdpyG3bpvr886o7duSa\\n/dBDqq77k2qLFqppaUEuXykzbdo0BXTdunWalpamR44c0Z9//ll79OihtWrV0v3792eeu2zZMq1S\\npYp26dJFZ82apR9//LFefvnlGhkZqYmJiaqqevDgQa1evbr26tVLP/roI124cKFOmzZNR4wYoaqq\\nW7du1WHDhimgixYt0sWLF+vixYtPWEZAGzZsqH369NG5c+fqlClTtGrVqtqrVy8999xzdfz48bpg\\nwQIdOXKkAvrxxx9nvnflypVaqVIl7dChg86aNUvfffddTUhI0EqVKumKFSsyz5s8ebICOnToUP3k\\nk0904sSJWq9ePa1WrZpef/31medt2bJF4+PjtU2bNvrWW2/pp59+qjfccIOKiH744YeZ540ZM0bd\\n//LGqAKJmp/v/vycVJK3EhEk8rBnj2pMjC9QTJ0a6hKVbN4gkX2rW7euLlmyxO/cCy+8UFu1aqVH\\njx7NTEtPT9dWrVpp3759VVV16dKlCujKlSsDXtP7BZqWzwgOaPPmzf3OHz16tAI6fvz4zLS0tDSN\\nj4/XoUOHZqb1799fY2JidO/evZlp+/fv1+rVq2u/fv1UVfX48eNav3597dWrl991Z86cqYBfkLjx\\nxhs1Li5OU1JS/M7t2bOnnnHGGTnu0RjV/AeJ/A6m+1t+tqKp25RSaWnw9tvuQXa2+bCqV/fv6TRu\\nHBw9GuTylUIffPABS5cuZcmSJcyePZvWrVvTp08f1q5dC0BqaipfffUVf/nLX6hQoQLp6emkp6ej\\nqvTs2ZOvv/4agObNmxMbG8tNN93E22+/zdatW4ukfBdddBHh4b45Mlu1agVAr169MtPCw8Np1qyZ\\n3zW//vprLrvsMmJjYzPTqlWrxhVXXMFXX30FwLZt29i2bRsDBw70u2b//v39rgnw6aef0qdPH2Ji\\nYjL/Bunp6fTq1YuVK1dy4ED2ZVuMyb/8PpP4B/AAcDtwR4Dt9uIoYKkhAvff7wJFLu3Go0a5WTwA\\nNm+2JU7zo23btiQkJNCxY0f69u3LRx99hKoyduxYAPbs2cPx48cZP348ERERftukSZPYu3cvGRkZ\\nxMTEsHDhQurWrcutt95Kw4YNadu2Le8VcmWo6tWr+x1HegZX5pZ+JMtoyj179nDKKaeQXZ06ddi7\\ndy8ASUlJANSuXdvvnPDwcGp6u8x5JCcn8+abb+b4G9x7770A/PFHyZtt35Qe+Z0qfCnQBvgYmKKq\\ni4qvSKVUeDjceCM8/ribh+P88/2yq1aFBx/0jcR+/HG44QY347jJH+8D51WrVgEQGxtLhQoVuO22\\n27juuuuE2UwBAAAgAElEQVRyfU8FzwqCZ555Ju+99x7p6ekkJiby1FNPMXDgQFauXEnbtm2Ddg8A\\nNWrUYOfOnTnSd+7cmRlgvEFk165dfuekp6fn+NKvWbMmXbt25f77c5/0oK53VKcxBZDfWWA7AZ2A\\nvcD7IvKLiNwnIrXzeGv5MmyYq1G8+y7k8uvtllv8R2G/+GKQy1fKHT58mI0bN+JdjTAqKoquXbuy\\ncuVKOnToQEJCQo4tu/DwcDp37sz48ePJyMjIbLqq6FktKjU1tdjvo1u3bsybN4+DWWZ+PHjwIHPm\\nzKF79+4A1K9fnwYNGvC/bLMMewNdVpdccgmrVq2iTZs2uf4NvPdmTEHke9EhVV0D/E1E7gf6AjcC\\n40TkM2Cgqlore6NG0KuXGy/x1lv+gySAypVhzBjfMqdPPeXiSp06wS9qabBixQpSUlJQVZKSkpg0\\naRJ79uzhjjvuyDznhRde4Pzzz6dXr14MGzaMU045hZSUFH788UeOHz/O008/zdy5c3nttde48sor\\nady4MX/++ScvvfQSVatW5ZxzzgGgdevWADz//PP07t2bsLCwXINMUfj73//O3Llz6dGjB/fffz8i\\nwjPPPMPhw4d59NFHAVcDGjNmDMOHD+eGG25g0KBBbNiwgaeffjqzK7DXY489xtlnn83555/P7bff\\nTqNGjdi7dy+rV69m06ZNTM2yzK4xJy0/T7dz24CLcYPs0oHYgn5OYbcS17vpvfdUGzcO2IUpLU21\\nTRtfT6fhw4NcvlIgt95N8fHxesEFF+inn36a4/yff/5Zr776ao2Pj9fIyEitV6+eXn755ZndTtet\\nW6cDBw7URo0aacWKFTUuLk579+6t33//feZnpKen66233qrx8fEqInn2AgL04YcfzrXc69ev90vv\\n1q2bdunSxS/t+++/1x49emhUVJRWqVJFL7zwQv3hhx9yXGfChAnasGFDrVixop511ln6zTff6Kmn\\nnurXu0nV1423bt26GhERoXXq1NGePXvqW2+9lXmO9W4yWZHP3k35WpnOS0Qa4WoQ13uS3gSmqupv\\nhQ9XBROylekCOX7cNTlVCNySN38+XHKJ2xeB5cvdwG1jjAmWIl2ZzrN2xBfAz0BL4Cagkar+PZQB\\nokQKC3MB4uhRWLAg11N69fIFCVX3MPskYrUxxgRNfte4zgC2AP8BUgKdp6ovFF3R8qfE1STAjZlo\\n3tz1df31V99MsVmsWePWwvbO5fThh3DFFUEupzGm3CrSmgQuQCjwV2ycRN4iIuDCC93+xIm5ntKm\\njZsl1mvUKDh8OAhlM8aYk5DfLrCNVLXxiTagWzGXtXQZNcq9Tpvm1jDNxfjxUKOG2//9d3jyyeAU\\nzRhj8iu/NYmARKSOiEwCfi2C8pQdZ5wB3bvDoUMuUOQiLg6eftp3/NxzrnXKGGNKivw+uI4VkXdE\\nZLeI7BCRO8UZA2wCOuN6PZ3oMxqIyEIR+VlE1ojIqFzOERF5SUQ2iMgqEelQoLsqKby1ic8+C3jK\\nsGHQqZPbP3bMTS1uD7FLvtTUVH777Te/7fjx46EuljFFLr81iSeBrsAbwB7gReAjXBNTb1VNUNW8\\n1l1LB+5W1da4oHKbiLTOdk5v3LoVzYGRwCv5LF/JdPnl7on0nDkBT6lQAV55xddjdsECt8idKdnu\\nuusuWrVqRbt27WjXrh0tW7bkrVzWEzGmtMtvkLgUuFFV7wGuAATYqKoXqupX+fkAVU1S1R89+weB\\ntXiWRM2iL/CmZ6zH90CsiOScCa20CAtzXZbCwuDPPwNWEdq3B8/6NwDceSfs3h2kMpoCOXz4MMeO\\nHePQoUMcOnSI8PBwjtrUvqYMym+QqIsbI4GqbgKOAK8X9KKeQXnt8V8nG1zQyDqP8zZyBpLSZ9Ik\\naNgQPvkk4ClPPAENGrj9lBQXKIwxJtTyGyQqAFnXjDwOFKjDpohEA+8Bd6lqgSa6F5GRIpIoIom7\\nS8NP7tRUtwb2U08FPKVaNXj1Vd/xzJmupcoYY0Ipv0FCgLdF5CMR+QioBLzuPc6SfuIPEYnABYh3\\nVPX9XE7ZDjTIclzfk+ZHVV/zPAdJ8M4IWqLdfDPExsKiRW4LoHdvyDrj9S23gGd5AWOMCYn8Bok3\\ngB3AH57tbVyz0B/ZtoDErSw/BVh7gpHZHwHXeXo5dQb2q2pSPstYclWt6rotwQlrE+CmD/euM5OU\\n5J5VWG8nY0yo5GuqcFW9oQiu1QW4FvhJRFZ40h4CGnqu8W9gHtAH2IBrziqK65YMo0bBCy/AvHmw\\nejUEWOimRg3497+hXz93PHMmXHopDBkSxLIaY4xHvteTKCx1q9lJHucocFtwShRkcXHwj39A48Zu\\nTo4TuPJKt8iddxmAW2+FLl3cW40xJpgKPeLanIRbbnHTv4rk2Yb0z39Cs2Zu/+BBV5PItiCZMcYU\\nOwsSwbZvH9x3X55TvkZHwzvvuCEWAN9957rJGmNMMFmQCIXXX4e5c2HhwhOedvbZMG6c7/ixx/J8\\nizHGFCkLEsEWG+tWGQL4+9/zbHZ64AHo2tXtZ2TAoEGwPUenYGOMKR4WJEJh1Cj3IPvbb+GjEw8v\\nCQuDGTOgVi13nJwMAwe6yQCNMaa4WZAIhapV4dFH3f799/uWpwugXj3473/9n0/cd18xl9EYY7Ag\\nETo33+y6LL35pm8K2BPo3t1/HN4//+lqGMYYU5wsSIRKRAS89ZZ7Og35GlZ9zz2+QXbgxlL8kH2K\\nRGOMKUIWJEJt504YMQIefjjPU0XcInctW7rjI0dcT9rffy/eIhpjyi8LEqG2bRtMngzPPw8bN+Z5\\nekwMfPwx1KzpjpOT4bLLAi6jbYwxhWJBItQSEtzUr8eOuUUk8tHs1LQpfPCBa7ECWLMGrr7aRmQb\\nY4qeBYmS4Nln3YIS8+blexGJrl1hyhTf8fz5MHx4nh2ljDHmpFiQKAlq14bHH3f7992X72/6a6+F\\nRx7xHb/xBvztbza1uDGm6FiQKCluucWtOTF3br66xHo99hgMG+Y7/uc/ffHGGGMKy4JESREeDhMn\\nQosWripwIH8ru4q4ZU8HDPClPfqo+yhjjCksCxIlze7dcNVV0KsXHD+er7eEhcHbb8NFF/nS7rwT\\nJk0qpjIaY8oNCxIlTWQkLFkC339/UtWBihXh/fehc2df2h13wIQJxVBGY0y5YUGipImJce1HAA89\\nlK+xE17R0fDpp/6BYvRoeO65Ii6jMabcsCBREl12GVxzDaSmuqfSJ9GvNSbGdYft0sWXdt99bl0K\\n6/VkjDlZFiRKqn/+E+LjYcUKWL/+pN5arZqrUXTr5ksbO9bNKWgD7owxJ8OCREkVFwezZsGqVb7J\\nmk5CdLSbvuPii31pr70G/fvD4cNFWE5jTJlmQaIk69YNGjZ07UTTp7vmp5MQFQVz5sDgwb60jz5y\\nvaCSk4u2qMaYssmCRGlw++1www1uLdOTFBnplqy4915f2nffQceOriXLGGNOJGhBQkSmikiyiKwO\\nkN9dRPaLyArP9miwylbi3XCDG2z30kuuanCSKlRw00NNmOAG3wFs2QLnngv/+18Rl9UYU6YEsyYx\\nHbgkj3O+UdUzPdtjQShT6ZCQAE884favuw42bSrQx4wa5WJMtWruODXVzR774IP2QNsYk7ugBQlV\\n/RrYE6zrlTn33ONWGNq3DwYOzPdo7OwuvdStZteihS/t6afhggtg69YiKqsxpswoac8kzhWRVSLy\\niYi0CXVhSpQKFdzD6w4dYMwYNxdHAbVq5QLFJVnqdYsWwZlnFqg1yxhThpWkIPEj0FBV2wETgdmB\\nThSRkSKSKCKJu3fvDloBQ656dVi6FC6/3B0nJRX4o2JjXRfZJ57wxZs9e1xl5dZb4dChIiivMabU\\nKzFBQlUPqOohz/48IEJE4gKc+5qqJqhqQnx8fFDLGXLeacQnToQmTeDLLwv1UQ895D6ifn1f+iuv\\nQLt28NVXhSqpMaYMKDFBQkTqiLi+NyJyNq5sf4S2VCXY5s1w5IgbHXcS8zvl5rzzXHfYvn19ab/9\\nBt27u9lk8zlruTGmDApmF9gZwGKgpYhsE5FhInKziNzsOWUAsFpEVgIvAYNUbbahgJ55xj2F3rPH\\nzfW0b1+hPq5mTbdu9ptvuqYor4kT3TOMmTNt7idjSpKMjJMeX1sg4cV/CUdV/5pH/iTAVkDIr7Aw\\n+M9/3Ex+q1e7hwkLFrg5wwtIxC2JeuGFMHKkW3Ib3KOPv/4VXn8dXn7ZBQ1jTPFKTXU9DjdvduOa\\nsr9u3erG2b7wQvGWI2hBwhSDatXc0+cuXaBrVze8ugjUq+dWUZ0xA+6+G3budOn/93/uWcXNN7u1\\ntWvVKpLLlQqHDx9my5Ytmcf79+/Pcc7OnTtZt25d5nHz5s0JK0QvNFN2qcIffwQOAJs3u/XH8rJ5\\nc/GXVUp7i05CQoImJiaGuhihtWcP1Kjh9pOSoE4d39DqQjpwwPW4nTjRf2hGdLQbuvG3v0HVqkVy\\nqRLtscce47HHHqNKlSoAqCqHsnQBCwsLy8wDOHToEHPmzOHSSy8NellN6KWlwbZt7gs/tyCwZUvR\\nTLTZowd8/nnB3isiy1Q1Ic/zLEiUIcuWQe/eMHSoe2ZRRIEC3GS0d9wBX3/tnx4fD/ffDzfd5AJH\\nWbVt2zaaN2/OkSNH8nV+rVq12LZtGxEREcVcMhMKBw6cuBawY0fhn+GFh7teh6ee6ub5zP7aoIGb\\nxLOg8hskrLmpLNm9G/budUvRRUTA448XWaBo1851lZ03z80zuHq175L33OPGW9x5pwskNWsWySVL\\nlPr16zN48GDefPNN0tLSTnhuVFQUTzzxhAWIUiojw1XIA9UCNm+GXFobT1rVqu4LP1AQOOWUQo2Z\\nLTJWkyhr3n0XBg1ybUMPPABPPlmkNQpwH/322/Doo+5/mqyiotxierfd5j/1R1mQ39qE1SJKttTU\\nEweAbdtcc1FhiLgvee8Xfm5BIGsvwlCw5qby7L33XKBIT4d//QtuuaVYLnP0qOsy+8wzuQ/VuPhi\\nFywuvbRk/CIqCsOHDz9hbSIqKooJEyYwfPjwIJfMgGviSUnJ2f5/sg+E81KpUu5f/N7X+vWLrB9J\\nsbEgUd598IFbAvWjj3zTvhaT9HS3iN5TT8FPP+XMb9DAda297roCLbJXouRVm7BaRPHK+kA40DOB\\nohg7EBd34iAQH1/kFfSgsyBh3M8qETcR07PPwsMPF2ocRX4ut2CBG0sxZ07uD+46dXIBo39/1wmr\\nNApUm4iKiuLFF19kxIgRISpZ6bd/f+DeQMX9QNi7X9gHwqWFBQnjc/XVbnWh7t1h9myIiSn2S/7+\\nO/z73zB5susPnp0InHMOXHUV9OvnpqEqLQLVJqwWcWLHj/s/EM4tGJSnB8KhZkHC+Kxc6brGJiXB\\n6ae7kXINGwbl0seOuR5Rb7zhxv0FeiB4+unuGcbFF7txgZUrB6V4BZa9NlHen0Wouplhtm71DwJZ\\nj7dvL/AyKJm8D4RP1BQU6gfCpYUFCePv99/dAhK//OIaVD/80P2UD6KUFPjvf10HrK+/dl0Nc1Ox\\nopt0sEcPt8Rqx46QZZxaiZC9NlHWaxFHj/oPDsv65e/dL4rp5QM9EPbul4YHwqWFBQmT0549rtfT\\nkiVu1aEQPkXevds9t/jgA/jsM1fjCCQszC2IdM45bmvfHpo3d23LoeStTURGRpbaWkRGhvtvsX27\\na+8P9JqSUjTXi4/3DQTLrUmoLDwQLi0sSJjcpae72kSbNq6NYOpUuOaakLbvHDrkBuotWOC2tWvz\\nfk+lSu4W2rWDM86A1q2hWTP35ROs4LFt2zaaNm1KbGxsiatFHD4Mycmwa5d79e4nJfl/+SclFd36\\n5lWq+H71N2iQc79+/ZLfjFieWJAweXvhBTeDX9u2bi7wNiVjxdht29x8NN9+C4sXw5o1+X9veDg0\\nbuwCRrNm0KgR1K3rJi2sW9dtRflF9eijj9KuXTsGDBhQdB+aTVqaG0i/Z0/O1927/YOB97WoVxYM\\nC3N/u0ABoGFDt3Ci1QJKDwsSJm8rVrieT7/+6r45J0yAESNK3P/p+/a51rHFi11L2apV7pdwQVWv\\nDrVruzkRq1f3vXr3o6LcnyO3LTLS/Xly2ypUcA9mjx3zbWlp/sdHj7ov8BNtBw/6B4PiXkq2Rg1f\\nEA30WquW9QgqayxImPw5dMhNuDR9ujsePtwtHFHC/fGHG7i3cqULGuvXw4YNhVr2u0yJjHRf7LVr\\n53ytV8+/ZlWpUqhLa0LBJvgz+RMdDdOmQc+ecOut0KuXS8/I8P1ELoFq1nTDPrp3908/dAg2bXIB\\nY8MG1/Nmxw5fO3xRtsEHS4UK/jWeGjV8+zVrui/+7MEgJqbE/qczpY2qlurtrLPO0oICFNDExERV\\nVR0xYoQCOmLECFVVTUxMzDzHq0OHDgroq6++qqqqr776qgLaoUOHUv+5yz//PPNz7wFd1aCB6saN\\nJba8BfncV155VXfuVB0z5n8K52mTJnfptGmqL7ygCo8pTNTLL9+tV1+teuqpyxXma+3av2pCgmqT\\nJocVflH4VZs2VW3SRDUycqvCJq1Z84A2aKAaF7dfYZ1WqrReO3RQ7dxZFb5U+EzPPXef9u2r2qzZ\\nYoVX9PTT5+vYsap33bVFYaTCNTp7turnn6u2anWNQiOdMGGqHj9eev6+9rnB/dzCABI1H9+xQVvj\\n2pR8xz2jkCLS0xkNnL51K7RuzSmvvkpZaZGoUMH92q5bdy+wiNjYrxk6FEaPBngUuIMxYzYzcyZc\\nfPG/gF5cccVzLF0K//vfz0BLoAUbNrhJDdu27Qs04cknZ7BlCzzxxEygFa1bX82yZe45CnQHLual\\nlzYwezZccMFU4BY6d36XMWNgyJBk4DXgP/Tt68aHVKmyDvidypXTqGD/l5oQsmcSJndJSXDffW5O\\ncHDdhKZOhQsuCGmxjDFFI7/PJOw3isndKafAW2+5odHt2rkR296ftKWtUd8YU2AWJMyJde3qlkWd\\nPRu6dXNpd9/tHnAvWxbashljip0FCZO38HDo29ftHz4MM2a4uTQSEqBPH1fbKOXNlsaY3AUtSIjI\\nVBFJFpHVAfJFRF4SkQ0iskpEOgSrbOYkVKni5s245x63/8knrobx6KOhLpkxphgEsyYxHbjkBPm9\\ngeaebSTwShDKZAqiZk147jm3CMCYMa7T/lVXubxVq9x0H3v3hraMxpgiEbQgoapfA3tOcEpf4E1P\\nF97vgVgROSU4pTMFEhcHY8e6UWrt27u0CRPcM4t69dzo7e+/t6YoY0qxkvRMoh6wNcvxNk+aKemy\\nzuswYIBbOSg1FaZMcXN7JyQEXjzCGFOilaQgkW8iMlJEEkUkcffu3aEujsmqTx+YP99NGnjPPW4h\\n67Ztfd1nhw+Hf/3LzZNhjCnxgjqYTkQaAXNVtW0uea8CX6rqDM/xL0B3VT3hlG02mK6ES0+HAwfc\\nc4uVK93qQV7eRa4HDgzacqrGGKc0Dqb7CLjO08upM7A/rwBhSoHwcBcgAJo0cYtd9+3rmqgWL4Z7\\n73U1D4CdO91C2H/+GbryGmP8BG0WWBGZgZvEJk5EtgFjgAgAVf03MA/oA2wADgM3BKtsJkiqVoXr\\nrnPbn3/Cp5/Ce+/BFVe4/A8+cDPRRkb6Frnu2hU6dbKFjY0JEZu7yZQcM2fCiy/C0qX+PaK2bnVr\\nX373netae+65bp5sY0yB2XoSpvQZNMhtf/zh1i/9+mu3OET9+i5/wgSYNcvtN2kCZ50FHTu6B+S2\\neIIxxcJqEqb0ePZZ+OgjN2fUkSMurXlz15MK4Oab3SLPbdr4tpYtoWLF0JXZmBLKli81ZVd6upsa\\nxPvf/YYbXPPUKae4IJFVp05uQB/ApEluJb5mzdxWu7bVQEy5Zc1NpuwKD4fTT3dbVgsWwOrVsGaN\\nb2vr6W2tCg89BAcP+s6PioLrr4eXX3bH06dDfDw0aOC65NoaoMZYkDBlhEjugcNbU05Pd8vPeRe/\\n3rAB9uyBsDCXn5YGN97o/8C8alXX2+rpp13600+72kqdOr4tPt73GcaUQRYkTNnmrQlERMC4cf55\\ne/b4FlBKTYVhw2DLFt928KCrtYDrVfXQQzk/f/RoN6FhaqobGJg9gLRvD6ed5qYl+fNP19xltRNT\\niliQMOWXd5AfQLVq8PrrvmNV/5lsVd1yrklJbtDfzp1u/xTPHJQ7d7pxH9mNG+emUd+50016GBHh\\nJkasWdNtN90Ef/2rC0ivvea69sbEQGyse23UyJ1vTIhYkDAmNyL+QaRmTXjmmZzneZun4uPdaHFv\\n8EhKgpQU3zQk+/dD5cquxuHNB99iTtu3u6682T37rBuVvmGDW0Y2JsY/iNxyC1x5pes2/K9/uZpK\\n1q1dOzj1VDh2zD3U96ZHRBTd38qUaRYkjCkMb9NRdLSb3DCQ005zq/qlprov9JQU99q0qe/9o0bB\\nvn0uoOzf7/a9c1rt3+/em5rqApGXN8hs3Zr7wk+TJsFtt8Evv7iA4RUZ6a753HPuWcymTTBihEuL\\ninIBrUoVN26lSxfYvdutSFilisvz5rdt68axHDkC27b551esaE1rZYAFCWOCqXJl96XqHSDoVb++\\nGywYSIcOcOiQL3h4X1u3dvk1a8Ijj7hzsm7eIJSe7q5x6JBr2jp2zD2T8dq9G/7v/3Jet107FyR+\\n+80FsexefRVGjnS9yjp29M8TcU1ow4e7/KuucoGjYkU3d1fFiq721Lu3C1Ljxvnyvef06+cGTSYn\\nw/vv++dXrAhnnOF6o/35J6xf7/vciAi3xcS4NFW3VShJ09WVDhYkjCkNRNwv/KgoqFs3Z36DBjB+\\nfOD3t2/vahvgviyPHXMBo3Jll9aqletC7A0u3lrLuee6/Lg4uP12X7q3VtSoke8ajRv756el+ebc\\n2rfPfYlnd+217nXnTnjzzZz5TZu6ILFxo2tay27KFFcTWrXKV9aspk5142i+/97lV6jgCyAREa77\\n81//CsuXu9eseRER8PDDLoj98gs8+GDO/BtucLMZb9niAqY3PTzcbZdc4gZ17toFc+f60r1bQoJr\\nDty3z437yZ7fuLH7AZCa6mpq4eG+z4+L83WsKEYWJIwpb0R8v8S9YmKgZ8/A72nSBCZODJyfkOBq\\nA1kdP+57ZnPWWW4A5NGj/pu3JtS0KUyb5tKOHPHld/AsdR8f72os2d9/6qkuv2JFV+vxpqeluS0q\\nyuV7e7FlZPjOyZp+8KALBNmlpLjX5GQ3AWV2553ngsTmzfDkkznza9VyQWL9elejyu7NN12gXL0a\\nLrooZ/4bb7gJMZctc5NdZrVqVc4u38XAgoQxpnhkHT9SubKrrQRSuzYMHRo4v1kz90s9kA4d3Hol\\ngXTt6gLE8eO+AHLsmC+IJCTAzz/78rz5LVu6/NNOczMWe9O953Tu7PIbNnQ1uWPHXOA5fty9eoNg\\nfLyr8aSn+2/eIFetmpv1OHu+t2dbZKQLpFnzgjQzsk3LYYwx5VBpXHTIGGNMCWNBwhhjTEAWJIwx\\nxgRkQcIYY0xAFiSMMcYEZEHCGGNMQBYkjDHGBGRBwhhjTEClfjCdiOwGNhfw7XFAShEWpzSwey4f\\n7J7Lh8Lc86mqGp/XSaU+SBSGiCTmZ8RhWWL3XD7YPZcPwbhna24yxhgTkAUJY4wxAZX3IPFaqAsQ\\nAnbP5YPdc/lQ7Pdcrp9JGGOMObHyXpMwxhhzAuU2SIjIJSLyi4hsEJEHQl2eoiIiU0UkWURWZ0mr\\nISILRGS957V6lrwHPX+DX0SkV2hKXXAi0kBEForIzyKyRkRGedLL8j1XEpElIrLSc8/jPOll9p69\\nRCRMRJaLyFzPcZm+ZxH5XUR+EpEVIpLoSQvuPatquduAMGAj0ASIBFYCrUNdriK6t/OBDsDqLGnP\\nAg949h8AnvHst/bce0WgsedvEhbqezjJ+z0F6ODZrwr86rmvsnzPAkR79iOAH4DOZfmes9z734D/\\nAHM9x2X6noHfgbhsaUG95/Jakzgb2KCqm1T1GDAT6BviMhUJVf0a2JMtuS/whmf/DeDKLOkzVfWo\\nqv4GbMD9bUoNVU1S1R89+weBtUA9yvY9q6oe8hxGeDalDN8zgIjUBy4FJmdJLtP3HEBQ77m8Bol6\\nwNYsx9s8aWVVbVVN8uzvBGp79svU30FEGgHtcb+sy/Q9e5pdVgDJwAJVLfP3DEwA7gMysqSV9XtW\\n4HMRWSYiIz1pQb3n8MJ+gCldVFVFpMx1aRORaOA94C5VPSAimXll8Z5V9ThwpojEAh+ISNts+WXq\\nnkXkMiBZVZeJSPfczilr9+xxnqpuF5FawAIRWZc1Mxj3XF5rEtuBBlmO63vSyqpdInIKgOc12ZNe\\nJv4OIhKBCxDvqOr7nuQyfc9eqroPWAhcQtm+5y7AFSLyO655+EIReZuyfc+o6nbPazLwAa75KKj3\\nXF6DxFKguYg0FpFIYBDwUYjLVJw+Aq737F8PfJglfZCIVBSRxkBzYEkIyldg4qoMU4C1qvpClqyy\\nfM/xnhoEIlIZuAhYRxm+Z1V9UFXrq2oj3P+v/6eqQyjD9ywiUSJS1bsPXAysJtj3HOqn96HagD64\\nnjAbgYdDXZ4ivK8ZQBKQhmuTHAbUBL4A1gOfAzWynP+w52/wC9A71OUvwP2eh2u3XQWs8Gx9yvg9\\ntwOWe+55NfCoJ73M3nO2+++Or3dTmb1nXO/LlZ5tjfd7Ktj3bCOujTHGBFRem5uMMcbkgwUJY4wx\\nAVmQMMYYE5AFCWOMMQFZkDDGGBOQBQljShgRUREZEOpyGAMWJIzxIyLTPV/S2bfvQ102Y0LB5m4y\\nJqfPgWuzpR0LRUGMCTWrSRiT01FV3Zlt2wOZTUG3i8jHInJYRDaLyJCsbxaR00XkcxFJFZE9ntpJ\\nTLZzrvcsJnNURHaJyBv4qyEis0TkTxHZlP0axgSLBQljTt443Dw5Z+IWon9TRBIgc46d+cAh3GRs\\n/cXvAWMAAAHJSURBVIBzganeN4vITcCrwDTgdNzkfKuyXeNR3Jw8ZwD/BaaKSMPiuyVjcmfTchiT\\nhYhMB4YAR7Jlvayq93umZZ6sqiOyvOdzYKeqDhGREcA/gPrqFkHCM7X1QqC5qm4QkW3A26qa67K5\\nnms8raoPeo7DgQPASFV9uwhv15g82TMJY3L6GhiZLW1flv3F2fIW41ZMAzgNWOUNEB7f4RbKaS0i\\nB3ALwXyRRxkyaxaqmi4iu4Fa+Su+MUXHgoQxOR1W1Q3F8LknU21Py+W91jxsgs7+0Rlz8jrncrzW\\ns78WON27DoDHubj/19aqWzxmO9Cj2EtpTBGwmoQxOVUUkTrZ0o6r6m7P/lUishT4EhiA+8Lv5Ml7\\nB/dg+00ReRSojntI/X6W2skTwIsisgv4GKgC9FDV54vrhowpKAsSxuTUE7dwU1bbcctBAowF+gMv\\nAbuBG1R1KYCqHhaRXsAE3KpgR3C9lEZ5P0hVXxGRY8DdwDPAHmBecd2MMYVhvZuMOQmenkd/UdV3\\nQ10WY4LBnkkYY4wJyIKEMcaYgKy5yRhjTEBWkzDGGBOQBQljjDEBWZAwxhgTkAUJY4wxAVmQMMYY\\nE5AFCWOMMQH9P9YSwqpZNS0iAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Early Stopping -- stop training when minimum validation error reached\\n\",\n \"\\n\",\n \"# build dataset\\n\",\n \"rnd.seed(42)\\n\",\n \"m = 100\\n\",\n \"X = 6 * rnd.rand(m, 1) - 3\\n\",\n \"y = 2 + X + 0.5 * X**2 + rnd.randn(m, 1)\\n\",\n \"\\n\",\n \"X_train, X_val, y_train, y_val = train_test_split(X[:50], y[:50].ravel(), test_size=0.5, random_state=10)\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"from sklearn.pipeline import Pipeline\\n\",\n \"\\n\",\n \"poly_scaler = Pipeline((\\n\",\n \" (\\\"poly_features\\\", PolynomialFeatures(\\n\",\n \" degree=90, \\n\",\n \" include_bias=False)),\\n\",\n \" (\\\"std_scaler\\\", StandardScaler()),\\n\",\n \" ))\\n\",\n \"\\n\",\n \"X_train_poly_scaled = poly_scaler.fit_transform(X_train)\\n\",\n \"X_val_poly_scaled = poly_scaler.transform(X_val)\\n\",\n \"\\n\",\n \"sgd_reg = SGDRegressor(n_iter=1,\\n\",\n \" penalty=None,\\n\",\n \" eta0=0.0005,\\n\",\n \" warm_start=True,\\n\",\n \" learning_rate=\\\"constant\\\",\\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"n_epochs = 500\\n\",\n \"train_errors, val_errors = [], []\\n\",\n \"\\n\",\n \"for epoch in range(n_epochs):\\n\",\n \" sgd_reg.fit(X_train_poly_scaled, y_train)\\n\",\n \"\\n\",\n \" y_train_predict = sgd_reg.predict(X_train_poly_scaled)\\n\",\n \" y_val_predict = sgd_reg.predict(X_val_poly_scaled)\\n\",\n \"\\n\",\n \" train_errors.append(mean_squared_error(y_train_predict, y_train))\\n\",\n \" val_errors.append(mean_squared_error(y_val_predict, y_val))\\n\",\n \"\\n\",\n \"best_epoch = np.argmin(val_errors)\\n\",\n \"best_val_rmse = np.sqrt(val_errors[best_epoch])\\n\",\n \"\\n\",\n \"plt.annotate('Best model',\\n\",\n \" xy=(best_epoch, best_val_rmse),\\n\",\n \" xytext=(best_epoch, best_val_rmse + 1),\\n\",\n \" ha=\\\"center\\\",\\n\",\n \" arrowprops=dict(facecolor='black', shrink=0.05),\\n\",\n \" fontsize=16,\\n\",\n \" )\\n\",\n \"\\n\",\n \"best_val_rmse -= 0.03 # just to make the graph look better\\n\",\n \"plt.plot([0, n_epochs], [best_val_rmse, best_val_rmse], \\\"k:\\\", linewidth=2)\\n\",\n \"plt.plot(np.sqrt(val_errors), \\\"b-\\\", linewidth=3, label=\\\"Validation set\\\")\\n\",\n \"plt.plot(np.sqrt(train_errors), \\\"r--\\\", linewidth=2, label=\\\"Training set\\\")\\n\",\n \"plt.legend(loc=\\\"upper right\\\", fontsize=14)\\n\",\n \"plt.xlabel(\\\"Epoch\\\", fontsize=14)\\n\",\n \"plt.ylabel(\\\"RMSE\\\", fontsize=14)\\n\",\n \"#save_fig(\\\"early_stopping_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Logistic Regression\\n\",\n \"* commonly used to est probability of instance belonging to specified class. positive if >50% (labeled \\\"1\\\"), otherwise labeled \\\"0\\\".\\n\",\n \"\\n\",\n \"![Logistic Regression model probability](logistic-regression-probability.png)\\n\",\n \"\\n\",\n \"* logistic is a sigmoid function, outputs 0\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# train a LR model\\n\",\n \"\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"log_reg=LogisticRegression()\\n\",\n \"log_reg.fit(X,y)\\n\",\n \"\\n\",\n \"# predict probability of flowers with petal widths = 0-3cm \\n\",\n \"X_new = np.linspace(0, 3, 1000).reshape(-1, 1)\\n\",\n \"y_proba = log_reg.predict_proba(X_new)\\n\",\n \"print(y_proba)\\n\",\n \"decision_boundary = X_new[y_proba[:, 1] >= 0.5][0]\\n\",\n \"\\n\",\n \"plt.plot(X_new, y_proba[:, 1], \\\"g-\\\", label=\\\"Iris-Virginica\\\")\\n\",\n \"plt.plot(X_new, y_proba[:, 0], \\\"b--\\\", label=\\\"Not Iris-Virginica\\\")\\n\",\n \"plt.text(decision_boundary+0.02, 0.15, \\\"Decision boundary\\\", fontsize=14, color=\\\"k\\\", ha=\\\"center\\\")\\n\",\n \"plt.xlabel(\\\"Petal width (cm)\\\", fontsize=14)\\n\",\n \"plt.ylabel(\\\"Probability\\\", fontsize=14)\\n\",\n \"plt.legend(loc=\\\"center left\\\", fontsize=14)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# what's the prediction for petal length = 1.5 or 1.7cm?\\n\",\n \"print(log_reg.predict([[1.5], [1.7]]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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i50cxMB9RBBH1YQEStmiOzU5EKPOywmUPy5r6cY/IeeGPyHvlh84HNx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\\nzIs555xzzrkM5sWcc84551wG+3+SFi5K2+3VdQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Logistic Regressin contour plot\\n\",\n \"# with multiple decision boundaries (not just 50%)\\n\",\n \"\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"\\n\",\n \"X = iris[\\\"data\\\"][:, (2, 3)] # petal length, petal width\\n\",\n \"y = (iris[\\\"target\\\"] == 2).astype(np.int)\\n\",\n \"\\n\",\n \"log_reg = LogisticRegression(C=10**10)\\n\",\n \"log_reg.fit(X, y)\\n\",\n \"\\n\",\n \"x0, x1 = np.meshgrid(\\n\",\n \" np.linspace(2.9, 7, 500).reshape(-1, 1),\\n\",\n \" np.linspace(0.8, 2.7, 200).reshape(-1, 1),\\n\",\n \" )\\n\",\n \"\\n\",\n \"# ravel(): return contiguous flattened array\\n\",\n \"X_new = np.c_[x0.ravel(), x1.ravel()]\\n\",\n \"\\n\",\n \"y_proba = log_reg.predict_proba(X_new)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 4))\\n\",\n \"plt.plot(X[y==0, 0], X[y==0, 1], \\\"bs\\\")\\n\",\n \"plt.plot(X[y==1, 0], X[y==1, 1], \\\"g^\\\")\\n\",\n \"\\n\",\n \"zz = y_proba[:, 1].reshape(x0.shape)\\n\",\n \"contour = plt.contour(x0, x1, zz, cmap=plt.cm.brg)\\n\",\n \"\\n\",\n \"\\n\",\n \"left_right = np.array([2.9, 7])\\n\",\n \"boundary = -(log_reg.coef_[0][0] * left_right + log_reg.intercept_[0]) / log_reg.coef_[0][1]\\n\",\n \"\\n\",\n \"plt.clabel(contour, inline=1, fontsize=12)\\n\",\n \"plt.plot(left_right, boundary, \\\"k--\\\", linewidth=3)\\n\",\n \"plt.text(3.5, 1.5, \\\"Not Iris-Virginica\\\", fontsize=14, color=\\\"b\\\", ha=\\\"center\\\")\\n\",\n \"plt.text(6.5, 2.3, \\\"Iris-Virginica\\\", fontsize=14, color=\\\"g\\\", ha=\\\"center\\\")\\n\",\n \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n \"plt.axis([2.9, 7, 0.8, 2.7])\\n\",\n \"#save_fig(\\\"logistic_regression_contour_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"### Softmax Regression (Multinomial Logistic Regression)\\n\",\n \"\\n\",\n \"* **Predicts one class at a time (multiclass, not multioutput). Use only for mutually exclusive classes.**\\n\",\n \"* Scoring for K classes: ![softmax-score-for-class K](pics/softmax-score-for-class-k.png)\\n\",\n \"* Softmax function (aka normalized exponential): ![softmax-function](softmax-function.png)\\n\",\n \"* Prediction: ![softmax prediction](pics/softmax-prediction.png)\\n\",\n \"* Uses **cross entropy** to minimize cost function. (Same as log loss, used for Logistic Regression, when k=2.)\\n\",\n \"![cross entropy](pics/cross-entropy-cost-function.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 6.33134078e-07, 5.75276067e-02, 9.42471760e-01]])\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# use Softmax to classify iris flowers\\n\",\n \"\\n\",\n \"X = iris[\\\"data\\\"][:, (2, 3)] # petal length, width\\n\",\n \"y = iris[\\\"target\\\"]\\n\",\n \"\\n\",\n \"# Scikit LR can be switched to Softmax with \\\"multinomial\\\" setting.\\n\",\n \"# also defaults to L2 regularization (control with C parameter)\\n\",\n \"\\n\",\n \"softmax_reg = LogisticRegression(multi_class=\\\"multinomial\\\",solver=\\\"lbfgs\\\", C=10)\\n\",\n \"softmax_reg.fit(X, y)\\n\",\n \"\\n\",\n \"# predict iris 5cm long, 2cm wide:\\n\",\n \"\\n\",\n \"softmax_reg.predict([[5, 2]])\\n\",\n \"softmax_reg.predict_proba([[5,2]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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mXc4PAO3LeuIrXveJJGPVF+OCuLEBQ0C+Tc4Hs58uJXRL35M0nDHkZ1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\\nTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# softmax contour plot\\n\",\n \"\\n\",\n \"x0, x1 = np.meshgrid(\\n\",\n \" np.linspace(0, 8, 500).reshape(-1, 1),\\n\",\n \" np.linspace(0, 3.5, 200).reshape(-1, 1),\\n\",\n \" )\\n\",\n \"X_new = np.c_[x0.ravel(), x1.ravel()]\\n\",\n \"\\n\",\n \"\\n\",\n \"y_proba = softmax_reg.predict_proba(X_new)\\n\",\n \"y_predict = softmax_reg.predict(X_new)\\n\",\n \"\\n\",\n \"zz1 = y_proba[:, 1].reshape(x0.shape)\\n\",\n \"zz = y_predict.reshape(x0.shape)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 4))\\n\",\n \"plt.plot(X[y==2, 0], X[y==2, 1], \\\"g^\\\", label=\\\"Iris-Virginica\\\")\\n\",\n \"plt.plot(X[y==1, 0], X[y==1, 1], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n \"plt.plot(X[y==0, 0], X[y==0, 1], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n \"\\n\",\n \"from matplotlib.colors import ListedColormap\\n\",\n \"custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\\n\",\n \"\\n\",\n \"plt.contourf(x0, x1, zz, cmap=custom_cmap, linewidth=5)\\n\",\n \"contour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg)\\n\",\n \"plt.clabel(contour, inline=1, fontsize=12)\\n\",\n \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n \"plt.legend(loc=\\\"center left\\\", fontsize=14)\\n\",\n \"plt.axis([0, 7, 0, 3.5])\\n\",\n \"#save_fig(\\\"softmax_regression_contour_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Raw Cell Format\",\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch05-support-vector-machines.html", "content": "\n\n\nch05-support-vector-machines\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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SVM Classification (Linear)

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  • Well suited for complex, small/medium dataset classification.
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In [1]:
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%matplotlib inline\nimport matplotlib.pyplot as plt\n
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In [2]:
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# Large margin classification:\n\nfrom sklearn.svm import SVC\nfrom sklearn import datasets\n\niris = datasets.load_iris()\nX = iris["data"][:, (2, 3)]  # petal length, petal width\ny = iris["target"]\n\nsetosa_or_versicolor = (y == 0) | (y == 1)\nX = X[setosa_or_versicolor]\ny = y[setosa_or_versicolor]\n\n# SVM Classifier model\nsvm_clf = SVC(kernel="linear", C=float("inf"))\nsvm_clf.fit(X, y)\n
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SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n  max_iter=-1, probability=False, random_state=None, shrinking=True,\n  tol=0.001, verbose=False)
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# Bad models\n\nimport numpy as np\n\nx0 = np.linspace(0, 5.5, 200)\npred_1 = 5*x0 - 20\npred_2 = x0 - 1.8\npred_3 = 0.1 * x0 + 0.5\n\ndef plot_svc_decision_boundary(svm_clf, xmin, xmax):\n    w = svm_clf.coef_[0]\n    b = svm_clf.intercept_[0]\n\n    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n    # => x1 = -w0/w1 * x0 - b/w1\n    x0 = np.linspace(xmin, xmax, 200)\n    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n\n    margin = 1/w[1]\n    gutter_up = decision_boundary + margin\n    gutter_down = decision_boundary - margin\n\n    svs = svm_clf.support_vectors_\n    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n    plt.plot(x0, decision_boundary, "k-",  linewidth=2)\n    plt.plot(x0, gutter_up,         "k--", linewidth=2)\n    plt.plot(x0, gutter_down,       "k--", linewidth=2)\n\nplt.figure(figsize=(12,2.7))\n\nplt.subplot(121)\nplt.plot(x0, pred_1, "g--", linewidth=2)\nplt.plot(x0, pred_2, "m-", linewidth=2)\nplt.plot(x0, pred_3, "r-", linewidth=2)\nplt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs", label="Iris-Versicolor")\nplt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo", label="Iris-Setosa")\nplt.xlabel("Petal length", fontsize=14)\nplt.ylabel("Petal width", fontsize=14)\nplt.legend(loc="upper left", fontsize=14)\nplt.axis([0, 5.5, 0, 2])\n\nplt.subplot(122)\nplot_svc_decision_boundary(svm_clf, 0, 5.5)\nplt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs")\nplt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo")\nplt.xlabel("Petal length", fontsize=14)\nplt.axis([0, 5.5, 0, 2])\nplt.show()\n\n# On left:\n# dashed line = basically useless decision boundary.\n# solid lines = OK for this dataset, but no margins. Probably will not work well on new instances.\n\n# On right: SVM finds widest possible "street" between classes.\n
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In [4]:
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# sensitivity to feature scaling:\n\nXs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\nys = np.array([0, 0, 1, 1])\nsvm_clf = SVC(kernel="linear", C=100)\nsvm_clf.fit(Xs, ys)\n\nplt.figure(figsize=(12,3.2))\nplt.subplot(121)\nplt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], "bo")\nplt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], "ms")\nplot_svc_decision_boundary(svm_clf, 0, 6)\nplt.xlabel("$x_0$", fontsize=20)\nplt.ylabel("$x_1$  ", fontsize=20, rotation=0)\nplt.title("Unscaled", fontsize=16)\nplt.axis([0, 6, 0, 90])\n\nfrom sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\nX_scaled = scaler.fit_transform(Xs)\nsvm_clf.fit(X_scaled, ys)\n\nplt.subplot(122)\nplt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], "bo")\nplt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], "ms")\nplot_svc_decision_boundary(svm_clf, -2, 2)\nplt.xlabel("$x_0$", fontsize=20)\nplt.title("Scaled", fontsize=16)\nplt.axis([-2, 2, -2, 2])\n\n# SVMs are sensitive to feature scaling. \n# Plot on right has much more robust feature boundary.\n
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[-2, 2, -2, 2]
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In [5]:
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X_scaled\n
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array([[-1.50755672, -0.11547005],\n       [ 0.90453403, -1.5011107 ],\n       [-0.30151134,  1.27017059],\n       [ 0.90453403,  0.34641016]])
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# "hard" margin classification:\n# - all instances need to be "out of the street".\n# - all instances need to be "on the right side of the street".\n# problem: doable only if data is linearly separable\n# problem: very sensitive to outliers\n\nX_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\ny_outliers = np.array([0, 0])\nXo1 = np.concatenate([X, X_outliers[:1]], axis=0)\nyo1 = np.concatenate([y, y_outliers[:1]], axis=0)\nXo2 = np.concatenate([X, X_outliers[1:]], axis=0)\nyo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n\nsvm_clf2 = SVC(kernel="linear", C=10**9)#float("inf"))\nsvm_clf2.fit(Xo2, yo2)\n\nplt.figure(figsize=(12,2.7))\n\nplt.subplot(121)\nplt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], "bs")\nplt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], "yo")\nplt.text(0.3, 1.0, "Impossible!", fontsize=20, color="red")\nplt.xlabel("Petal length", fontsize=14)\nplt.ylabel("Petal width", fontsize=14)\nplt.annotate("Outlier",\n             xy=(X_outliers[0][0], X_outliers[0][1]),\n             xytext=(2.5, 1.7),\n             ha="center",\n             arrowprops=dict(facecolor='black', shrink=0.1),\n             fontsize=16,\n            )\nplt.axis([0, 5.5, 0, 2])\n\nplt.subplot(122)\nplt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], "bs")\nplt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], "yo")\nplot_svc_decision_boundary(svm_clf2, 0, 5.5)\nplt.xlabel("Petal length", fontsize=14)\nplt.annotate("Outlier",\n             xy=(X_outliers[1][0], X_outliers[1][1]),\n             xytext=(3.2, 0.08),\n             ha="center",\n             arrowprops=dict(facecolor='black', shrink=0.1),\n             fontsize=16,\n            )\nplt.axis([0, 5.5, 0, 2])\n
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In [7]:
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X_scaled\n
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array([[-1.50755672, -0.11547005],\n       [ 0.90453403, -1.5011107 ],\n       [-0.30151134,  1.27017059],\n       [ 0.90453403,  0.34641016]])
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# soluton to "hard margins" problem:\n# control hardness with C hyperparameter\n\nfrom sklearn import datasets\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.svm import LinearSVC\n\niris = datasets.load_iris()\nX = iris["data"][:, (2, 3)]  # petal length, petal width\ny = (iris["target"] == 2).astype(np.float64)  # Iris-Virginica\n\nscaler = StandardScaler()\nsvm_clf1 = LinearSVC(C=100, loss="hinge")\nsvm_clf2 = LinearSVC(C=1, loss="hinge")\n\nscaled_svm_clf1 = Pipeline((\n        ("scaler", scaler),\n        ("linear_svc", svm_clf1),\n    ))\nscaled_svm_clf2 = Pipeline((\n        ("scaler", scaler),\n        ("linear_svc", svm_clf2),\n    ))\n\nscaled_svm_clf1.fit(X, y)\nscaled_svm_clf2.fit(X, y)\n\nscaled_svm_clf2.predict([[5.5, 1.7]])\n
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array([ 1.])
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In [9]:
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X_scaled\n
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array([[-1.50755672, -0.11547005],\n       [ 0.90453403, -1.5011107 ],\n       [-0.30151134,  1.27017059],\n       [ 0.90453403,  0.34641016]])
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In [10]:
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# Convert to unscaled parameters\nb1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\nb2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\nw1 = svm_clf1.coef_[0] / scaler.scale_\nw2 = svm_clf2.coef_[0] / scaler.scale_\nsvm_clf1.intercept_ = np.array([b1])\nsvm_clf2.intercept_ = np.array([b2])\nsvm_clf1.coef_ = np.array([w1])\nsvm_clf2.coef_ = np.array([w2])\n\n# Find support vectors (LinearSVC does not do this automatically)\nt = y * 2 - 1\nsupport_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\nsupport_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\nsvm_clf1.support_vectors_ = X[support_vectors_idx1]\nsvm_clf2.support_vectors_ = X[support_vectors_idx2]\n
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In [11]:
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plt.figure(figsize=(12,3.2))\nplt.subplot(121)\nplt.plot(X[:, 0][y==1], X[:, 1][y==1], "g^", label="Iris-Virginica")\nplt.plot(X[:, 0][y==0], X[:, 1][y==0], "bs", label="Iris-Versicolor")\nplot_svc_decision_boundary(svm_clf1, 4, 6)\nplt.xlabel("Petal length", fontsize=14)\nplt.ylabel("Petal width", fontsize=14)\nplt.legend(loc="upper left", fontsize=14)\nplt.title("$C = {}$".format(svm_clf1.C), fontsize=16)\nplt.axis([4, 6, 0.8, 2.8])\n\nplt.subplot(122)\nplt.plot(X[:, 0][y==1], X[:, 1][y==1], "g^")\nplt.plot(X[:, 0][y==0], X[:, 1][y==0], "bs")\nplot_svc_decision_boundary(svm_clf2, 4, 6)\nplt.xlabel("Petal length", fontsize=14)\nplt.title("$C = {}$".format(svm_clf2.C), fontsize=16)\nplt.axis([4, 6, 0.8, 2.8])\n
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Out[11]:
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[4, 6, 0.8, 2.8]
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SVM Classification (Non-Linear)

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# some (most?) datasets are not linearly separable. simple example below.\n\nX1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n\nX2D = np.c_[X1D, X1D**2] # adds 2nd, non-linear dimension.\n\ny = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n\nplt.figure(figsize=(10, 4))\n\nplt.subplot(121)\nplt.grid(True, which='both')\nplt.axhline(y=0, color='k')\nplt.plot(X1D[:, 0][y==0], np.zeros(4), "bs")\nplt.plot(X1D[:, 0][y==1], np.zeros(5), "g^")\nplt.gca().get_yaxis().set_ticks([])\nplt.xlabel(r"$x_1$", fontsize=20)\nplt.axis([-4.5, 4.5, -0.2, 0.2])\n\nplt.subplot(122)\nplt.grid(True, which='both')\nplt.axhline(y=0, color='k')\nplt.axvline(x=0, color='k')\nplt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], "bs")\nplt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], "g^")\nplt.xlabel(r"$x_1$", fontsize=20)\nplt.ylabel(r"$x_2$", fontsize=20, rotation=0)\nplt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\nplt.plot([-4.5, 4.5], [6.5, 6.5], "r--", linewidth=3)\nplt.axis([-4.5, 4.5, -1, 17])\n\nplt.subplots_adjust(right=1)\n\n#save_fig("higher_dimensions_plot", tight_layout=False)\nplt.show()\n\n# result: adding 2nd dimension (on right) makes dataset linearly separable\n
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# test on "moons" dataset\n\nfrom sklearn.datasets import make_moons\nX, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n\ndef plot_dataset(X, y, axes):\n    plt.plot(X[:, 0][y==0], X[:, 1][y==0], "bs")\n    plt.plot(X[:, 0][y==1], X[:, 1][y==1], "g^")\n    plt.axis(axes)\n    plt.grid(True, which='both')\n    plt.xlabel(r"$x_1$", fontsize=20)\n    plt.ylabel(r"$x_2$", fontsize=20, rotation=0)\n\nplot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\nplt.show()\n
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# do this in Scikit with a Pipeline. Contents:\n# 1) Polynomial Features\n# 2) StandardScaler\n# 3) LinearSVC\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import PolynomialFeatures\n\npolynomial_svm_clf = Pipeline((\n        ("poly_features", PolynomialFeatures(degree=3)),\n        ("scaler", StandardScaler()),\n        ("svm_clf", LinearSVC(C=10, loss="hinge"))\n    ))\n\npolynomial_svm_clf.fit(X, y)\n\ndef plot_predictions(clf, axes):\n    x0s = np.linspace(axes[0], axes[1], 100)\n    x1s = np.linspace(axes[2], axes[3], 100)\n    x0, x1 = np.meshgrid(x0s, x1s)\n    X = np.c_[x0.ravel(), x1.ravel()]\n    y_pred = clf.predict(X).reshape(x0.shape)\n    y_decision = clf.decision_function(X).reshape(x0.shape)\n    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n\nplot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\nplot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n\n#save_fig("moons_polynomial_svc_plot")\nplt.show()\n
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Solving polynomial-feature problems (aka combinatorial explosion) via the kernel trick

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from sklearn.svm import SVC\n\n# train SVM classifier using 3rd-degree polynomial kernel\npoly_kernel_svm_clf = Pipeline((\n    ("scaler", StandardScaler()),\n    ("svm_clf", SVC(\n        kernel="poly", degree=3, coef0=1, C=5))))\n\n# train SVM classifier using 10th-degree polynomial kernel (for comparison)\npoly100_kernel_svm_clf = Pipeline((\n        ("scaler", StandardScaler()),\n        ("svm_clf", SVC(kernel="poly", degree=10, coef0=100, C=5))\n    ))\n\npoly_kernel_svm_clf.fit(X, y)\npoly100_kernel_svm_clf.fit(X, y)\n\nplt.figure(figsize=(11, 4))\n\nplt.subplot(121)\nplot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\nplot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\nplt.title(r"$d=3, r=1, C=5$", fontsize=18)\n\nplt.subplot(122)\nplot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\nplot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\nplt.title(r"$d=10, r=100, C=5$", fontsize=18)\n\n#save_fig("moons_kernelized_polynomial_svc_plot")\nplt.show()\n\n# left: 3rd-degree polynomial; right: 10th-degree polynomial.\n# if overfitting, reduce polynomial degree. if underfitting, bump it up.\n# "coef0": controls high- vs low-degree polynomial influence.\n
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Adding Similarity Features

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  • similarity function: measures how much an instance resembles specified landmark.
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# define similarity function to be Gaussian Radial Basis Function (RBF)\n# equals 0 (far away) to 1 (at landmark)\n\ndef gaussian_rbf(x, landmark, gamma):\n    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n\ngamma = 0.3\n\nx1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\nx2s = gaussian_rbf(x1s, -2, gamma)\nx3s = gaussian_rbf(x1s, 1, gamma)\n\nXK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\nyk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n\nplt.figure(figsize=(11, 4))\n\nplt.subplot(121)\nplt.grid(True, which='both')\nplt.axhline(y=0, color='k')\nplt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c="red")\nplt.plot(X1D[:, 0][yk==0], np.zeros(4), "bs")\nplt.plot(X1D[:, 0][yk==1], np.zeros(5), "g^")\nplt.plot(x1s, x2s, "g--")\nplt.plot(x1s, x3s, "b:")\nplt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\nplt.xlabel(r"$x_1$", fontsize=20)\nplt.ylabel(r"Similarity", fontsize=14)\nplt.annotate(r'$\\mathbf{x}$',\n             xy=(X1D[3, 0], 0),\n             xytext=(-0.5, 0.20),\n             ha="center",\n             arrowprops=dict(facecolor='black', shrink=0.1),\n             fontsize=18,\n            )\nplt.text(-2, 0.9, "$x_2$", ha="center", fontsize=20)\nplt.text(1, 0.9, "$x_3$", ha="center", fontsize=20)\nplt.axis([-4.5, 4.5, -0.1, 1.1])\n\nplt.subplot(122)\nplt.grid(True, which='both')\nplt.axhline(y=0, color='k')\nplt.axvline(x=0, color='k')\nplt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], "bs")\nplt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], "g^")\nplt.xlabel(r"$x_2$", fontsize=20)\nplt.ylabel(r"$x_3$  ", fontsize=20, rotation=0)\nplt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n             xy=(XK[3, 0], XK[3, 1]),\n             xytext=(0.65, 0.50),\n             ha="center",\n             arrowprops=dict(facecolor='black', shrink=0.1),\n             fontsize=18,\n            )\nplt.plot([-0.1, 1.1], [0.57, -0.1], "r--", linewidth=3)\nplt.axis([-0.1, 1.1, -0.1, 1.1])\n    \nplt.subplots_adjust(right=1)\n\n#save_fig("kernel_method_plot")\nplt.show()\n
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x1_example = X1D[3, 0]\nfor landmark in (-2, 1):\n    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n    print("Phi({}, {}) = {}".format(x1_example, landmark, k))\n
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Phi(-1.0, -2) = [ 0.74081822]\nPhi(-1.0, 1) = [ 0.30119421]\n
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Using a Gaussian RBF Kernel

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rbf_kernel_svm_clf = Pipeline((\n        ("scaler", StandardScaler()),\n        ("svm_clf", SVC(kernel="rbf", gamma=5, C=0.001))\n    ))\nrbf_kernel_svm_clf.fit(X, y)\n\nfrom sklearn.svm import SVC\n\ngamma1, gamma2 = 0.1, 5\nC1, C2 = 0.001, 1000\nhyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n\nsvm_clfs = []\nfor gamma, C in hyperparams:\n    rbf_kernel_svm_clf = Pipeline((\n            ("scaler", StandardScaler()),\n            ("svm_clf", SVC(kernel="rbf", gamma=gamma, C=C))\n        ))\n    rbf_kernel_svm_clf.fit(X, y)\n    svm_clfs.append(rbf_kernel_svm_clf)\n\nplt.figure(figsize=(11, 7))\n\nfor i, svm_clf in enumerate(svm_clfs):\n    plt.subplot(221 + i)\n    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n    gamma, C = hyperparams[i]\n    plt.title(r"$\\gamma = {}, C = {}$".format(gamma, C), fontsize=16)\n\n#save_fig("moons_rbf_svc_plot")\nplt.show()\n\n# below: model trained with different values of gamma and C.\n# GAMMA:\n# bigger gamma = narrower bell curve, so each instance's area of influence = smaller.\n# smaller gamma: bigger bell curve = smoother decision boundary.\n
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Computational Complexity

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  • LinearSVC class: based on liblinear library. Doesn't support kernel trick. Scales linearly to #instances and #features; training complexity ~O(mxn).
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  • SVC class: based on libsvm library. Does support kernel trick. Training complexity is O(m^2xn) to O(m^3xn) = MUCH slower on larger training datasets.
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SVM Regression (Linear & Non-Linear)

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  • Objectives: 1) fit max #instances on the street; 2) find min #margin violations (instances \"off\" the street\").
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  • Width controlled by epsilon hyperparameter.
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  • Below: random linear dataset. two training results with different vals of epsilon.
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from sklearn.svm import LinearSVR\nimport numpy.random as rnd\n\nrnd.seed(42)\nm = 50\nX = 2 * rnd.rand(m, 1)\ny = (4 + 3 * X + rnd.randn(m, 1)).ravel()\n\nsvm_reg1 = LinearSVR(epsilon=1.5)\nsvm_reg2 = LinearSVR(epsilon=0.5)\nsvm_reg1.fit(X, y)\nsvm_reg2.fit(X, y)\n\ndef find_support_vectors(svm_reg, X, y):\n    y_pred = svm_reg.predict(X)\n    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n    return np.argwhere(off_margin)\n\nsvm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\nsvm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n\neps_x1 = 1\neps_y_pred = svm_reg1.predict([[eps_x1]])\n
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def plot_svm_regression(svm_reg, X, y, axes):\n    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n    y_pred = svm_reg.predict(x1s)\n    plt.plot(x1s, y_pred, "k-", linewidth=2, label=r"$\\hat{y}$")\n    plt.plot(x1s, y_pred + svm_reg.epsilon, "k--")\n    plt.plot(x1s, y_pred - svm_reg.epsilon, "k--")\n    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n    plt.plot(X, y, "bo")\n    plt.xlabel(r"$x_1$", fontsize=18)\n    plt.legend(loc="upper left", fontsize=18)\n    plt.axis(axes)\n\nplt.figure(figsize=(9, 4))\nplt.subplot(121)\nplot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\nplt.title(r"$\\epsilon = {}$".format(svm_reg1.epsilon), fontsize=18)\nplt.ylabel(r"$y$", fontsize=18, rotation=0)\n#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], "k-", linewidth=2)\nplt.annotate(\n        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n    )\nplt.text(0.91, 5.6, r"$\\epsilon$", fontsize=20)\nplt.subplot(122)\nplot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\nplt.title(r"$\\epsilon = {}$".format(svm_reg2.epsilon), fontsize=18)\n#save_fig("svm_regression_plot")\nplt.show()\n
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# Use kernel-ized SVM model to handle nonlinear regression jobs.\n
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from sklearn.svm import SVR\n\n# random quadratic training set.\nrnd.seed(42)\nm = 100\nX = 2 * rnd.rand(m, 1) - 1\ny = (0.2 + 0.1 * X + 0.5 * X**2 + rnd.randn(m, 1)/10).ravel()\n\nsvm_poly_reg1 = SVR(kernel="poly", degree=2, C=100, epsilon=0.1)\nsvm_poly_reg2 = SVR(kernel="poly", degree=2, C=0.01, epsilon=0.1)\nsvm_poly_reg1.fit(X, y)\nsvm_poly_reg2.fit(X, y)\n
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SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
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In [23]:
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plt.figure(figsize=(9, 4))\nplt.subplot(121)\nplot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\nplt.title(r"$degree={}, C={}, \\epsilon = {}$".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\nplt.ylabel(r"$y$", fontsize=18, rotation=0)\nplt.subplot(122)\nplot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\nplt.title(r"$degree={}, C={}, \\epsilon = {}$".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n#save_fig("svm_with_polynomial_kernel_plot")\nplt.show()\n\n# left: little regularization (large C)\n# right: much more regularization (little C)\n
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Under the Hood

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  • conventions: b = bias term; w = feature weights vector.
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iris = datasets.load_iris()\nX = iris["data"][:, (2, 3)]  # petal length, petal width\ny = (iris["target"] == 2).astype(np.float64)  # Iris-Virginica\n
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In [25]:
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from mpl_toolkits.mplot3d import Axes3D\n\ndef plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n    x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n    X_crop = X[x1_in_bounds]\n    y_crop = y[x1_in_bounds]\n    x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n    x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n    x1, x2 = np.meshgrid(x1s, x2s)\n    xs = np.c_[x1.ravel(), x2.ravel()]\n    df = (xs.dot(w) + b).reshape(x1.shape)\n    m = 1 / np.linalg.norm(w)\n    boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n    margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n    margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n    ax.plot_surface(x1s, x2, 0, color="b", alpha=0.2, cstride=100, rstride=100)\n    ax.plot(x1s, boundary_x2s, 0, "k-", linewidth=2, label=r"$h=0$")\n    ax.plot(x1s, margin_x2s_1, 0, "k--", linewidth=2, label=r"$h=\\pm 1$")\n    ax.plot(x1s, margin_x2s_2, 0, "k--", linewidth=2)\n    ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, "g^")\n    ax.plot_wireframe(x1, x2, df, alpha=0.3, color="k")\n    ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, "bs")\n    ax.axis(x1_lim + x2_lim)\n    ax.text(4.5, 2.5, 3.8, "Decision function $h$", fontsize=15)\n    ax.set_xlabel(r"Petal length", fontsize=15)\n    ax.set_ylabel(r"Petal width", fontsize=15)\n    ax.set_zlabel(r"$h = \\mathbf{w}^t \\cdot \\mathbf{x} + b$", fontsize=18)\n    ax.legend(loc="upper left", fontsize=16)\n\nfig = plt.figure(figsize=(11, 6))\nax1 = fig.add_subplot(111, projection='3d')\nplot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n\n#save_fig("iris_3D_plot")\nplt.show()\n
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Training Objectives

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  • Slope of a decision function equals a weight vector's norm (||w||)
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  • Divide slope by 2 ==> any points where decision function = +1/-1 will be 2x away from decision boundary.\n\"example\"
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  • So we want minimal ||w|| to get max margins
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  • If we also want zero margin violations, then decision function needs to be GT1 (positive) and LT1 (negative).
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  • if soft margins OK - need to define a slack variable (C) for tradeoff.
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Quadratic programming

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  • Hard- & soft-margin problems = convex quadratic optimization problems with linear constraints, ie quadratic programming (QP) problems. See Convex Optimization for more info.
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todo: The dual problem

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todo: Kernelized SVM

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todo: Online (incremental learning) SVMs

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  • Linear SVM classifiers often use SGD to find a min-cost solution. SGD converges much more slowly than QP-based methods.
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\n\n\n" }, { "path": "ch05-support-vector-machines.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### SVM Classification (Linear)\\n\",\n \"\\n\",\n \"* Well suited for complex, small/medium dataset classification.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\\n\",\n \" decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\\n\",\n \" max_iter=-1, probability=False, random_state=None, shrinking=True,\\n\",\n \" tol=0.001, verbose=False)\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Large margin classification:\\n\",\n \"\\n\",\n \"from sklearn.svm import SVC\\n\",\n \"from sklearn import datasets\\n\",\n \"\\n\",\n \"iris = datasets.load_iris()\\n\",\n \"X = iris[\\\"data\\\"][:, (2, 3)] # petal length, petal width\\n\",\n \"y = iris[\\\"target\\\"]\\n\",\n \"\\n\",\n \"setosa_or_versicolor = (y == 0) | (y == 1)\\n\",\n \"X = X[setosa_or_versicolor]\\n\",\n \"y = y[setosa_or_versicolor]\\n\",\n \"\\n\",\n \"# SVM Classifier model\\n\",\n \"svm_clf = SVC(kernel=\\\"linear\\\", C=float(\\\"inf\\\"))\\n\",\n \"svm_clf.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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/fz/vvvF53n5eV104YsY8eOZe/evXTs2JF169YRExPDvHnz7PMNKXAr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\\nUkolkA6wlVJKKaWUSqD/B19QbNfFrLLLAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Bad models\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"x0 = np.linspace(0, 5.5, 200)\\n\",\n \"pred_1 = 5*x0 - 20\\n\",\n \"pred_2 = x0 - 1.8\\n\",\n \"pred_3 = 0.1 * x0 + 0.5\\n\",\n \"\\n\",\n \"def plot_svc_decision_boundary(svm_clf, xmin, xmax):\\n\",\n \" w = svm_clf.coef_[0]\\n\",\n \" b = svm_clf.intercept_[0]\\n\",\n \"\\n\",\n \" # At the decision boundary, w0*x0 + w1*x1 + b = 0\\n\",\n \" # => x1 = -w0/w1 * x0 - b/w1\\n\",\n \" x0 = np.linspace(xmin, xmax, 200)\\n\",\n \" decision_boundary = -w[0]/w[1] * x0 - b/w[1]\\n\",\n \"\\n\",\n \" margin = 1/w[1]\\n\",\n \" gutter_up = decision_boundary + margin\\n\",\n \" gutter_down = decision_boundary - margin\\n\",\n \"\\n\",\n \" svs = svm_clf.support_vectors_\\n\",\n \" plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\\n\",\n \" plt.plot(x0, decision_boundary, \\\"k-\\\", linewidth=2)\\n\",\n \" plt.plot(x0, gutter_up, \\\"k--\\\", linewidth=2)\\n\",\n \" plt.plot(x0, gutter_down, \\\"k--\\\", linewidth=2)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,2.7))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.plot(x0, pred_1, \\\"g--\\\", linewidth=2)\\n\",\n \"plt.plot(x0, pred_2, \\\"m-\\\", linewidth=2)\\n\",\n \"plt.plot(x0, pred_3, \\\"r-\\\", linewidth=2)\\n\",\n \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n \"plt.legend(loc=\\\"upper left\\\", fontsize=14)\\n\",\n \"plt.axis([0, 5.5, 0, 2])\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_svc_decision_boundary(svm_clf, 0, 5.5)\\n\",\n \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"bs\\\")\\n\",\n \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"yo\\\")\\n\",\n \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \"plt.axis([0, 5.5, 0, 2])\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# On left:\\n\",\n \"# dashed line = basically useless decision boundary.\\n\",\n \"# solid lines = OK for this dataset, but no margins. Probably will not work well on new instances.\\n\",\n \"\\n\",\n \"# On right: SVM finds widest possible \\\"street\\\" between classes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[-2, 2, -2, 2]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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cemhBDucrP9tuwMKYQQ+cybE22lVChwQmutlVLNARtQRV/jj4n02blL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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# sensitivity to feature scaling:\\n\",\n \"\\n\",\n \"Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\\n\",\n \"ys = np.array([0, 0, 1, 1])\\n\",\n \"svm_clf = SVC(kernel=\\\"linear\\\", C=100)\\n\",\n \"svm_clf.fit(Xs, ys)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,3.2))\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \\\"bo\\\")\\n\",\n \"plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \\\"ms\\\")\\n\",\n \"plot_svc_decision_boundary(svm_clf, 0, 6)\\n\",\n \"plt.xlabel(\\\"$x_0$\\\", fontsize=20)\\n\",\n \"plt.ylabel(\\\"$x_1$ \\\", fontsize=20, rotation=0)\\n\",\n \"plt.title(\\\"Unscaled\\\", fontsize=16)\\n\",\n \"plt.axis([0, 6, 0, 90])\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"scaler = StandardScaler()\\n\",\n \"X_scaled = scaler.fit_transform(Xs)\\n\",\n \"svm_clf.fit(X_scaled, ys)\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \\\"bo\\\")\\n\",\n \"plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \\\"ms\\\")\\n\",\n \"plot_svc_decision_boundary(svm_clf, -2, 2)\\n\",\n \"plt.xlabel(\\\"$x_0$\\\", fontsize=20)\\n\",\n \"plt.title(\\\"Scaled\\\", fontsize=16)\\n\",\n \"plt.axis([-2, 2, -2, 2])\\n\",\n \"\\n\",\n \"# SVMs are sensitive to feature scaling. \\n\",\n \"# Plot on right has much more robust feature boundary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[-1.50755672, -0.11547005],\\n\",\n \" [ 0.90453403, -1.5011107 ],\\n\",\n \" [-0.30151134, 1.27017059],\\n\",\n \" [ 0.90453403, 0.34641016]])\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_scaled\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0, 5.5, 0, 2]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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Pjhh/rll1/WK1as0Pfv37e5PSGeZVkhZivj2qyrVq1aOjAwMKO7IYTI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\\npZRSSinlRv8fA5yJyZCAgNYAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# \\\"hard\\\" margin classification:\\n\",\n \"# - all instances need to be \\\"out of the street\\\".\\n\",\n \"# - all instances need to be \\\"on the right side of the street\\\".\\n\",\n \"# problem: doable only if data is linearly separable\\n\",\n \"# problem: very sensitive to outliers\\n\",\n \"\\n\",\n \"X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\\n\",\n \"y_outliers = np.array([0, 0])\\n\",\n \"Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\\n\",\n \"yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\\n\",\n \"Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\\n\",\n \"yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\\n\",\n \"\\n\",\n \"svm_clf2 = SVC(kernel=\\\"linear\\\", C=10**9)#float(\\\"inf\\\"))\\n\",\n \"svm_clf2.fit(Xo2, yo2)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,2.7))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \\\"bs\\\")\\n\",\n \"plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \\\"yo\\\")\\n\",\n \"plt.text(0.3, 1.0, \\\"Impossible!\\\", fontsize=20, color=\\\"red\\\")\\n\",\n \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n \"plt.annotate(\\\"Outlier\\\",\\n\",\n \" xy=(X_outliers[0][0], X_outliers[0][1]),\\n\",\n \" xytext=(2.5, 1.7),\\n\",\n \" ha=\\\"center\\\",\\n\",\n \" arrowprops=dict(facecolor='black', shrink=0.1),\\n\",\n \" fontsize=16,\\n\",\n \" )\\n\",\n \"plt.axis([0, 5.5, 0, 2])\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \\\"bs\\\")\\n\",\n \"plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \\\"yo\\\")\\n\",\n \"plot_svc_decision_boundary(svm_clf2, 0, 5.5)\\n\",\n \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \"plt.annotate(\\\"Outlier\\\",\\n\",\n \" xy=(X_outliers[1][0], X_outliers[1][1]),\\n\",\n \" xytext=(3.2, 0.08),\\n\",\n \" ha=\\\"center\\\",\\n\",\n \" arrowprops=dict(facecolor='black', shrink=0.1),\\n\",\n \" fontsize=16,\\n\",\n \" )\\n\",\n \"plt.axis([0, 5.5, 0, 2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[-1.50755672, -0.11547005],\\n\",\n \" [ 0.90453403, -1.5011107 ],\\n\",\n \" [-0.30151134, 1.27017059],\\n\",\n \" [ 0.90453403, 0.34641016]])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_scaled\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1.])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# soluton to \\\"hard margins\\\" problem:\\n\",\n \"# control hardness with C hyperparameter\\n\",\n \"\\n\",\n \"from sklearn import datasets\\n\",\n \"from sklearn.pipeline import Pipeline\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"from sklearn.svm import LinearSVC\\n\",\n \"\\n\",\n \"iris = datasets.load_iris()\\n\",\n \"X = iris[\\\"data\\\"][:, (2, 3)] # petal length, petal width\\n\",\n \"y = (iris[\\\"target\\\"] == 2).astype(np.float64) # Iris-Virginica\\n\",\n \"\\n\",\n \"scaler = StandardScaler()\\n\",\n \"svm_clf1 = LinearSVC(C=100, loss=\\\"hinge\\\")\\n\",\n \"svm_clf2 = LinearSVC(C=1, loss=\\\"hinge\\\")\\n\",\n \"\\n\",\n \"scaled_svm_clf1 = Pipeline((\\n\",\n \" (\\\"scaler\\\", scaler),\\n\",\n \" (\\\"linear_svc\\\", svm_clf1),\\n\",\n \" ))\\n\",\n \"scaled_svm_clf2 = Pipeline((\\n\",\n \" (\\\"scaler\\\", scaler),\\n\",\n \" (\\\"linear_svc\\\", svm_clf2),\\n\",\n \" ))\\n\",\n \"\\n\",\n \"scaled_svm_clf1.fit(X, y)\\n\",\n \"scaled_svm_clf2.fit(X, y)\\n\",\n \"\\n\",\n \"scaled_svm_clf2.predict([[5.5, 1.7]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[-1.50755672, -0.11547005],\\n\",\n \" [ 0.90453403, -1.5011107 ],\\n\",\n \" [-0.30151134, 1.27017059],\\n\",\n \" [ 0.90453403, 0.34641016]])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_scaled\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Convert to unscaled parameters\\n\",\n \"b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\\n\",\n \"b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\\n\",\n \"w1 = svm_clf1.coef_[0] / scaler.scale_\\n\",\n \"w2 = svm_clf2.coef_[0] / scaler.scale_\\n\",\n \"svm_clf1.intercept_ = np.array([b1])\\n\",\n \"svm_clf2.intercept_ = np.array([b2])\\n\",\n \"svm_clf1.coef_ = np.array([w1])\\n\",\n \"svm_clf2.coef_ = np.array([w2])\\n\",\n \"\\n\",\n \"# Find support vectors (LinearSVC does not do this automatically)\\n\",\n \"t = y * 2 - 1\\n\",\n \"support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\\n\",\n \"support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\\n\",\n \"svm_clf1.support_vectors_ = X[support_vectors_idx1]\\n\",\n \"svm_clf2.support_vectors_ = X[support_vectors_idx2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[4, 6, 0.8, 2.8]\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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47w+uuv4+HhYe9miSrMUZYzLavC7bY3idlCCFtxxrhdGTHblquyKGA+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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12,3.2))\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"g^\\\", label=\\\"Iris-Virginica\\\")\\n\",\n \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n \"plot_svc_decision_boundary(svm_clf1, 4, 6)\\n\",\n \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n \"plt.legend(loc=\\\"upper left\\\", fontsize=14)\\n\",\n \"plt.title(\\\"$C = {}$\\\".format(svm_clf1.C), fontsize=16)\\n\",\n \"plt.axis([4, 6, 0.8, 2.8])\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"g^\\\")\\n\",\n \"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"bs\\\")\\n\",\n \"plot_svc_decision_boundary(svm_clf2, 4, 6)\\n\",\n \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \"plt.title(\\\"$C = {}$\\\".format(svm_clf2.C), fontsize=16)\\n\",\n \"plt.axis([4, 6, 0.8, 2.8])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### SVM Classification (Non-Linear)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAosAAAETCAYAAABeN0ykAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAFmdJREFUeJzt3X+MpHd9H/D3Jz4sCKa6pqZ3AdMYix+KSzAtV+IUqNcQ\\nikMQbiFRcQoNCpGBlNY0SRGGtFCdkEghAZJQWVaCXQUEojS0lBIwNLdYqh3A5g5sCkcdVMPBHQaR\\nJT4Ivtzep3/s2jpf7sG3v55nd+f1kkbeeeaZfX9mdvbr9z0zO1PdHQAAOJ0fmnoAAAA2L2URAIBB\\nyiIAAIOURQAABimLAAAMUhYBABikLAIAMEhZBABgkLIIAMCgHSvZ+dxzz+3zzz9/1WHf/e5389CH\\nPnTV11+LKbPly/fYX33+rbfe+q3ufvg6jrTpbOW1Vf60+QcPHszi4mIuvPDCSfJn+b7fDvlnvL52\\n9xmfnvzkJ/da7Nu3b03X36rZ8uV77K9eklt6BevUVjxt5bVV/rT5l1xySV900UWT5c/yfb8d8s90\\nffU0NAAAg5RFAAAGKYsAAAxSFgEAGKQsAgAwSFkEAGCQsggAwCBlEWCVquqdVXVXVd1+yvZ/VVVf\\nrKrPV9V/3Ijs3buTqqXTpZfO3ff17t0bkQZsFlP87iuLAKt3fZLLTt5QVZcmuTzJRd39d5O8ZSOC\\nv/GNlW0HtocpfveVRYBV6u4bk3z7lM2vSPKm7r5neZ+7Rh8MYB2t6LOhAXhAj0vy9Kp6Y5LvJ/n1\\n7v70qTtV1ZVJrkySXbt2ZX5+foUxc4OXrPx7rc3Ro0dHz5S/ZGFhIYuLi5Plz/J9P13+3OAlGzWL\\nsgiwvnYk+ZEkFyf5B0neV1UXLH8O6326+9ok1ybJnj17em5ubt0GWM/vdSbm5+dHz5S/ZOfOnVlY\\nWJgsf5bv+82Qf6qNmsXT0ADr61CSP+oln0pyIsm5E88EsGrKIsD6+m9JLk2SqnpckrOTfGu9Q3bt\\nWtl2YHuY4ndfWQRYpap6T5Kbkzy+qg5V1UuTvDPJBctvp/PeJL946lPQ6+HIkaR76bRv3/x9Xx85\\nst5JwGYyxe++1ywCrFJ3XzFw0YtGHQRgAzmyCADAIGURAIBByiIAAIOURQAABimLAAAMUhYBABik\\nLAIAMEhZBABgkLIIAMAgZREAgEHKIgAAg5RFAAAGKYsAAAxSFgEAGKQsAgAwSFkEAGCQsggAwCBl\\nEQCAQcoiAACDlEUAAAYpiwAADFIWAQAYpCwCADBIWQQAYJCyCADAIGURAIBByiIAAIOURQAABimL\\nAAAMUhYBABikLAIAMEhZBABgkLIIAMAgZREAgEHKIgAAg5RFAAAGKYsAAAxSFgEAGKQsAgAwSFkE\\nWKWqemdV3VVVt5+07c1V9cWq+lxVfaCqdk45I8BaKYsAq3d9kstO2faxJE/o7icm+VKSq8ceCmA9\\nKYsAq9TdNyb59inbbuju48tn/zTJeaMPBrCOlEWAjfNLSf546iEA1mLH1AMAbEdV9bokx5O8e+Dy\\nK5NcmSS7du3K/Pz8qrOOHj26puuvlfzp8hcWFrK4uDhZ/izf97OUrywCrLOqekmS5yZ5Znf36fbp\\n7muTXJske/bs6bm5uVXnzc/PZy3XXyv50+Xv3LkzCwsLk+XP8n0/S/nKIsA6qqrLkrw6ySXd/b2p\\n5wFYK69ZBFilqnpPkpuTPL6qDlXVS5P8XpKHJflYVR2oqmsmHRJgjRxZBFil7r7iNJv/YPRBADaQ\\nI4sAAAxSFgEAGKQsAgAwSFkEAGCQsggAwCBlEQCAQcoiAACDlEUAAAYpiwAADFIWAQAYpCwCADBI\\nWQQAYJCyCADAIGURAIBByiIAAIOURQAABimLAAAMUhYBABikLAIAMEhZBABgkLIIAMAgZREAgEHK\\nIgAAg5RFAAAGKYsAAAxSFgEAGKQsAiSpqhuqqqvqBadsr6q6fvmyN001H8BUlEWAJf82yYkke6vq\\nrJO2vyXJLya5trtfM8lkABNSFgGSdPdnk/xhkh9P8uIkqarXJvnVJO9L8orpptt8du9OqpZOl146\\nd9/Xu3dPPRlsrFl87O+YegCATeTfJflnSV5fVeckeWOSjyZ5cXefmHSyTeYb31jZdtguZvGx78gi\\nwLLu/mqStyU5P8nvJrkpyfO7+9jJ+1XV1VX16ar6i6r6ZlX9j6p6wvgTA2w8ZRHg/r550tcv7e7v\\nnWafuST/Kck/TPKMJMeTfLyqfmTjxwMYl7IIsKyqfiFLf9ByZHnTVafbr7uf3d3Xdfft3X1bll7j\\n+PAkTx1nUoDxKIsASarqOUmuT3J7kicmOZjkl6vq8Wdw9YdlaT398w0bEGAiyiIw86rqaUnen+RQ\\nkmd39zeT/EaW/gjwN8/gW7w9yYEkN2/YkJvMrl0r2w7bxSw+9pVFYKZV1ZOSfCjJd5I8q7sPJ0l3\\nvz/JLUkur6qn/4Dr/3aSpyV5QXcvjjDypnDkSNK9dNq3b/6+r48ceeDrwlY2i499ZRGYWVX1mCQf\\nSdJZOqL4Z6fscvXyf988cP23JrkiyTO6+8sbNijAhLzPIjCzuvuOJINvpdvdH09Sp7usqt6epfdk\\nvLS7v7gxEwJMT1kEWKGqekeW/gL6nyT586q6t3Ae7e6j000GsP48DQ2wcr+Spb+A/l9JDp90+vV7\\nd6iqf1NVn6+q26vqPVX14GlGBVibDS+LU36G4mb5/MbDdx/OVQeuypGj07z6Vf7s5U/92J86f6N1\\ndw2c3pAkVfXIJP86yZ7ufkKSs5K8cMKRAVZtw8vilJ+huFk+v3HvjXtz23duy95P7B03WP7M5k/9\\n2J86f5PYkeQhVbUjyQ8n+frE8wCsyopes3jw4MHMzc2tMGJ+8JKVf6+VmjJ7yT1n35NPXfyp9Fmd\\naz55Tfa/fX/OPnb2KNnyZzl/fvCScR77U+dPq7u/VlVvSfKVJH+Z5IbuvmHisQBWxR+4bLA7z78z\\nnU6SdDp3/tideez/fax8+WxjVfU3k1ye5NFJFpL8l6p6UXe/66R9rkxyZZLs2rUr8/Pzq847evTo\\nmq6/VvKny19YWMji4uJk+bN8389SfnX3Ge+8Z8+evuWWW1YWcNo3nViyguhVmTI7WXqt2gW/c0G+\\nf/z79217yI6H5MtXfTm7z9n4F2/Jn938qR/765lfVbd29561TTSuqvr5JJd190uXz/+LJBd396+c\\nbv/VrK0nm5+fn/SIrfzp8ufm5rKwsJADBw5Mkj/L9/12yD/T9dVfQ2+gvTfuzYk+cb9ti7042mvX\\n5M92PpP6SpKLq+qHq6qSPDPJFyaeCWBVNrwsTvkZilN/fuPNh27OscVj99t2bPFYbjp0k3z5G2rq\\nx/7U+VPr7k9m6bOmP5PktiyttddOOhTAKm34axZP/qzEsQ/XTpmdJPtftl++/Enyp37sT52/GXT3\\n65O8fuo5ANbK09AAAAxSFgEAGKQsAgAwSFkEAGCQsggAwCBlEQCAQcoiAACDlEUAAAYpiwAADFIW\\nAQAYpCwCADBIWQQAYJCyCDC1r389qRo+PeIR99//DW+43+Vzl166ov1X+v0faP/zr79+Q7//A+3/\\nUz/3c6Pe3s12/7/8yJHJ7v+5Sy8d/faevP9fu+8nuP+n3H9d7v8zoCwCADBIWQQAYFh3n/HpyU9+\\ncq/Fvn371nT9rZotX77H/uoluaVXsE5txdNWXlvlT5t/ySWX9EUXXTRZ/izf99sh/0zXV0cWAQAY\\npCwCADBIWQQAYJCyCADAIGURAIBByiIAAIOURQAABimLAAAMUhYBABikLAIAMEhZBABgkLIIAMAg\\nZREAgEHKIgAAg5RFAAAGKYsAAAxSFgEAGKQsAgAwSFkEAGCQsggAwCBlEQCAQcoiAACDlEUAAAYp\\niwAADFIWATZAVZ1VVfur6kNTz7KdHb77cK46cFWOHD0y9SiMzM9+PMoiwMa4KskXph5iu9t7497c\\n9p3bsvcTe6cehZH52Y9HWQRYZ1V1XpKfTfL7U8+ynR2++3CuO3BdOp3rDlznCNMM8bMf146pBwDY\\nht6W5NVJHja0Q1VdmeTKJNm1a1fm5+dXHXb06NE1XX+tpsp/65femuOLx5Mkf7X4V3n5e16eVz32\\nVaPPMeX9v7CwkMXFxcny/exn43dPWQRYR1X13CR3dfetVTU3tF93X5vk2iTZs2dPz80N7vqA5ufn\\ns5brr9UU+YfvPpwb/vcNOd5LheF4H88Nd92Qa664JrvP2T3qLFPe/zt37szCwsJk+X72s/G752lo\\ngPX11CTPq6r/l+S9SZ5RVe+adqTtZ++Ne3OiT9xv22Ivev3aDPCzH5+yCLCOuvvq7j6vu89P8sIk\\nf9LdL5p4rG3n5kM359jisfttO7Z4LDcdummiiRiLn/34PA0NwJaz/2X77/t66qcCGZef/fiURYAN\\n0t3zSeYnHgNgTTwNDQDAIGURAIBByiIAAIOURQAABimLAAAMUhYBABikLAIAMEhZBABgkLIIAMAg\\nZREAgEHKIgAAg5RFAAAGKYsAAAxSFgEAGKQsAgAwSFkEAGCQsggAwCBlEQCAQcoiAACDlEUAAAYp\\niwAADFIWAQAYpCwCADBIWQQAYJCyCADAIGURAIBByiIAAIOURQAABimLALBKh+8+nKsOXJUjR49M\\nPcroZvm2zxplEQBWae+Ne3Pbd27L3k/snXqU0c3ybZ81yiIArMLhuw/nugPXpdO57sB1M3WEbZZv\\n+yxSFgFgFfbeuDcn+kSSZLEXZ+oI2yzf9lmkLALACt17ZO3Y4rEkybHFYzNzhG2Wb/usUhYBYIVO\\nPrJ2r1k5wjbLt31WKYsAsEI3H7r5viNr9zq2eCw3HbppoonGM8u3fVbtmHoAANhq9r9s/31fz8/P\\nZ25ubrphRjbLt31WObIIAMAgZREAgEHKIgAAg5RFgA1QVZdV1cGquqOqXjP1PACrpSwCrLOqOivJ\\nO5L8TJILk1xRVRdOOxXb0T1n35M7nn6H9zhkQymLAOvvKUnu6O4vd/exJO9NcvnEM7EN3Xn+nfnu\\n3/qu9zhkQ3nrHID198gkXz3p/KEkPzm088GDB9f09iMLCwvZuXPnqq+/VvKnyb/n7Hty+CcPJ5Vc\\n88lrsv/t+3P2sbNHnWFW7/tZy1cWASZQVVcmuTJJHvSgB2VhYWHV32txcXFN118r+dPkH7ro0H1f\\nn8iJfOlHv5TzPnveqDPM6n0/a/nKIsD6+1qSR510/rzlbffp7muTXJske/bs6VtuuWXVYVO/MbL8\\n8fMP3304F/zOBcnx5Q1nJd973PfykXd8JLvP2T3aHLN432+n/Ko6o/28ZhFg/X06yWOr6tFVdXaS\\nFyb54MQzsY34fGbG5MgiwDrr7uNV9cokH01yVpJ3dvfnJx6LbcTnMzMmZRFgA3T3h5N8eOo52J7u\\n/Xzmubm5LCws5MCBAxNPxHbmaWgAAAYpiwAADFIWAQAYpCwCADBIWQQAYJCyCADAIGURAIBB1d1n\\nvnPVN5PcuYa8c5N8aw3XX4sps+XL99hfvR/r7oev1zCb0RZfW+XPdv4s3/btkH9G6+uKyuJaVdUt\\n3b1ntMBNki1fvsf+dPmzYOr7WP7s5s/ybZ+lfE9DAwAwSFkEAGDQ2GXx2pHzNku2fPke+2ykqe9j\\n+bObP8u3fWbyR33NIgAAW4unoQEAGDRJWayqX6uqrqpzR87dW1Wfq6oDVXVDVT1i5Pw3V9UXl2f4\\nQFXtHDn/56vq81V1oqpG+eutqrqsqg5W1R1V9ZoxMk/Jf2dV3VVVt0+Q/aiq2ldV/2f5fr9q5PwH\\nV9Wnquqzy/n/Ycz85RnOqqr9VfWhsbNnlfV1/PV1irV1OXey9XXKtXU53/o64vo6elmsqkcl+cdJ\\nvjJ2dpI3d/cTu/tJST6U5N+PnP+xJE/o7icm+VKSq0fOvz3J85PcOEZYVZ2V5B1JfibJhUmuqKoL\\nx8g+yfVJLhs5817Hk/xad1+Y5OIk/3Lk239Pkmd090VJnpTksqq6eMT8JLkqyRdGzpxZ1tfJ1tdR\\n19ZkU6yv12e6tTWxviYjrq9THFl8a5JXJxn9xZLd/RcnnX3o2DN09w3dfXz57J8mOW/k/C9098ER\\nI5+S5I7u/nJ3H0vy3iSXj5if7r4xybfHzDwp+3B3f2b567uz9Ev9yBHzu7uPLp990PJptMd8VZ2X\\n5GeT/P5YmVhfl8+Our5OsLYmE6+vU66ty/nW1xHX11HLYlVdnuRr3f3ZMXNPmeGNVfXVJP884//L\\n92S/lOSPJ8wfwyOTfPWk84cy4i/zZlJV5yf5e0k+OXLuWVV1IMldST7W3WPmvy1LxeXEiJkzy/p6\\nP9bXGWJ93Xg71vsbVtXHk+w+zUWvS/LaLD1FsmF+UH53//fufl2S11XV1UlemeT1Y+Yv7/O6LB1C\\nf/d6Zp9pPuOqqnOS/Nckrzrl6MuG6+7FJE9afv3WB6rqCd294a8xqqrnJrmru2+tqrmNzpsV1tfp\\n1ldr6+ZkfR1nfV33stjdP3267VX1E0keneSzVZUsPUXwmap6Sncf2ej803h3kg9nnRezB8qvqpck\\neW6SZ/YGvG/RCm7/GL6W5FEnnT9vedvMqKoHZWkhe3d3/9FUc3T3QlXty9JrjMZ4QfpTkzyvqp6T\\n5MFJ/kZVvau7XzRC9rZlfZ1ufd1ka2tifbW+jri+jvY0dHff1t1/u7vP7+7zs3TI/O+v50L2QKrq\\nsSedvTzJF8fKXs6/LEuHjZ/X3d8bM3sin07y2Kp6dFWdneSFST448UyjqaX/a/9Bki90929PkP/w\\ne/8itKoekuRZGekx391Xd/d5y7/rL0zyJ4rixrG+Wl9jfR07f6bW11l7n8U3VdXtVfW5LD1dM+qf\\n2if5vSQPS/Kx5beXuGbM8Kr6p1V1KMlPJfmfVfXRjcxbfrH5K5N8NEsvPn5fd39+IzNPVVXvSXJz\\nksdX1aGqeumI8U9N8uIkz1j+eR9Y/pfgWH40yb7lx/uns/SaGm9hw0aZ2fV17LU1mX59nXhtTayv\\no/IJLgAADJq1I4sAAKyAsggAwCBlEQCAQcoiAACDlEUAAAYpiwAADFIWAQAYpCyyLqrqhqrqqnrB\\nKdurqq5fvuxNU80HsFVZX5maN+VmXVTVRUk+k+Rgkp9Y/oD1VNVvJfnVJNd298smHBFgS7K+MjVH\\nFlkX3f3ZJH+Y5Mez9BFMqarXZmkhe1+SV0w3HcDWZX1lao4ssm6q6lFJvpTkSJLfSvK7Wfrc0ud1\\n97EpZwPYyqyvTMmRRdZNd381yduSnJ+lheymJM8/dSGrqn9UVR+sqq8tv9bmJaMPC7CFWF+ZkrLI\\nevvmSV+/tLu/d5p9zklye5KrkvzlKFMBbH3WVyahLLJuquoXkrwlS0+TJEuL1V/T3R/u7td29/uT\\nnBhrPoCtyvrKlJRF1kVVPSfJ9Vn6F+0Ts/RXe79cVY+fci6Arc76ytSURdasqp6W5P1JDiV5dnd/\\nM8lvJNmR5DennA1gK7O+shkoi6xJVT0pyYeSfCfJs7r7cJIsPwVyS5LLq+rpE44IsCVZX9kslEVW\\nraoek+QjSTpL/+L9s1N2uXr5v28edTCALc76ymayY+oB2Lq6+44ku3/A5R9PUuNNBLA9WF/ZTJRF\\nRldV5yR5zPLZH0ryd5afbvl2d39luskAtjbrKxvBJ7gwuqqaS7LvNBf95+5+ybjTAGwf1lc2grII\\nAMAgf+ACAMAgZREAgEHKIgAAg5RFAAAGKYsAAAxSFgEAGKQsAgAwSFkEAGCQsggAwKD/DwXXHSvq\\noBrCAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# some (most?) datasets are not linearly separable. simple example below.\\n\",\n \"\\n\",\n \"X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\\n\",\n \"\\n\",\n \"X2D = np.c_[X1D, X1D**2] # adds 2nd, non-linear dimension.\\n\",\n \"\\n\",\n \"y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 4))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.grid(True, which='both')\\n\",\n \"plt.axhline(y=0, color='k')\\n\",\n \"plt.plot(X1D[:, 0][y==0], np.zeros(4), \\\"bs\\\")\\n\",\n \"plt.plot(X1D[:, 0][y==1], np.zeros(5), \\\"g^\\\")\\n\",\n \"plt.gca().get_yaxis().set_ticks([])\\n\",\n \"plt.xlabel(r\\\"$x_1$\\\", fontsize=20)\\n\",\n \"plt.axis([-4.5, 4.5, -0.2, 0.2])\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.grid(True, which='both')\\n\",\n \"plt.axhline(y=0, color='k')\\n\",\n \"plt.axvline(x=0, color='k')\\n\",\n \"plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \\\"bs\\\")\\n\",\n \"plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \\\"g^\\\")\\n\",\n \"plt.xlabel(r\\\"$x_1$\\\", fontsize=20)\\n\",\n \"plt.ylabel(r\\\"$x_2$\\\", fontsize=20, rotation=0)\\n\",\n \"plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\\n\",\n \"plt.plot([-4.5, 4.5], [6.5, 6.5], \\\"r--\\\", linewidth=3)\\n\",\n \"plt.axis([-4.5, 4.5, -1, 17])\\n\",\n \"\\n\",\n \"plt.subplots_adjust(right=1)\\n\",\n \"\\n\",\n \"#save_fig(\\\"higher_dimensions_plot\\\", tight_layout=False)\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# result: adding 2nd dimension (on right) makes dataset linearly separable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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nv0/HNmdn4eceXZ36G+lWJLMjJIiShfz3zymZmDdvutKAftKo/oCppM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\\nUzIREZHElExERCQxJRMREUns/wONji5/tsi2YAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# test on \\\"moons\\\" dataset\\n\",\n \"\\n\",\n \"from sklearn.datasets import make_moons\\n\",\n \"X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\\n\",\n \"\\n\",\n \"def plot_dataset(X, y, axes):\\n\",\n \" plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"bs\\\")\\n\",\n \" plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"g^\\\")\\n\",\n \" plt.axis(axes)\\n\",\n \" plt.grid(True, which='both')\\n\",\n \" plt.xlabel(r\\\"$x_1$\\\", fontsize=20)\\n\",\n \" plt.ylabel(r\\\"$x_2$\\\", fontsize=20, rotation=0)\\n\",\n \"\\n\",\n \"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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lx3q9tl2WJCIDbD0fZWoi+9YftgfT70jBLlNRomKi+p1siRRbsLXv8R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\\nAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# do this in Scikit with a Pipeline. Contents:\\n\",\n \"# 1) Polynomial Features\\n\",\n \"# 2) StandardScaler\\n\",\n \"# 3) LinearSVC\\n\",\n \"\\n\",\n \"from sklearn.pipeline import Pipeline\\n\",\n \"from sklearn.preprocessing import PolynomialFeatures\\n\",\n \"\\n\",\n \"polynomial_svm_clf = Pipeline((\\n\",\n \" (\\\"poly_features\\\", PolynomialFeatures(degree=3)),\\n\",\n \" (\\\"scaler\\\", StandardScaler()),\\n\",\n \" (\\\"svm_clf\\\", LinearSVC(C=10, loss=\\\"hinge\\\"))\\n\",\n \" ))\\n\",\n \"\\n\",\n \"polynomial_svm_clf.fit(X, y)\\n\",\n \"\\n\",\n \"def plot_predictions(clf, axes):\\n\",\n \" x0s = np.linspace(axes[0], axes[1], 100)\\n\",\n \" x1s = np.linspace(axes[2], axes[3], 100)\\n\",\n \" x0, x1 = np.meshgrid(x0s, x1s)\\n\",\n \" X = np.c_[x0.ravel(), x1.ravel()]\\n\",\n \" y_pred = clf.predict(X).reshape(x0.shape)\\n\",\n \" y_decision = clf.decision_function(X).reshape(x0.shape)\\n\",\n \" plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\\n\",\n \" plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\\n\",\n \"\\n\",\n \"plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\\n\",\n \"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n \"\\n\",\n \"#save_fig(\\\"moons_polynomial_svc_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Solving polynomial-feature problems (aka combinatorial explosion) via the kernel trick\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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gZxKLwo5WNCV2dJESosYWJoipkVS07m22OaqkjN9H4h7BZqDxM4tI/S\\nyoOcu6XSqEWrUqhmqViCN5HMpxIm0NXF8zPndcWQkzKcL/wm1B6m/ODTdK446vhhKtmQQjULxRi+\\nIAEs3NW3Zx8jod30NY/jxsC/rPjPnt+H+VV0+px6yUbhJxNdPSxfBf0tzSw1rEgFKVRn5OciVXoG\\nhIniQ1AjY/uJbKmiZt0djF505+dPVvzPzK8ZOW2T/WqZUy/8L1pn0oD/NVKopuHH8JVeAeEFE109\\nzCn7gNCdZazcsA2A7osRl1slUvFbTqa9eY9ccKE1QjjD9LUAUqim4KfwnRa8ldYWp3XzxtOuXBWi\\nENULKxm/SY5KNZVvMxJnbuAlO4VpVHUAGHG7GSlJoZrEDwGcLnhVxNoVpbKNirCD6Xf3xc4PGQnT\\nc9Lp0SXJTmGK8OFTBIKdHGo+xizM2Dc1mRSqCbwcwDLfVPiJyXf3xcoPC6YkJ4W4Jth2jMCZF/mg\\npYu5N6+j0cCFVCCF6hSvFqlu9goIYZdeLgDz3G6GmOTVfIyTnBRiuniRGmzpZu56c4tUkEIV8GYI\\nS/AKvwm1h6k68AIjJe/Sv76KQL3MUTWBF/MxTnJSiPSWNs/h8vq1RhepIIWqp0JYQlf4VXzD6Y5G\\nMzacjvSHZGsqvJWPiSQrhcjMS2sBirpQ9UoIF1voyjnYxak2MEz/7WZuOF1svJKNyeJZWQw5mYpk\\np8hGsO0YY6HdvNU8buwCqkRFW6h6IYiLrUCNk3Owi5epG04XEy9kY7JiL1DjJDtFJvHDVMYSDlNp\\nqJVC1UheCGIJXiGE8zRgdjYmkpwUIjdz5lwgdPO1w1S8oOgKVdOLVAleUYxKBi+h51S63QyBudmY\\nSHJSiDyNDHjuMJUStxvgJJOLVBUKSviKohRqD1N55KccqX/f7aYIw0lOCpGf+LB/36zgzA82TNH0\\nqJpepIIEryg+8b38OlafYe76FuO3SRHuKNb5+kJYIb6rSueK2K4qXsvZoihUTS1SpUBNTc7BLg6h\\n9jBzek/T29JNQIpUkYbkZPYkO0WyeE9q6dpBapq9uatKURSqIEWql8g2KsVjwcJSQvOqWebB8BT2\\nUtEx1EBQMjIHkp0ilQULSxkKlDFrzRq3m5IX3xeqiedTm0AKVCGu0cNXic6f7XYzhEGmhvkrJSeF\\nED4vVE0b8pci1T6y0bUQ3peYkSoie386QbLT//TwVaj17h7VPi5UzdoP0A9FqsmBJhtde0/J4CW3\\nmyAM4oeMTEeyU7ito7KXAN7alirOx4WqFKlWk0ATVomv9n+zpYu5C9e53RzhIr/kYyaSncIN0ZFx\\nKvbumMpZL5xClYrr+6gqpX6glLqklDqe5uNKKfU9pdRppdS7SqlPON3GfKlQEBUdo7yuxtchLEQu\\ngm3HGAk+Q7Clm7nr1xm32j/SH0IvqHO7GRn5JTeLoUgVwg3BtmNMDF00Nmdz4XqhCuwCfinDxx8A\\nmibfHgF2ONCmgsUDWJXJHbMQieb0nmbFLTVUr9/g6fB02S48nJuycb8Q9prTe5ryylICWx/wfM66\\nXqhqrX8C9GV4yFbgL3XMm0CtUmqxM63LjwSwEDMbr692uwme5eXcTMxHyUghxEy8MEd1KdCd8O+e\\nyfddSH6gUuoRYr0H1NfXczGSclTMVio6BpWTPamRC0T0CN2RU463I5XC29KY9iO5Xtfqr8u82kWE\\nr1y/zdG82tEZX8df/0fWsastE7+whJLK+Yydq2D0YiS7toxouk9m99hC6YlKdJbtMpiRuZmcj5n4\\n62dBstNu0pbpJjYtJFpenlPOmsoLhWrWtNZPAU8BNK2+US8qd3aRRqqe1O7IKZaXNzvajnTyaUu6\\n1aqJ6uaN53xdq78u+//6Ysr3b9y2jAe2/uJ1709cbev1/yO72NWWkZd3UXnnJXo/sybryf3dJyMs\\nX1NueVtSifQPGD9H1UpO5WauI01e/1nwcnbG2j77uuxM3qXA6/9HdjGhLSOv7CL4qzcwe2nYs4uo\\n4rxQqJ4Dlif8e9nk+4zi1+H+TEF7/OWzDrYkP/mstjV5Kxnf8PCefh5hTG76NRtn4uXslNz0tr49\\n+5g9dp7Rkkb8cJyKFwrV3cBjSqkfAncBV7XWmceMHFasQZyNdOE1r3ZR2l5Qt8lWMsIHjMhNycb8\\neS07JTfNEGw7xlhoN333jaNmlXm+NxUMKFSVUn8NbAIWKKV6gN8DZgForZ8EXgI+C5wGhoDt7rQ0\\nNTeD2K472GyGrLKV7jqp5kTNRO7YvS/UHqZi7DwdlcOe3XzaBF7ITZOLVDuyxMrcBMlOkbtg2zHG\\nenYycU8ZNfduYvSieT97+XC9UNVaf2mGj2vgXzjUnLy4FcR23cGaegcsd+zeFt/k/8J9vcxa2OSL\\nO323mJ6bJhepYE+WmJxDkp3Fo35lJeF776Khtolujy+iinO9UPUyFQoaG8RCmCS+yf9HLSFqNj4g\\nRaqPmV6kCiG8RQrVPMXD2Eu2fXVjymGjfId/Nm5bZvywUd288bRDXsJZC28IMLD+JuqkSPUtvxap\\nxZadkpveVDocQi8x4+h4K0mhmgevhnG6uU35Dv94Ydgon18GEtL2UEMfud0EYSOv5mI2ii07JTeF\\nSaRQzZObYWz1pH07pQuvebWjLrQmOyb3dHidnEblT14pUiU77SO56a6+PfsYCe2mr3kcs38KcyeF\\nao5MmJeazSbShUgXkPlIF16xUzty2xDZ6jv2a7+0rj81RlbDCpEdrxSpYG92WpmbINkpshNqDxM4\\ntI/SyoNEtlRRs+4O360BkEI1B16Yl2rFRtKvt/YY2fNgdfhl+vxM+9yFMJGXitSZFJqd8XyS7DTr\\nc/e7ia4e5pR9QNfmBlbevcXt5tiixO0GeI0fAjkbEjbCSqXDIbebICzmpyLVSpKdwmnVCyuJ1vn3\\ntD8pVLNkwpB/NtY90MjGbctSfizd3KZ8h39kkrzIha7232rUYueFTMyWZKcQZpKh/yx4Ycg/Ubo7\\n+ta/eJ3l5bnNbUql0CGy2NBYcc5tkhNihB945cY9V5KdZpLcTK90OAQ+X6MqPapZMimUvX43Xsyn\\npBTz5y78wWs37okkO72pWD/vbASCnVyqvup2M2wlheoMTAzl11t7LFk0Vewy/dLy+i80Iezg9Xmp\\nkp3WkOx0X6g9zHDrc3Q07uf8ygiV9SvcbpJtZOg/C14N5UJ4ffPmbIaK4n92R05ZMqwnUlNXeqn8\\n6LLbzRAF8nqR6hTJTmG3UHuY8oNP07niKIH7W2hs2ux2k2wlhWoGJvamOsXL834ybQ8jQ0XOCrWH\\nqTrwMu3r3qRmZT3+XZdaHKRInZlkp3BC7fwy+m9vZqnPi1SQQnVGJgezm3fuJk9ul0A1Q7DtGBXd\\ne+loPMrc9f6/6/czv920S3amJtnpISMDgHK7FY6QQjUN04PZ7bAr5K47318Sbn/OVvD6sGCuVqyt\\npr/ZW3f95TV1RC6H0Avq3G6KEfw25O92jkh25q7YcjNbft47NZEUqhmYHMyZwm7jtmVGh8/rrT15\\nzW3yw5CUyf8vQqRjchbmyss5UqzZKblZ3KRQTcH03tSZeCV8MglGLvH104/x7dWPs6C8we3m5M3r\\nPRmF0kMDRXPX70d+3S/VDcG2Y5N/u34f1Li+PftmvE70rjn0/ez6x41XxkYAQqqP/zr7j/i90X/P\\nguW3UNc0L4/Wuq/YszOTksFLbjfBUVKopiHh7K4nz32PdwYOsePc9/jdVf/NsusmDhVdC8LG6x5j\\nVRB6vSdDFC+v37CbItQeJnBoHyMlb7NosQYeTPvYOUv2z3i9wVmfuu5xamBk6u9/XvZzjpW288zs\\nb/Fv995HsGsLmYrjbEl2mkXPqXS7CY6RQlUYJxi5xPPBv0WjeT74LI8u/c2celUzzWdKDFEJQiEy\\nkxv23IXaw1N/Lxm8RMWhH9G5+gzVK+rov+lW+B/pn3vlwVtnvH70XGXax4VGrvLCK3+DnoDnyzvZ\\ncttpFp7YSabiOJFkpzdUHvkpoaW9wGK3m+IIKVSTyFBXduyc3P7kue8xwQQAE4zn3Kta7MNCQhRK\\nelPzE2w7RuDMi9TUXsvGU/f1Eri5hWWTCwrrFowSujz7uufWLRilobZpxtcYvRhJ+7j/9cbjaDQA\\nGs2zKyP8s9VV8MPMba7ffAsg2Wm62HZ/L9DRuJ/K25aw0kOLVAshhapHpSsU7RbvLfi7/xLO8JiZ\\nrxNdOE7obOwaiXOo4r2pY3oMgDE9NtWrWjcv/ZylXG3ctizn5whrBYf6+Pq+P+Dbm77JgsB8t5sj\\nksgN+8zieVgyeAl97gJjod0EW4a5vH7t1GNq6jdPKyxff3uvLW0JDvXx/OlXGZuIAjA2EeXvu37O\\nv/n8o9TO6+dK+Pr/z3nVVxjr2cnIzo/z0YatU+/PNK9VstMd8Skk7699nT+qbufxT/wTt5vkGClU\\nE3ipF+H11p6Mk83tED8No3Z+wrfNyEDuF6qoZmjux2k4sY8rfVGCXfdN3dEn9qbGxXtVX2+1bq6q\\nnUV+pk2zxTVPHmnlnd732HGkld9d/5jbzRGT/D6qZNVoUHLv6eXafiJbqgis25RVz6jVnjzSyoRO\\nyk49wY4jrRw4kv5GsPNAFR1H9rPucG/sHRXVDB5azNAdm1IWrJKd7lmwsJRnyrt4r/9cUeWmFKpJ\\nvBTQTg7TxEM52NJNf/OqqfdH66pyuk5ZaAgYJDJrnK5Ng+hLl6k+9hcMt97D0B2bODryzlRvatyY\\nHuPo4DtWfBqOyHY/RL8rHQ5BdeqPxXt/NJrnT7/Ko7dtk15VI2i3G2C7QnMzPvxaXnuc8C+UcPmm\\n+BnrVa4OxR4NnpjqTY0bm4hyNHgi4/NWbtjG2YVtfMi1fO5/aw/LDnZP60RwgmRnZr2jPbw62lF0\\nuSmFqkgp2HaMOb2nUaNXARgreZdgyzCBrQ8U1lsw+dTukxGWrtkCwLmGF+l/aw8r9p7gr2b9Gnr2\\nXIC0d/R2szMIj7981rZrm0hVB4CR696f2PsT7/Uplt4B03npZt0JofYwE9VXGP7xc6jRq1SMnadj\\n9Rnjzlh/dusTeT936vNIiPZQ9QnK3+8k2LY962JVstNefxbYOzXeWEy5KYWqmCY+D2ZsbD+he2Lf\\nHtH5s4EqVm74Z7a85tK7txCtCxC8rZeyvvcBGOgKsWLvCYJdWxy9owdZUGC3VHPpiql3QHhH/Bjg\\n0S3NhO78KRDLw8BCs4pUqy29ewuX1rQTmXeAsYPX5rDO1HEg2Wmf0ESQZysOE50sVYspN6VQneT3\\neVnZCLWHqdi7g87VZ2IrCjdsc+y1G5s2T7ubP9veRrD6Pcrf30nfnoemNrMGHC9chbUyzaUrht4B\\nk6kiOTs8G8G2Y4wEn+GjtSFm1dzBsk9td7tJjmqobYKHmuic30rHkf2s2HueEI9ixZ6sInd/2f89\\nJpKm5hRLbkqhKoDYiSgV59/gwuRWKm73FjQ2beZS/QqGbjhE/bFXp95/8YJiuPU0lds+V9D15exo\\n9+Q7l04IJySOKql7yqi+aQND48V7ulp8Dmuw+j3q9+5gfuWf0Dd8/Wbzkp32Oj5xgjE1/WtcLLkp\\nhWqRU1d6GX7uZUZK3qXvvnFqNm52ZcVqKg21TbChiUvrYvtdlQYHUCeO0tm1h5Wthc1hTRyiyufs\\n7EykCM6skLl0QtgpvrNJ54qjVK+oY9lDsV7UoZMRl1vmrnjHwYXaNn44+8vMn/g4Fdsflux0SKg9\\nzA/f/wrDS9u5enuJo6OdJpBCFW9tS2Wl8OFTVB75KR+se5OaG+qpWXeHMUVqoqk21QJNn+Dcmy/S\\n6dKq1GzIPC0hvCc+HzW09gQ1zc0svXuL200ySkNtE2yEobpDdBzZz6qdEP2VO6HcuteQ7Lxe/Oap\\no/EogftbimaT/0RSqE4qtvmpfXv2Mfv8G3zQ0sXc9etcH+rPRXzxVeiN93JelSqcEWoPUxXs5FDz\\nMWZh3s2PEIlC7eGpIrV6/Qbqmj7hdpOMFB/lOruwjY5X9hMYWEWoN+zK7izFInBoH2Wregl82v0p\\neW4pcbsBwnl9e/YxEtrNhft6PVekxjU2bSaw9QEm7iljrGcnw63Pud0kMSnYdoyKvTu4uO5nzLq5\\nyZPfX6L41AaGmdW8SorULDQ2bSZwfwtjgQijb3yLYNsxt5vkS6H2MAsWllK+YC6V9StmfoJPSaFa\\nRKIj44xYi1TZAAAgAElEQVTs3MVIaDeRLVXUbNzs6SKiobaJZQ9tZ+KeMjoX7WFg1/emjjQU7ogP\\nnwZbuglsfcDT31+ieKhTRwGI1hXvoqlcNTZtpqS2RjoLhO1k6L9IhNrDlASCdDTud3zrKbste2g7\\n5958kVD1CeoOPu3YvNVMR9gW81yrFWur6W9eZeR8ZyGSjezcxUjJu5zZUkWg/ia3m+Mps0orWPbQ\\ndnrYSWfXHpbt6iZyz1cyTgWQ3MyNHr7qdhNcJz2qRSC+P+pYIBKbjO2jIjVu6d1bCGx9gNDaE4z1\\n7HRkKCrdcX9yVrUQ5gu1hxnZuYuOxv1EtlSxcsM2ubnK07KHtlNzezOhtScoP/h0xpEtyc081BZ3\\nT7/rhapS6peUUqeUUqeVUr+d4uOblFJXlVJHJt/+k6WvXwQb/U909bC0eQ5qToVxQ7HBoT6++tI3\\nuDzUN+3v+WiobSKw9QHqV16/x58QfuN2dnpZ4jxqr968W5mdVlh69xZmNa9ixdpq19og/MnVoX+l\\nVCnwBPAZoAc4pJTarbV+P+mh+7XWDzreQJ8oHQ6hlph5R/bkkVbe6X2PHUdaAab+XshJG7q6gtLz\\nIauaKIRxJDvzN3XiVEuIwNYHPNuLakd2FipaF0APDcS2EhQFKxm85HYTjOD2HNU7gdNa6w8BlFI/\\nBLYCyWFrFC/NsYlvQ3WouReFdRszWyF+5rtGx/7UeurvhZxf3FV9mdnDf09gZycV2x+2ttFCmMFz\\n2WlKbs7pPU3DrTX0fuZOzxapdmWnFY4ETlC/dwfBri2ybWABQu1hqo78lDfXvUlNZT0BinfVv9uF\\n6lKgO+HfPcBdKR63Xin1LnAO+LrW+r1UF1NKPQI8AlBfX8/FyPEZG6Aqx1CR3ObGhMKpzzoOhUvp\\njpya9r6IHqE7coptX91I+Mrs654zr3aU1r94PafXz9ZE3xXGPxll4t47KamuRo/NptuQE1YiI5rH\\n9z3N+ETszPfI+NjUx8YnJvjjfX/FYx/7Wh5XbqRk9TZGlw7RNRxh9vk3mJhTS1lF+v/j+P9RPq+V\\nTn7XK6Qt1sunLdFbx3mvtJmRWR+z9HstMqId/d7VE5Xoi2b8rKRhWXYm56Zd33+55CbYl50TmxZS\\nUjWfsXMVjGb5f+z0918m9mVnfm1J/LqU8CnGbr+DC6v7UaNBhi7+jJL5yd2r1ucmeD87E0X7h1FV\\nA4QeWkxVzT+mdHYNoxehO8dMMun7thBuF6rZeAdYobUeVEp9FngeUu8grrV+CngKoGn1jXpR+boZ\\nL64GrJ2jmnycXPyIuVRBCxC+MtvSI+jiQu1hlp/6OR+sOUb5JzfQULuU7pMRlq+x8BiRLAWH+vj6\\nvj/g25u+OXWnf/Tdi7x2+cdEdezMd42eenxUR3ntchvf2PTlPHsGyoEazra3MfTKYVacvoGhG9Lf\\n3ed7DGCm4/7y/T+1+kjCQuTTluDRY9xYc4rjzUGWrrHuZB+nv3cj/QPoBXWOvZ5NssrOxNy8cfWN\\n2o3vv1SvaUd2BtuOMdbzLJEtVcxZmv1JfMWTnblJ/XUp59KVXiI/OUDz2Q10rbhx2i4AduQmeD87\\nEwWPHmNuzX6CzWUsvTX/HHXr+9Zqbheq54DlCf9eNvm+KVrr/oS/v6SU+nOl1AKt9WWH2ugIu4bF\\novNns8zl4a3EuVTx+VOtPc8woSfSPmdCTxQ836qxaTNngWD1e5S/v9PyE6xMm+ZhCj00IPtR2k+y\\nc1I22RlqDxM4tI/yyoOM3lNGwNDjopO5lZ2FaqhtoocDKT8muZkdNfQRs9akGiQpPm6v+j8ENCml\\nVimlyoEvArsTH6CUWqSUUpN/v5NYm323UibTlh3rHmhk3QONbNy2LOvrVR14gQtjR61qXt6S51LF\\nV6WeGDjF2EQ07fPGJqIcDZ4o+PUTT7Cq6N4rBwIIv5DsnJRNdv7D317DyLzDDG5uYNlD2z1RpLqd\\nnYWKzp8te4AKS7jao6q1jiqlHgNeAUqBH2it31NKfW3y408CnwceVUpFgWHgi1prnfaiPpbNPnPx\\nnoNOQzb2f/JI69Tdf+Kd/hO3/oljQxINtU2cvamL2nc+QtZQCj+Q7MxNeKiCOYFxJuqWut2UrJmQ\\nnUKYwO2hf7TWLwEvJb3vyYS/Pw487nS7Msk0x8ZN8SI1UnmQmtubWXq3dXME8xHvEYjf/Y9NRKdW\\npcIcx9vTNysYOyqxaZPjry2E1byWnSbk5ni9N/b4NC078zU6dF4yNw+lwyHwxreqI7IuVJVS/4/Y\\nnn2f11r/KOH9CtgJfBX4Q631dRtPmyrfzf7zmWPjVEgvWFjK0KIGRtessfS6+UjsEYiL9ww8PP8R\\nR9tSWb+C8MZeyl/czXBrmKE7NmU85k8Iq/gxO/OR79xEEwpcp5mUnfkKrLuDU6E2Am/EMrdy2+fc\\nbpKn6OoKt5tgjFx6VL9BbBXpt5RSz2ut4ynxP4kF7VN+D9pCFOME8qPBE9fNpZqaP+XwNn8NtU2w\\noYmevtiZ1CsPQQgpVoUjJDsLYFV2eukXv0nZma+G2iYatjbRo3Yy0n4YWpEOgiwFgp2wxO1WmCPr\\nQlVrfVQp9TSxYP0KsEsp9TvAbwF/AzxqTxOLQ7peAy97dusTaT/m1t5uyx7aTs/unSzQpXS50gJr\\nmbKJukhPstNekp1mK793A/NHTxIoNydzTc3NUHuYqgMv0NG4n8qVS1jpgUV/Tsh11f/vAiPA701O\\n5P99YpP5v6J1hv0yxIxeb+3h+MtnOf7y2bRDWtkMdckqy5n5aTVqphXPbigdtmZRuZtnlweH+vin\\nr/83q19bstMm2WTn/Mphh1slTGZabkJsj9/yg09zcd3PCNzfkvdCaLez047Xzmkxlda6Wyn1J8Bv\\nA38GvAH8I631tFs8pdQ3gX8ENAOjwJvAN7XWMx8V5WGp79Iac75LK/iOrlb2sMxELVzI24F9LDsY\\nJsRXMh2U4phr3zvTG+P2HX4+VHWAWE2Wv1T7RzrlySOtHL58iu+/3cpvf8qa15bszMzO7BzZuYu+\\nknfpqh7HuqNdhCn8lJ0r1lbT37yKpU2b875GYnY6PZ/ZrtzOZx/VYMLff11rPZTiMZuAPwfWA78A\\nRIHXlFIemV2THxPv0sT1Gps2E7i/hdDaE5QffJpov7W9LRu3LZvavzHxLdM+uPK9c026/SPTPdbK\\nO/jE1959KvNr53P5hL9Ldiaw4/s/1B5muPU5Ohr3E9lSxeqtX/PE/qnFrNizUw8NFPT85Ozsi6Te\\nN9yOns9ccjtXOfWoKqW2EVsAcBFYBPwrUsyv0lrfn/S8rwBXgXuAF/NtrBBWaWzaTIh5zOYsJdHR\\nnJ+faY6Tn4LTDan2j0zXM2D1HXzya1vVqyrZ6byJrh7mzLlgxFZ9Ra82gO6ITbeS7MyskFP9kvOr\\ntecZbv34b6Z8nNU9n+n2/bVC1j2qk2dF7wKOAx8HTgH/VCmVzYG21ZOvJccCCWOM11czO5DfVsIS\\nqPZIt39kqp4Bq+/gU722Fb2qkp3uqVlc6XYTRBLJTnukyq9Xg23X5ZcdPZ/pctuqXtWsClWl1Abg\\nWaAHuF9rHQT+I7Ee2T/M4hJ/ChwBfppnO4UQKRSy8M5qofYwgWAnh6qP5X2NdPtHtvY8k/Gx8Tv4\\nQqR77e+/nf91JTuFMI9JuRlX6ELUTHvvpnucFbmZy2vna8buJKXUbcAeYsNPn9FaXwDQWj+rlHoL\\n2KqU+pTWen+a538H2ABsSNg/UAhzTHj329KUxQKh9jAVe3fQsfoMgZtbaMxzMUC6/SNPDJyc9r5M\\nJ/csCOQ3nTPt3pW9+Z2bLtkphJlMyc1khSxETZVfUT259+4kO3Iz3WtP7ftrgYyFqlJqNfD3gCbW\\nG3Am6SHfBF4F/hi4O8Xzvwt8EbhPa/2hJS02mNsnqJQMXoLqOcR+L4psdCweQA2PEWoPu7oRtdvf\\nO4WIH93b29JNYH3+RSpc2z/yW288zt+ceolfa/4sv7v+sev2jsx0B5/vvKjEvSsj/SH0grq8rgOS\\nnbny8ve/cJcfvndC7WGqgp30LrkA5Pd7KFV2Pjz/EZavKZ96jB25mfjadslYqGqtTxOb+J/u468B\\nKtXHlFJ/CnyBWNCeTPUYv0l1l9YdOcXy8mymohWH4FAfX9/3B/zWsq+znIWutqWhtomzC7sYuxRh\\n9LVvEezaTv3mWwq+bj7BGf/e8er3y4KFpYTmVbOsgCI1LnkOVarzze2+gy+UZGduJDtnZlJ22qUY\\nszNxNKpy5RJWFpChydn50G2/Ou17xfTcTCe/lSQzUEo9QewEln8IhJVS8cAe1FoP2vGawhviqw1b\\nSb0a0WmNTZv5MNLPxD1ljLz1DME2sipWMwWqqcNKXpHNqn+77+DdItkp0jEtO/Ml2XlNsO0YFd17\\nCVowGgUzr/r3am7aUqgC/3zyz7ak9/8X4D/b9Jp5iYT6Ka+TbaCdkHi392qwjW8MfbmgeTFWmVVa\\nQeVNt1IdPsvV/uyeU2yBOhOrTvpKN4cquWfAxzyTncI5pmZnPiQ7p1u+ioI3+Yf0q/69/L0SZ0uh\\nqrVOOaRlGl1XjwoFZ35gCqaeFWwyO/dZK9R4fTVq6CO3m+Fp0fmzC75GplX/Xu5FypZXsrMQkp25\\nMzk7ReEK2Ts1zq75pybI52QqgewFl6vku72otnafNeEP2a76F94l2ZkbyU6RjWxW/XuVXUP/Qkzj\\nlbu9Qveys0ux9EKlm0OVvOpfiGLhlew0lcnZWTocQi3Jf0uqRKmys/tkZNqqf6+SHlXhCC+sNtTV\\nFW43IS2Te6FKBi+53QQhUup6b4CxUx2cbU+e8usdXshOk5manaH2MLPPv1HQASnFQnpUfWRiTgOc\\n+7nbzUgp+W7PL3d6YlL1nJkfI4SD6jffQqh9GXUHn6Zn4DBnoeBV1W6Q7PSfYNsxxnp2MtAyzKyb\\n13ry+9JJ0qMqxKSu6svMCh+mb88+t5viKZVHfsq50bfcboYQ16lrmkfknq+w6uynGHu/nUtX2t1u\\nkihyofYwFd17mbinjMDWB6RIzYIUqnky8axgkb+G2iZqNm6m9+5ORkK7CbbJcMxMQu1hRnbuoqNx\\nP1dvL2Hlhm1uN8kSkX4z5yn7hdPZWdc0j6H6lawYWGDL9YXIVW1gmPGbVtBQ2+R2UzxBhv7z5PYk\\nbGG9htom2AgRdYDAj18kSHab/xej+LGppR9rJ/DpwjeqNk0hx6eKzCQ7RTGb6Oqhb1YQqHK7KZ4h\\nharhTF6x6EcNtU2cvamLpecmaI/O/HinmHiedV3NIMML5lJZv8K1NgiRzvTs/Grs7T9A3YJRXn97\\nr5tNEw4yJTvjN/djY/v5aEsVAcnNrEmhajhTVyzmIn5G9bc3fdMzJ2QE6c1rqyq7biyMvSmpLXyj\\naiHskDY7Lxd+MIUTvJibhfBzdsaL1EjlQSbuLPPNNCmnFP0cVV1XTySU5bmZIi/xM6p3HGl1uylZ\\nqaxfwblbSigd28Nw63OE2sNZP9cPNxZCCPd5LTcL5ffsXLCwlNobGyi/d4PbTfEc6VG1QKY7wb/a\\ndcqFFpkj8Yzq50+/yqO3bTO+d6Chtgk2NHGu7EX639rDsoPdhPgKdU3z3G6aMUoGL01uSXXV7aYI\\nD5PsTM2LuSky08OSlfkq+h5VKxh3J3hlyJ3XTSHVGdVesfTuLdTc3syKtdVMdLk/fGSKUHuY8kM/\\n4s3AK3RU9srKVZE347LTEF7OTTFdqD1M+cGneTuwj47FA5KXeZBC1WdU5Vy3mzAl+YzqsQlvnlGt\\nhwbcboIxgm3HqNi7gwv39TJ3/TrH5lr1Rfr46kvf8Nz3jjBT5wFzCz+/5KaI5WX5wacJrT1B4P4W\\nV+amBoe8n51SqBrOy/u1Zjqj2iuidbJYKC4YucRvqcfob75MzcbNjm5J1drzjCPz9SL9IdmayifS\\nZeT82YOsGl5o7Ob/fsjN6xg0yuekOb2nqVjVS+T2j/Of2v/elWLRD3OdZY7qJDu3gSrk2iasWMxX\\nxjOqPTbdKtsdAEzZCsUOT577HkcCXfx/c2p5hE2OvW5wqI9XL/1Y5usZymvZObJzl9GFU8bc9LCZ\\nRvv8mJ1q9CrlC+ayK3hgqlj83fWPOfb6fRF/zHWWQnWSnXOlinUeVvIZ1Ym6T0YcbElh3lpyhmUH\\nagi1h2dcUOXlG4tMgpFLPNf7DFpp/m7oOJ8f6afBodd+8kgrE0yfr+dk2IvMvJadevZcrnxwnKHF\\nA7DBvPmCmXLTz/yWncG2YwTGzvN2fYi/P/tzV4rF1p5nrpvr7MXslKF/YltUFcLLw/Mis8amzcy6\\nuYkP1r1Jxd4dRXu06hNt/5wJHft+nlDwtx/+1JHXjc/Xi2qZr+dHbmRn5bbPMfvKOspf/IjOA63G\\nTgEQ3jXRd4Wxnp0EW7p5lotodOz9Dk7hiI9E+WGus/SoWiDTnWC3wx2Hl3vHKa2JMnbyJNxtXm+B\\nFzU2beYsEOQ9yt/fSbBte9EcrRpqDzP0zgu80PgOURW7M49OjDvWM5Bpvp4XewbEdG5lZ8X2h6nY\\ns4+q1/cQ5hCX1iGrsYUlgm3H0LcMMnFPGVWf/jKvPLv9umLRsezEH9kphaqP1DXNI8Qmyg920z9w\\nlGhdwHdnsLulsWkzl+pXEJl3gLGDOxlu/RRDd2wybm9VK+cLRvuHKT/4NL+/+hl0iWKyUwBwLvDS\\nzdd7u9f6nu1If+4nkQnv0s23svhUN7XDV+l1uzHCdVZlZ+lwiJJSRfm9G1y90T4aPDE1EhVnV3ba\\nTYb+faauaR6Re77CqrOf4uobxznb3uZ2k2zl5NYbDbVNLHtoOxP3lDEy7zCBQ/tyOrXKCVbN6Qu2\\nHUNFBgitPcGZOaNE9fShWKcWdzy79QmOb3+Zl//BCxzf/jJfaP5lFIpPLrSnR1tW/BeXvuC40Qur\\n7OSHbYusZEV2qiu9BIKdUBorrdxcGPfs1iemctOJ7LST6z2qSqlfAv4UKAW+r7X+H0kfV5Mf/yww\\nBDystX7H6nbUzR0jdHXW9e+3YK6U06sZ65rmER78B9zdP5f3e98BH49oJW69ke0daqFnaJffu4H5\\noycJlJfSlfOzvaN8NujmVbxw92+63RRATutJZkx22phvtl67aR7Brk8yfvBF+svbGL65q6hGoNzI\\nTj8LHz5F5ZGf8sG6NwmUN7KitsmYhXFez05XC1WlVCnwBPAZoAc4pJTarbV+P+FhDxArtZqAu4Ad\\nk39a6tUnTlJeV2P1ZQH/rWY0Rb4/fPkEdCp+PhKvdDjEGKVG7SOb6rQer821sopJ2WlnvtmdnfWb\\nbyG0YhmL9+7gzJXjnIWiKFbdzk6/6duzj9nn36Dnvl7m3ryOknGzikCvZ6fbQ/93Aqe11h9qrSPA\\nD4GtSY/ZCvyljnkTqFVKLba6Ibqunkio3+rLumZiTgMMDLrdDFvlc8xgckDnM+zVUNtEx+IB3g7s\\no2LvDuOG/wvVt2cfI6HdDM0apbJ+hdvNAa7tB2jXClYPzk81Jju9rq5pHpE7foWWjlVuN8UxbmWn\\nH8Xz8sJ9vY4fhJINP5x05vbQ/1KgO+HfPVx/x5/qMUuBC8kXU0o9AjwCUF9fz8XI8ZwaoyrHUBFr\\n9zaN6BG6I6csvWY2ogvHaa+9gWjFsqk9SyMj2pj9SwttS1+kj+c+eJWxhG2Lnmt/lYfm/Crzy9Mv\\ncHr8w6cZn4gF9PjEBH+87694ZMlv5NyW0gWfp/xT/VxoGWHW0Lv0X55LWU1l3p9PXOHfL41pPzLT\\ndaMj45QMXmH8k1Em5m6BsrmMXqyn+6L73zNPdz4z9f8WF///e+xjXyv4+nqiEl1WBgZ8rlmyLDuT\\nc9ONvErFyeyM3jDO4JK7ifan3uNZsjP/7BwbX8josrmULKwgUn6BocilvNueidPZGc9Lfec40Tlb\\nKKkMMHqxgu6LEWO+XyIjmsf3PW1rdjrB7ULVUlrrp4CnAJpW36gXla/L6flqIGj58H935BTLy5st\\nvWY2QmfDLD/1Nu+v/4Dlt8fOF+4+GWH5mnLH25JKoW3Z9cazaDUxbSW6ZoLdg3+bdkgjONTHaz//\\n8dRKyKiO8trlNrYt+wK3rlmYRysWcOlKO/2v7yHwWinzJz5OxfaH87jONYV+v2Sa05fpuuHDpyg/\\n9DxnWrqYu34dH2vaPOP/kRXz1bK9xgdHT123gjWqo5wZO2XJ93Skf6BoF1Il5uaNq2/UbuRVKk5m\\nZ+hsmMChU3Qu2kPF7c0svXvL9LZIduadnZeunGXh+ycJXFxD14obbdspxcnsDLWHqdi7g67VZwjc\\n38LqpF5UU7Kz+2SEM2Mf2JqdTnC7UD0HLE/497LJ9+X6GOExfZE+/uNL3877BzWf1ZTptgpp7XmG\\nWz+e34Khhtom2AhDdYfoOLKfVTvhow1bXdu2Kp85ffH5VRcm51dlO3RlxXy1bK/xxK1/4plQdYhk\\np4XiW/utPATh5/bTGW0lsO4O4/ZWDQ718Y3j/53HV/xO3gWOKdlpmmyzM9h2jMCZFwm2dBNY35LX\\nUL+T2WnKgq5CuF2oHgKalFKriAXoF4FtSY/ZDTymlPohsaGtq1rr64b9RWplfaNuNyGl1p5ncvpB\\nTb57zOeHL11Anxg4mfO1EjXUNsGGJs4ubKPjlf2s2HueYNcWYw8FCLWHmejqoXQ4RCDYyUjJu/Td\\nN07Nxs1Z/2K2YhWpKStRI/0hL/amSnZarK5pHjR9jsG21fDiTvpDbbDRrIMAnjzSynsD7xe0Ut+k\\n7PSa4dbnGBvbT/gXygjc+0Be3xt+yk6nuFqoaq2jSqnHgFeIbbHyA631e0qpr01+/EngJWLbq5wm\\ntsXKdjvbFAn127b632mqcq7bTUgpfrRbLj9kVtyBpgtoq+YSxU+wurC8ncAbO+nb8xDzH9xkybWt\\nEGoPEzi0j6GSt1m0WEM1nG0epKyuhtUbkmuczKxYRer1lahuMjE7/aJ+8y307XmIuXvf4AKxbatK\\n+JTbzXJ1pb7d2Wm6UHuYqgMv0Nm4n8rblrAyx7xMJNmZO7dX/aO1fklrfaPW+gat9e9Pvu/JyaBl\\ncsXqv5j8+C1a67dsa0tdvV2XFgkSj3bLZsWpl1abNjZtpmbjZiJbqhgJ7WZk5y7Ch08Rag87vjtA\\n/DXjb+UHn6Zz0R7U7RHCX7qL8Jfuombj5pxD14pVpH5Yieo2k7LTb+Y/uInIHb/CsgMtjL3fTmTU\\n/R1hZKW+O4Jtx6jYu4OOxv0E7m8pqEiV7MyP64WqKC7xH7Kozv6HLJ+ATvW6Tp5gtXLDNiJbqvhg\\n3ZssaH+ehsNP0XD4KUZ27nKkYI0Xpg0n/nrqtUNrTxC4v4VlD22nobZp6i1X333rB0TGp/ekRMYj\\nfOetH2R9jUxHCzrJo8P+wgHzWpoZql/J7edvcLspeRcnhWanlbnpxX2n+/bsY6xnJ8GWbuq+vLXg\\nradSZef4xHhO/y+mZKeTpFAVjsr1h8yqu8fE4S+nrNywjbnr1/HhP66aeuto3E/F3h2ED9u37U68\\nByC09gRdmwbp+iXFh/+4isDWByzZ4+8nPYcSFwwDsQXEP+n5edbXcPNoQSGyNV5Zx0DvMEQnZn6w\\njfIpTqzITstys9acg0OyEWoPM7JzFyOh3US2xLLTirnKqbIzqsdzyr1izE63F1OJIpPrD1mmgM5l\\nMYFbE88bmzaz8ZP3Ebo8e/oHnoP5swf5f1u/Pe3dEw/cxPCPn8v5ddTotd6KsZJ3C5rsn0lwqI/h\\n6AgAs0vLaf3l77Dt//4Wo+MRhqOjXB7qy+pra8JKVA9u8i8cVrJiGYO9NzI+NELP7p2U37vBlcVV\\nVq7Uz2UBq9sLdjZuW5Z2yygrTy2Lz9+HWJZWjJ2nY3LrqZUWbeCfmJ3lJbNAQWR8jNml5Tz5mW9l\\nfR0TstNpUqim4KcFVdGaAOriFfSiWrebAlz7Ict2L0Ar7h7dnnh+XZE6qW90Dhe3npn6d1nfKNGq\\n5YTu/GlerxOdH3+dqoLmUWWS/LX8d6//kacn9cuwv8gkvhPA7Is/o+RglKHwy1za6vxOAInFiVPZ\\naUdulgxeArLfui9VkZrp/fkIth2jonsvnSuOUr0ilgfR+bMJLMxv66l0Er+eYxNR1OT7vZibuSq0\\nU0AK1SS6rh4VCrrdDEtc7h2nNBilu/85yu/dQKaTN0xV6N1juuEvU7bzSC4ou09GWHaXmYuzU30t\\nz1ztmvq4aV9bIaxSMr+WWcu2M/fwiwR5mbPru4w7KjNZIdlpZW421DbRUXmI/oXHueFQN6E5j7q2\\nz3SyYNsxxnp28tHaYWqarz/owbLXSfp6avTUFADJzZnJHFWfqmuaR+W2z1FR/wVKDkbpf72NsfER\\nV9vk5IKmuGKceG6XVF/LZKZ9bdN9z8mwv8hV/eZbGLnvUeoPL2folcN0HnDu+9zp7LQ6N+Pz9S/c\\n10vF3h0E245Z0cy8xeegjvXsZOKeMgJbH7CtSIWZszPXBVVOsOp7zoqslR5Vm1ybWzO9F9PquTUz\\nie8JuHbgNG9F3d3zLnFi/sPzH8npufkeOVeME8/tkuprmcy0r22mPSRl2N880+ckXstOp3MzndgJ\\nVo+y6sAL9HW/SyfOnGDldHbakZuNTZu5VL+C4JWXKX9/J8Otn6Jy2+fyvl4uEg85Aag4/wYdq88U\\nvCdqtmbKzlwXVDnBiv134wrNWilUbeLE3BovSZ6Y/9Btv8pyMp8RnSjfH5pinHhul1Rfy+BQH7/0\\n7HZGxyPMLi3nlc/vzOlGwoozrzNdO9ViEOlNNZcXcjM2b/VhZrUdc+QEKzey067cbKht4tJWGLrh\\nEJ1H9rBq51Xbj5wOHz5FxaEfEa3vIzB/Nrq6gq7mywRutnYOaibZZGcuC6rszM349a1YSGdV1srQ\\nfxqRkPsbPPtJ8sT81p5nsn6u1zetrluQ+hjbdO/3kkL3abRz27BMbZPeVFGo+s23MGvZdhbvXUjo\\nr1hAT2QAABukSURBVF7gbHubLa/jt+yM7zMduL9laru+vj37CLYdu+5tXiD1dLV5gZHrHhvtH576\\ne9+efVNv5Yd+xIX7ernyaysJf+kurjx4KzUbN7s+x7iQ7LR7u0Ur9i6PsyJrpUc1BT8tqIrTA0Ou\\nvXaqifmvBtv4xtCXsz4C0Msry19/e6/bTQCsvwsvdMGFndvfpGvbr69+gAUVZuyAIbyvfvMthFYs\\nY9WBF+h4ZT+dvb2WDiX7OTsTj5xuPP/qdR/XgSrafvvFGa+jhj4CYFhtprYsdrOgl1RMffz8+gg1\\n6zYX1ONtUnbavW2YVQvprBy5kh7VIjBe6W7vUSET84vxuDi7WH0XXuiCCyvv2rNt2/8+8Zz0pgpL\\n1TXNo2L7w6w6+ynKX/yIzgOtXLrSbsm1/Z6d8SOn40c6x9/6P30zA+sbs3qLPyc6v2rq71cevHXq\\nbeWGbQVPyzApO+3MzULblsyqrJUeVWG7VBPJozq7iflWbPgv7LkLL2TBhd3bhqVr27uh0wVfW4hU\\nKrY/TMWefcx99g0uhNoYvrnwLayKITtTFpF5DHqMXozYMk/YpOx0YrtFKxbSWX00tRSqNqmbN572\\nRI1ik2oiuZMb/jvN7onu+bBjCLCQBRd2/xJN1Tarw1NYz+u5Of/BTYTab2Xx3h10dR/mLBRUrEp2\\nus+k7HTi5qPQhXR2LFaVQjWDQk6oim+l0h05xfLyZiubVVS8uGrfym09rGDioQdO/xKVlf7ekLgF\\nlVezc9oWVt9/l84tvY5sYZVMsrNwpmWnV24+rO4QkEI1DT8uqBL2M+F87GQmDgG68UtUelNndu28\\nHFGI+BZWFXv2wYu7bd/Cyg8kO2dm+s2HXR0CsphKeIobp1vlwu6J7vnwyl24XaQ3NTeyNZ915j+4\\niYq6h1i8dyH9r7fZtoVVNiQ7c1fs2ZmLeM7a0SEgParCU0wbGkpk2jBRnOl34U6Q3tRsKbcb4Dvx\\neavLDrxAR7f1W1hlS7Izd5KdubErZ6VHVXiGiZtXJ7L6fGxROFlAJUxg5xZW2ZDsFHaye9RKCtUZ\\nyDCYOUwcGkokw0RmkSH//Enu2aNi+8NU1D1E/bOVjk4FkOwUdrFzyD9Ohv4zkAVV5jB1aCiR14aJ\\nTNwKxmrSm5o7yT17zX9wE+HDi1m890ecuXK84C2sZiLZab1iyM5c2J2z0qMqPEGGhqxn93nRbpLe\\nVGGyeS3NjNz3KDcev5ua/3vB1qkAkp3W83N25sKpnJVCVXiCDA1Zy/Q5a4VwYijK73RdvQz/2yw+\\nb1VHPsO814cZDnbZ8jqSndbyc3bmwsmclaF/4QleGxqykxXDTnactmISKVKFV+jmW5l/+GcMhobA\\nhm1WJTtjrBqu93t2ZsPpzgDpURWiQE7vT1josFO6OWt+6BmQVf7Wkl5VZ1wZqkRfukyo/R23m+Io\\nJ7PTiuF6P2dnrpzMWSlUZyBDYGIm2QagFaFsxbCTX+esybxUa+m6erebUBTqmuYxsvw+9FvlDLxx\\noKiKVaey06rher9mZy7cyFkpVIXIQXJg5hKAVtzRW7HNjB/nrMm8VOFl9ZtvoaL+C9S9dxOXXvmx\\nqydY2cXN7LRqey4/Zmcu3MpZKVSFyEFyYGYbgFbc0Vs17PTs1ic4vv3l696ymctm4jGMUqTaS0aU\\nnFG/+RYi93yFlRcfZOiVw3Qe8FcvnVvZaeVwvd+yMxdu5qwUqsJ42f6A2x0EyYF5KvRh1gFoxR29\\nCcNOpm3LIkWqvWT431l1TfOo3PY5y06wkuw0Izfj7TApO/PhVs5KoSqMl+0PuN1BkByY/+71P8oq\\nAK26o8912MnqXz6mbssiRar9pFfVWfETrFo6Gwvatkqy0/3cjF/TxOzMltuLVKVQLQKlwyFUdcDt\\nZuQl2x9wu4MgVWB+eLUrqwC06o4+12Enq3/5mHYMo9vhWSykV9Ud45V16IGhvJ8v2Rnjdm7Gr2lS\\ndubChJyVQjVL0qPgjmx+wINDffzq7n/JuI1BkCowy0pK+ULzL88YgG5MwJ/pl0+uvQambctiQngK\\nYTLJztxlU7R7PTtzYcpOKlKoZkF6FNyR7Q/4d9/6AZeH+4jaGASFBGYhE/DzNdMvqVx7DUyZ5wVS\\npLpBtulzT1ko915Vyc78ZFPcezk7c2HS/H85mcrnQu1hqoKd9C65ADS73ZycZPoBj58EEhzqY8+Z\\nvdc91+oTQ7x0uku6X1KP3raNBYH51/UaxN+fiSnbskiRKorJwNUy9KXLXLrSTkNt9sdWSXbmbqbc\\nTHyMF7MzFyYVqSCFqq+F2sOUH3yajsajVK5cQvnsGreblJNsfsCfPNLKBBPJTzU+COw00y+pfI4A\\nNOGXjRSp7ouE+imv81aOeFX95lsYbD1NycHT9Os22EjWxapkZ+6yKe69mp25MK1IBRcLVaXUfOAZ\\nYCXQCfya1jqc4nGdwAAwDkS11rc710rvCrWHCRzax7m7OqlZ2szSu7fQfTLidrNyMtMPePzuNtHs\\n0nJe+fzOgs5y9rpMv6Sy6TXIRj7nZud71nakP4SeqDQqON3kVnbqunpUKFjIJUSOKrd9juE986h+\\n8zC9xIpVaJzxeZKduZupuLciO/PNwHyflysTi1Rwd47qbwNtWusmoG3y3+ncp7W+TYrU3CxYWErF\\nrDKW3r3F7abYwqtzf+yWaV6XVV+zfFbG5vOcqeAsc3/wZ3AkxOCIEYsLJDuLyPwHNzE2r4UVAwss\\nu6Zk5/Vmmg9rxdcs3x0FnNiD1dQiFdwtVLcCfzH5978A/qGLbREe5MW5P26z4muWz3Y2+TzHxOAs\\nn2tEW1zLTllU5Q+Snbkr9GuW7zZgTuzBamLWJlJaa3deWKkrWuvayb8rIBz/d9LjOoCrxIav/pfW\\n+qkM13wEeASgvr7+k7u+b93dh4qOocpKc35eRI9Qriosa0e2oiPjlEcGGJk9SPnc2J14ZERTXqEc\\nb0sq0pbUktvSF+njDz74n3zzxm8wv3yeq22Je/zDHbxy6TWiOkqZKuP+hs/w2Me+lvFauT5HT/5C\\niPekRoc1ZZXu/R9NTLZHlZbx4Ke3vu1mD6XV2ZlrbuabhblyKztTcbst0asDlM4aJDq3Ej0229i8\\nclNiW9zMzeS2xOWTm4U8L107kiVnrV1++TP556atLVNKvQYsSvGh/5D4D621Vkqlq5g3aK3PKaUa\\ngFeVUie11j9J9cDJIH4KoGn1jXpR+boCWj+dGojNzcp1IUF35BTLy51fbR86G2ZF12lONR5g2V3b\\nY205GWH5mnLH25KKl9ti53yho+9e5Dsffnvq2rveeJb3Bt5n9+DfWrYKN1upvi7BoT5e+/mPiepY\\nuEV1lNcut/GNTV9O+7XI9Tmp7u4vHo+waJ073y/x4X4ne1OdzM5cczPfLMyVW9mZittt6fvZPuYs\\n2c+VB29l9GKjZGcKidm5+4h7uQnXf13yyc1CnpeuHclM70mNs3XoX2v9aa31uhRvLwC9SqnFAJN/\\nXkpzjXOTf14CngPutLPN6XhxL1U9fNXtJviSnfOFWnuembq2icfu5TNPK9vnRPpDUyv7TQlON4pU\\nMDs7vZiFwgxOZOd339rpi9ws5HnZ8EqRCu7OUd0NfHXy718FXkh+gFKqSilVHf878IvAccda6APR\\n+bPdboKv2Fk8Bof6ePXSj6eu/d23fmDcsXv5zNPK5jkmh6Yh81ITGZGdMlfVGepKL+PV183s8Byn\\nsnPPhz9mfGIc8HZuFvK8mZict6m4uZT2fwB/o5T6deAs8GsASqklwPe11p8FFgLPxaZhUQa0aq3/\\n3qX2Cg+xa4gpn330crr25L6G43qCPWf2Tv07322krJbPnoCZnpN4RJ9poTk4EjKxSAUDslO2qnJO\\n6MAJKsKH6br7Mk7sYOuH7Izzcm4W8rxMvFakgos9qlrrkNZ6s9a6aXKYq2/y/ecngxat9Yda61sn\\n39ZqrX/frfYKb7FjiMnOM5vj156aizQRvW4zblN6B6ySGJimhaYh21ClJNlZPIZbn2MktJvQL/dR\\ns3FzTqdT5cvr2ZnMb7lZCC8WqeDu0L+wUdWBFzhXftLtZrjCriEmO+cLpbp2Mr9sHxOfiwpmBqZb\\n81K9SIb/7RNsO8acOReYuKeMlRu2OVKk+jE7/ZKbhTI5c2fi/i7awlLxE6k6G/dTedsSAuvucLtJ\\njrNriCndfKG3ewuf+pfq2gBr5n/Mc0fwZWJ6WEqRmj0Z/rdfzeJKVIN1G/3PRLLTn0zP3ZlIoeoj\\n8SJ1ZN5ham5v9u2JVJlYdURoKsmh9603HudvTr3EJxcWvg1a/NombdtlJZPnosZJkZqfSKjf9q2q\\nipUeGnDstSQ7/UdPRIn0DxibudmSoX+fWbCwlPkr5lBRt9TtprjCqaMBTdw6ykTJw/ymBqYUqfmR\\nrarsF60LOPI6kp3+4vVe1ERSqPrUeH21201whVNHA6YaIjNRcKiPr770DVd+GXihQAUpUoUAyc5k\\nbmZnoaay1+bTppzij89CiElOzEmyc4jMaokreJ06ocVLd/JSpFpDhv+tVzocAgf7GyQ7p3MjO60w\\nLX8vRlxujTWkR9Vn5DQq+zk1RFYop4fYTDxZKhMpUq0hw//2UdXODPs7RbLTPl7L31xIoepHtf4K\\nN9M4NURWKKeG2LwYkFKkWk+2qrJOsO0YI6HdHKo+RmX9CrebYxnJTnt4aRQrHzL0L0SOvLDlid1D\\nbF5YxZ+OFKnWk62qrBHfuWVsbD+RLVXUrLvDkf1TnSLZaT2/F6kgPaq+Et/kv6Oy1+2mCJfZNcTm\\nlVX86UiRKkw20dXj+Cb/YjqvTE8w/eCURIWe9Cc9qj6QvMn/yg3b3G6ScJnVQ2xe7kGNkyLVfrKo\\nqnDVc6OObvIvpvPC9ASvFKhgzXHUUqj6QODQPsqWvkVNS3Fu8i+uZ8UQW3yzaPBGIGYiRar9ZPjf\\nOk7tnSquZ/r0BC8WqYXmrhSqPlG+YC6z1qxxuxnC4xJ7TqHSE2GYiRSoQgg/8FKBCtZmrxSqQhS5\\n6cXptSDUHt+DT4pUd8jwf/5Kh0NcWnIVmOd2U4RBvFikWpm7Uqj6gBqVvVNFbtIVp34hRao7ZPg/\\nP/F1BqWVBzm/spKAj7akEvnzWoEK1hepIIWq54Xaw1SB7J0qZuT34hSmT9yXIlV4wdRi2EV7CNzf\\nwsqmzW43SRjAa0WqnZ0DUqh6WKg9TMXeHXSsPkNl5RJW1krAiemKoTiNk15Uc8jwf27mzLlAze3N\\nLJUiteh5rUAF+7NXClWPCrYdo6J7L8GWbgLrW2iUgBMUV2GaSIpUc8jwfx5GBgDldiuEy6RITU0K\\nVQ+bvXaQ6vUbqGv6hNtNES4p1sI0TgpU4ReyJVXx8mKBCs7lrxSqHqaGPmK8vtrtZgiHJBel4L1g\\ns5IUqWaT4f/slAxecrsJwiVePkjFyfyVQlUIQ0lhmpqbBWrfROGnrBQDGf7PTqg9TNWRn3J4XQdz\\nWed2c4SDvNqLCs5nsBSqHlU6HEIvqXC7GcIC0+6qJyp9cxqU1dxc0R8vUKsq5P9EWCPYdozAmRf5\\noKWLuevXyTqDIiEFau6kUPWgUHuYivNv0NV8GRlY85ZUvaQwfZN9LwaY3SYmz952sxdVilRhlVB7\\nmDm9pylr/kiK1CLh5WF+cHckSwpVj4nfhQdbupl181oaapvcbpJIIV1BCt4MKbdc60WtlCLVY3Rd\\nPZFQUOapplFXM8jw6sVUyub+vuflXlRwfz2AFKoeMm1Lqq0PSJFqiJl6SUXukof5Vanzx7lKkSrs\\nok4dZXToPB2VgwSQQtWvvF6ggvtFKkih6jnLV0F/8yopUl0gBakzTAhGKVKFXYJtxxgL7ebU+nFm\\nLWySLPchPRH1/FoDk075k0I1SyatYJX99uwlBak7pED1J9mm6prh1ucYG9vPxD1l1Ny7SYpUn7n2\\nu6PS078vTMjiRFKo5sDtsC0dDoFsm2qpSH9o2kr7OC+HjNeYEopSpFpPtqm63tLbF9F77xopUn0k\\neaGUvuj8VCWrmJLHiaRQ9RhVHQBG3G6GJ6XtKS0rk8LUBSYFohSpwglq9KrbTRAW8vpK/kQmDfUn\\nk0LVI/r27GMktJtDzePMQu7Es5H1hvkevvv1IhMLVJAiVdgn1B4mcGgf4ZJ3iSyuQiZveZufClQw\\nK5NTkULVA4Zbn2NkbD+RLVXUrLtDhozSKPZz701m4t269KIKJ4Taw5QffJrOFUcJ3N/CStkz1dP8\\nsJI/kelFKkiharxQe5iGsg8I3VnGyg3b3G6OUaQwNZ+JBSpIkeq0Yl5QNdHVQ8WqXmpamlkqRapn\\n+bVABbOyORUpVD1i/CbZay/roXzhOlNDUApU58mCqphZa9a43QSRB78VqOCNXtREUqhmwa2QDbWH\\nqTrwAn3/f3v3H3tVXcdx/PkSczOhVJj8CiQnY8YqJWbEyPFH9oOtSFtbayvdbGabzdZfZK2/tbS1\\nmm2x1aTWasx+kWEkzdYfTScxUJAQKJcwFPzSQKdJxrs/zvnSDc79fg987/mczz339djuvufee+C8\\n7od7398358f9XHoUuKSVDG3zXtPhkWtzOs5NqqU2tu+fvPnAbzh03TGflzpkutygQp41uh83qjWl\\nPmw1fl7T36/cycXXzhup85rcnA6X3IufG1RrQ+9Mglrp6a6HRRcbVBi+vai9Lmhrw5I+KWm3pFOS\\nlk+w3ocl7ZW0X9K6lBnbMn6F6NjSPbxl+ZKROTf15Imx/ysS4zfLzyv/Gjt9u+itM0/fcnLs1Fgn\\nm1TXzvyNN6nHVx1kxspVXDlCOxqG0fjvnpMnxjr3e2e8TsNwNqnQ7h7VXcDNwPf7rSBpGvAAcCNw\\nEHhS0qaIeCZNxPbMmj2NsctmMH/FR9uO0qiufc1Hl/XuOYW8i14XG9Qerp1DYOHSGey6YhYzFy9r\\nO4r10fXfP6dOvQHkMdvfVLTWqEbEHgBJE612PbA/Iv5WrvszYC3Q+WIbr3X7i6G7MBfyqMj90H6v\\njjeowPDWzlG78j9efdnTXWesq4f4xxV1++IsmtSp1uPcz1GdDzzfc/8g8N5+K0u6Hbi9vPv6ez4y\\nd1eD2eqaBbx03n/6ixsHl2SqWQbLWao5S7WcsixpO0ANtWtnpnUT8vo3P78s9wDcl0eWZjhLtVyy\\n5JIDplA3G21UJW0F5lQ89dWI+PWgtxcR64H15ba3RUTf87dSySUHOEs/zlLNWapJ2pZgG8lqZ451\\nE5ylH2ep5iz55oCp1c1GG9WI+MAU/4pDwIKe+28rHzMz6yzXTjOzQmtX/df0JLBY0tslXQR8CtjU\\nciYzs9y5dppZJ7T59VQ3SToIvA/4raQt5ePzJG0GiIg3gDuBLcAeYGNE7K65ifUNxD4fueQAZ+nH\\nWao5S7VWszRcOz3O1ZylmrNUyyVLLjlgClkUEYMMYmZmZmY2ELkf+jczMzOzEeVG1czMzMyy1IlG\\n9RymFHxO0tOSdjT1FTM5TW8o6XJJj0raV/68rM96jY3LZK9The+Uzz8lqbFpXGpkWS3peDkOOyR9\\nvaEcP5R0RFLl91UmHpPJsqQakwWSHpP0TPn5uatinSTjUjNLknFpmmtn3224dtbPkeyz4NpZuZ3u\\n186IGPobcA3Fl8n+EVg+wXrPAbPazgJMAw4AVwEXATuBdzSQ5RvAunJ5HXBvynGp8zqBNcAjgIAV\\nwBMN/bvUybIaeLjJ90e5nRuAZcCuPs8nGZOaWVKNyVxgWbk8A3i2xfdKnSxJxiXBuLt2Vm/HtbN+\\njmSfBdfOyu10vnZ2Yo9qROyJiL1t54DaWU5PbxgRJ4Hx6Q0HbS2woVzeAHy8gW1MpM7rXAv8KAqP\\nA5dKmttSliQi4k/AsQlWSTUmdbIkERGHI2J7ufwyxZXq889YLcm41MzSCa6dfbl21s+RjGtnZY7O\\n185ONKrnIICtkv6iYtrAtlRNb9jEL8LZEXG4XH4BmN1nvabGpc7rTDUWdbezsjw08oikpQ3kqCPV\\nmNSVdEwkLQKuA54446nk4zJBFsjjvZKKa2e1rtfOYaqb4Nq5iA7WzkZnphokDWZKwVURcUjSFcCj\\nkv5a/q+ojSwDMVGW3jsREZL6fRfZQMalA7YDCyPiFUlrgF8Bi1vO1LakYyJpOvBz4EsRcaKp7Qwg\\ny9C8V1w7zz1L7x3XzkkNzWchMdfOAdXOoWlUY+pTChIRh8qfRyT9kuKwxjkXlQFkGdj0hhNlkfSi\\npLkRcbjczX+kz98xkHGpUOd1pprqcdLt9H6gImKzpO9JmhURLzWQZyLZTH+ZckwkvYmiuP0kIn5R\\nsUqycZksS0bvlUm5dp57FtfO+tvI7LPg2tnB2jkyh/4lXSJpxvgy8EGg8mq9BFJNb7gJuKVcvgU4\\na49Fw+NS53VuAj5bXpW4Ajjec8htkCbNImmOJJXL11N8PsYayDKZVGMyqVRjUm7jB8CeiPhWn9WS\\njEudLBm9Vxrn2jnStXOY6ia4dnazdkaCK/WavgE3UZxz8TrwIrClfHwesLlcvoriisWdwG6KQ02t\\nZIn/XYX3LMUVlU1lmQn8AdgHbAUuTz0uVa8TuAO4o1wW8ED5/NNMcOVxgix3lmOwE3gcWNlQjp8C\\nh4F/l++V21ock8mypBqTVRTn+z0F7Chva9oYl5pZkoxL07c69arpGnEuWcr7rp2R9POQRd0st+Xa\\neXaOztdOT6FqZmZmZlkamUP/ZmZmZjZc3KiamZmZWZbcqJqZmZlZltyompmZmVmW3KiamZmZWZbc\\nqJqZmZlZltyompmZmVmW3Kha50n6vaSQ9IkzHpekB8vn7mkrn5lZjlw7LQf+wn/rPEnvBrYDe4F3\\nRsR/ysfvB74MrI+Iz7cY0cwsO66dlgPvUbXOi4idwI+Ba4DPAEi6m6LQbgS+0F46M7M8uXZaDrxH\\n1UaCpAUU81W/ANwPfBfYAnwsIk62mc3MLFeundY271G1kRARzwPfBhZRFNo/AzefWWgl3SBpk6RD\\n5flXtyYPa2aWCddOa5sbVRslR3uWb4uIVyvWmQ7sAu4CXkuSyswsb66d1ho3qjYSJH0auI/i8BUU\\nxfQsEbE5Iu6OiIeAU6nymZnlyLXT2uZG1TpP0hrgQYr/7b+L4grWz0la0mYuM7OcuXZaDtyoWqdJ\\nWgU8BBwEPhQRR4GvARcC97aZzcwsV66dlgs3qtZZkq4FHgaOAzdGxGGA8tDUNmCtpPe3GNHMLDuu\\nnZYTN6rWSZKuBn4HBMXegANnrPKV8uc3kwYzM8uYa6fl5sK2A5g1ISL2A3MmeH4roHSJzMzy59pp\\nuXGjatZD0nTg6vLuBcDC8jDYsYj4R3vJzMzy5dppTfHMVGY9JK0GHqt4akNE3Jo2jZnZcHDttKa4\\nUTUzMzOzLPliKjMzMzPLkhtVMzMzM8uSG1UzMzMzy5IbVTMzMzPLkhtVMzMzM8uSG1UzMzMzy5Ib\\nVTMzMzPLkhtVMzMzM8vSfwGwlT1AkpZfwAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.svm import SVC\\n\",\n \"\\n\",\n \"# train SVM classifier using 3rd-degree polynomial kernel\\n\",\n \"poly_kernel_svm_clf = Pipeline((\\n\",\n \" (\\\"scaler\\\", StandardScaler()),\\n\",\n \" (\\\"svm_clf\\\", SVC(\\n\",\n \" kernel=\\\"poly\\\", degree=3, coef0=1, C=5))))\\n\",\n \"\\n\",\n \"# train SVM classifier using 10th-degree polynomial kernel (for comparison)\\n\",\n \"poly100_kernel_svm_clf = Pipeline((\\n\",\n \" (\\\"scaler\\\", StandardScaler()),\\n\",\n \" (\\\"svm_clf\\\", SVC(kernel=\\\"poly\\\", degree=10, coef0=100, C=5))\\n\",\n \" ))\\n\",\n \"\\n\",\n \"poly_kernel_svm_clf.fit(X, y)\\n\",\n \"poly100_kernel_svm_clf.fit(X, y)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11, 4))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\\n\",\n \"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n \"plt.title(r\\\"$d=3, r=1, C=5$\\\", fontsize=18)\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\\n\",\n \"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n \"plt.title(r\\\"$d=10, r=100, C=5$\\\", fontsize=18)\\n\",\n \"\\n\",\n \"#save_fig(\\\"moons_kernelized_polynomial_svc_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# left: 3rd-degree polynomial; right: 10th-degree polynomial.\\n\",\n \"# if overfitting, reduce polynomial degree. if underfitting, bump it up.\\n\",\n \"# \\\"coef0\\\": controls high- vs low-degree polynomial influence.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Adding Similarity Features\\n\",\n \"\\n\",\n \"* **similarity function**: measures how much an instance resembles specified landmark.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Rk+R65c0LKl0S3nwgVjYK0QQgghPIe02HshrTXDNgyjYGBBRjUZZXYc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\\nRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# define similarity function to be Gaussian Radial Basis Function (RBF)\\n\",\n \"# equals 0 (far away) to 1 (at landmark)\\n\",\n \"\\n\",\n \"def gaussian_rbf(x, landmark, gamma):\\n\",\n \" return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\\n\",\n \"\\n\",\n \"gamma = 0.3\\n\",\n \"\\n\",\n \"x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\\n\",\n \"x2s = gaussian_rbf(x1s, -2, gamma)\\n\",\n \"x3s = gaussian_rbf(x1s, 1, gamma)\\n\",\n \"\\n\",\n \"XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\\n\",\n \"yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11, 4))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.grid(True, which='both')\\n\",\n \"plt.axhline(y=0, color='k')\\n\",\n \"plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\\\"red\\\")\\n\",\n \"plt.plot(X1D[:, 0][yk==0], np.zeros(4), \\\"bs\\\")\\n\",\n \"plt.plot(X1D[:, 0][yk==1], np.zeros(5), \\\"g^\\\")\\n\",\n \"plt.plot(x1s, x2s, \\\"g--\\\")\\n\",\n \"plt.plot(x1s, x3s, \\\"b:\\\")\\n\",\n \"plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\\n\",\n \"plt.xlabel(r\\\"$x_1$\\\", fontsize=20)\\n\",\n \"plt.ylabel(r\\\"Similarity\\\", fontsize=14)\\n\",\n \"plt.annotate(r'$\\\\mathbf{x}$',\\n\",\n \" xy=(X1D[3, 0], 0),\\n\",\n \" xytext=(-0.5, 0.20),\\n\",\n \" ha=\\\"center\\\",\\n\",\n \" arrowprops=dict(facecolor='black', shrink=0.1),\\n\",\n \" fontsize=18,\\n\",\n \" )\\n\",\n \"plt.text(-2, 0.9, \\\"$x_2$\\\", ha=\\\"center\\\", fontsize=20)\\n\",\n \"plt.text(1, 0.9, \\\"$x_3$\\\", ha=\\\"center\\\", fontsize=20)\\n\",\n \"plt.axis([-4.5, 4.5, -0.1, 1.1])\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.grid(True, which='both')\\n\",\n \"plt.axhline(y=0, color='k')\\n\",\n \"plt.axvline(x=0, color='k')\\n\",\n \"plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \\\"bs\\\")\\n\",\n \"plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \\\"g^\\\")\\n\",\n \"plt.xlabel(r\\\"$x_2$\\\", fontsize=20)\\n\",\n \"plt.ylabel(r\\\"$x_3$ \\\", fontsize=20, rotation=0)\\n\",\n \"plt.annotate(r'$\\\\phi\\\\left(\\\\mathbf{x}\\\\right)$',\\n\",\n \" xy=(XK[3, 0], XK[3, 1]),\\n\",\n \" xytext=(0.65, 0.50),\\n\",\n \" ha=\\\"center\\\",\\n\",\n \" arrowprops=dict(facecolor='black', shrink=0.1),\\n\",\n \" fontsize=18,\\n\",\n \" )\\n\",\n \"plt.plot([-0.1, 1.1], [0.57, -0.1], \\\"r--\\\", linewidth=3)\\n\",\n \"plt.axis([-0.1, 1.1, -0.1, 1.1])\\n\",\n \" \\n\",\n \"plt.subplots_adjust(right=1)\\n\",\n \"\\n\",\n \"#save_fig(\\\"kernel_method_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Phi(-1.0, -2) = [ 0.74081822]\\n\",\n \"Phi(-1.0, 1) = [ 0.30119421]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x1_example = X1D[3, 0]\\n\",\n \"for landmark in (-2, 1):\\n\",\n \" k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\\n\",\n \" print(\\\"Phi({}, {}) = {}\\\".format(x1_example, landmark, k))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Using a Gaussian RBF Kernel\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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8kApPubSj2fiVgkhnzYo6LYDJx6EU4n17yo/KSXmvL43yktKbZ4RXGM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\\nCIIgCF9CQpUgCIIgCILwJf8f2Z1Pv+cLroAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rbf_kernel_svm_clf = Pipeline((\\n\",\n \" (\\\"scaler\\\", StandardScaler()),\\n\",\n \" (\\\"svm_clf\\\", SVC(kernel=\\\"rbf\\\", gamma=5, C=0.001))\\n\",\n \" ))\\n\",\n \"rbf_kernel_svm_clf.fit(X, y)\\n\",\n \"\\n\",\n \"from sklearn.svm import SVC\\n\",\n \"\\n\",\n \"gamma1, gamma2 = 0.1, 5\\n\",\n \"C1, C2 = 0.001, 1000\\n\",\n \"hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\\n\",\n \"\\n\",\n \"svm_clfs = []\\n\",\n \"for gamma, C in hyperparams:\\n\",\n \" rbf_kernel_svm_clf = Pipeline((\\n\",\n \" (\\\"scaler\\\", StandardScaler()),\\n\",\n \" (\\\"svm_clf\\\", SVC(kernel=\\\"rbf\\\", gamma=gamma, C=C))\\n\",\n \" ))\\n\",\n \" rbf_kernel_svm_clf.fit(X, y)\\n\",\n \" svm_clfs.append(rbf_kernel_svm_clf)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11, 7))\\n\",\n \"\\n\",\n \"for i, svm_clf in enumerate(svm_clfs):\\n\",\n \" plt.subplot(221 + i)\\n\",\n \" plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\\n\",\n \" plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\\n\",\n \" gamma, C = hyperparams[i]\\n\",\n \" plt.title(r\\\"$\\\\gamma = {}, C = {}$\\\".format(gamma, C), fontsize=16)\\n\",\n \"\\n\",\n \"#save_fig(\\\"moons_rbf_svc_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# below: model trained with different values of gamma and C.\\n\",\n \"# GAMMA:\\n\",\n \"# bigger gamma = narrower bell curve, so each instance's area of influence = smaller.\\n\",\n \"# smaller gamma: bigger bell curve = smoother decision boundary.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Computational Complexity\\n\",\n \"\\n\",\n \"* **LinearSVC** class: based on *liblinear* library. Doesn't support kernel trick. Scales linearly to #instances and #features; training complexity ~O(mxn).\\n\",\n \"* **SVC** class: based on *libsvm* library. Does support kernel trick. Training complexity is O(m^2xn) to O(m^3xn) = MUCH slower on larger training datasets.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### SVM Regression (Linear & Non-Linear)\\n\",\n \"\\n\",\n \"* Objectives: 1) fit max #instances *on* the street; 2) find min #margin violations (instances \\\"off\\\" the street\\\").\\n\",\n \"* Width controlled by epsilon hyperparameter.\\n\",\n \"* Below: random linear dataset. two training results with different vals of epsilon.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.svm import LinearSVR\\n\",\n \"import numpy.random as rnd\\n\",\n \"\\n\",\n \"rnd.seed(42)\\n\",\n \"m = 50\\n\",\n \"X = 2 * rnd.rand(m, 1)\\n\",\n \"y = (4 + 3 * X + rnd.randn(m, 1)).ravel()\\n\",\n \"\\n\",\n \"svm_reg1 = LinearSVR(epsilon=1.5)\\n\",\n \"svm_reg2 = LinearSVR(epsilon=0.5)\\n\",\n \"svm_reg1.fit(X, y)\\n\",\n \"svm_reg2.fit(X, y)\\n\",\n \"\\n\",\n \"def find_support_vectors(svm_reg, X, y):\\n\",\n \" y_pred = svm_reg.predict(X)\\n\",\n \" off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\\n\",\n \" return np.argwhere(off_margin)\\n\",\n \"\\n\",\n \"svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\\n\",\n \"svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\\n\",\n \"\\n\",\n \"eps_x1 = 1\\n\",\n \"eps_y_pred = svm_reg1.predict([[eps_x1]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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DjmDow1UUd27NhB48aNCQkJYeHChaxatSrbaoohOnL8+HGaNm3KihUr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\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def plot_svm_regression(svm_reg, X, y, axes):\\n\",\n \" x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\\n\",\n \" y_pred = svm_reg.predict(x1s)\\n\",\n \" plt.plot(x1s, y_pred, \\\"k-\\\", linewidth=2, label=r\\\"$\\\\hat{y}$\\\")\\n\",\n \" plt.plot(x1s, y_pred + svm_reg.epsilon, \\\"k--\\\")\\n\",\n \" plt.plot(x1s, y_pred - svm_reg.epsilon, \\\"k--\\\")\\n\",\n \" plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\\n\",\n \" plt.plot(X, y, \\\"bo\\\")\\n\",\n \" plt.xlabel(r\\\"$x_1$\\\", fontsize=18)\\n\",\n \" plt.legend(loc=\\\"upper left\\\", fontsize=18)\\n\",\n \" plt.axis(axes)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(9, 4))\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\\n\",\n \"plt.title(r\\\"$\\\\epsilon = {}$\\\".format(svm_reg1.epsilon), fontsize=18)\\n\",\n \"plt.ylabel(r\\\"$y$\\\", fontsize=18, rotation=0)\\n\",\n \"#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \\\"k-\\\", linewidth=2)\\n\",\n \"plt.annotate(\\n\",\n \" '', xy=(eps_x1, eps_y_pred), xycoords='data',\\n\",\n \" xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\\n\",\n \" textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\\n\",\n \" )\\n\",\n \"plt.text(0.91, 5.6, r\\\"$\\\\epsilon$\\\", fontsize=20)\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\\n\",\n \"plt.title(r\\\"$\\\\epsilon = {}$\\\".format(svm_reg2.epsilon), fontsize=18)\\n\",\n \"#save_fig(\\\"svm_regression_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Use kernel-ized SVM model to handle nonlinear regression jobs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\\n\",\n \" kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.svm import SVR\\n\",\n \"\\n\",\n \"# random quadratic training set.\\n\",\n \"rnd.seed(42)\\n\",\n \"m = 100\\n\",\n \"X = 2 * rnd.rand(m, 1) - 1\\n\",\n \"y = (0.2 + 0.1 * X + 0.5 * X**2 + rnd.randn(m, 1)/10).ravel()\\n\",\n \"\\n\",\n \"svm_poly_reg1 = SVR(kernel=\\\"poly\\\", degree=2, C=100, epsilon=0.1)\\n\",\n \"svm_poly_reg2 = SVR(kernel=\\\"poly\\\", degree=2, C=0.01, epsilon=0.1)\\n\",\n \"svm_poly_reg1.fit(X, y)\\n\",\n \"svm_poly_reg2.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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uuvJyEhASklQggWLFhAUlISX3/9NQaDwd9VVAQqBw86Fx5FpULpiMJr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\\nh8PhcDguyf8DVi4ANQd6MdoAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(9, 4))\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\\n\",\n \"plt.title(r\\\"$degree={}, C={}, \\\\epsilon = {}$\\\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\\n\",\n \"plt.ylabel(r\\\"$y$\\\", fontsize=18, rotation=0)\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\\n\",\n \"plt.title(r\\\"$degree={}, C={}, \\\\epsilon = {}$\\\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\\n\",\n \"#save_fig(\\\"svm_with_polynomial_kernel_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# left: little regularization (large C)\\n\",\n \"# right: much more regularization (little C)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Under the Hood\\n\",\n \"\\n\",\n \"* conventions: b = bias term; w = feature weights vector.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"iris = datasets.load_iris()\\n\",\n \"X = iris[\\\"data\\\"][:, (2, 3)] # petal length, petal width\\n\",\n \"y = (iris[\\\"target\\\"] == 2).astype(np.float64) # Iris-Virginica\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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n6/H3V1dUx4mC0E9Xo9PB4Pkx+ZrHo4G4Jiv4m92Jy9SwW7YITk+xFi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Ly8PGaCuqWvpVAolGekRXYJ1GePQukmmJiYwNXVVaPQA8BYfygLPU03ezY2NvD2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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n \"\\n\",\n \"def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\\n\",\n \" x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\\n\",\n \" X_crop = X[x1_in_bounds]\\n\",\n \" y_crop = y[x1_in_bounds]\\n\",\n \" x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\\n\",\n \" x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\\n\",\n \" x1, x2 = np.meshgrid(x1s, x2s)\\n\",\n \" xs = np.c_[x1.ravel(), x2.ravel()]\\n\",\n \" df = (xs.dot(w) + b).reshape(x1.shape)\\n\",\n \" m = 1 / np.linalg.norm(w)\\n\",\n \" boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\\n\",\n \" margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\\n\",\n \" margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\\n\",\n \" ax.plot_surface(x1s, x2, 0, color=\\\"b\\\", alpha=0.2, cstride=100, rstride=100)\\n\",\n \" ax.plot(x1s, boundary_x2s, 0, \\\"k-\\\", linewidth=2, label=r\\\"$h=0$\\\")\\n\",\n \" ax.plot(x1s, margin_x2s_1, 0, \\\"k--\\\", linewidth=2, label=r\\\"$h=\\\\pm 1$\\\")\\n\",\n \" ax.plot(x1s, margin_x2s_2, 0, \\\"k--\\\", linewidth=2)\\n\",\n \" ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \\\"g^\\\")\\n\",\n \" ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\\\"k\\\")\\n\",\n \" ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \\\"bs\\\")\\n\",\n \" ax.axis(x1_lim + x2_lim)\\n\",\n \" ax.text(4.5, 2.5, 3.8, \\\"Decision function $h$\\\", fontsize=15)\\n\",\n \" ax.set_xlabel(r\\\"Petal length\\\", fontsize=15)\\n\",\n \" ax.set_ylabel(r\\\"Petal width\\\", fontsize=15)\\n\",\n \" ax.set_zlabel(r\\\"$h = \\\\mathbf{w}^t \\\\cdot \\\\mathbf{x} + b$\\\", fontsize=18)\\n\",\n \" ax.legend(loc=\\\"upper left\\\", fontsize=16)\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(11, 6))\\n\",\n \"ax1 = fig.add_subplot(111, projection='3d')\\n\",\n \"plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\\n\",\n \"\\n\",\n \"#save_fig(\\\"iris_3D_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training Objectives\\n\",\n \"* Slope of a decision function equals a weight vector's **norm** (||w||)\\n\",\n \"* Divide slope by 2 ==> any points where decision function = +1/-1 will be **2x away from decision boundary.**\\n\",\n \"![example](pics/small-weight-vector-large-margin.png)\\n\",\n \"* So we want minimal ||w|| to get max margins\\n\",\n \"* If we also want zero margin violations, then decision function needs to be GT1 (positive) and LT1 (negative).\\n\",\n \"* if soft margins OK - need to define a *slack variable* (C) for tradeoff.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Quadratic programming\\n\",\n \"* Hard- & soft-margin problems = convex quadratic optimization problems with linear constraints, ie *quadratic programming* (QP) problems. See [Convex Optimization for more info](http://goo.gl/FGXuLw).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### todo: The dual problem\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### todo: Kernelized SVM\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### todo: Online (incremental learning) SVMs\\n\",\n \"\\n\",\n \"* Linear SVM classifiers often use **SGD** to find a min-cost solution. SGD converges **much more slowly** than QP-based methods.\\n\",\n \"* [implementation:](http://goo.gl/JEqVui)\\n\",\n \"* [implementation:](https://goo.gl/hsoUHA)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch06-decision-trees.html", "content": "\n\n\nch06-decision-trees\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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Intro

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import os\nimport numpy.random as rnd\n
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Training & Visualization

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# load iris dataset & train a DT classifier\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\n\niris = load_iris()\nX = iris.data[:, 2:] # petal length and width\ny = iris.target\ntree_clf = DecisionTreeClassifier(max_depth=2)\ntree_clf.fit(X, y)\n
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DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n            max_features=None, max_leaf_nodes=None,\n            min_impurity_split=1e-07, min_samples_leaf=1,\n            min_samples_split=2, min_weight_fraction_leaf=0.0,\n            presort=False, random_state=None, splitter='best')
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# graph it into a .dot file\n\nfrom sklearn.tree import export_graphviz\n\ndef image_path(fig_id):\n    #return os.path...\n    return fig_id\n\nexport_graphviz(\n    tree_clf,\n    out_file=image_path("iris_tree.dot"),\n    feature_names=iris.feature_names[2:],\n    class_names=iris.target_names,\n    rounded=True,\n    filled=True\n)\n
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# convert to PDF or PNG using command-line tool.\n! dot -Tpng iris_tree.dot -o iris_tree.png\n
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\"result\"

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Predictions

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  • DTs require very little data prep. No feature scaling & centering.
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  • SciKit uses CART algorithm. (only two children per node.) Other algos, ex ID3, can build DTs with >2 children per node.
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  • gini attribute refers to a node's \"impurity\" (gini=0 if all applicable training instances belong to same class.)
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# Plot DT decision boundaries\n# Depth=0: root node (petal length=2.45cm)\n# Depth=1: right node splits @ 1.75cm\n# Stops at max_depth = 2.\n# Vertical dotted line shows boundary if max_depth set = 3.\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\n\ndef plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\n    x1s = np.linspace(axes[0], axes[1], 100)\n    x2s = np.linspace(axes[2], axes[3], 100)\n    x1, x2 = np.meshgrid(x1s, x2s)\n    X_new = np.c_[x1.ravel(), x2.ravel()]\n    y_pred = clf.predict(X_new).reshape(x1.shape)\n    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap, linewidth=10)\n    if not iris:\n        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n    if plot_training:\n        plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo", label="Iris-Setosa")\n        plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs", label="Iris-Versicolor")\n        plt.plot(X[:, 0][y==2], X[:, 1][y==2], "g^", label="Iris-Virginica")\n        plt.axis(axes)\n    if iris:\n        plt.xlabel("Petal length", fontsize=14)\n        plt.ylabel("Petal width", fontsize=14)\n    else:\n        plt.xlabel(r"$x_1$", fontsize=18)\n        plt.ylabel(r"$x_2$", fontsize=18, rotation=0)\n    if legend:\n        plt.legend(loc="lower right", fontsize=14)\n\nplt.figure(figsize=(8, 4))\nplot_decision_boundary(tree_clf, X, y)\nplt.plot([2.45, 2.45], [0, 3], "k-", linewidth=2)\nplt.plot([2.45, 7.5], [1.75, 1.75], "k--", linewidth=2)\nplt.plot([4.95, 4.95], [0, 1.75], "k:", linewidth=2)\nplt.plot([4.85, 4.85], [1.75, 3], "k:", linewidth=2)\nplt.text(1.40, 1.0, "Depth=0", fontsize=15)\nplt.text(3.2, 1.80, "Depth=1", fontsize=13)\nplt.text(4.05, 0.5, "(Depth=2)", fontsize=11)\n\n#save_fig("decision_tree_decision_boundaries_plot")\nplt.show()\n
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Estimating Class Probabilities

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# Probability of instance 5cm long, 1.5cm wide belonging to any one of three nodes above:\nprint(tree_clf.predict_proba([[5, 1.5]]))\n\n# Return class of highest probability (in this case, class #1.)\nprint(tree_clf.predict([[5, 1.5]]))\n
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Training: CART algorithm

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\"CART\"

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  • \"Greedy\" algorithm; searches for optimum at each level w/o regard for lower levels. Not guaranteed to find optimum solution.
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Computational Complexity

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Gini Impurity, or Entropy?

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  • Can use entropy measure by setting criterion parameter to \"entropy\".\n\"entrpopy\"

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  • Dataset's entropy = 0 when it contains instances of only one class.

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Regularization Hyperparameters

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In [7]:
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# Train two DTs on moons dataset.\n# left: default params = no restrictions (case of overfitting)\n# right: min_samples_leaf = 4. (better generalization)\n\nfrom sklearn.datasets import make_moons\nXm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\n\ndeep_tree_clf1 = DecisionTreeClassifier(random_state=42)\ndeep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\ndeep_tree_clf1.fit(Xm, ym)\ndeep_tree_clf2.fit(Xm, ym)\n\nplt.figure(figsize=(11, 4))\nplt.subplot(121)\nplot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\nplt.title("No restrictions", fontsize=16)\nplt.subplot(122)\nplot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\nplt.title("min_samples_leaf = {}".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\n\n#save_fig("min_samples_leaf_plot")\nplt.show()\n
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Regression

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  • Task: Predict a value (instead of a class) for each node.
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In [8]:
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from sklearn.tree import DecisionTreeRegressor\n\n# Quadrat!ic training set + noise\nrnd.seed(42)\nm = 200\nX = rnd.rand(m, 1)\ny = 4 * (X - 0.5) ** 2\ny = y + rnd.randn(m, 1) / 10\n\ntree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)\ntree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)\ntree_reg1.fit(X, y)\ntree_reg2.fit(X, y)\n\ndef plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel="$y$"):\n    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n    y_pred = tree_reg.predict(x1)\n    plt.axis(axes)\n    plt.xlabel("$x_1$", fontsize=18)\n    if ylabel:\n        plt.ylabel(ylabel, fontsize=18, rotation=0)\n    plt.plot(X, y, "b.")\n    plt.plot(x1, y_pred, "r.-", linewidth=2, label=r"$\\hat{y}$")\n\nplt.figure(figsize=(11, 4))\nplt.subplot(121)\nplot_regression_predictions(tree_reg1, X, y)\nfor split, style in ((0.1973, "k-"), (0.0917, "k--"), (0.7718, "k--")):\n    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\nplt.text(0.21, 0.65, "Depth=0", fontsize=15)\nplt.text(0.01, 0.2, "Depth=1", fontsize=13)\nplt.text(0.65, 0.8, "Depth=1", fontsize=13)\nplt.legend(loc="upper center", fontsize=18)\nplt.title("max_depth=2", fontsize=14)\n\nplt.subplot(122)\nplot_regression_predictions(tree_reg2, X, y, ylabel=None)\nfor split, style in ((0.1973, "k-"), (0.0917, "k--"), (0.7718, "k--")):\n    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\nfor split in (0.0458, 0.1298, 0.2873, 0.9040):\n    plt.plot([split, split], [-0.2, 1], "k:", linewidth=1)\nplt.text(0.3, 0.5, "Depth=2", fontsize=13)\nplt.title("max_depth=3", fontsize=14)\n\nplt.show()\n\n# Predicted value for each region (red line) = avg target value of instances in that region.\n
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In [9]:
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tree_reg1 = DecisionTreeRegressor(random_state=42)\ntree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\ntree_reg1.fit(X, y)\ntree_reg2.fit(X, y)\n\nx1 = np.linspace(0, 1, 500).reshape(-1, 1)\ny_pred1 = tree_reg1.predict(x1)\ny_pred2 = tree_reg2.predict(x1)\n\nplt.figure(figsize=(11, 4))\n\nplt.subplot(121)\nplt.plot(X, y, "b.")\nplt.plot(x1, y_pred1, "r.-", linewidth=2, label=r"$\\hat{y}$")\nplt.axis([0, 1, -0.2, 1.1])\nplt.xlabel("$x_1$", fontsize=18)\nplt.ylabel("$y$", fontsize=18, rotation=0)\nplt.legend(loc="upper center", fontsize=18)\nplt.title("No restrictions", fontsize=14)\n\nplt.subplot(122)\nplt.plot(X, y, "b.")\nplt.plot(x1, y_pred2, "r.-", linewidth=2, label=r"$\\hat{y}$")\nplt.axis([0, 1, -0.2, 1.1])\nplt.xlabel("$x_1$", fontsize=18)\nplt.title("min_samples_leaf={}".format(tree_reg2.min_samples_leaf), fontsize=14)\n\n#save_fig("tree_regression_regularization_plot")\nplt.show()\n\n# left: no regularization (default params): overfitting\n# right: more reasonable.\n
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Instability

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  • DTs strongly favor orthogonal decision boundaries. They are sensitive to training set rotations.
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In [10]:
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rnd.seed(6)\nXs = rnd.rand(100, 2) - 0.5\nys = (Xs[:, 0] > 0).astype(np.float32) * 2\n\nangle = np.pi / 4\nrotation_matrix = np.array(\n    [[np.cos(angle), -np.sin(angle)], \n     [np.sin(angle), np.cos(angle)]])\n\nXsr = Xs.dot(rotation_matrix)\n\ntree_clf_s = DecisionTreeClassifier(random_state=42)\ntree_clf_s.fit(Xs, ys)\ntree_clf_sr = DecisionTreeClassifier(random_state=42)\ntree_clf_sr.fit(Xsr, ys)\n\nplt.figure(figsize=(11, 4))\nplt.subplot(121)\nplot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\nplt.subplot(122)\nplot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n\n#save_fig("sensitivity_to_rotation_plot")\nplt.show()\n\n# left: std linearly separable dataset\n# right: dataset rotated by 45degrees.\n
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In [ ]:
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\n\n\n" }, { "path": "ch06-decision-trees.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import numpy.random as rnd\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training & Visualization\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\\n\",\n \" max_features=None, max_leaf_nodes=None,\\n\",\n \" min_impurity_split=1e-07, min_samples_leaf=1,\\n\",\n \" min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n \" presort=False, random_state=None, splitter='best')\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# load iris dataset & train a DT classifier\\n\",\n \"\\n\",\n \"from sklearn.datasets import load_iris\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"\\n\",\n \"iris = load_iris()\\n\",\n \"X = iris.data[:, 2:] # petal length and width\\n\",\n \"y = iris.target\\n\",\n \"tree_clf = DecisionTreeClassifier(max_depth=2)\\n\",\n \"tree_clf.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# graph it into a .dot file\\n\",\n \"\\n\",\n \"from sklearn.tree import export_graphviz\\n\",\n \"\\n\",\n \"def image_path(fig_id):\\n\",\n \" #return os.path...\\n\",\n \" return fig_id\\n\",\n \"\\n\",\n \"export_graphviz(\\n\",\n \" tree_clf,\\n\",\n \" out_file=image_path(\\\"iris_tree.dot\\\"),\\n\",\n \" feature_names=iris.feature_names[2:],\\n\",\n \" class_names=iris.target_names,\\n\",\n \" rounded=True,\\n\",\n \" filled=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# convert to PDF or PNG using command-line tool.\\n\",\n \"! dot -Tpng iris_tree.dot -o iris_tree.png\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![result](pics/iris_tree.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Predictions\\n\",\n \"\\n\",\n \"* DTs require very little data prep. No feature scaling & centering.\\n\",\n \"* SciKit uses CART algorithm. (only two children per node.) Other algos, ex ID3, can build DTs with >2 children per node.\\n\",\n \"* *gini* attribute refers to a node's \\\"impurity\\\" (gini=0 if all applicable training instances belong to same class.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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bHninIXqDGzSeG3DjxtZttL7c4D2gPvpyg2EUlQ02ah2aC/Xvs1Lz3z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\\nIiKSo/4/RII8+u+gl4oAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot DT decision boundaries\\n\",\n \"# Depth=0: root node (petal length=2.45cm)\\n\",\n \"# Depth=1: right node splits @ 1.75cm\\n\",\n \"# Stops at max_depth = 2.\\n\",\n \"# Vertical dotted line shows boundary if max_depth set = 3.\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from matplotlib.colors import ListedColormap\\n\",\n \"\\n\",\n \"def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\\n\",\n \" x1s = np.linspace(axes[0], axes[1], 100)\\n\",\n \" x2s = np.linspace(axes[2], axes[3], 100)\\n\",\n \" x1, x2 = np.meshgrid(x1s, x2s)\\n\",\n \" X_new = np.c_[x1.ravel(), x2.ravel()]\\n\",\n \" y_pred = clf.predict(X_new).reshape(x1.shape)\\n\",\n \" custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\\n\",\n \" plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap, linewidth=10)\\n\",\n \" if not iris:\\n\",\n \" custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\\n\",\n \" plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\\n\",\n \" if plot_training:\\n\",\n \" plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n \" plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"bs\\\", label=\\\"Iris-Versicolor\\\")\\n\",\n \" plt.plot(X[:, 0][y==2], X[:, 1][y==2], \\\"g^\\\", label=\\\"Iris-Virginica\\\")\\n\",\n \" plt.axis(axes)\\n\",\n \" if iris:\\n\",\n \" plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \" plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n \" else:\\n\",\n \" plt.xlabel(r\\\"$x_1$\\\", fontsize=18)\\n\",\n \" plt.ylabel(r\\\"$x_2$\\\", fontsize=18, rotation=0)\\n\",\n \" if legend:\\n\",\n \" plt.legend(loc=\\\"lower right\\\", fontsize=14)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(8, 4))\\n\",\n \"plot_decision_boundary(tree_clf, X, y)\\n\",\n \"plt.plot([2.45, 2.45], [0, 3], \\\"k-\\\", linewidth=2)\\n\",\n \"plt.plot([2.45, 7.5], [1.75, 1.75], \\\"k--\\\", linewidth=2)\\n\",\n \"plt.plot([4.95, 4.95], [0, 1.75], \\\"k:\\\", linewidth=2)\\n\",\n \"plt.plot([4.85, 4.85], [1.75, 3], \\\"k:\\\", linewidth=2)\\n\",\n \"plt.text(1.40, 1.0, \\\"Depth=0\\\", fontsize=15)\\n\",\n \"plt.text(3.2, 1.80, \\\"Depth=1\\\", fontsize=13)\\n\",\n \"plt.text(4.05, 0.5, \\\"(Depth=2)\\\", fontsize=11)\\n\",\n \"\\n\",\n \"#save_fig(\\\"decision_tree_decision_boundaries_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Estimating Class Probabilities\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0. 0.90740741 0.09259259]]\\n\",\n \"[1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Probability of instance 5cm long, 1.5cm wide belonging to any one of three nodes above:\\n\",\n \"print(tree_clf.predict_proba([[5, 1.5]]))\\n\",\n \"\\n\",\n \"# Return class of highest probability (in this case, class #1.)\\n\",\n \"print(tree_clf.predict([[5, 1.5]]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training: CART algorithm\\n\",\n \"* Split training set in two using feature *k* and threshold *t_k*.\\n\",\n \"* Searches for pair *(k, t_k)* that returns purest subsets, weighted by size.\\n\",\n \"* Cost function to minimize shown below.\\n\",\n \"\\n\",\n \"![CART](pics/CART-algorithm.png)\\n\",\n \"\\n\",\n \"* \\\"Greedy\\\" algorithm; searches for optimum at each level w/o regard for lower levels. Not guaranteed to find optimum solution.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Computational Complexity\\n\",\n \"* Typical: O(log2(m)) = independent of #features. (So: very fast prediction times.)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Gini Impurity, or Entropy?\\n\",\n \"* Can use entropy measure by setting *criterion* parameter to \\\"entropy\\\".\\n\",\n \"![entrpopy](pics/entropy.png)\\n\",\n \"\\n\",\n \"* Dataset's entropy = 0 when it contains instances of only one class.\\n\",\n \"* Can use either; Gini impurity = slightly faster. Entropy tends to build slightly more balanced trees.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Regularization Hyperparameters\\n\",\n \"\\n\",\n \"* *max_depth* controls max depth of the DT. Reducing *max_depth* regularizes the model, therefore reduces risk of overfit.\\n\",\n \"* Also: *min_samples_split*, *min_samples_leaf*, *min_weight_fraction_leaf*, *max_leaf_nodes*, *max_features* -- increasing min_* or reducing max_* params will regularize the model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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iIikjpqpIqIiIhI6qiRKiIiIiKpo0aqiIiIiKSOGqkiIiIikjpqpIqI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\\naboZ5RwAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Train two DTs on moons dataset.\\n\",\n \"# left: default params = no restrictions (case of overfitting)\\n\",\n \"# right: min_samples_leaf = 4. (better generalization)\\n\",\n \"\\n\",\n \"from sklearn.datasets import make_moons\\n\",\n \"Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\\n\",\n \"\\n\",\n \"deep_tree_clf1 = DecisionTreeClassifier(random_state=42)\\n\",\n \"deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\\n\",\n \"deep_tree_clf1.fit(Xm, ym)\\n\",\n \"deep_tree_clf2.fit(Xm, ym)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11, 4))\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\\n\",\n \"plt.title(\\\"No restrictions\\\", fontsize=16)\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\\n\",\n \"plt.title(\\\"min_samples_leaf = {}\\\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\\n\",\n \"\\n\",\n \"#save_fig(\\\"min_samples_leaf_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Regression\\n\",\n \"* Task: Predict a value (instead of a class) for each node.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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0ynoqJOx1opYhe96MVuSkicJukxQUjAxIkTmTNnDk1NTV3ixK1du5aR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\\nIiIiXvh/vOp4FA6l6REAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.tree import DecisionTreeRegressor\\n\",\n \"\\n\",\n \"# Quadrat!ic training set + noise\\n\",\n \"rnd.seed(42)\\n\",\n \"m = 200\\n\",\n \"X = rnd.rand(m, 1)\\n\",\n \"y = 4 * (X - 0.5) ** 2\\n\",\n \"y = y + rnd.randn(m, 1) / 10\\n\",\n \"\\n\",\n \"tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)\\n\",\n \"tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)\\n\",\n \"tree_reg1.fit(X, y)\\n\",\n \"tree_reg2.fit(X, y)\\n\",\n \"\\n\",\n \"def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\\\"$y$\\\"):\\n\",\n \" x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\\n\",\n \" y_pred = tree_reg.predict(x1)\\n\",\n \" plt.axis(axes)\\n\",\n \" plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \" if ylabel:\\n\",\n \" plt.ylabel(ylabel, fontsize=18, rotation=0)\\n\",\n \" plt.plot(X, y, \\\"b.\\\")\\n\",\n \" plt.plot(x1, y_pred, \\\"r.-\\\", linewidth=2, label=r\\\"$\\\\hat{y}$\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11, 4))\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_regression_predictions(tree_reg1, X, y)\\n\",\n \"for split, style in ((0.1973, \\\"k-\\\"), (0.0917, \\\"k--\\\"), (0.7718, \\\"k--\\\")):\\n\",\n \" plt.plot([split, split], [-0.2, 1], style, linewidth=2)\\n\",\n \"plt.text(0.21, 0.65, \\\"Depth=0\\\", fontsize=15)\\n\",\n \"plt.text(0.01, 0.2, \\\"Depth=1\\\", fontsize=13)\\n\",\n \"plt.text(0.65, 0.8, \\\"Depth=1\\\", fontsize=13)\\n\",\n \"plt.legend(loc=\\\"upper center\\\", fontsize=18)\\n\",\n \"plt.title(\\\"max_depth=2\\\", fontsize=14)\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_regression_predictions(tree_reg2, X, y, ylabel=None)\\n\",\n \"for split, style in ((0.1973, \\\"k-\\\"), (0.0917, \\\"k--\\\"), (0.7718, \\\"k--\\\")):\\n\",\n \" plt.plot([split, split], [-0.2, 1], style, linewidth=2)\\n\",\n \"for split in (0.0458, 0.1298, 0.2873, 0.9040):\\n\",\n \" plt.plot([split, split], [-0.2, 1], \\\"k:\\\", linewidth=1)\\n\",\n \"plt.text(0.3, 0.5, \\\"Depth=2\\\", fontsize=13)\\n\",\n \"plt.title(\\\"max_depth=3\\\", fontsize=14)\\n\",\n \"\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# Predicted value for each region (red line) = avg target value of instances in that region.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Instead of trying to minimize impurity (classification) DTs now try to minimize MSE:\\n\",\n \"![CART regression](pics/CART-regression-cost.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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G1yS5haFtPDvn8J+6acDEBbcWnMeqHnaW1txefLrUiRnsbv99OWx6Ko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\\nRK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tree_reg1 = DecisionTreeRegressor(random_state=42)\\n\",\n \"tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\\n\",\n \"tree_reg1.fit(X, y)\\n\",\n \"tree_reg2.fit(X, y)\\n\",\n \"\\n\",\n \"x1 = np.linspace(0, 1, 500).reshape(-1, 1)\\n\",\n \"y_pred1 = tree_reg1.predict(x1)\\n\",\n \"y_pred2 = tree_reg2.predict(x1)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11, 4))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.plot(X, y, \\\"b.\\\")\\n\",\n \"plt.plot(x1, y_pred1, \\\"r.-\\\", linewidth=2, label=r\\\"$\\\\hat{y}$\\\")\\n\",\n \"plt.axis([0, 1, -0.2, 1.1])\\n\",\n \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \"plt.ylabel(\\\"$y$\\\", fontsize=18, rotation=0)\\n\",\n \"plt.legend(loc=\\\"upper center\\\", fontsize=18)\\n\",\n \"plt.title(\\\"No restrictions\\\", fontsize=14)\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.plot(X, y, \\\"b.\\\")\\n\",\n \"plt.plot(x1, y_pred2, \\\"r.-\\\", linewidth=2, label=r\\\"$\\\\hat{y}$\\\")\\n\",\n \"plt.axis([0, 1, -0.2, 1.1])\\n\",\n \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \"plt.title(\\\"min_samples_leaf={}\\\".format(tree_reg2.min_samples_leaf), fontsize=14)\\n\",\n \"\\n\",\n \"#save_fig(\\\"tree_regression_regularization_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# left: no regularization (default params): overfitting\\n\",\n \"# right: more reasonable.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Instability\\n\",\n \"* DTs strongly favor orthogonal decision boundaries. They are **sensitive to training set rotations**.\\n\",\n \"* More generally: DTs are sensitive to training data variations.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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zz/XjN7wMx+YmZ3mdnJIeLMUy8jiWk1l8/jdJFeC8h2BXNeXQDaCTE9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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rnd.seed(6)\\n\",\n \"Xs = rnd.rand(100, 2) - 0.5\\n\",\n \"ys = (Xs[:, 0] > 0).astype(np.float32) * 2\\n\",\n \"\\n\",\n \"angle = np.pi / 4\\n\",\n \"rotation_matrix = np.array(\\n\",\n \" [[np.cos(angle), -np.sin(angle)], \\n\",\n \" [np.sin(angle), np.cos(angle)]])\\n\",\n \"\\n\",\n \"Xsr = Xs.dot(rotation_matrix)\\n\",\n \"\\n\",\n \"tree_clf_s = DecisionTreeClassifier(random_state=42)\\n\",\n \"tree_clf_s.fit(Xs, ys)\\n\",\n \"tree_clf_sr = DecisionTreeClassifier(random_state=42)\\n\",\n \"tree_clf_sr.fit(Xsr, ys)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11, 4))\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\\n\",\n \"\\n\",\n \"#save_fig(\\\"sensitivity_to_rotation_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# left: std linearly separable dataset\\n\",\n \"# right: dataset rotated by 45degrees.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch07-ensemble-learning.html", "content": "\n\n\nch07-ensemble-learning\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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Intro

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In [1]:
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import numpy as np\nimport numpy.random as rnd\nimport matplotlib.pyplot as plt\n
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Voting Classifiers

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  • Good classifiers can be built by aggregating predictions of various weaker classifiers, and returning the class that gets the most votes. (A \"hard voting\" classifier.)
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In [2]:
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heads_proba = 0.51\ncoin_tosses = (rnd.rand(10000, 10) < heads_proba).astype(np.int32)\ncumulative_heads_ratio = np.cumsum(\n    coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)\n#cumulative_heads_ratio\n
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In [3]:
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plt.figure(figsize=(8,3.5))\nplt.plot(cumulative_heads_ratio)\nplt.plot([0, 10000], [0.51, 0.51], "k--", linewidth=2, label="51%")\nplt.plot([0, 10000], [0.5, 0.5], "k-", label="50%")\nplt.xlabel("Number of coin tosses")\nplt.ylabel("Heads ratio")\nplt.legend(loc="lower right")\nplt.title("The law of large numbers:")\nplt.axis([0, 10000, 0.42, 0.58])\n#save_fig("law_of_large_numbers_plot")\nplt.show()\n
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In [4]:
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# build a voting classifier in Scikit using three weaker classifiers\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import make_moons\n\n# use moons dataset\nX, y = make_moons(\n    n_samples=500, \n    noise=0.30, \n    random_state=42)\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, random_state=42)\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\n\nlog_clf = LogisticRegression(random_state=42)\nrnd_clf = RandomForestClassifier(random_state=42)\nsvm_clf = SVC(probability=True, random_state=42)\n\n# voting classifier = logistic + random forest + SVC\n\nvoting_clf = VotingClassifier(\n        estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n        voting='soft'\n    )\nvoting_clf.fit(X_train, y_train)\n\n# let's see how each individual classifier did:\n\nfrom sklearn.metrics import accuracy_score\n\nfor clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n    clf.fit(X_train, y_train)\n    y_pred = clf.predict(X_test)\n    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))\n    \n# voting classifier did better than 3 individual ones!\n
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LogisticRegression 0.864\nRandomForestClassifier 0.872\nSVC 0.888\nVotingClassifier 0.912\n
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  • If all classifiers can estimate class probabilities (they have a predict_proba() method), use Scikit to predict highest class probability, averaged over all individual classifiers. (soft voting)

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  • Often better than hard voting because it gives more weight to highly confident votes. Replace voting=\"hard\" with \"soft\" & ensure all classifiers can estimate class probabilities. (SVC cannot by default -set probability param to True.)

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  • This tells SVC to use cross-validation to estimate class probabilities. Slows training times & adds a predict_proba() method).

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Bagging & Pasting

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  • Another approach: use same training algorithm, but apply it to different subsets of the training dataset.
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  • bagging: sampling the dataset with replacement.
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  • pasting: sampling the dataset without replacement.
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  • Final prediction = based on an aggregation function.
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  • Predictions can be made in parallel -- good scaling properties.
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In [5]:
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from sklearn.datasets import make_moons\nfrom sklearn.ensemble import BaggingClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Train ensemble of 500 Decision Tree classifiers\n# each using 100 training instances - randomly sampled from training set\n# with replacement.\n\nbag_clf = BaggingClassifier(\n        DecisionTreeClassifier(random_state=42), \n    n_estimators=500,\n    max_samples=100, \n    bootstrap=True, # set to False for pasting instead of bagging.\n    n_jobs=-1, \n    random_state=42)\n\nbag_clf.fit(X_train, y_train)\ny_pred = bag_clf.predict(X_test)\nprint(accuracy_score(y_test, y_pred))\n
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0.904\n
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In [6]:
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tree_clf = DecisionTreeClassifier(random_state=42)\ntree_clf.fit(X_train, y_train)\ny_pred_tree = tree_clf.predict(X_test)\nprint(accuracy_score(y_test, y_pred_tree))\n
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0.856\n
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In [7]:
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from matplotlib.colors import ListedColormap\n\ndef plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n    x1s = np.linspace(axes[0], axes[1], 100)\n    x2s = np.linspace(axes[2], axes[3], 100)\n    x1, x2 = np.meshgrid(x1s, x2s)\n    X_new = np.c_[x1.ravel(), x2.ravel()]\n    y_pred = clf.predict(X_new).reshape(x1.shape)\n    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap, linewidth=10)\n    if contour:\n        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n    plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo", alpha=alpha)\n    plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs", alpha=alpha)\n    plt.axis(axes)\n    plt.xlabel(r"$x_1$", fontsize=18)\n    plt.ylabel(r"$x_2$", fontsize=18, rotation=0)\n
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In [8]:
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plt.figure(figsize=(11,4))\nplt.subplot(121)\nplot_decision_boundary(tree_clf, X, y)\nplt.title("Decision Tree", fontsize=14)\n\nplt.subplot(122)\nplot_decision_boundary(bag_clf, X, y)\nplt.title("Decision Trees with Bagging", fontsize=14)\n#save_fig("decision_tree_without_and_with_bagging_plot")\nplt.show()\n
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Out of Bag Evaluation

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  • Bagging: some instances may be sampled multiple times - others not at all. On avg, ~63% of training samples are used. Remainder 37% = \"out of bag\".
    • \n
  • use oob_score=True in Scikit to do automatic oob evaluation after training.
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In [9]:
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# oob_score_: predicts classifier results on test set.\nbag_clf = BaggingClassifier(\n    DecisionTreeClassifier(),\n    n_estimators=500,\n    bootstrap=True,\n    n_jobs=-1,\n    oob_score=True\n)\nbag_clf.fit(X_train, y_train)\nbag_clf.oob_score_\n
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Out[9]:
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0.89866666666666661
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In [10]:
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# did oob_score_ do a good job?\nfrom sklearn.metrics import accuracy_score\ny_pred = bag_clf.predict(X_test)\naccuracy_score(y_test,y_pred)\n
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Out[10]:
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0.90400000000000003
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In [11]:
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# oob decision functionfor each training instance\nbag_clf.oob_decision_function_\n
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Out[11]:
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array([[ 0.36363636,  0.63636364],\n       [ 0.38586957,  0.61413043],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.06632653,  0.93367347],\n       [ 0.30769231,  0.69230769],\n       [ 0.03015075,  0.96984925],\n       [ 0.99444444,  0.00555556],\n       [ 0.94708995,  0.05291005],\n       [ 0.79      ,  0.21      ],\n       [ 0.00507614,  0.99492386],\n       [ 0.77456647,  0.22543353],\n       [ 0.84269663,  0.15730337],\n       [ 0.95480226,  0.04519774],\n       [ 0.06557377,  0.93442623],\n       [ 0.        ,  1.        ],\n       [ 0.98      ,  0.02      ],\n       [ 0.95505618,  0.04494382],\n       [ 1.        ,  0.        ],\n       [ 0.01086957,  0.98913043],\n       [ 0.3372093 ,  0.6627907 ],\n       [ 0.89949749,  0.10050251],\n       [ 1.        ,  0.        ],\n       [ 0.96666667,  0.03333333],\n       [ 0.        ,  1.        ],\n       [ 0.99375   ,  0.00625   ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.64285714,  0.35714286],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.14444444,  0.85555556],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.33152174,  0.66847826],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.22702703,  0.77297297],\n       [ 0.41212121,  0.58787879],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.02777778,  0.97222222],\n       [ 1.        ,  0.        ],\n       [ 0.00561798,  0.99438202],\n       [ 0.99418605,  0.00581395],\n       [ 0.89265537,  0.10734463],\n       [ 0.96273292,  0.03726708],\n       [ 0.9494382 ,  0.0505618 ],\n       [ 0.        ,  1.        ],\n       [ 0.03846154,  0.96153846],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.00540541,  0.99459459],\n       [ 1.        ,  0.        ],\n       [ 0.81182796,  0.18817204],\n       [ 0.44776119,  0.55223881],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.63387978,  0.36612022],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.845     ,  0.155     ],\n       [ 1.        ,  0.        ],\n       [ 0.55      ,  0.45      ],\n       [ 0.13372093,  0.86627907],\n       [ 0.68390805,  0.31609195],\n       [ 0.87700535,  0.12299465],\n       [ 0.        ,  1.        ],\n       [ 0.19487179,  0.80512821],\n       [ 0.88324873,  0.11675127],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.07017544,  0.92982456],\n       [ 0.0326087 ,  0.9673913 ],\n       [ 0.29120879,  0.70879121],\n       [ 1.        ,  0.        ],\n       [ 0.00487805,  0.99512195],\n       [ 0.87700535,  0.12299465],\n       [ 0.00543478,  0.99456522],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.26395939,  0.73604061],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.92227979,  0.07772021],\n       [ 0.78494624,  0.21505376],\n       [ 0.005     ,  0.995     ],\n       [ 1.        ,  0.        ],\n       [ 0.1957672 ,  0.8042328 ],\n       [ 0.6631016 ,  0.3368984 ],\n       [ 0.        ,  1.        ],\n       [ 0.03529412,  0.96470588],\n       [ 0.4974359 ,  0.5025641 ],\n       [ 1.        ,  0.        ],\n       [ 0.01785714,  0.98214286],\n       [ 0.99465241,  0.00534759],\n       [ 0.23626374,  0.76373626],\n       [ 0.5270936 ,  0.4729064 ],\n       [ 1.        ,  0.        ],\n       [ 0.01694915,  0.98305085],\n       [ 0.99568966,  0.00431034],\n       [ 0.25988701,  0.74011299],\n       [ 0.92982456,  0.07017544],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.80748663,  0.19251337],\n       [ 1.        ,  0.        ],\n       [ 0.02105263,  0.97894737],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.98477157,  0.01522843],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.92655367,  0.07344633],\n       [ 1.        ,  0.        ],\n       [ 0.01485149,  0.98514851],\n       [ 0.29145729,  0.70854271],\n       [ 0.96216216,  0.03783784],\n       [ 0.29608939,  0.70391061],\n       [ 0.9893617 ,  0.0106383 ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.73913043,  0.26086957],\n       [ 0.40251572,  0.59748428],\n       [ 0.46031746,  0.53968254],\n       [ 0.88297872,  0.11702128],\n       [ 0.92090395,  0.07909605],\n       [ 0.06818182,  0.93181818],\n       [ 0.82634731,  0.17365269],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.01169591,  0.98830409],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.00529101,  0.99470899],\n       [ 0.        ,  1.        ],\n       [ 0.00540541,  0.99459459],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.95238095,  0.04761905],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.34065934,  0.65934066],\n       [ 0.23529412,  0.76470588],\n       [ 0.00534759,  0.99465241],\n       [ 0.00512821,  0.99487179],\n       [ 0.31213873,  0.68786127],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.00613497,  0.99386503],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.00578035,  0.99421965],\n       [ 0.63313609,  0.36686391],\n       [ 0.9027027 ,  0.0972973 ],\n       [ 0.        ,  1.        ],\n       [ 0.98963731,  0.01036269],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.07978723,  0.92021277],\n       [ 1.        ,  0.        ],\n       [ 0.03645833,  0.96354167],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.01818182,  0.98181818],\n       [ 1.        ,  0.        ],\n       [ 0.96276596,  0.03723404],\n       [ 0.77173913,  0.22826087],\n       [ 0.65536723,  0.34463277],\n       [ 0.        ,  1.        ],\n       [ 0.14832536,  0.85167464],\n       [ 1.        ,  0.        ],\n       [ 0.96296296,  0.03703704],\n       [ 0.97849462,  0.02150538],\n       [ 1.        ,  0.        ],\n       [ 0.00564972,  0.99435028],\n       [ 0.        ,  1.        ],\n       [ 0.48924731,  0.51075269],\n       [ 0.86243386,  0.13756614],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.00543478,  0.99456522],\n       [ 0.        ,  1.        ],\n       [ 0.96208531,  0.03791469],\n       [ 0.        ,  1.        ],\n       [ 0.21590909,  0.78409091],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.96875   ,  0.03125   ],\n       [ 0.8021978 ,  0.1978022 ],\n       [ 1.        ,  0.        ],\n       [ 0.00568182,  0.99431818],\n       [ 0.05670103,  0.94329897],\n       [ 1.        ,  0.        ],\n       [ 0.01898734,  0.98101266],\n       [ 0.        ,  1.        ],\n       [ 0.08888889,  0.91111111],\n       [ 1.        ,  0.        ],\n       [ 0.77439024,  0.22560976],\n       [ 0.        ,  1.        ],\n       [ 0.86666667,  0.13333333],\n       [ 0.99441341,  0.00558659],\n       [ 0.14634146,  0.85365854],\n       [ 0.19487179,  0.80512821],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.26111111,  0.73888889],\n       [ 0.96391753,  0.03608247],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.52147239,  0.47852761],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.07177033,  0.92822967],\n       [ 0.13333333,  0.86666667],\n       [ 0.99435028,  0.00564972],\n       [ 0.01142857,  0.98857143],\n       [ 1.        ,  0.        ],\n       [ 0.43820225,  0.56179775],\n       [ 0.11290323,  0.88709677],\n       [ 0.5875    ,  0.4125    ],\n       [ 0.60752688,  0.39247312],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.58125   ,  0.41875   ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.18032787,  0.81967213],\n       [ 0.8150289 ,  0.1849711 ],\n       [ 0.07216495,  0.92783505],\n       [ 1.        ,  0.        ],\n       [ 0.84444444,  0.15555556],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.07772021,  0.92227979],\n       [ 0.02061856,  0.97938144],\n       [ 0.        ,  1.        ],\n       [ 0.99473684,  0.00526316],\n       [ 0.9076087 ,  0.0923913 ],\n       [ 0.15517241,  0.84482759],\n       [ 0.96174863,  0.03825137],\n       [ 0.00578035,  0.99421965],\n       [ 0.60487805,  0.39512195],\n       [ 0.03553299,  0.96446701],\n       [ 0.98958333,  0.01041667],\n       [ 0.82795699,  0.17204301],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.96174863,  0.03825137],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.32960894,  0.67039106],\n       [ 0.98907104,  0.01092896],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 0.83240223,  0.16759777],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.79213483,  0.20786517],\n       [ 0.94565217,  0.05434783],\n       [ 1.        ,  0.        ],\n       [ 0.73224044,  0.26775956],\n       [ 0.57142857,  0.42857143],\n       [ 0.        ,  1.        ],\n       [ 0.91666667,  0.08333333],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.89820359,  0.10179641],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.77272727,  0.22727273],\n       [ 0.14371257,  0.85628743],\n       [ 0.52348993,  0.47651007],\n       [ 0.26701571,  0.73298429],\n       [ 0.        ,  1.        ],\n       [ 0.86486486,  0.13513514],\n       [ 0.83536585,  0.16463415],\n       [ 0.00591716,  0.99408284],\n       [ 1.        ,  0.        ],\n       [ 0.99431818,  0.00568182],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.02659574,  0.97340426],\n       [ 0.96067416,  0.03932584],\n       [ 0.95767196,  0.04232804],\n       [ 1.        ,  0.        ],\n       [ 0.47928994,  0.52071006],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.99459459,  0.00540541],\n       [ 0.03157895,  0.96842105],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 0.96601942,  0.03398058],\n       [ 0.        ,  1.        ],\n       [ 0.04651163,  0.95348837],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 1.        ,  0.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.02040816,  0.97959184],\n       [ 1.        ,  0.        ],\n       [ 0.13917526,  0.86082474],\n       [ 0.        ,  1.        ],\n       [ 0.01734104,  0.98265896],\n       [ 0.        ,  1.        ],\n       [ 0.40952381,  0.59047619],\n       [ 0.06818182,  0.93181818],\n       [ 0.23195876,  0.76804124],\n       [ 1.        ,  0.        ],\n       [ 0.98795181,  0.01204819],\n       [ 0.20430108,  0.79569892],\n       [ 0.99438202,  0.00561798],\n       [ 0.        ,  1.        ],\n       [ 0.        ,  1.        ],\n       [ 1.        ,  0.        ],\n       [ 0.97311828,  0.02688172],\n       [ 0.31460674,  0.68539326],\n       [ 0.98870056,  0.01129944],\n       [ 1.        ,  0.        ],\n       [ 0.00581395,  0.99418605],\n       [ 0.99009901,  0.00990099],\n       [ 0.        ,  1.        ],\n       [ 0.0304878 ,  0.9695122 ],\n       [ 0.97849462,  0.02150538],\n       [ 1.        ,  0.        ],\n       [ 0.03508772,  0.96491228],\n       [ 0.64864865,  0.35135135]])
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Random Patches - Random Subspaces

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  • BaggingClassifier supports feature sampling. Params: max_features and bootstrap.
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  • Very useful when handling high-dimensional datasets.
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  • \"Random patches\": sampling features & sampling instances.
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  • \"Random subspaces\": sampling features & keeping all instances.
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Random Forests

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  • RF = ensemble of Decision Trees
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  • RandomForestClassifier: designed for DT classification
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  • RandomForestRegressor: designed for regression
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In [12]:
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# Train an RF classifier with 500 trees limited to 16 max nodes each.\n# splitter="random": tells RF to search for best feature among\n# a random subset of features.\n\nbag_clf = BaggingClassifier(\n    DecisionTreeClassifier(\n        splitter="random", \n        max_leaf_nodes=16, \n        random_state=42),\n    \n    n_estimators=500, \n    max_samples=1.0, \n    bootstrap=True,\n    n_jobs=-1,\n    random_state=42)\n\nbag_clf.fit(X_train, y_train)\ny_pred = bag_clf.predict(X_test)\n
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In [13]:
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from sklearn.ensemble import RandomForestClassifier\n\nrnd_clf = RandomForestClassifier(\n    n_estimators=500, \n    max_leaf_nodes=16, \n    n_jobs=-1, \n    random_state=42)\n\nrnd_clf.fit(X_train, y_train)\ny_pred_rf = rnd_clf.predict(X_test)\n
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In [14]:
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# almost identical predictions\nnp.sum(y_pred == y_pred_rf) / len(y_pred)\n
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Out[14]:
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0.97599999999999998
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Feature importance

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  • important features likely to appear closer to root of tree
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  • unimportant features likely to appear closer to leaves - if at all.
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  • Scikit finds avg depth of feature appearance across all trees in an RF.
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In [15]:
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# rank features by importance in iris\n# #1: petal length: 44%\n\nfrom sklearn.datasets import load_iris\niris = load_iris()\n\nrnd_clf = RandomForestClassifier(\n    n_estimators=500, \n    n_jobs=-1, \n    random_state=42)\n\nrnd_clf.fit(iris["data"], iris["target"])\n\nfor name, importance in zip(\n    iris["feature_names"], \n    rnd_clf.feature_importances_):\n        print(name, "=", importance)\n
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sepal length (cm) = 0.112492250999\nsepal width (cm) = 0.0231192882825\npetal length (cm) = 0.441030464364\npetal width (cm) = 0.423357996355\n
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In [16]:
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rnd_clf.feature_importances_\n
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Out[16]:
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array([ 0.11249225,  0.02311929,  0.44103046,  0.423358  ])
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In [17]:
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plt.figure(figsize=(6, 4))\n\nfor i in range(15):\n    tree_clf = DecisionTreeClassifier(\n        max_leaf_nodes=16, \n        random_state=42+i)\n    \n    indices_with_replacement = rnd.randint(\n        0, \n        len(X_train), \n        len(X_train))\n    \n    tree_clf.fit(\n        X[indices_with_replacement], \n        y[indices_with_replacement])\n    \n    plot_decision_boundary(\n        tree_clf, X, y, \n        axes=[-1.5, 2.5, -1, 1.5], \n        alpha=0.02, \n        contour=False)\n\nplt.show()\n
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Boosting - AdaBoost

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  • One strategy: pay more attention to training instances that predecessor underfitted - forces new predictors to concentrate more on the \"hard cases\".
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  • Disadvantage: results depend on previous classifier (sequential), so algo cannot be parallelized. Not great for scaling.
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In [18]:
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# Plot decision boundaries of five predictors on moons dataset\n\nm = len(X_train)\n\nplt.figure(figsize=(11, 4))\nfor subplot, learning_rate in ((121, 1), (122, 0.5)):\n    sample_weights = np.ones(m)\n    for i in range(5):\n        plt.subplot(subplot)\n        \n        svm_clf = SVC(\n            kernel="rbf", \n            C=0.05)\n        \n        svm_clf.fit(\n            X_train, y_train, \n            sample_weight=sample_weights)\n        \n        y_pred = svm_clf.predict(\n            X_train)\n        \n        sample_weights[y_pred != y_train] *= (1 + learning_rate)\n        \n        plot_decision_boundary(\n            svm_clf, \n            X, y, \n            alpha=0.2)\n        \n        plt.title("learning_rate = {}".format(learning_rate - 1), \n                  fontsize=16)\n\nplt.subplot(121)\nplt.text(-0.7, -0.65, "1", fontsize=14)\nplt.text(-0.6, -0.10, "2", fontsize=14)\nplt.text(-0.5,  0.10, "3", fontsize=14)\nplt.text(-0.4,  0.55, "4", fontsize=14)\nplt.text(-0.3,  0.90, "5", fontsize=14)\n#save_fig("boosting_plot")\nplt.show()\n\n# left: 1st clf gets many wrong, so 2nd clf gets boosted values.\n# right: same sequence, but learning rate cut in half.\n
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In [19]:
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# train AdaBoost classifier on 200 decision stumps (DS)\n# DS = decision tree with max_depth=1\n\nfrom sklearn.ensemble import AdaBoostClassifier\n\nada_clf = AdaBoostClassifier(\n        DecisionTreeClassifier(max_depth=1), n_estimators=200,\n        algorithm="SAMME.R", learning_rate=0.5, random_state=42\n    )\nada_clf.fit(X_train, y_train)\nplot_decision_boundary(ada_clf, X, y)\nplt.show()\n
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Boosting - Gradient Boosting

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  • Similar to AdaBoost (continually correcting the predecessors in an ensemble. Instead of tweaking instance weights on each iteration, GB fits the predictor to the residual errors of the previous predictor.
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In [20]:
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from sklearn.tree import DecisionTreeRegressor\n\n# training set: a noisy quadratic function\nrnd.seed(42)\nX = rnd.rand(100, 1) - 0.5\ny = 3*X[:, 0]**2 + 0.05 * rnd.randn(100)\n\n# train Regressor\ntree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\ntree_reg1.fit(X, y)\n\n# now train 2nd Regressor using errors made by 1st one.\ny2 = y - tree_reg1.predict(X)\ntree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\ntree_reg2.fit(X, y2)\n\n# now train 3rd Regressor using errors made by 2nd one.\ny3 = y2 - tree_reg2.predict(X)\ntree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\ntree_reg3.fit(X, y3)\n\nX_new = np.array([[0.8]])\n\n# now have ensemble w/ three trees.\ny_pred = sum(tree.predict(X_new) for tree in (\n    tree_reg1, tree_reg2, tree_reg3))\n\nprint(y_pred)\n
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[ 0.75026781]\n
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In [21]:
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def plot_predictions(\n    regressors, X, y, axes, \n    label=None, \n    style="r-", \n    data_style="b.", \n    data_label=None):\n    \n    x1 = np.linspace(axes[0], axes[1], 500)\n    \n    y_pred = sum(\n        regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n            \n    plt.plot(X[:, 0], y, data_style, label=data_label)\n    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n    if label or data_label:\n        plt.legend(loc="upper center", fontsize=16)\n    plt.axis(axes)\n\nplt.figure(figsize=(11,11))\n\nplt.subplot(321)\nplot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label="$h_1(x_1)$", style="g-", data_label="Training set")\nplt.ylabel("$y$", fontsize=16, rotation=0)\nplt.title("Residuals and tree predictions", fontsize=16)\n\nplt.subplot(322)\nplot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label="$h(x_1) = h_1(x_1)$", data_label="Training set")\nplt.ylabel("$y$", fontsize=16, rotation=0)\nplt.title("Ensemble predictions", fontsize=16)\n\nplt.subplot(323)\nplot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label="$h_2(x_1)$", style="g-", data_style="k+", data_label="Residuals")\nplt.ylabel("$y - h_1(x_1)$", fontsize=16)\n\nplt.subplot(324)\nplot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label="$h(x_1) = h_1(x_1) + h_2(x_1)$")\nplt.ylabel("$y$", fontsize=16, rotation=0)\n\nplt.subplot(325)\nplot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label="$h_3(x_1)$", style="g-", data_style="k+")\nplt.ylabel("$y - h_1(x_1) - h_2(x_1)$", fontsize=16)\nplt.xlabel("$x_1$", fontsize=16)\n\nplt.subplot(326)\nplot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label="$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$")\nplt.xlabel("$x_1$", fontsize=16)\nplt.ylabel("$y$", fontsize=16, rotation=0)\n\n#save_fig("gradient_boosting_plot")\nplt.show()\n\n# 1st row: ensemble = only one tree: predictions match 1st tree.\n# 2nd row: new tree trained on residual errors of 1st tree.\n# 3rd row: "                                              "\n# result: ensemble predictions get better as trees are added.\n
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  • learning_rate param controls contribution of each tree. Low values (ex: 0.1) = need more trees in ensemble to fit training set, but predictions usually generalize better. (This is called shrinkage.)
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In [22]:
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# two GBRT ensembles trained with low learning rate\n\nfrom sklearn.ensemble import GradientBoostingRegressor\n\ngbrt = GradientBoostingRegressor(\n    max_depth=2, \n    n_estimators=3, \n    learning_rate=0.1, \n    random_state=42)\n\ngbrt.fit(X, y)\n\ngbrt_slow = GradientBoostingRegressor(\n    max_depth=2, \n    n_estimators=200, \n    learning_rate=0.1, \n    random_state=42)\n\ngbrt_slow.fit(X, y)\n\nplt.figure(figsize=(11,4))\n\nplt.subplot(121)\nplot_predictions(\n    [gbrt], X, y, \n    axes=[-0.5, 0.5, -0.1, 0.8], \n    label="Ensemble predictions")\nplt.title("learning_rate={}, n_estimators={}".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\n\nplt.subplot(122)\nplot_predictions(\n    [gbrt_slow], X, y, \n    axes=[-0.5, 0.5, -0.1, 0.8])\nplt.title("learning_rate={}, n_estimators={}".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\n\n#save_fig("gbrt_learning_rate_plot")\nplt.show()\n\n# left: not enough trees (underfits)\n# right: too many trees (overfits)\n
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  • To find optimal number of trees - use early stopping method.
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  • staged_predict method: returns iterator
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In [23]:
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from sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\n\nX_train, X_val, y_train, y_val = train_test_split(X, y)\n\n# train GRBR regressor with 120 trees\n\ngbrt = GradientBoostingRegressor(\n    max_depth=2, \n    n_estimators=120, \n    learning_rate=0.1, \n    random_state=42)\n\ngbrt.fit(X_train, y_train)\n\n# measure MSE validation error at each stage\nerrors = [mean_squared_error(y_val, y_pred) for y_pred in gbrt.staged_predict(X_val)]\nerrors\n
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Out[23]:
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[0.05877146809545241,\n 0.050146609664278821,\n 0.042693525239940654,\n 0.036758764317358611,\n 0.032342621749728441,\n 0.028407668512271105,\n 0.024897554253370889,\n 0.022344405311247584,\n 0.019535997367701449,\n 0.017423553892941333,\n 0.015298227412102105,\n 0.013614891608372095,\n 0.01241865401978786,\n 0.01114950733723946,\n 0.010131360091843384,\n 0.0091854704682465919,\n 0.0085684302891776056,\n 0.0078525358395017328,\n 0.0072105819722258777,\n 0.0067708705683962693,\n 0.0062415649764643415,\n 0.0058360573276457243,\n 0.0053862983457847987,\n 0.0051345071507873903,\n 0.0048692096567381805,\n 0.0045993749990593299,\n 0.0043550054844811968,\n 0.0041542481413648245,\n 0.0039794595160053785,\n 0.0038058301746231277,\n 0.0036528925611761264,\n 0.0035903310836105469,\n 0.0035078898256137104,\n 0.0034145667924260869,\n 0.0033091498103360911,\n 0.0032216349333429491,\n 0.0031684358902285465,\n 0.0031067035318094903,\n 0.0030811367114601672,\n 0.0030602631146299077,\n 0.003000040093686018,\n 0.0029246869254349805,\n 0.0028559321605494477,\n 0.0028308419421558683,\n 0.0028218777360194264,\n 0.0027941065824977074,\n 0.0027733228935542496,\n 0.0027805517665357811,\n 0.0027523772234700978,\n 0.0027297064654860348,\n 0.0027248578787871292,\n 0.0027111390401517179,\n 0.0027041926119007326,\n 0.0026930464329994377,\n 0.0027047076934144398,\n 0.0027194180251317295,\n 0.0027010027055809748,\n 0.0026976053707465464,\n 0.0026946405089738347,\n 0.0026713744909731395,\n 0.0026633491003786457,\n 0.0026694977341077202,\n 0.0026594592750579836,\n 0.0026425819418378605,\n 0.0026524409142755744,\n 0.0026418897165154491,\n 0.0026483360802177103,\n 0.0026456393608631189,\n 0.0026465080389023671,\n 0.0026396693211148074,\n 0.002649273120700455,\n 0.002643721514468783,\n 0.0026463988198929221,\n 0.0026333618213948747,\n 0.0026314011519099879,\n 0.0026349113355268257,\n 0.0026387528659342825,\n 0.0026345585421650142,\n 0.0026355886319374901,\n 0.0026310345391991532,\n 0.0026519658939712061,\n 0.0026467700098620557,\n 0.00264498239665715,\n 0.0026475491456891486,\n 0.0026474836942911913,\n 0.0026530458155365681,\n 0.0026478335004093052,\n 0.0026564768881028435,\n 0.0026574608795571115,\n 0.0026537575609276061,\n 0.0026559108292476983,\n 0.0026528848367343987,\n 0.0026533895549644779,\n 0.0026520896622857252,\n 0.0026416985817433059,\n 0.0026497886163651938,\n 0.0026430582537166087,\n 0.0026548742317473117,\n 0.002660275592603878,\n 0.0026582571161537366,\n 0.0026570823709750535,\n 0.0026557538081706522,\n 0.002675470519360824,\n 0.0026762761989050578,\n 0.0026742086578626454,\n 0.0026957941482744232,\n 0.0026964801899977998,\n 0.0026939578807501376,\n 0.0026959742963617757,\n 0.0026949319702616616,\n 0.0026988916344244736,\n 0.0027169473218451121,\n 0.0027148017926961689,\n 0.0027192710134859655,\n 0.0027358435370699618,\n 0.0027346474658663323,\n 0.0027351047440069571,\n 0.0027459941366245631,\n 0.0027441324932851491,\n 0.002756368378237764]
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In [24]:
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# train another GBRT ensemble using optimal #trees\n\nbest_n_estimators = np.argmin(errors)\nmin_error = errors[best_n_estimators]\n\ngbrt_best = GradientBoostingRegressor(\n    max_depth=2, \n    n_estimators=best_n_estimators, \n    learning_rate=0.1, \n    random_state=42)\n\ngbrt_best.fit(X_train, y_train)\n
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Out[24]:
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GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n             learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\n             max_leaf_nodes=None, min_impurity_split=1e-07,\n             min_samples_leaf=1, min_samples_split=2,\n             min_weight_fraction_leaf=0.0, n_estimators=79, presort='auto',\n             random_state=42, subsample=1.0, verbose=0, warm_start=False)
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In [25]:
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plt.figure(figsize=(11, 4))\n\nplt.subplot(121)\nplt.plot(errors, "b.-")\nplt.plot([best_n_estimators, best_n_estimators], [0, min_error], "k--")\nplt.plot([0, 120], [min_error, min_error], "k--")\nplt.plot(best_n_estimators, min_error, "ko")\nplt.text(best_n_estimators, min_error*1.2, "Minimum", ha="center", fontsize=14)\nplt.axis([0, 120, 0, 0.01])\nplt.xlabel("Number of trees")\nplt.title("Validation error", fontsize=14)\n\nplt.subplot(122)\nplot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\nplt.title("Best model (55 trees)", fontsize=14)\n\n#save_fig("early_stopping_gbrt_plot")\nplt.show()\n
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  • Another method: actually stopping training early
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In [26]:
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gbrt = GradientBoostingRegressor(\n    max_depth=2, \n    n_estimators=1, \n    learning_rate=0.1, \n    random_state=42, \n    warm_start=True)\n\nmin_val_error = float("inf")\nerror_going_up = 0\n\n# 120 estimators.\n# stop training with validation error doesn't improve for\n# five consecutive iterations\n\nfor n_estimators in range(1, 120):\n    gbrt.n_estimators = n_estimators\n    gbrt.fit(X_train, y_train)\n    y_pred = gbrt.predict(X_val)\n    val_error = mean_squared_error(y_val, y_pred)\n\n    if val_error < min_val_error:\n        min_val_error = val_error\n        error_going_up = 0\n    else:\n        error_going_up += 1\n        if error_going_up == 5:\n            break  # early stopping\n            \nprint(gbrt.n_estimators)\n
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Stacking

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  • Instead of using a voting function to aggregate an ensemble's predictor outputs, instead train a model to do the aggregation. (\"blending\".)
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In [27]:
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# todo: stacking implementation\n
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In [ ]:
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\n\n\n" }, { "path": "ch07-ensemble-learning.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import numpy.random as rnd\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Voting Classifiers\\n\",\n \"\\n\",\n \"* Good classifiers can be built by aggregating predictions of various *weaker* classifiers, and returning the class that gets the most votes. (A \\\"hard voting\\\" classifier.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"heads_proba = 0.51\\n\",\n \"coin_tosses = (rnd.rand(10000, 10) < heads_proba).astype(np.int32)\\n\",\n \"cumulative_heads_ratio = np.cumsum(\\n\",\n \" coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)\\n\",\n \"#cumulative_heads_ratio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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eYI064KgoicLiI7RGS3iNwTIH+iiJSLyAb364Em+WYRWS8iXxwRgbpP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\\nzMza+H+QDz0zi2bafwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,3.5))\\n\",\n \"plt.plot(cumulative_heads_ratio)\\n\",\n \"plt.plot([0, 10000], [0.51, 0.51], \\\"k--\\\", linewidth=2, label=\\\"51%\\\")\\n\",\n \"plt.plot([0, 10000], [0.5, 0.5], \\\"k-\\\", label=\\\"50%\\\")\\n\",\n \"plt.xlabel(\\\"Number of coin tosses\\\")\\n\",\n \"plt.ylabel(\\\"Heads ratio\\\")\\n\",\n \"plt.legend(loc=\\\"lower right\\\")\\n\",\n \"plt.title(\\\"The law of large numbers:\\\")\\n\",\n \"plt.axis([0, 10000, 0.42, 0.58])\\n\",\n \"#save_fig(\\\"law_of_large_numbers_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"LogisticRegression 0.864\\n\",\n \"RandomForestClassifier 0.872\\n\",\n \"SVC 0.888\\n\",\n \"VotingClassifier 0.912\\n\"\n ]\n }\n ],\n \"source\": [\n \"# build a voting classifier in Scikit using three weaker classifiers\\n\",\n \"\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.datasets import make_moons\\n\",\n \"\\n\",\n \"# use moons dataset\\n\",\n \"X, y = make_moons(\\n\",\n \" n_samples=500, \\n\",\n \" noise=0.30, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(\\n\",\n \" X, y, random_state=42)\\n\",\n \"\\n\",\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"from sklearn.ensemble import VotingClassifier\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.svm import SVC\\n\",\n \"\\n\",\n \"log_clf = LogisticRegression(random_state=42)\\n\",\n \"rnd_clf = RandomForestClassifier(random_state=42)\\n\",\n \"svm_clf = SVC(probability=True, random_state=42)\\n\",\n \"\\n\",\n \"# voting classifier = logistic + random forest + SVC\\n\",\n \"\\n\",\n \"voting_clf = VotingClassifier(\\n\",\n \" estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\\n\",\n \" voting='soft'\\n\",\n \" )\\n\",\n \"voting_clf.fit(X_train, y_train)\\n\",\n \"\\n\",\n \"# let's see how each individual classifier did:\\n\",\n \"\\n\",\n \"from sklearn.metrics import accuracy_score\\n\",\n \"\\n\",\n \"for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\\n\",\n \" clf.fit(X_train, y_train)\\n\",\n \" y_pred = clf.predict(X_test)\\n\",\n \" print(clf.__class__.__name__, accuracy_score(y_test, y_pred))\\n\",\n \" \\n\",\n \"# voting classifier did better than 3 individual ones!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* If all classifiers can estimate class probabilities (they have a predict_proba() method), use Scikit to predict highest class probability, averaged over all individual classifiers. (*soft voting*)\\n\",\n \"\\n\",\n \"* Often better than hard voting because it gives more weight to highly confident votes. Replace voting=\\\"hard\\\" with \\\"soft\\\" & ensure all classifiers can estimate class probabilities. (SVC cannot by default -set probability param to True.)\\n\",\n \"\\n\",\n \"* This tells SVC to use cross-validation to estimate class probabilities. Slows training times & adds a predict_proba() method).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Bagging & Pasting\\n\",\n \"\\n\",\n \"* Another approach: use same training algorithm, but apply it to different subsets of the training dataset.\\n\",\n \"* **bagging**: sampling the dataset **with** replacement.\\n\",\n \"* **pasting**: sampling the dataset **without** replacement.\\n\",\n \"* Final prediction = based on an aggregation function.\\n\",\n \"* Predictions can be made in parallel -- good scaling properties.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.904\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.datasets import make_moons\\n\",\n \"from sklearn.ensemble import BaggingClassifier\\n\",\n \"from sklearn.metrics import accuracy_score\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"\\n\",\n \"# Train ensemble of 500 Decision Tree classifiers\\n\",\n \"# each using 100 training instances - randomly sampled from training set\\n\",\n \"# with replacement.\\n\",\n \"\\n\",\n \"bag_clf = BaggingClassifier(\\n\",\n \" DecisionTreeClassifier(random_state=42), \\n\",\n \" n_estimators=500,\\n\",\n \" max_samples=100, \\n\",\n \" bootstrap=True, # set to False for pasting instead of bagging.\\n\",\n \" n_jobs=-1, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"bag_clf.fit(X_train, y_train)\\n\",\n \"y_pred = bag_clf.predict(X_test)\\n\",\n \"print(accuracy_score(y_test, y_pred))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.856\\n\"\n ]\n }\n ],\n \"source\": [\n \"tree_clf = DecisionTreeClassifier(random_state=42)\\n\",\n \"tree_clf.fit(X_train, y_train)\\n\",\n \"y_pred_tree = tree_clf.predict(X_test)\\n\",\n \"print(accuracy_score(y_test, y_pred_tree))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from matplotlib.colors import ListedColormap\\n\",\n \"\\n\",\n \"def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\\n\",\n \" x1s = np.linspace(axes[0], axes[1], 100)\\n\",\n \" x2s = np.linspace(axes[2], axes[3], 100)\\n\",\n \" x1, x2 = np.meshgrid(x1s, x2s)\\n\",\n \" X_new = np.c_[x1.ravel(), x2.ravel()]\\n\",\n \" y_pred = clf.predict(X_new).reshape(x1.shape)\\n\",\n \" custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\\n\",\n \" plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap, linewidth=10)\\n\",\n \" if contour:\\n\",\n \" custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\\n\",\n \" plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\\n\",\n \" plt.plot(X[:, 0][y==0], X[:, 1][y==0], \\\"yo\\\", alpha=alpha)\\n\",\n \" plt.plot(X[:, 0][y==1], X[:, 1][y==1], \\\"bs\\\", alpha=alpha)\\n\",\n \" plt.axis(axes)\\n\",\n \" plt.xlabel(r\\\"$x_1$\\\", fontsize=18)\\n\",\n \" plt.ylabel(r\\\"$x_2$\\\", fontsize=18, rotation=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Aepq5jF8DXqRJRnJLIbwwiRzZ7CtmcxzSiBwDZCoQvb7pem8z50Gs1a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\\nqiAIgiAIguA5/j8s+egymsv++QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(11,4))\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_decision_boundary(tree_clf, X, y)\\n\",\n \"plt.title(\\\"Decision Tree\\\", fontsize=14)\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_decision_boundary(bag_clf, X, y)\\n\",\n \"plt.title(\\\"Decision Trees with Bagging\\\", fontsize=14)\\n\",\n \"#save_fig(\\\"decision_tree_without_and_with_bagging_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Out of Bag Evaluation\\n\",\n \"* Bagging: some instances may be sampled multiple times - others not at all. On avg, ~63% of training samples are used. Remainder 37% = \\\"out of bag\\\".\\n\",\n \"* use *oob_score=True* in Scikit to do automatic oob evaluation after training.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.89866666666666661\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# oob_score_: predicts classifier results on test set.\\n\",\n \"bag_clf = BaggingClassifier(\\n\",\n \" DecisionTreeClassifier(),\\n\",\n \" n_estimators=500,\\n\",\n \" bootstrap=True,\\n\",\n \" n_jobs=-1,\\n\",\n \" oob_score=True\\n\",\n \")\\n\",\n \"bag_clf.fit(X_train, y_train)\\n\",\n \"bag_clf.oob_score_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.90400000000000003\"\n ]\n },\n \"execution_count\": 10,\n 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0.02150538],\\n\",\n \" [ 1. , 0. ],\\n\",\n \" [ 0.03508772, 0.96491228],\\n\",\n \" [ 0.64864865, 0.35135135]])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# oob decision functionfor each training instance\\n\",\n \"bag_clf.oob_decision_function_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Random Patches - Random Subspaces\\n\",\n \"\\n\",\n \"* **BaggingClassifier** supports feature sampling. Params: *max_features* and *bootstrap*. \\n\",\n \"* Very useful when handling high-dimensional datasets.\\n\",\n \"* \\\"Random patches\\\": sampling features & sampling instances.\\n\",\n \"* \\\"Random subspaces\\\": sampling features & keeping all instances.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Random Forests\\n\",\n \"* RF = ensemble of Decision Trees\\n\",\n \"* Typically trained via bagging\\n\",\n \"* **RandomForestClassifier**: designed for DT classification\\n\",\n \"* **RandomForestRegressor**: designed for regression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Train an RF classifier with 500 trees limited to 16 max nodes each.\\n\",\n \"# splitter=\\\"random\\\": tells RF to search for best feature among\\n\",\n \"# a random subset of features.\\n\",\n \"\\n\",\n \"bag_clf = BaggingClassifier(\\n\",\n \" DecisionTreeClassifier(\\n\",\n \" splitter=\\\"random\\\", \\n\",\n \" max_leaf_nodes=16, \\n\",\n \" random_state=42),\\n\",\n \" \\n\",\n \" n_estimators=500, \\n\",\n \" max_samples=1.0, \\n\",\n \" bootstrap=True,\\n\",\n \" n_jobs=-1,\\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"bag_clf.fit(X_train, y_train)\\n\",\n \"y_pred = bag_clf.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"\\n\",\n \"rnd_clf = RandomForestClassifier(\\n\",\n \" n_estimators=500, \\n\",\n \" max_leaf_nodes=16, \\n\",\n \" n_jobs=-1, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"rnd_clf.fit(X_train, y_train)\\n\",\n \"y_pred_rf = rnd_clf.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.97599999999999998\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# almost identical predictions\\n\",\n \"np.sum(y_pred == y_pred_rf) / len(y_pred)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Feature importance\\n\",\n \"* important features likely to appear closer to root of tree\\n\",\n \"* unimportant features likely to appear closer to leaves - if at all.\\n\",\n \"* Scikit finds avg depth of feature appearance across all trees in an RF.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sepal length (cm) = 0.112492250999\\n\",\n \"sepal width (cm) = 0.0231192882825\\n\",\n \"petal length (cm) = 0.441030464364\\n\",\n \"petal width (cm) = 0.423357996355\\n\"\n ]\n }\n ],\n \"source\": [\n \"# rank features by importance in iris\\n\",\n \"# #1: petal length: 44%\\n\",\n \"\\n\",\n \"from sklearn.datasets import load_iris\\n\",\n \"iris = load_iris()\\n\",\n \"\\n\",\n \"rnd_clf = RandomForestClassifier(\\n\",\n \" n_estimators=500, \\n\",\n \" n_jobs=-1, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"rnd_clf.fit(iris[\\\"data\\\"], iris[\\\"target\\\"])\\n\",\n \"\\n\",\n \"for name, importance in zip(\\n\",\n \" iris[\\\"feature_names\\\"], \\n\",\n \" rnd_clf.feature_importances_):\\n\",\n \" print(name, \\\"=\\\", importance)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0.11249225, 0.02311929, 0.44103046, 0.423358 ])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"rnd_clf.feature_importances_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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9GohvFVjcJJf5QwOrzG8AIg7D8fYhBcdEGyjtqa1rEYHAtxCmjWKJQm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\\n9WIjkST1YiORJPXyD+FxqYxxJhBjAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(6, 4))\\n\",\n \"\\n\",\n \"for i in range(15):\\n\",\n \" tree_clf = DecisionTreeClassifier(\\n\",\n \" max_leaf_nodes=16, \\n\",\n \" random_state=42+i)\\n\",\n \" \\n\",\n \" indices_with_replacement = rnd.randint(\\n\",\n \" 0, \\n\",\n \" len(X_train), \\n\",\n \" len(X_train))\\n\",\n \" \\n\",\n \" tree_clf.fit(\\n\",\n \" X[indices_with_replacement], \\n\",\n \" y[indices_with_replacement])\\n\",\n \" \\n\",\n \" plot_decision_boundary(\\n\",\n \" tree_clf, X, y, \\n\",\n \" axes=[-1.5, 2.5, -1, 1.5], \\n\",\n \" alpha=0.02, \\n\",\n \" contour=False)\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Boosting - AdaBoost\\n\",\n \"* One strategy: pay more attention to training instances that predecessor underfitted - forces new predictors to concentrate more on the \\\"hard cases\\\".\\n\",\n \"* **Disadvantage**: results depend on previous classifier (sequential), so algo cannot be parallelized. Not great for scaling.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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3GcFKbpxzTLJ1THSPz+alx3cKg9AK47iN+vVznnMqlUmoEBF3yec/81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\\nRQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot decision boundaries of five predictors on moons dataset\\n\",\n \"\\n\",\n \"m = len(X_train)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11, 4))\\n\",\n \"for subplot, learning_rate in ((121, 1), (122, 0.5)):\\n\",\n \" sample_weights = np.ones(m)\\n\",\n \" for i in range(5):\\n\",\n \" plt.subplot(subplot)\\n\",\n \" \\n\",\n \" svm_clf = SVC(\\n\",\n \" kernel=\\\"rbf\\\", \\n\",\n \" C=0.05)\\n\",\n \" \\n\",\n \" svm_clf.fit(\\n\",\n \" X_train, y_train, \\n\",\n \" sample_weight=sample_weights)\\n\",\n \" \\n\",\n \" y_pred = svm_clf.predict(\\n\",\n \" X_train)\\n\",\n \" \\n\",\n \" sample_weights[y_pred != y_train] *= (1 + learning_rate)\\n\",\n \" \\n\",\n \" plot_decision_boundary(\\n\",\n \" svm_clf, \\n\",\n \" X, y, \\n\",\n \" alpha=0.2)\\n\",\n \" \\n\",\n \" plt.title(\\\"learning_rate = {}\\\".format(learning_rate - 1), \\n\",\n \" fontsize=16)\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.text(-0.7, -0.65, \\\"1\\\", fontsize=14)\\n\",\n \"plt.text(-0.6, -0.10, \\\"2\\\", fontsize=14)\\n\",\n \"plt.text(-0.5, 0.10, \\\"3\\\", fontsize=14)\\n\",\n \"plt.text(-0.4, 0.55, \\\"4\\\", fontsize=14)\\n\",\n \"plt.text(-0.3, 0.90, \\\"5\\\", fontsize=14)\\n\",\n \"#save_fig(\\\"boosting_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# left: 1st clf gets many wrong, so 2nd clf gets boosted values.\\n\",\n \"# right: same sequence, but learning rate cut in half.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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/6f2z631wXJBWB71d/HS595rALV2kEud4pi7SpJMLgXny9GoTCHlBCJ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\\nRFEURakJVSSKoihKTfx/YZjHeZyt/a0AAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# train AdaBoost classifier on 200 decision stumps (DS)\\n\",\n \"# DS = decision tree with max_depth=1\\n\",\n \"\\n\",\n \"from sklearn.ensemble import AdaBoostClassifier\\n\",\n \"\\n\",\n \"ada_clf = AdaBoostClassifier(\\n\",\n \" DecisionTreeClassifier(max_depth=1), n_estimators=200,\\n\",\n \" algorithm=\\\"SAMME.R\\\", learning_rate=0.5, random_state=42\\n\",\n \" )\\n\",\n \"ada_clf.fit(X_train, y_train)\\n\",\n \"plot_decision_boundary(ada_clf, X, y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Boosting - Gradient Boosting\\n\",\n \"* Similar to AdaBoost (continually correcting the predecessors in an ensemble. Instead of tweaking instance weights on each iteration, GB fits the predictor to the *residual errors* of the previous predictor.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.75026781]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.tree import DecisionTreeRegressor\\n\",\n \"\\n\",\n \"# training set: a noisy quadratic function\\n\",\n \"rnd.seed(42)\\n\",\n \"X = rnd.rand(100, 1) - 0.5\\n\",\n \"y = 3*X[:, 0]**2 + 0.05 * rnd.randn(100)\\n\",\n \"\\n\",\n \"# train Regressor\\n\",\n \"tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\\n\",\n \"tree_reg1.fit(X, y)\\n\",\n \"\\n\",\n \"# now train 2nd Regressor using errors made by 1st one.\\n\",\n \"y2 = y - tree_reg1.predict(X)\\n\",\n \"tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\\n\",\n \"tree_reg2.fit(X, y2)\\n\",\n \"\\n\",\n \"# now train 3rd Regressor using errors made by 2nd one.\\n\",\n \"y3 = y2 - tree_reg2.predict(X)\\n\",\n \"tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\\n\",\n \"tree_reg3.fit(X, y3)\\n\",\n \"\\n\",\n \"X_new = np.array([[0.8]])\\n\",\n \"\\n\",\n \"# now have ensemble w/ three trees.\\n\",\n \"y_pred = sum(tree.predict(X_new) for tree in (\\n\",\n \" tree_reg1, tree_reg2, tree_reg3))\\n\",\n \"\\n\",\n \"print(y_pred)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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3+y2inAk2b2a/yZ/p3x/cW+gT8ZJFPf057A/WZ2I/5M/8n4kxFuz+K1\\nZfv8v8H/GHvc/NA0sR9p2ezJ+DG+EG0OnmMx8CX8iXGxYXvmAPuY2aH4Q3LLnHMLkyzrEXyBfEPw\\nI2w2/v38NjA5yx8A7czsJnwfxWZ8d5Ad8D/UHk/3OKkZI+K6BcSbmdCHNS0zewD/3XoRv/duJL7/\\n+o0Azrm3zewq4Brzwzr9C3+EZ2v8oeab488TCMHXgxx8HN+d6+f4I2JvJZvZOefM7CzggaD/71/x\\n24j++Fx+zzl3dYbn/Bz4RfB+vgUcD+yHP/Es2dGjnJ8/x21f4nM8ZWb3AFcHP9z/iT+PYF/gIefc\\nNDZcvOgsM7sNv9Pl1SRdoMCPFPMQ8KCZXYc/3+QS/GhHv87Unnj51AGCRjko9Eb6syBjnc7jRwUY\\nhD9Dewl+D9MH+F/rJwT3bwHchj/JaRX+UMW/gAPjljGWuFEOgmn1+CLuQ3zRMQ0/NFOHMzGDeY/A\\nDwGzGh+6B5AwygF+r9jl+KJ0VdCGkfgi/NYkr39I8Pch+L6JH+CLoEX4flBbZXgf++I78n8a3O7E\\nB1Pimbe3AouTPL5D+4Np+wEvBe1YAJxBilEekizvCHyYrY9vQ/A8z6Z4TB3ww+A9XYMPslfwQ0dt\\nGjdfV3xx/UbQtk/wJ5FMIu4M2STLHxK05Xv4w5dLg8/mIWDbhHkXkmQUgVyeH3+SwzPBa1mCD+xL\\nyDDKQTBtJP4Ess+C9ewNfFEfu3/HYNmrgsffGkyflGT5vfCjE3yA/87Mww+JZEm+E/ul+H7G1s+T\\ngs/w4+C1v4Mv3nuVO0t0K9+N9KMcODaMphGbb/uEx3dYb/HDMD2P/2G8Gr/HcRL+4ibxjzsxmO8L\\n/Nn6c4N1fVDcPA64PEV7E9sxjbh8iptvX/wQWP8Jvu/XAt3i5htC8tFEmvA7ND4NcmAhPqebMryf\\nt+J/yH4Zny1r8EcHf5DN68jl+cly25f4GQXTGvAjPszDZ8tS/Inbw+Lm+Tk+/1rpmCXJcu8g/I/l\\n1fj8fyB+Wck+o7jpC9mQgxnrAN02vsWGzhCRiLMNF334jvOHMEVEIsf8hVL2c84NKndbpHaoD62I\\niIiIVLTIF7TmLyX3ppnNN7OfJrl/U/OXZH3FzGabWSEnEomIVAVlp4jUkkh3OQjOFpyH7yS/GN8X\\n53jn3Jy4eS7A9088Pzhp5E381ZGyGVZERKTqKDtFpNZEfQ/tXvixUBcEIXsXcHjCPA7oGYzVtgm+\\n83TWZ6OKiFQhZaeI1JSoF7QD8WfJxyxm4wHbrwF2wp+N/xrwQ5d68H4RkVqg7BSRmlIN49AeiL9C\\n0n/hx9J8wsyecc6tTJzRzE4HTgfo0aPHHjvuuGNJGyoi1W/WrFnLnHP9Ms9Zdlllp3JTRIotjNyM\\nekG7BD/QdMwgNr7CyCnAlc53Bp5vZu/gx7h8IWE+nHM3ATcBNDY2upkzZxal0SJSu8zs3XK3gRCz\\nU7kpIsUWRm5GvcvBDGComW0bXC3kOGBqwjzvAeMAzKw//hKuC0raShGRaFF2ikhNifQeWudci5md\\njb/yVD1wi3NutpmdGdx/A/4yrrcGl0w1/NWIcrocpohINVF2ikitiXRBC+Ccexh/Kbr4aTfE/f99\\n/KVbRUQkoOwUkVoS9S4HIiIiIiJp1WxB+8UXMHkyNDeXuyUiIpWjuVnZKSLRE/kuB8Xy5ptw0UXQ\\nuTM8+SQ0NZW7RSIi0fbFFzBuHKxbp+wUkWip2YLWOWht9cE8bVpthfKaNWtYunQpa9asoaVFFwaS\\n8HTq1IktttiCXr16lbspJdHc7PNj7NjayJDPP/eZWe3ZqYyUKKjmPC1GdtZsQWsGdXV+L8PYseVu\\nTemsWLGCjz76iH79+rHlllvS0NCAv/KlSGGcc6xevZolS/xwp9UYwvGam2tvb2XPnvDJJxteczVm\\npzJSoqCa8zRZdoahZgvaYcNgwoTa2bMSs2zZMgYNGkT37t3L3RSpMmZG9+7dGThwIO+//35VBXAy\\n06bVxt7KeD16+I1PNe+VVkZKFFRznibLzjDUbEHbowdMnLjh71o5dLhu3Tq6detW7mZIFevWrRvr\\n168vdzOKbuxYv3ehmvdWJtPU1DEjqy07lZESJdWYp8XKzpotaOPV2qFDHT6TYqqV9aupqfr3VmZS\\nrdlZK+uwRF81rovFyk4VtNTmoUMRKVzi3spao+wUkXwUIztrdhzaeLHd3/X1tXXoUESkEMpOEYkK\\nFbRs2P192WXVc8isVphZxtuQIUNCea41a9ZgZlx55ZU5P/bRRx/FzHj++edDaUupLVu2jEmTJvHq\\nq6+WuykSIcrO6FNGloYysvzU5SBQ7Sc6VKvmhMsVHXnkkey+++5MmjSpfVqXLl1Cea4uXbrQ3NzM\\n4MGDc35sU1MTzc3N7LrrrqG0pdSWLVvGJZdcwvbbb89uu+1W7uZIhCg7o00ZWRrKyPJTQZtEtZ7o\\nUI1Gjx7d4e8uXbrQt2/fjaansnbt2qzD3MyyXm6iTTfdNO/HilQKZWf0KCOlVqjLQRLFGiNNyuu4\\n445j++235+mnn2b06NF069aNiy++GIDbb7+dMWPG0K9fP3r27Mkee+zBn/70pw6PT3Y47ac//SkN\\nDQ289dZbHHjggfTo0YNtt92WyZMn45xrny/Z4bTRo0ez33778cgjjzBixAi6d+/O8OHDeeihhzZq\\n++23384OO+xA165d2X333XnkkUcYPXo0Bx10UNrXvH79eiZOnMiXvvQlunbtSt++fdlnn33497//\\n3T6Pc45rr72W4cOH07VrV7bYYgvOOOMMVqxYAcAbb7zBTjvtBMCJJ57Yfpjyrrvuyvatlxqh7Kxs\\nykhlZCXTHtokanV8yUJUymHGZcuWceKJJ3L++eez884706NHDwAWLFjAsccey9ChQ6mrq+Opp57i\\nxBNPZN26dZx88slpl+mc46ijjuK0007jvPPO49577+WCCy5gyJAhHH/88WkfO3fuXH7yk58wceJE\\nevfuzVVXXcVRRx3FvHnz2GabbQB48MEHOemkkzjmmGOYMmUKH330Ed/97ndZs2YNI0aMSLv8Sy+9\\nlGuvvZbJkyez6667smLFCl544QU++eST9nnOPfdcrrvuOs4991zGjRvHokWLuPDCC5kzZw7/+te/\\nGDJkCHfddRfHHXcckyZN4sADDwRg6NChmd5uqTHKztSUkcpIKTLnXE3e9thjD5fM9OnOXXGFczfe\\n6P+dPj3pbBVrzpw5oS9z+nTnunVzrr7e/1vO92ybbbZx48ePT3rff//3fzvAPfroo2mX0dra6tav\\nX+9OOOEEt9dee7VPX716tQPc5MmT26edf/75DnB/+tOf2qe1tbW5oUOHusMOO6x92iOPPOIA19zc\\n3D5t7733dp07d3YLFy5sn7Zo0SIHuF//+tft00aOHOlGjRrVoY3PPfecA9yBBx6Y9rWMGzfOHX/8\\n8Snvf/PNN52ZuauuuqrD9H/84x8OcI888ohzzrm5c+c6wP3xj39M+3wxxVjPKgUw00Ug44pxy5Sb\\n06d3/H8lUkYqI+MVKyNzUQt5GkZuqstBnFj/r4sugnPOif4v6aiopMOM3bt3b//1HO+NN97gm9/8\\nJltttRUNDQ106tSJO+64gzfffDOr5R5yyCHt/zczdtllF957772Mj9tll13a9zIADBo0iM0226z9\\nsWvXruXll1/mmGOO6fC4L3/5ywwYMCDj8vfcc0/uv/9+Lr74YqZPn77RFWcee+wxnHOMHz+elpaW\\n9tu+++5Lly5dePrppzM+h9S2+NwcN85PmzhR2RlPGamMlOJTQRunkkInSippLMott9xyo2mfffYZ\\n++23H2+88Qa//OUvefbZZ5kxYwbjx49nzZo1GZdZX1+/0XW2u3TpktVjN998842mxT/2ww8/xDnH\\nFltssdF8/fv3z7j8SZMmceGFF3L33Xfzla98hb59+/Kd73yHTz/9FICPP/4Y8BuJTp06td86d+7M\\n2rVrWb58ecbnqBXNzTB5sv9XNlBuZqaMVEbWslJlp/rQxlH/r/xU0iVAk11G8JlnnmHJkiXcf//9\\nNDY2tk+PwvWz+/fvj5m1h2q8jz76KGNgd+nShQsvvJALL7yQDz74gKlTp/KjH/2IdevWcdttt9Gn\\nTx8Apk2b1t5XLl6/fv3CeSEVTmfvp6bczEwZWTzKyGgrZXaqoI1TSaETNZV8CdBVq1YB0KlTp/Zp\\nH3/8MQ8//HC5mtSua9eujBgxgrvvvpuJEye2T3/uuef44IMPchrvcMCAAZxxxhk88MADvP766wAc\\ncMABmBmLFy9m/PjxKR8bG7Zn9erVeb6SyqZLvKam3MyOMrI4lJHRVsrsVEGboJJDR/Kzzz770KNH\\nD8444wwuvvhiVq5cyaWXXkr//v1ZvHhxuZvHpZdeymGHHcaxxx7Lqaeeyocffsgll1xC//79qatL\\n32vo4IMPZu+992bkyJFsttlmzJw5k3/+85+ce+65AOy8886cc845nH766bz++uvss88+dOnShffe\\ne4/HH3+c73//+3z5y19m0KBB9OrVizvvvJNhw4bRvXt3tttuO3r37l2Kt6DstBcyPeVmdVNGKiPz\\nVcrsrNmC9u1P3+aovxyV12M/+QSWLoV+/WCrLbrys31/xs79dg65hVIqW221Fffccw8/+clPOPro\\noxk0aBD/8z//w7vvvsuUKVPK3TwOPfRQbr31Vi6//HKOOOIIdthhB6655hrOO+88Nt1007SP3Xff\\nfbn//vv53e9+x5o1axg8eDA/+9nPOuzJuPrqq9l11125/vrr+e1vf0t9fT2DBw9m3LhxbLvttoDf\\nM3PzzTdz0UUXMW7cOFpaWvjzn//McccdV9TXHhXaCxl4+204Kr/cBFj+CSxbCn37QZ+hfeCqqyBJ\\nH0mJFmWkMjJfpcxO86Ml1B7byhxnhLOs7+/1fX538O/CWViRzZ07t30AaKlc77zzDjvssANXXHEF\\n5513Xrmbs5FaXs/MbJZzrjHznJWn0czNDHOBt94KJ50U5hILVsvrbjWJekbmohbWyTBys2b30G63\\n+Xb84pu/yPlx99wLd/0Z2trAtnsC13gDq9erz4wUz4oVK7jgggsYN24cm2++OW+//TZXXXUVm222\\nWcYBzUVCtd128IvccxPgnnvgz3f57PwuN7A/T4D6G0oIlJECNVzQbtZ1M47aKfdDZwP+C+77X98f\\npK7nZ6znBlpcSxFaKOJ16tSJxYsXc9ZZZ7F8+XI22WQTxowZw+TJk3WGrZTWZpvl3eVgqwHw8H0+\\nOw/kSWh9AlqUnVI4ZaRADRe0uYq/bGGsP8jqHTpx2evQ0qZQluLp3r07DzzwQLmbIZKzxMu9xrLz\\nGy93gr+iglZCoYwUUEGblWTjqE2cCH9+rQFU0IqIbCTV+JNNTcCPg02PCloRCYmuFJaFVFfCaajz\\noby+tfyDS4uIREnaK4g1BAVtBAbmF5HqoII2C6kuWxgraLWHVkSko7SXe23QHloRCZe6HGQh1Thq\\nner9VVNU0IqIdJR2/MnYFadU0IpISCJf0JrZQcBvgXrgZufclUnmGQtMAToBy5xzY8JuR7Ir4WgP\\nrUjtSDzBKeqikJ0pryCmPbQiNaNU2RnpgtbM6oFrgf2BxcAMM5vqnJsTN89mwHXAQc6598xsi1K0\\nrbkZ/vbPoA9tW+p+YJW2ERSRjaU6wSmqop6da55p4GuQtg+tslOk8pUyOyNd0AJ7AfOdcwsAzOwu\\n4HBgTtw83wLudc69B+Cc+7jYjYp9QGsHNMAE+OSz5HsZKm0jKCLJC6lkJzhF/Lsc6ew8a40vaN9/\\nr4Wt0syn7BSpHOXOzqifFDYQWBT39+JgWrwdgN5mNs3MZpnZhGI3KvYBtbX43wOpCtq0Z/mKSOTE\\nCqmf/QzGjIGbbvLT057gFE2Rzs51zmfn++8pO0WqQRSyM+oFbTYagD2AQ4ADgYvMbIdkM5rZ6WY2\\n08xmLl26NO8njH1Adc6f2LBJz+ShXIEbwYo2d+5czIwnnniioOX84Ac/4NBDDw2pVRtMmTKF4cOH\\n09bWFvqyJRzTpsHatf7yrOvXw1ln+aCOneB02WVVtbcwq+wMKzdhQya2ms/OQVsqO0spl4wsRg6W\\nMgPD2h5A5b8XpRCF7Ix6QbsE2Dru70HBtHiLgcecc18455YBTwO7J1uYc+4m51yjc66xkMvhxT6g\\n757h9zJyEnyQAAAgAElEQVR07ZE8lKt0IxhZs2bNAqCxsTHvZbz99tvccMMNTJo0KaRWbXDGGWew\\ndOlSbrvtttCXLYVrbob33gOzDdPa2jbsHWxq8hdUqZDvcWjZGVZuwoZMPORwn51b9lV2llK2GVms\\nHCxlBoaxPYDqeC+KqbkZJk+GPn38D9CYcmRn1AvaGcBQM9vWzDoDxwFTE+Z5APiqmTWYWXdgb2Bu\\nsRvW1AR77elDecXnqU9sqLCNYEWbNWsW2223Hb179857GVOmTGH33XcvOAST6datGxMmTOBXv/pV\\n6MuWwsQOl/3+91BX54O5rg66dKnYvYORzs6hO/vs/HiJsrOUss3IYuVgKTMwjO0BVMd7USyx3Lzo\\nIjjnHDj3XD+ASbmyM9IFrXOuBTgbeAwftH91zs02szPN7MxgnrnAo8CrwAv44WleD7MdsV8gzc0d\\np53+bR/KCxa2dLhPyuPFF19kzz335I9//COjRo2iW7du7Lzzzjz11FNZPX7t2rXccccdfOtb3+ow\\nff78+XTq1ImLL764w/Tvfve79OzZk5kzZ2bdxuOOO445c+Ywffr0rB8jxRffZ7OtDb7zHbj88srd\\nOxj17Lzylz47//GYsrOUssnIYudgqTKw0O0BVM97USyJfd032wyefrqM2emcq8nbHnvs4bIxfbpz\\n3bo5V1/v/50+3U+/4grn6vq96ZiE4/tD3RVXZLW4spszZ065m1AUbW1trmfPnm7w4MHuwAMPdPfc\\nc4+bOnWqGzZsmBs0aFBWy5g2bZoD3IwZMza678wzz3Q9e/Z0y5Ytc845d8kll7jOnTu7J554Iqd2\\ntra2up49e7qLLroop8dVmkpbz1J9z/MBzHQRyLhi3LLNTefSZ+e37E/OgfuzHRe57Ky0dTdb2WZk\\nsXMwUwa2tbW59evXZ7y1tLQU/FozKfd7ERPVdTJquVn2gCzXLdtgvuIK/2GB/zcWvtOnO9dly7cd\\nk3B2zrYFfZClFNUvRqHeeOMNB7ijjjqqw/Rrr73WAW7VqlUZl3HllVc6M3Nr167d6L7333/fde/e\\n3f34xz92v//9711dXZ37y1/+kldbv/rVr7r9998/r8dWikpcz6ZP99/vQr/LKmi9dNn5rc5/dQ7c\\nPXXHRC47K3HdzUa2GVmKHEyXgU899ZQDMt7GjBlT8GvNpNzvRUyU18ko5WbUx6Etu9jZtrHxEGN9\\nQpqa4M93NnDUM9Cv//qKPCwZzy6xzDOVgPu5y+txL774IgBXXHFFh+nLli2jV69edOvWDYDLLruM\\nP/7xj8yfP597772XI444on3e999/n169etG5c+eNlj9gwADOOeccfv3rX9PS0sLvfvc7vvnNb3aY\\nJ92y4/Xr14958+bl9TqleFJe1Uryki47N7+kASbC2K+sZ/NKec8tGhmJK25GFpKDYWTgHnvswYwZ\\nMzK+np49e6a8L4ztAZT/vagEUcpNFbQZpLse+d57NsAzsGZ9CwceCEcfDaefXq6W1rZZs2YxZMgQ\\nhg0b1mH6Sy+9xG677db+9/7778/48eM59dRTN1rGmjVr6NKlS8rnGDp0KGvXruWrX/0qZ5111kb3\\np1t2vG7durF69epML0kiRFetyl267By2i9/0fP5ZC8crO0si24wsJAfDyMBNNtmEESNGZHo5WJof\\nGGFsD6D870U1KGV2qqDNQqpfIJ3q/FiKKz9v4fHH4fHH/fRKDOZ894xGxaxZsxg1atRG01966SUO\\nP/zw9r9Hjx6dchl9+vThs88+S3rfk08+yRlnnEFTUxPPPfccr776aodgzLTseJ988gl9+/bNal4p\\nP121Kn8p99508tk597UWHn+tQrIzzz2jUZFtRhaSg2Fk4L/+9S++9rWvZVzGmDFjmJbiihthbA+g\\n/O9FpSt1dkZ6lIOoa6gLfg/UbRhL8Z57ytSYGuac46WXXmLkyJEdpn/66ae8++67G01PZccdd2Td\\nunUsXry4w/QXX3yRI488km9/+9tMmzaNwYMHM3HixLzb+84772y050CiS1etKoIGn50NKDtLIZeM\\nLEUOpsvAWJeDTLcbb7yx4NeaSbnfi0pX6uxUQVuA9oK2fsNYikcfXabG1LC3336bFStWbPSL/KWX\\nXgJI+ks9mX333ReAF154oX3a/PnzOfjggznggAP4f//v/9G5c2d+/vOf8/DDD/P000/n3NbPPvuM\\nefPmtT+XlEbi8FHJhpNKRVetKoKgoO2EsrMUcsnIYudgpgzs2bMnjY2NGW+pisCwtgdQ/vciCiop\\nO1XQFiBW0DZ0buGAA+DGGyN+yKxKxa4IkyzAunTpws4775zVcoYMGcJee+3F3//+dwA+/PBDDjjg\\nAHbaaSfuvPNO6ur812XChAnsuOOO/PSnP825rQ899BCdO3fmyCOPzPmxkp/4wb/HjfPXGI//O1Mw\\n66pVRRAUtMO2U3aWQi4ZWewcLHYGhrU9gMp/LwpVcdlZ6DAJlXrLZfiZVNa1rHNMwtVfUl/wskol\\nysN/lNKYMWPcfffdt9H0P/zhD65Xr17uiy++CH3Zzjl30EEHuRNOOCHvZVeKKK1nicNHHXBA8uGk\\nwoKG7cqsudl/AHvtFc7yQhSldbdcCs3BSsvAdO2NwntRrnWylNkZRm6aX07taWxsdLlc4SkZ5xx1\\nl/pfaG0Xt6U96zIq5s6dy0477VTuZpTNpEmTuPnmm1m6dCk9e/aka9euPP/88wwaNAiAlpYWhg8f\\nzmmnncaPf/zjUJf98ssvs/feezN79my233770F9blOS6nv3znX9y/j/OZ23L2tDbsmoVLFjgz+kx\\ng622gvff3/D3l74E3buH93yvfe+1Wc658K+dHAFh5CYAs2ZBYyOMGuX/HyG1npGQfw5WWgZmai9E\\n471Iu07edBNce21RTlr8Iovs7BFSdtprheemCtoCNVzaQKtrZf1F6zf0qY0whXVmzz//PC+++CLf\\n+973Ql3uo48+yqeffsrxxx8f6nKjKNf17KT7T+L2V24vYotKaBIqaDN55RUYMQJ2283/P0KUkV4x\\ncrBSM7Dc70XadXLXXWH27NDaVS5G4bkZ/Qos4hrqGmhtbWV9a2UUtJLZ6NGjsx5yJRcHHXRQ6Mus\\nFq1trQBc/rXLOWzYYWVuTWF2n7R7uZsQfUEfWtavTz+flE0xcrBSMzDS70Wrz07uu8/vMq1Uuxee\\nm6rACtRQ18Da1rW0tLVknllEkmpzbQAM2WwIu/XfLcPcUvFiBW2LclOkIG0+O9lpJ6jS4b+ypVEO\\nCtSp3g8Qnk1Bm8twFyK1JFbQ1pkiqSYEF1bIpqBVboqkESto65Sd2kNboFg3g0wFra42JJKaw/fl\\nL/TESl2itkJkuYdWuSmSQew8KGWnCtpCxQra9W3p+4Ilu2JGpa40ImELYw+tip8KkmUfWuWmSAYh\\n7KGtluzUPuoCZbuHNkpXG6rVkS2kNPJZv3IpaFMdgtYlaitIlntoy5WbykiJiozrYg4FbbVnp/bQ\\nFqhTne8LNubWMe3/T6XvJX5MzO7d4aSZQJLRb3butzP3fPMe6uvqi9Ba6Ny5M6tXr6Z7mANvisRZ\\nvXo1nTql/y4kyragTbcnIVb8xO7TJWojLLZ+LF8OO+yQcrYmYHlfWL0KunWHbielWeZRR8GVVxbc\\nNGWkREnGPM2yoK2F7FRBW6Dd+u/GO5+9w8LPFmb9mOWrgFXJ73vrk7eY/8l8hvUtztmKffv2ZfHi\\nxfTt25eePXvS0NBQEReEkOhzzrF69WqWLFlC//79c3pstgVtukPQscssVno/sJrQsydssw28+y68\\n9VbaWbsFN5ZnWOY114RS0CojJQqyztMsC9payE4VtAW655v3sODTBe0ntWTrpZfghRdgr71g5Eg/\\n7eA7D2bBpwvaN+7FsOmmm9KlSxeWLl3K8uXLadGwORKiTp060b9/f3r16pXT42KH1TIVtJn2JDQ1\\nVW4Y15SGBpg7FxYtyvmhG2Xn6tX+Ig1t4eSmMlKiIqs8zfKksFrIThW0Baqvq2don6E5Paa5GU75\\nxsa7/rs2dAUoakEL0LVrV7beeuuiPodILmLrvJE+lKtlT4IA3bql7W6QTHMzjDslITtHrvF3hlTQ\\ngjJSKkiWe2hrITtV0JZBql3/sb1TxS5oRaIml5PCqmFPguQnaXbuEawzIRa0IhUjh5PCqj07NcpB\\nGaQ6c1cFrdQqXVhBspE0O+tU0EoN04UV2mkPbRFkGqA41a5/FbRSq1TQCuSZnW0qaKWGqaBtp4I2\\nZNkOUJxs178KWqlVsZMqVdDWrryzM3YyjHP+phEJpJaEdKWwaqCtR8gKGaBYBa3UqvaTwkIK5VQD\\niEt05Z2dZh2LWpFaEvIe2krOTu2hDVkhAxSroJVaFWaXg2q5jGOtKWhw97o6Xwm3tenQq9SWEAva\\nSs9OFbQhK2RoDBW0UqvCLGjTDSAu0VXQsELxBa1ILQmxoK307FRBWwTZDo2ReAKEClqpVWEWtNVy\\nGcdalE12Jj1xTCMdSK0KsaCt9OxUQVsmyXbtq6CVWpXtlcKyUQsDiNeqlIdEVdBKrQrxpLBKz04V\\ntGWSbNd+3QAfyq2utaxtEym1sIftqvYBxGtVykOiKmilVoV8UlglZ2dZe8+b2eNm9nyS6cPNbL2Z\\njTezg8zsTTObb2Y/TbOsPc2sxcyOKW6rw5FsgHDtoZVale2lbyW73Az+rrrsTHVRGhW0UrM0Dm27\\ncu+hfQ64wMy6OOfWApgft+c6YDpwFzAP2B9YDMwws6nOuTnxCzGzeuAq4PFSNj5fsT5gU6bA8uUb\\ndu3Xv1UP5FbQZhqIXKQS6MIKOUmbm865O4NMvJYqy06Ak07y/06YUFgfWmWnVAUVtO2iUNB2BkYC\\nsT0OE4DRwbS9gPnOuQUAZnYXcDgwJ2E53wfuAfYsQZsLkm5YjJUr/Qr5+uw29vtSYcsSqSQqaHOS\\nKTehBrJzwoQN97W01dEAzPh3G3semPuylJ1SsVTQtiv3O/A80IoPYsxsM+AXwDXOudeBgcCiuPkX\\nB9PamdlA4Ejg+lI0uFCpBg9vboZZM/zHcf5P27Ia1LiQiziIRImuFJaTTLkJNZadn33u15ujjlB2\\nSg2Jv5CIrhRW3oLWOfcf4BWCYAb+F2gDfp7DYqYA5zuX+Ti9mZ1uZjPNbObSpUtzbm8YUvUBmzYN\\n2lr9x9HS2pZVwKbsTyZSYbSHNnsh5SZkmZ1RyE3IkJ3BpqxlnbJTaogue9tBubscgD989g0zGwWc\\nCZzknFsZ3LcE2Dpu3kHBtHiNwF3BJTP7Al83sxbn3P2JT+Scuwm4CaCxsbEs10hMNSzG2LFQN6eO\\nNqChU1tWAVvpQ2yIxIR96dsakC43IcTsjEJuQvrsdEFB27WzslNqiLobdBCFgvZZfD+u24HnnHN3\\nxN03AxhqZtviw/g44FvxD3bObRv7v5ndCjyYrJiNkmTDYjQ1wZdfquPZpXDZ5W1ZB2wlD7EhEqM9\\ntDlLl5tQY9m5rm8dLIO//aWNRmWn1AoVtB1EoaB9Lvh3R2BU/B3OuRYzOxt4DKgHbnHOzTazM4P7\\nbyhpS4usz+Z1sBR2GKahZ6S25FrQ6gz11LkJtZednbv49aZxlLJTakgeBW01Z2cUCtr/AOuA651z\\nrybe6Zx7GHg4YVrSMHbOnVyMBpZKsnFoq3nlE4nJpaDVGepAhtyE2srOZMN2KTul6sXW9yy7alV7\\ndkahoL0Y+ITcT2ioOokFbbWvfCIxsUvfvvJKHQ/MSF+EpLxaVG1RbsZLKGiVnVITYieF1dVl9QOu\\n2rOzLAWtmXUHdgf2AX4IHOucW1GOtkRJYkFb7SufSExsnT/lZKPlw/RFSOwM9VixUitnqCs300go\\naJWdUhOC9b2Vuqx+wFV7dpZrD+1+wAP4kxV+6Jy7r0ztiJTEgrbaVz6RmNg6v35dHW0ZipAaPkNd\\nuZlKQkGr7JSaEKzv61vrWNeS+QdctWdnWQpa59xU0EXbEyUWtNmsfOonJtUgts53aqijJYuxQWvx\\nDHXlZhoJBa2yU2pCsL43dK6jc112P+CqOTuj0IdWAslOCktH/cSkWsTW+Tv/WMe8F1RkSI6SnBSW\\njrJTqkKsoO1Ux5OP6QeaCtoIyfWkMPUTk2oRu/Rt4x51HD2uzI2RypPjSWHKTqkKcVcKq+Y9r9lS\\nQRsh2ZwUFps+dqz6iUn10IUVpCBZnBQWm67slKqhCyt0oII2QjKdFNanz8Z7Haq5g7fUDl36VgqS\\n4aQwZadUJRW0HaigjZBMJ4Ul2+swcaLCWCqf9tBKQTKcFKbslKqkgrYDFbQRkuyksMR+MTpMJtVI\\nBa0UJLZBb21tn6TslKqngrYDFbQRkmmUg2ofQ05qT2zopHXByQ0qaCUvGUY5UHZKtWluhln3O86G\\nrC99W+1U0EZINsN2xe910DiKUsniz0Rv/VEbdFdBK3nKYtguZadUi1h2brm2jbOBtevr6FLuRkWA\\nCtoIiW3MH32sjZEt6YNW4yhKpYvv14gFJ4VhKjYkd0FBe/utbQxtU3ZKdYtlpwt+wK1e5wvaWs9O\\nFbQR8tFHPpQffKiNf1yRPmgzDUtTiyuzVJb4M9Fbg4J25sw6jjxIxYbkZuWqenoBN9/UxszblJ1S\\n3WLZ2WltG7RB1251+qEG6PhehLy/2H8cjrYOQZtMbIWur+84LM1FF/l/m5tL0mSRvMX6NV52GXTv\\n4Qva6c/WJS02RNJZsTLIzjZlp1S/WHb+zzk+N7t2r0v5Q62WqKCNkK239h+H1bVldT3mWDHw5JOw\\nfLlWZqk8TU1++KS6en9S2D771HUoNnQ2umRj094+OzspO6VGNDXBmWdsuFJY4g+1WsxOdTmIkEFb\\n1cG7cNDBbVz0q8yHCzQsjVSaWB+vPn18IRE7xNs+9vLedTobXXLWazNf0J52Shv/e5qyU6pP0uzs\\nvWHYLo3koYI2UmInhY39WlvOK6NWZom6WB+vtWv9yeh1ddCli19v48eh1TXJJWfBSWHjj28DZadU\\nmVTZOf3mNkZA+/pf69mpgjZCshm2K51kK3Otn/Uo0RHr4xUbWamtbcMhXl36VgqSxbBd6Sg7JcpS\\nZeeMf3csaGudCtoIKbSgTaSzHiVKYn284vcyxA7xtv1DVwqTAhRY0CZSdkqUpMrOvRp1pbB4Kmgj\\nJOyCNtlZjwplKZf4Q7uJfWjdE7pSmBQg5IJW2SlRkio7d+++4aQwUUEbKWEXtPHjfOpkB4mCVH28\\n4vvQiuQs5IJW2SlRkzQ7X9Ie2ngqaCMkjII2sd+XTnaQqHPO4fB7GgztaZA8hFDQKjul4rSpoI2n\\ngjZCCi1oU/X7UhhLlMWKWdBJYZKnAgtaZadUJBW0HehdiJBCC1pdKUQqkbobSMEKLGiVnVKRVNB2\\noHchQgotaHWlEKlEzumEMClQgQWtslMqktNJYfHU5SBCwhiHVv2+pBDlGHtTe2ilYCGMQ6vslHyV\\nbcxi7aHtQAVthIRxUpj6fUm+yjX2pgpaKVgIJ4UpOyUfZR2zWAVtB3oXIiTsYbtEclGMfoTNzTB5\\nsv83FRW0UrCQh+0SyVa5chNQQZtAe2gjRAWtlFOysTcLOZSW7Z6L9sveasguyZcKWimTVGMW55ud\\nOe3xVUHbQeQLWjM7CPgtUA/c7Jy7MuH+8cD5gAGfA991zr1S8oaGICoFra5hXpsS+xFCYYfSsr3a\\nUmzYLu2hDVctZWdUClplZ+1J1v+6kG4IOV2lTieFdRDpgtbM6oFrgf2BxcAMM5vqnJsTN9s7wBjn\\n3KdmdjBwE7B36VtbuGJcWCGfx+sa5rUrvh/h5MmFXf4z26stqctB+GotO4txYYV8Hq/srE2J/a8L\\nuXRyTlep0x7aDiJd0AJ7AfOdcwsAzOwu4HCgPZSdc9Pj5n8eGFTSFoaoWBdWyIWuYS4xhV7+M9sz\\nx1XQFkVNZWexLqyQC2WnxBSSnTmNuKGCtoOoF7QDgUVxfy8m/R6E04BHitqiIopt0FvbWvN6fBiB\\nqmuYS0y+Qxkl7unK9DgVtEVRU9lZjAsrKDslX2Fk58SJWTxABW0HUS9os2ZmX8OH8lfTzHM6cDrA\\n4MGDS9Sy7IV1YYVCAlXjMUq8bArS+BCG3Pd0tZ8Upn5gZZEpO6Oem0BoF1ZQdkpYSpGdKmg7inpB\\nuwTYOu7vQcG0DsxsN+Bm4GDn3PJUC3PO3YTvJ0ZjY6NLNV+51Fs9UP4LK2g8RslW4qHaAw+ENWv8\\nuQrZ7unSlcKKIrTsjHpuApG5sIKyU7IVRnbqpLCOol7QzgCGmtm2+DA+DvhW/AxmNhi4FzjROTev\\n9E0Mjy6sIJUm/lDt2rXw979vyNj6+uz2dKnLQVHUVHbqwgpSacLITu2h7SjSBa1zrsXMzgYeww89\\nc4tzbraZnRncfwNwMdAHuC44ZNninGssV5sL0V7QUrljKWrYmuqS6fOMP1RbVwctLRvuO/XU7NYB\\nFbThq7XsjMqwXflSblafXLITNqy6ZtlnpwrajiJd0AI45x4GHk6YdkPc/78NfLvU7SqGqIxDm6/E\\nQyhTpsDy5QrpSpXNmd/xh2o/+wx+8YsN940cmd3zqKAtjlrKzkouaJN9z0AFbiXLJTtvvx3+7//8\\nnlqATp1gwoQsn0gFbQeRL2hrSaUXtImHUM4+23/fNCZjZYr/PNes8cGb7DOMHaqdPNnnalub/3d5\\nyt7sHamglYJVcEGbOMLC7bfDbbdpPNtKlkt2TpuW595ZUEGbQO9ChFR6QRs7hFJf779fra3hXt9a\\nSmvsWGgIfvI6B7fckv7a4mPH+r0LZv7fbM8Uj10pTJe+lbxVcEEbn5udO/tpiUOISWXJJTtjn39d\\nnX9Mtke22hcOOiksoII2QqJe0DY3+71wqb6YsUMol10G114LXbpsCGmNyVh5mprglFM2ZGVra+aN\\nayxfXQ7nwmsPrRQs4gVtuuyMz80nn/SHm+MLXGVn5cklO5uafPe82E6gc85Jv+OgA+2h7UBdDiIk\\nygVttlfSiT9TePhw9QOrdBMmdDz8mW7jOm2aD2TnNgS4TgqTkohwQZttf8r4aRrPtvLlkp3Ll/vc\\nbGvL8cIeKmg7UEEbIVEuaPO5ko6Gwal8uYzPme/g9CpopWARLmiVnbWpFNmpgrYjFbQREsWCNjb0\\nSJ8+uqxjNUs3xEy2G9d8B6dXQSsFi2BBq+ysDeXMThW0HamgjZCoFbQahqs2ZNudJBv57FnSpW+l\\nYBEraJWdtaHc2dlheATRSWFRErWCNvFQ2fLlMHGi/9JlOkEMsptHkivle5fskGgp6dK3UrCIFbTK\\nzvKppexsP/tWe2gB7aGNlKgVtKn69WTzqzTMX661ptTvXd79t0KiLgdSsIgVtMrO8qi17FSXg45U\\n0EZIbIM+Y8kMjv7r0WVujbf3r2HpUujXD361CFgEb74Bq78BOFht8J0nYNiijo/LZh5JrhzvXbLP\\nuVRWrFkBqKCVAsQ26HfdBa++Wt62AE3Aor03fKf6/MpP7/0m3LEaHGCrofd3gGEdH5vNPJJcqd+7\\nVJ9zySxc6P9VQQuooI2UAT0HAPDRFx9x79x7y9yaBB8Ht5idNvx3toPZc5M8Jpt5JLlyvXeJn3MJ\\nDdhkQHmeWCrfgGDdeeMNf4uAPsEt3o7Brd3s4JbjPJJcOd67ZJ9zyQ1QdoIK2kgZueVImk9rZsnK\\nJeVuSkZvzoPZs2GXXfzfl0yClhZ/pZOfT4JhO3ScZ9gO5Wxt5am1987MGLPNmHI3QyrVqafCttvC\\nypXlbklGb77pv9s9e8Itf9iQm5N+DsOGdZxnl102TJPs1Nx717Wr72chKmijxMwYPWh0uZuRnZ2B\\nI/x/J0+G1tehrRVa68HmwtFHdJxHclTi9y7d0DMikdfQAPvvX+5WZGVYcJs8Gf7WCq1tUN8KuxlM\\nPLrjPJK7Ur93ys7oUEErBSt7x3gpiE5CESk95WblU3ZGi3oSS8ESr0WuL3Q0pRrOpuxDz4jUIOVm\\n5VB2VgbtoZVQ6FKN0ZZuT4L2FImUh3Iz+pSdlUMFrUgNSHc9+bwvuygiUuWUnZVDBa0UTJ3ioy/T\\nngTtKRIpPWVn9Ck7K4cKWilIGJ3iFerFpz0JItFSaHYqN0tD2Vk5VNBKQdIdjslGtZ0lWqyNTBjL\\n1Z4EkegoJDurLTdB2SmFU0ErBSm0U3yhBXGUFGsjU40bL5FaV0h2VlNugrJTwqGCVvIS/6s3djim\\nT58Nw5ZkGxrVdJZosTYy1bbxEqllYWRnNeUmKDslHCpoJWfJfvWOHZvfL+Fk/ZMqtW9Y2BuZ2PvQ\\np091bbxEalVY2ZmqX6ey01N21iYVtJKzVINJ5/tLOL5/UiUfIgrz5IHE92HKFFi+vPI2VCKyQZjZ\\nmdivU9npKTtrlwpayVmqX9OF/hJuboZJk2DtWmhrq8xDRGGdPJC44Vu+HCZOLHy5IlI+ys7UlJ1S\\nKBW0krNUv6YL+YUd+1UdC+S6uso9RBTGYb9q6yMnIsrOTJSdUggVtJKXZL+mC/mFHftVHQvk/fbz\\nexwqbUzbsA77FXIIrtzvgYikpuxM/fzKTimEClopumxCIvFXdb6BXO6+U2GeVZvPRq6S+9GJSEfK\\nzvyWpeysTSpopaiyDYkwTgqID8S1a+Gss8C59M8b9i/ych/u0jA1ItVB2Vn4MnOh7Kx8KmilqHIJ\\niUJPCogPxLo6/5zpTpAoxi/yfDcuuW4cUs1f7o2CiIRD2Znd45SdEqOCVoqqlCERH4h9+sA556R/\\n3mL9Is9145LrxiHd/GEOfyMi5aPszEzZKfEiX9Ca2UHAb4F64Gbn3JUJ91tw/9eBVcDJzrkXS95Q\\nSarUIREfiMOHp3/eqPwiz3XjkGn+sIa/kcqm7Kxsys7MlJ0SL9IFrZnVA9cC+wOLgRlmNtU5Nydu\\ntoOBocFtb+D64F+JiHQhUcyzSjOFU1R+kee6cYjKxkSiS9lZHZSd6Sk7JV6kC1pgL2C+c24BgJnd\\nBRJAfEEAACAASURBVBwOxIfy4cDtzjkHPG9mm5nZAOfcB6VvruQi28NF5QzuVMJsU64bh6hsTCTS\\nlJ1VTNm5oQ3KTomJekE7EFgU9/diNt6DkGyegcBGoWxmpwOnAwwePDjUhkrusjlclM/JB8UeS7BY\\nJ0Q0NfllT56cue06NCYZhJadys3oUXZuoOyUmKgXtKFyzt0E3ATQ2NjoytycmpfN4Z9c+0iVYizB\\n+DatWQO33x7Oc2gcRIki5Wb0KDs7UnYKQF25G5DBEmDruL8HBdNynUciKHb457LLUgdQLLjr67Pr\\n85QsxMM2diw0BD8FnYNbbvGBWqhStF1qhrKziik7O1J2CkS/oJ0BDDWzbc2sM3AcMDVhnqnABPNG\\nAyvUB6xyNDXBxInpx1fMFNzxcg3xfDQ1wSmngJn/u7U1nAAtRdulZig7q5yycwNlp0DEuxw451rM\\n7GzgMfzQM7c452ab2ZnB/TcAD+OHnZmPH3rmlHK1V4ojlz5Pper0P2EC3HZbdmfLZtsvTScsSFiU\\nnQLKTqkt5k9wrT2NjY1u5syZ5W6GVLBswlZ9u2qPmc1yzjWWux3FoNyUMCg7JVEYuRnpPbQi5ZIq\\ncBOnZwrYYl1RR0QkipSdUi4qaEUSpNozkM8eAw3kLSK1Qtkp5RT1k8JESi7VGbP5nEmby4kZsXEU\\nwzjrV0Sk1JSdUk7aQyuSINWegXz3GGRzeE39xUSk0ik7pZxU0IokiO0ZuP325NOLcSat+ouJSKVT\\ndko5qaCVmpLLpR1jQ8vcdtuGX/3Fumyi+ouJSJQpOyXqVNBKzcjl0FSmX/1hX/M81Z4NEZFyCys7\\nw85N8MuZMgXuuQeOPlp7Z2uZClqpGbkcmkr3q7+YfbaS7dkQESmnMLKzWLnZ3AznnOOX+8wzMHy4\\ncrNWaZQDqRm5XB4x3Rm2xbpuuK5HLiJRFEZ2Kjel2LSHVmpGricmpOrzVaw+W+oLJiJRFEZ2Kjel\\n2HTpW5E8FKMvWDGXK6WjS9+KJKfclFTCyE0VtCIiIVJBKyKSmzByU31oRURERKSiqaAVERERkYpW\\ns10OzGwp8G6Jnq4vsKxEz1UOen2VTa8vXNs45/qV8PlKpsS5CVo3K51eX+WquNys2YK2lMxsZrX2\\nqQO9vkqn1ydRVe2fnV5fZavm11eJr01dDkRERESkoqmgFREREZGKpoK2NG4qdwOKTK+vsun1SVRV\\n+2en11fZqvn1VdxrUx9aEREREalo2kMrIiIiIhVNBW0RmNnmZvaEmb0V/Ns7zbz1ZvaSmT1YyjYW\\nIpvXZ2Zbm9lTZjbHzGab2Q/L0dZcmNlBZvammc03s58mud/M7HfB/a+a2ahytDNfWby+8cHres3M\\nppvZ7uVoZz4yvba4+fY0sxYzO6aU7ZPsKDsrLzuVm5Wbm1Bd2amCtjh+CjzpnBsKPBn8ncoPgbkl\\naVV4snl9LcCPnHM7A6OBs8xs5xK2MSdmVg9cCxwM7Awcn6S9BwNDg9vpwPUlbWQBsnx97wBjnHPD\\ngcuokD5UWb622HxXAY+XtoWSA2VnBWWnchOo0NyE6stOFbTFcThwW/D/24Ajks1kZoOAQ4CbS9Su\\nsGR8fc65D5xzLwb//xy/4RlYshbmbi9gvnNugXNuHXAX/nXGOxy43XnPA5uZ2YBSNzRPGV+fc266\\nc+7T4M/ngUElbmO+svnsAL4P3AN8XMrGSU6UnZWVncrNys1NqLLsVEFbHP2dcx8E//8Q6J9ivinA\\nT4C2krQqPNm+PgDMbAgwEvh3cZtVkIHAori/F7PxRiSbeaIq17afBjxS1BaFJ+NrM7OBwJFU0N6h\\nGqXsjFMB2anc7KiSchOqLDsbyt2ASmVm/wC2THLXhfF/OOecmW00lISZHQp87JybZWZji9PK/BX6\\n+uKWswn+l905zrmV4bZSisHMvoYP5q+Wuy0hmgKc75xrM7Nyt6WmKTs9ZWd1qdLchArKThW0eXLO\\n7ZfqPjP7yMwGOOc+CA6tJNtN/xXgG2b2daAr0MvM7nDOnVCkJuckhNeHmXXCB/Kdzrl7i9TUsCwB\\nto77e1AwLdd5oiqrtpvZbvjDuAc755aXqG2Fyua1NQJ3BYHcF/i6mbU45+4vTRMlRtlZVdmp3KRi\\ncxOqLDvV5aA4pgInBf8/CXggcQbn3ETn3CDn3BDgOOCfUQnkLGR8febX/v8D5jrnri5h2/I1Axhq\\nZtuaWWf8ZzI1YZ6pwITgrN3RwIq4w4dRl/H1mdlg4F7gROfcvDK0MV8ZX5tzblvn3JDg+3Y38L0o\\nBrIoOyssO5WblZubUGXZqYK2OK4E9jezt4D9gr8xs63M7OGytiwc2by+rwAnAv9lZi8Ht6+Xp7mZ\\nOedagLOBx/AnYfzVOTfbzM40szOD2R4GFgDzgd8D3ytLY/OQ5eu7GOgDXBd8XjPL1NycZPnapDIo\\nOysoO5WbQIXmJlRfdupKYSIiIiJS0bSHVkREREQqmgpaEREREaloKmhFREREpKKpoBURERGRiqaC\\nVkREREQqmgpaEREREaloKmhFREREpKKpoBURERGRiqaCVkREREQqmgpaEREREaloKmhFREREpKKp\\noBURERGRiqaCVkREREQqmgpaEREREaloKmhFREREpKKpoBURqUJmdpCZvWlm883sp0nu39TM/m5m\\nr5jZbDM7pRztFBEJgznnyt0GEREJkZnVA/OA/YHFwAzgeOfcnLh5LgA2dc6db2b9gDeBLZ1z68rR\\nZhGRQmgPrYhI9dkLmO+cWxAUqHcBhyfM44CeZmbAJsAnQEtpmykiEg4VtCIi1WcgsCju78XBtHjX\\nADsB7wOvAT90zrWVpnkiIuFqKHcDyqVv375uyJAh5W6GiFSZWbNmLXPO9St3O7JwIPAy8F/AdsAT\\nZvaMc25l/ExmdjpwOkCPHj322HHHHUveUBGpbmHkZs0WtEOGDGHmzJnlboaIVBkze7fcbQCWAFvH\\n/T0omBbvFOBK50+kmG9m7wA7Ai/Ez+Scuwm4CaCxsdEpN0UkbGHkprociIhUnxnAUDPb1sw6A8cB\\nUxPmeQ8YB2Bm/YFhwIKStlJEJCQ1u4dWRKRaOedazOxs4DGgHrjFOTfbzM4M7r8BuAy41cxeAww4\\n3zm3rGyNFhEpgApaEZEq5Jx7GHg4YdoNcf9/Hzig1O0SESkGdTkQERERkYqmglZERLLW3AyTJ/t/\\nRUSiQl0OREQkK198AePGwbp10LkzPPkkNDWVu1UiIipoJY2VK1fy8ccfs379+nI3RcqsU6dObLHF\\nFvTq1avcTZEy+vxzX8y2tvp/p00Lv6BV7ohESzHyv7nZ58fYseFliApaSWrlypV89NFHDBw4kG7d\\nuuGvjim1yDnH6tWrWbLED2OqorZ29ewJn3yyYQ/t2LHhLl+5IxItxcj/5uaNj/SEQQWtJPXxxx8z\\ncOBAunfvXu6mSJmZGd27d2fgwIG8//77KmhrWI8efuMT9p6VGOWOSLQUI/+nTdv4SE8YVNBKUuvX\\nr6dbt27lboZESLdu3XQYWGhq6ljIhnnoULkjEk1h5v/YsX7PbNhHelTQSko63CfxtD5IomSHDgst\\narWeiURPmN/LpqbiHOlRQSsiInlJduhQox6ISCaJR3rCEPlxaM3sIDN708zmm9lP08y3p5m1mNkx\\npWyfiEitih06rK8vzkliIiLZinRBa2b1wLXAwcDOwPFmtnOK+a4CHi9tC6WS3HrrrZhZ+61z585s\\nt912XHDBBaxZsyb055s2bRpmxrQserybGZMmTQq9DTGx175w4cKiPYfUntihw8su05i0IlJekS5o\\ngb2A+c65Bc65dcBdwOFJ5vs+cA/wcSkbJ5Xpb3/7G83NzTz00EMceOCBTJ48mfPOOy/05xk1ahTN\\nzc2MGjUq9GWLREVTE0ycuKGY1ZXENjZ37lzMjCeeeCLjvD/4wQ849NBDQ33+KVOmMHz4cNra2kJd\\nbjK5vNZM9F54xXgfoLTvRSlEvaAdCCyK+3txMK2dmQ0EjgSuL2G7pIKNGDGC0aNHs//++3Pdddex\\n3377ccstt4T+pe7VqxejR4/WMFdSM2IniV10kf9XRa03a9YsABobG9PO9/bbb3PDDTeEfrTmjDPO\\nYOnSpdx2222hLjeZbF9rJnovvGK9D1Da96IUol7QZmMKcL5zLmM1Ymanm9lMM5u5dOnSEjRNEhXz\\nsHq+Ro0axapVq1i2bFn7tHfeeYfx48fTr18/unTpwogRI7jvvvs6PG7evHkceeSRbLHFFnTt2pXB\\ngwdz7LHH0tLSAiTvctDa2srPfvYzBgwYQPfu3Rk7diyzZ8/eqE0nn3wyQ4YM2Wj62LFjGRvXUXHN\\nmjWce+657LrrrmyyySZsueWWHHbYYbzxxhsZX/ef/vQnRo4cySabbEKvXr0YPnw4N954Y8bHiaRS\\nrPElK92sWbPYbrvt6N27d9r5pkyZwu67715wMZioW7duTJgwgV/96lehLjeZbF9rJlF7L4YMGZLz\\n9iuM96JY7wOUdr0ohagXtEuAreP+HhRMi9cI3GVmC4FjgOvM7IhkC3PO3eSca3TONfbr168Y7ZUM\\nLrnkknI3YSMLFy5k0003pU+fPgAsWrSIvffem1deeYXf/OY3TJ06lVGjRnH00UczderU9scdcsgh\\nLFmyhOuvv57HHnuMK6+8ki5duqTd0ztp0iSuuOIKxo8fz/33388BBxzAN77xjbzbvnbtWlauXMnE\\niRN58MEHuf7661mzZg1NTU18+OGHKR/37LPPcsIJJzBmzBjuv/9+7r777v/P3p3HSVFdjf//nNlw\\nxEEIoiDIooKICwojAWOEqLjFn0ti1GhcYozgkoiJiZonODPBqN+4hOAS446YBE3UBBIS9TGgeWSI\\nzACiQsQBNQKjsgcVmO38/qiuobunl+ru6v28X69+9XT17apbd7qrT5+6dS/f/e532bp1a9J1McYu\\nEotsyZIlHHPMMcyaNYtRo0ZRWVnJiBEjmD9/fmeZXbt28dRTT3HhhReGvLapqYny8nJuueWWkOVX\\nXXUVVVVVNDQ0eKrDBRdcwIoVK1i4cGHqOxSDl32Nx9rCEa0dIP/aIiNUNWdvOMOKrQGGABXAG8Bh\\nMco/AZzrZd2jR49WE92KFSvSsl7nLZcdjz/+uAL673//W1tbW3Xz5s366KOPamlpqd57772d5S6/\\n/HLdZ599dOPGjSGvP+mkk3TkyJGqqrphwwYF9M9//nPU7c2fP18BnT9/vqqqbt68Wbt3766TJk0K\\nKXfHHXcooDU1NZ3LLr30Uh00aFCXdY4fP17Hjx8fdZttbW362Wef6V577aX33HNPl31/7733VFX1\\nzjvv1F69ekVdTzTpel8UEqBBc+D4mY5btOPmwoWqt93m3Af/nahCfH91dHRoVVWVDhw4UE855RR9\\n9tlndc6cOXrIIYfogAEDOsstWLBAAV28eHGXdUyePFmrqqo6j0l1dXVaUVGhL730kud6tLe3a1VV\\nlU6dOjVqPVtbW+Pe2traUt7XeLLdFpEMGjQo5Bgdjx9tEasdVDPfFun8fPpx3Mz6ATJuBeF0YBWw\\nGvifwLLJwOQIZS2g9Ymfb9yamhoFutwSOTj4wQ3qwm9XX311SLn9999fL7nkki4H8jvvvFMB3bZt\\nm3Z0dOiBBx6ohx56qD700EO6atWqLtsLD2hfeeUVBfTll18OKff++++nFNA+/fTTOmbMGN17771D\\n9is4cA4PaN0D5UUXXaRz587VLVu2eGrDQgw4/FZsAe3ChaqVlaqlpc59MoGsqxDfX//+978V0K99\\n7Wshy++//34F9PPPP1dV54etiOiuXbu6rGP9+vW655576g033KAPP/ywlpSU6NNPP51wXY477jid\\nOHFixOfc41W8W6wf1F73NZ5st0Wk4H7QoEE6depUz8G9H20Rqx1UM9MWwXI9oM31Lgeo6jxVHaaq\\nB6nqzwPLHlTVByOUvUxV/5j5WppYamtrg390dP6drf60zz//PIsXL2bevHmcdNJJPPDAAzz55JOd\\nz3/yySc8+eSTlJeXh9zckRA2bdrUeeVqdXU1N998M8OGDePAAw/k17+Ofm1ic3MzAPvtt1/I8vDH\\niZg7dy7nn38+hx56KL/73e/417/+xeLFi+nTp0/MocjGjx/PH/7wBz788EPOOecc+vTpw0knncTy\\n5cuTrospTmnvNyuSG7ckLVmyBIDbbrstZPnGjRvp0aNH51S/69evp0ePHlRUVHRZR79+/ZgyZQr3\\n3nsvkydPZsaMGZx33nmdz0+bNo1hw4ZRUlLCn/70p6h16dOnD+vXr4/43OjRo1m8eHHcW6x+9l73\\nNV59s90Wr7zySpfj/wcffMC0adNClp144okptcWWLVs444wzGDZsGCNHjuTkk0+mqanJUztkqi38\\nkKmRT2ymMFN0Dj/8cA4++GAATjjhBI488kh+9KMf8fWvf53u3bvTu3dvvvzlL3PjjTdGfP3+++8P\\nwIEHHsiTTz6JqvLGG29w3333cfXVVzN48GBOO+20Lq/r168fAB9//DGHHXZY5/KPP/64S9k99tiD\\nlpaWLss3bdrU2dcXYPbs2Rx88ME88cQTnctaW1vZvHlz3HY499xzOffcc/n0009ZsGABN954I6ee\\neipr166lpCTnf+uaHJGuedkLRWNjI4MHD+aQQw4JWb506VKOPPLIzsc7d+6kW7duUdczdOhQdu3a\\nxXHHHcc111wT8tzEiRO56KKLuPzyy2PWpbKykh07dkR8bq+99uKoo46Ktzsxp0D1uq/x6pvttnCD\\n+2BnnnkmZ5xxBldeeWXnsqqqqqjr99IWIsKUKVM46aSTAJgxYwZXXHFF54XE8doB0t8WqUrH9NjR\\n2LeWyaiamppsVyFEt27duPPOO/nkk0944IEHADj11FNZvnw5hx12GNXV1V1u4QcYEeGoo47innvu\\nAeCtt96KuK0jjzyS7t2788wzz4Qsnz17dpeygwYN4uOPPyZ4NI7Vq1fzzjvvhJT7/PPPKSsL/V06\\na9Ys2tvbPbaA80V2xhlnMGnSJJqbm9m0aZPn1xqT9skVVHPjlqTGxsaIY1EvXbo0ZHnv3r2jXpT5\\n8ssvM2nSJMaNG8drr73W5UzK2LFjOfDAA+PWZfPmzeyzzz4Rn4uUlYx0i5WV9Lqv8eqb7baoqqrq\\nctyvqKhg//33D1kWHqwG89IWPXv27AxmAY499tiQyW9itQNkpi1SlcmRTyxDazIqF4ftOvPMMznm\\nmGO4++67ufbaa/nZz37GmDFjOP7447n22msZPHgwW7Zs4a233mLNmjU89thjLF++nOuuu47zzz+f\\ngw8+mPb2dp544gnKyso44YQTIm6nZ8+eXH/99fz85z+nqqqKk08+mcWLF/Poo492KfuNb3yDqVOn\\n8q1vfYsf/OAHbNy4kdtvv73LQefUU0/lT3/6E9dffz1nnHEGDQ0N3HvvvfTs2TPmPt9yyy18/PHH\\nfOUrX2H//fdn7dq1zJgxg6OOOgobAcQkKh3zshcCVWXp0qXccMMNIcu3bNnCBx98wNFHH925bPjw\\n4bS0tLB27VoGDBjQuXzJkiWcc845XHHFFfzyl79k2LBh3Hzzzfz1r39NuD7vvfceY8aMifhcpKxk\\nJNGykonsazzZbotUJdsW06dP56yzds8dFa0dIH/aIpNncCygNQa49dZbOeWUU3jwwQe5/vrraWho\\noLa2lp/85Cds2LCB3r17c/jhh3PppZcC0LdvXwYOHMg999zD2rVr2WOPPTjiiCP4y1/+wujRo6Nu\\nx+1P/Mgjj3DffffxxS9+kblz54Z0QQA4+OCD+eMf/8hPf/pTzj77bIYNG8Y999zTpT/Wd7/7XT78\\n8EMee+wxfvOb33DMMccwd+5czjnnnJj7+8UvfpEZM2Zw/fXXs3nzZvbdd19OPvlkpk2blmQLmqLw\\n3ntw8cVJv3zDBvj4Y9hvP+gzvDfU1cHee/tYwdyyevVqtm3b1iVTt3TpUoCQ5ccffzwAr7/+emfw\\n0tTUxGmnncbJJ5/MvffeS0lJCTU1NVx++eW8+uqrna/xYuvWraxatapLkOVys5LJSmRf48l2W6Qq\\nmbaoq6tjzZo1PPTQQ53LIrUD5FdbuGdwFixwgtm0/vBN9aqyfL3ZKAexFeLVxiZ19r6Ij0Ie5cDv\\nE/lPPhnSdoX2/po9e7YC2tzcHLL8rrvu0m7dumlra2vI8jFjxuhll12mqqrNzc06ZMgQHT9+vO7c\\nubOzTFtbmw4fPlzHjRvXZXvjx4/X559/PmJdnnrqKe3WrVuX4Qj9kui+xqtvrrVFIsN2JdoW06ZN\\n0zFjxujWrVu7rCu4HVSz2xa5PsqBOOspPtXV1ep14OFitHLlSg499NBsV8PkGHtfxCcijarq/7Q+\\nOaB6yBBt+NnPknrt3Lnwxz9Ch8LlPM5XmA8PPgiTJnWWKfb31xNPPMF1111Hc3Mze+65Z8KvnzBh\\nAlOmTOHss7vOLXTaaaexzz77MGvWLD+q6otY9S2Wtqirq2PevHm8+OKL7B3hbEWq7QD+tUU6P5++\\nHDdTjYjz9WYZ2tgKLVNi/GHvi/go5AxtCsfN4PFqHyy9WhVUgyY0UbX3V2trqw4fPlzvvPPOhF5X\\nU1Oj/fv314qKCu3du7f2799fP/zww87nly5dqhUVFfruu+/6XeWkxKuvanG0xVtvvaWAHnTQQTpy\\n5EgdOXKkhn/Gkm0HVf/bItcztNaH1hhjTFrU14f2nXP70p25tAz+ALS1ZbeCOaasrIzHH3+8cwxT\\nr2pra2NecPvRRx/xxBNPdA5XmG3x6gvF0RaHHXYYTiwXXbLtAPnVFn6wgNYYY4zvoo0/OW4ccEO5\\nU8gC2i7Gjh3L2LFjfV3nqaee6uv6MsXawpGOdoD8bItYbBxaY4wxvos5/qQ7drIFtMYYn1hAa4wx\\nxnfu+JOlpRHGn3QD2tbWLNTMGFOIrMuBMcYY38Ucf9IytMYYn1mGtgDk4uxbxpjsEpFTReQdEWkS\\nkZuilJkgIstE5G0RecXvOowbBzffHGEw9XLrQ2tMsaivh9tvd+7TyQLaAlBXV5ftKhhjcoiIlAL3\\nA6cBI4BvisiIsDI9gQeAM1X1MOAbmahbfT3M/2f0DK171fenn0Jzs3NvjMmueKMxRONeHDp1qnOf\\nzqDWAlpjjCk8Y4AmVV2jqi3AbOCssDIXAs+p6n8AVPWTdFfK/XKb96IT0K7/ILQPbXl5OTt27ODT\\nT2HVKli3zrm3oNaY7NqxYwfl7pmVKCJlYmNeHOozC2jzVG1tLSKCiAB0/m3dD4wxQH/gw6DHawPL\\ngg0DeonIAhFpFJFL0l0p98utRQMB7X9CM7T77rsv69atY/Pmz+nocDJCHR2wfXu6a2aMiURV+fzz\\nz1m3bh377rtv1HLuj9Wf/hTGj4eHHnKWx7w41Gd2UVieCh4wWUSSPh1QbFauXMmIESN48cUXmThx\\nYtLr+f73v8+aNWv4y1/+4mPtYPr06Tz66KO88cYblJTY702TVmXAaOBEoBKoF5FFqroquJCIXAlc\\nCTBw4MCUNuh+ubXvLAeFAX1DA9oePXoAsHr1ejZsaEUERJxryLZuTWnTxpgklZeXs99++3V+PiNZ\\nsAB27XJ+gHZ0wDXXwBFHxLk41GcW0Jqi0tjYCEB1dfJTRq9evZoHH3yQhQsX+lWtTpMmTeKOO+5g\\n5syZfPvb3/Z9/aZorAMOCHo8ILAs2Fpgk6p+BnwmIq8CI4GQgFZVHwIeAqiurk7pl7P75bb1F2Xw\\nJ+i7T9c+tD169ODoo3uEzDJ21FGpbNUYky7u57R3bycL29HhLO/ocJa7k6mkM5B1WQooD8TrRlBT\\nU5OZihSAxsZGDjroIHr16pX0OqZPn87IkSNTCoqjqays5JJLLuGuu+7yfd2mqCwGhorIEBGpAC4A\\n5oSV+TNwnIiUiciewBeBlemu2LhxMHSEk0v5ZF30cWijjpBgjMkJwRd8TZkC11/vnE0pKYFu3dLb\\nvSASC2jzQLxRDKzfrHdLlizhmGOOYdasWYwaNYrKykpGjBjB/PnzPb1+165dPPXUU1x44YUhy5ua\\nmigvL+eWW24JWX7VVVdRVVVFQ0OD5zpecMEFrFixIi0ZYFMcVLUNuBZ4ASdIfUZV3xaRySIyOVBm\\nJfB3YDnwOvCIqr7lZz0iXSRSXw933OkEtP/7Qlvah/IxxqRH+AVfPXvCq6/Crbfunuo6k6zLgSka\\nqsrSpUt5//332bJlCz/96U8pLy/nRz/6EZdccgkffvhh3HUsWrSIrVu38uUvfzlk+cEHH8wVV1zB\\n9OnTue666+jduzc/+9nPeOyxx/jrX/+aUDb3qKOOoqqqir///e8ce+yxCe+nMQCqOg+YF7bswbDH\\ndwJ3pmP7bvampcXpN+t+wS1YADvbnK+eko62ztOSxpj84vaJdz/jbh/ZbH2eLaDNUbW1tSGZWXc0\\ng5qaGsvIJmnVqlVs376diRMn8uyzz3Yu//DDD7nmmmvYsWMHlZWVMdexaNEiRIQjjzyyy3O33HIL\\nTz75JHfccQeHHHIIdXV1/P73v+ekk05KqJ4lJSWMHDmSRYsWJfQ6Y3JJpOF6xo1zvvTeKi+HFqiQ\\ntoyfljSmoLz2Gjz9NGThwvBxwOqvwrq10H8A9Psd8LuMV6OTBbQ5KpFRDILLppPUSdq34YXWJPfB\\nXbJkCQC33XZbyPKNGzfSo0cPKisr2bJlCxdffDGrVq2isrKS/fbbjwceeICDDz4YgPXr19OjRw8q\\nKiq6rL9fv35MmTKFu+++m7a2NmbMmMF5550XUmbatGnMmjWLpqYmnnvuOc4+++yIde3Tpw+rVq2K\\n+Jwx+SBS9gacoPYLdWVwM0w4ro0vWHbWmORddx0ELnbOhn6BWy6wgLYA1NXVWdbWg8bGRgYPHswh\\nhxwSsnzp0qWdGVcRYcqUKZ1Z1RkzZnDFFVewIDAa9M6dO+nWrVvUbQwdOpRdu3Zx3HHHcc01c91I\\n6QAAIABJREFU13R5fuLEiVx00UVcfvnlMetaWVnJjh07Etk9kyWZ+kGZb2IN13PIYc5Xz/YtrXzz\\nFPj61+HKK7NSTWPy22efOfc/+Qn07ZvdukTw3nvw7rswdCgMGRKj4Pe/n/K2LKA1niWbGc0VjY2N\\njBo1qsvypUuXctZZziRKPXv2DOkicOyxx3LPPfd0Pu7duzdbowyI+fLLLzNp0iTGjRvHa6+9xvLl\\ny7t0TRg7dqynum7evJl99tnHU1mTXfaDMrqo/enKnK+elW+28eKb8OKLzmILao1JkHv29uKLYfjw\\n7NYlTH09nHhj4CzNy3EuFPMhoLVRDvKQmxGymcK8cy8IO/roo0OWb9myhQ8++KDLctf06dM7g12A\\n4cOH09LSwtq1a0PKLVmyhHPOOaczmztw4EBuvvnmpOv73nvvdckkG1MwAlNolrF7HNqgbu3GGK/c\\ngV9zcCKeTE57C3kQ0IrIqSLyjog0ichNEZ6/SESWi8ibIrJQREZmo55+ixWwuhkhVe3sW+v+bQFt\\nZKtXr2bbtm1dMrRLly4FiJi5raurY82aNdx+++2dy44//ngAXn/99c5lTU1NnHbaaZx88snce++9\\nVFRUUFNTw7x583j11VcTruvWrVtZtWpV57ZM7rEflCkKZGiDA9qvfz1blTEmj6U5oI009J5XmZz2\\nFtgdCOXiDSgFVgMHAhXAG8CIsDLHAr0Cf58G/MvLukePHq35wvk3eX/shxUrVvi+zmyaPXu2Atrc\\n3Byy/K677tJu3bppa2tryPJp06bpmDFjdOvWrV3WNWbMGL3ssstUVbW5uVmHDBmi48eP1507d3aW\\naWtr0+HDh+u4ceMi1mf8+PH6/PPPR3zuqaee0m7duunGjRsT2sdMKLT3hR8ifB4bNAeOn+m4+Xbc\\n/Oc/VUGbDzpWTz5Z9Te/8We1xhSdIUNUQXX1at9XvXChamWlammpc79woXO77Tbn3us6vJT347iZ\\n6xnaMUCTqq5R1RZgNnBWcAFVXaiqWwIPF+FM8VhwYmWEbKaw+M4//3xUlb5hneZ/+MMfsnPnTsrK\\ndncnr6urY+7cubz44ovsvffeXdZ11VVX8dxzz/H555/Tt29f1qxZw4IFC0IuFistLWXlypVJTY7w\\n1FNP8Y1vfIPevXsn/Fpj8kLg89a3dxsvvGB9Z41JWhoztOFdBp58cvfMYCee6C1rm8kZ/xJqAREZ\\nKyK1IvL3wGn+d0WkXkSeEJFvi0jy84lG1h8IHu1+bWBZNN8B/uZzHbLOHXvW/RUCoV0M7DSnf95+\\n+21qa2vZtGkT48eP56ijjuoyKcK3vvUt9t9/fx544IGE119bW8uAAQOor6/niiuuYMCAASH9cZct\\nW8Y//vGPovqRku/v32L6X/km0IeWtrbY5YwxsQViAsT/YTXDuwxAZvvEJspTQCsil4rIm8BC4Hpg\\nT+Bd4F/AFpw5wB8B1gWC21iDM6SFiHwFJ6C9MUaZK0WkQUQaNmzYkLnKJSj8Cz7WF36+BwO55rDD\\nDkNVaWpqYtmyZSxbtqzLtLVlZWU8/vjj7Lnnngmvv7a2lrVr17Jr1y42btzI2rVrGTBg90mFjz76\\niCeeeKJz3NtiEG9q51xnn8EkuGdELKA1JjVpzNC6Q+9Nm+bcX3JJhvvEJihuC4jIcuAOnCkURwM9\\nVfV4Vf26qn5LVU9X1UOBLwDfBfYFVojI+T7Ubx1wQNDjAYFl4XU8EiegPktVN0Vbmao+pKrVqlrd\\np08fH6qXHvG+4IMzQuFl7cs1M8aOHcvVV1/t+3pPPfVUvvnNb/q+XmNyihvQtrZmtx7G5Ls0XxQW\\n3GUgPMDNtSmrvbTAo8AQVb1RVZeqe847jKpuU9XfqurpwFgg8mCdiVkMDBWRISJSAVwAzAkuICID\\ngeeAi1W1KKZWihW05nu2yxQHGyWgyFmG1hh/ZHjYrkz2iU1U3BZQ1V+p6s5EVqqqb6jqC8lXq3M9\\nbcC1wAvASuAZVX1bRCaLyORAsVuA3sADIrJMRBqirC6nJfIFb8GAyXc27FyRSyCgTWXYIGMKXg6P\\nQ5tpOd8CqjpPVYep6kGq+vPAsgdV9cHA31eoai9VPSpwq469xtyUyBd8eFm3C4KbnbUA1+Qy931p\\n788i5vGisPr6xK+qNqao+HRRWCH8cExLQCsi+6djvSYyy3aZfOL+8HLvbZSAIuQxQ5vpmYaMyTs+\\nZGgL5YdjWfwiSVkEDEzTugvW+u3rGfPwGKiBkjqPb87wshFeq6qd3RO8+svJf+GzdZ9BhJf12qMX\\nB33hoITWZ/JflO7zKcunH142TJ5PPF4U5g4b1NKSm1dVG5N1CQS09fXOj8IJE0L7wEb64ZiLfWTj\\nSTqgFZEzYzy9R7LrLWaN6xtZt30dCCgeg4fwspFem8j6Ajbt2sS+bftCedfntu7043o/k2927NhB\\neXmEN4QH7pTNruD+37B7rOVc5047bVLkBrSbN8PRR0ctNg7YMAC2fwpVe0H3WAOLfO1rTorJmGLi\\nMaB1s7Duj8PgUQoK5YdjKhna54FXiJjDoyqF9Rat1g4nW3HWIWfx3PnP+bbe0tJS2tvbE3rNf//7\\nXz75+BP679OfyspKRARFWdK8JOHg2OQ3VWXHjh2sW7eO/fbbL6l1BGc2RaTzrEG6sr4mx/XoAf36\\nQXMzLFsWs2j3wC2ud9+1gNYUH48BbawsrDscV6TsbT5JJaBtAi5X1ffDnxCRD7sWN/G0tjsBbUVp\\nBSWSfH8YN3AIzoiVlpQC3jNhPffuSYmU0NzcTGvQacGNWzcCsHLbyqTrZ/JPeXk5++23Hz169Mh2\\nVTIuWnY5X7LKOamsDFauhDVrEn7pG29AYyOMHg0jR+J8O48da0OAmeLk8aKweFlYd5zZfJZKQDsL\\nZxKF9yM890gK6y1aboa2vDS507quurq6kIvCks2E9ejRo0sAc1jdYShK29S2ziDZmES4F4Hly8Vg\\nkbLLxgd77x2zu0Ek9fVw4tVhp02PCQSyFtCaYuQxQ1soWdhYEkoDisgo929VvVVVX49UTlVtdP8g\\nXrM4boa2vCS1gDad3CC2rcO+PExybNguk6yIox6UBn5Yt7fvzlYZUywSuCgslydF8EOi57Xni8hX\\n0lKTAuZ19q7ODG0SAW2syRb8zISVlThJ/XZNrE+uMYUgX7LKhco9bRoyl7zI7i9z98vdmGJhEyt0\\nSrQFfgfME5Gvhz8hIseJyP/5U63i1JmhTaLLQayxaP3MhJWKkw1p77CAtlBZ5jQ6a5v0ije4e9S5\\n5G0qXVOsLKDtlFALqOpVwO3AbHfqWRE5XETmAq8CvfyvYn5KZnraVDK0meJ2ObAMbeHyekbBGD95\\nHdw94mnT4G4HxhQTn2YKKwQJh/Sq+jPgKmCGiLwCLAMOBy4HjvC3eumVzmxLMrN3pZKhDZbO06Ju\\nlwPrQ2u8sqym8SKlWcEsQ2uKlc8Z2nyeAjfhFhCRXsBQoB34Ms6sYENV9QlVzasOTLmWiXIztBWl\\nFSmtx48AIto6rMtBYYp1RiHV91Oufc5MborYP9YrN0NrAa0pJqq+ZmjzfQrcREc5qAXeA64B7sbJ\\nylYD9/heswLiNWOaS6McRAtCrMtBYYp1RsEC0t0s25w+UfvHeuFmaK3LgSkmwcGsDwFtSmdJckCi\\nGdqf4FwYdrCq/lRVnwC+ClwqIk+LSPYjMY8aGxsBb31bU+V13S3tLUDqXQ7SuS+WoTVeJNOHPB9Y\\ncJ9eXoYVinhK1DK0phj53N0gpbMkOSDRVjhUVa9W1Y/cBar6MvAVYDzwdz8rl06jR48G0jcaQDL8\\nuigs2S9dL0GI9aEtfO4MWKkGpIn2Ic8l+VLPYhP1lKhlaE0x8vmCsJTOkuSAREc5WB1l+RLgOGCw\\nD3XKmlSyLxN8+Cnj10VhyfJyIZt1OSh87o+7RALS8OX5nskMrn+hZpvzUdRTonZRmClGaRiyK58n\\nX/CtFVS1CTjWr/Vlgp+jAbzyyispryNdEyv4ybocmEjCA8Bg+ToZQfCMZrmUbRaRF0VkUYTlR4hI\\nq4hcFHh8qoi8IyJNInJTjPUdIyJtInJuOuvth6inRK3LgSlGNgZtiLitICJzRMTThNuq+rGI7CEi\\nP3DHqc1lbiYqV7Iv6ZpYIRnRghDL0BYfrwGp+1lyg9vgz1U+CD8W1NXV5Wom9jXgaBHp5i4Qp9IP\\nAAtV9bciUgrcD5wGjAC+KSIjwlcUKPf/gBczUnMfXHopfPe7USZWSKDLQT4PT2QMYAFtGC+t8D6w\\nSET+JSLfF5FRIlIWXEBE9heRs0XkUaAZ+A6wxP/q+i+VQHDChAkRg+Fkux/k0sQK0fbfSx/aHAwA\\nTBpECgCDZTuTmSi3nu6xwP07uP45km1+DagAghMNlwBjcUagARgDNKnqGlVtAWYDZ0VY1/eAZ4FP\\n0lddf7j9Zx9+GGbODH3u8xbnh/ayBm8Z2nwfnsgYwALaMHFbQVW/j/ML/3WgFlgM7BSRzSLSLCI7\\ngA+B54DDgCnAkar6etpqnSHxvogXLFgQMRhekORYF36NQ5vOL10vXQ5inX42+Sdaf9jwH4PhcjS7\\nGVdwVjn8jE2O7M8inHHAxwKISE/gF8B9qvpWoEx/nOOya21gWScR6Q+cA/w63RX2Q7T+s/X1sPoD\\n54f2ld9p9xSc5vvwRMYANktYGE9hvaquVtXvAX2BE3CG73oS+DPOGLSXAUNUdayqzlTNz/PR4YFg\\npi9s8euisLQO25Vgl4N8vzjIOLy8p4KD25qamrzJzoZnmoPlYpZZVT8F3iAQ0AI/BzqARH/JTgdu\\njDchjohcKSINItKwYcOGhOvrl2j9ZxcsgDZ1jkva2uYpOM334YmMASxDGybRUQ5aVPUVVf2Fqk5R\\n1cmq+j+qOktVP0hXJTMllS+t8ePHp7z9XOpyEE20DG20vsgmPyXan3T8+PH50v+0i2jdjnLca8BY\\nERkFTAZ+pKr/DXp+HXBA0OMBgWXBqoHZIvI+cC7wgIicHb4hVX1IVatVtbpPnz5+7kNCog0pNGEC\\ndAR6wVWWt3kKTvN9eCJjAAtow0geHLjTorq6WhsaGrosjzYzkjs2Zzqd+fszmbtqLn++4M+ceciZ\\nad1Wsg6oPYC1spb/+/b/8aWBX4pYJlogm4k2NN7d+uqt/Lrh13GDt+bmZgD69evnab3Nzc2ey+aa\\n4Lpv376dqqqqxNdxQ3Ojqlb7XbdgInIe8DTwNrBZVY8Pe74MWAWciBPILgYuVNW3o6zvCeAvqvrH\\nWNuNdtzMtu2Hj6Pq7UW8+ZuFHHGlRaemSGzcCH36QO/ezt95TERSPm6WxS+SOBHZX1XXp2Pd6eYG\\ntKqKiGQ0U5MPGdq1/1kLg+J3OXDbLdNtaLx7fNnjrN/u4WMaiOmaP232tuKqBMrmmuC6C3z66afZ\\nrU90rwXuhwOjwp9U1TYRuRZ4ASgFHlPVt93RZ1T1wYzVNAOqejpnjo441IbtMkUkiQxtfb3TTWfC\\nhMI7M5GWgBbnooWBaVp3wcr2xArRhMyiFohNY10UliNXgps4drXtAuD1K16nf4/+3H333fzwhz/s\\nUu7uu+/mnnvuYd268DPWXcuE+8EPfhBxnfmof//+Mdugs1xt/7hlfPAp0AL8WlWXRyqgqvOAeWHL\\nIgayqnqZ3xXMqAjDdhXyF7cxwO6A1mP3Pnd0j5YWp+94wXW3cfuLJXoDzoxx+yTZ9WbqNnr0aA1W\\nU1OjOOFayK2mpkb9FGt9X37sy0otuuC9Bb5uM1UhbXIJSi3Kgd7axu/2M/7p84s+Si360faPVNX5\\nP0cT67lUymZSqu9Fr/sFNGiaj1/A3ThDJO6d7m0F38KPmznjhBNUQfWll1RVdeFC1cpK1dJS537h\\nwizXz5h0WL/eed/37asLF6redlvs9/pttzmfCXDub7stc1WNx4/jZio9iZ/HGaLr+gi3xDueRRFv\\nthtxzAg8vzxwkUTCMjUbUKyr/ju7HORYhhaCLpQJ/CD82wt/y26FikC6+xu3tLcA3oaJK4Sse/hn\\nz0v75tLEKyKyp4iME5EfA9cBV6vqtoxXJBeFTX1rw3KZohDI0La0lXgaV7nQR/dIJaBtAi5X1a+E\\n3wBfeid7nO3mNGBo4HYlGRpTMR1faG6Xg1THofVD1FELgroceBmSy4btSl66284NaL+w9xfiBmyJ\\nvN/zJfj10r45Nu3tScBC4PvAdar6fDYqkZPcqW8DXQ4K/YvbGKAzoN3ZWuLpB1yhj+6RSkA7C9g3\\nynOPpLDeYF5muzkLeDKQtV4E9BSRlC6x9vKFnEiw4TXLk0sXhUX7Ih928DAg8kVhNoJBfnED2tad\\nrb4GbLn0PsilDGuqVHWOqoqqDlDV+7Ndn5wSlqH18sVtU9+avBcIaLvtUeL5B9y4cXDzzYUXzEIK\\nF4Wp6q0xnvMrtRRptpsveijTH6d/WVRvfvwmg6YPivxkT3h8+uOxazaF6K+PsL6Bv3SukfvPB/9h\\n4CDn7+lbp4dsx73iPBe7HLgEJzA462znd4UbKNTU1FBXV9d5AVlwwB9cJh8DiUzKVNu1d7TTru0I\\n0jm2cCEKvqBRRDrfp24bJ9K++ZJ5LkoRLgqLpeAvjjHFwQ1oK0t4+Xm7CDKpgFZEuqnqLr8rk24i\\nciVOtwToB//Z9p/kV9YzydcHv05g27bQLnC9K3tzQI8DIrwwe4K/yA8fcTjvrHyHZ/74DOcdfl5n\\nZg92Z63Dg4jgMia2TLVdcP9ZN6jzY3KQXJdK+9qPsRzmdjkIZGjjBayR+tgWaxBg8ph7/BJh3Dh7\\nDycU0IrIBGAmMEBE/gssB5YASwP3KzTONIoJ8jLbjZcygDPjDfAQwJFHH6lzr5ubUGV+Of2XXD/l\\negAGDx7M+++/n9DrvayjT/c+7Fm+Z8LrTafgL/LwqW8tG5uf3IC2W1m3zmWvvPJKtqqTEZZhLWBh\\nGdpoF4W5GSy3j60b8FofW5OXbKawEIlmaO8HPgeuBfYBjgbOxrniFmAn4Gc0thgYKiJDcILUC4AL\\nw8rMAa4Vkdk43RG2qWrcUd0rSisY1NNjl4GAX9X9ium1050H20j49QA96cngXoM7H7t/50sAGDz1\\nrVvnWBkvCyKSl862S2SEg0IR/vmy92YBCcvQhgesvXt3zdi+/LKdojV5zgLaEIm2whDgBlX9tapO\\nU9WvqeoQ4As4V+D+1M/KqWobTvD8ArASeEYDs924M97gDBy+BmfUhYeBq/2sQzTxvgyjBac5dtV0\\nwoIztF6HPTLJSUfbuevc1e70GNr12a6CuWjK5bXu+byPJkyci8I2bYrcxaBQL44xRcIC2hCJtsJK\\noMsVS6q6VVX/oapdpwpKkarOU9VhqnqQqv48sOxBDcx4Exjd4JrA80eoqq8TjUcdviqOQh2uqqzE\\n+eJo6+g6xaRlvHKf+750M7S9e/XOyx9YsepXqJ89E0PYsF0QGrDaMF6mIFlAGyJuK4jIiSKyd+Dh\\nL3EvqioS6cyo5mMAGNzlIFjI9Lgm53X2oS3tFqdkbrKg1YQIy9CGK/TxN03xqa+Hhx/afVGY8Zah\\nfQnYLCKrcCYxOFREnhGRg9NbtfyT6JiX+RgAdga0YePQphpg5GNbRJNr+xLpfXno4YcCoX1o8/EH\\nlquQxps1SfAwbFdwxtbGoDX5zB3F4/57nQzt5zstQwveAtoRwCXAX3DGd/0CcC7wjoisFpE/iMhP\\nAlPURptooSCEf+FHmk0pH0/fJqKzD22Ht/EevSqkjFu69iXZ91Gk9+Xrja8DoQFtrr9PYwWtxfDZ\\nMzEEuhy89Le2uEGqGwzEmybUmFzljuKhgS4H2z93Qrli/6EWN6BV1X+r6m9V9QeqOkFV9waGAxcB\\nzwG9gR/hXJwVd3SBfBb+5ehH4JJvX7jBfWgtK5ZZfgbK+TjKgQWtJpr1nzjHpRfntcUNUiMN6VXs\\ngYDJL26f8PISJ6Ddq6rEfqiR5NS3qrpKVWer6o9U9QRV7QUMA77pb/Xyl9fTt/mWmQzvchAeYCQy\\n/FghBcT5si/u+zLSOLSFIp+7Tpjk/Ge9c1wSbY85lz10vUDMHdKrmAMBk1/cPuHXXOUEtN2rSqKO\\nvVxMfOt4oapNqvqMX+vLVV4Dl1wLZPwS3OUgUjCeSIAeK+OWb+2XruzhhAkTfA2U3dflY4Y2WKyg\\nNd/eOyZ1Bwx2MrQV0uZpLvt4Q3oZk+vGjYNvX7b7ojAbycPHgLZYhAcuNTU1CQcu+ZLNiyTaRWF+\\nZcXcNsi3zHW6vPLKK2kJlN1xaPM1oM2Hz4rJnP4DnePSySe2exrFwIb0MvnG7Rbz0ENB3WOChu2y\\nkTwSnymsaEXLGtbV1SX85VobmC5WVbvMrpXr2cmFry0EgZv/52Zg91S3sDsITWb6WzcgTqY9c00+\\nnPLO9wytF7n+WTI+CoxycHzDPXDR4wm9dBywuRfs2AGVlbDHRWmoHzhjhd50E1xxRZo2YAqV2z92\\n1y4nhi0pgW7d4F+/6uAI6ByHdty44gxkO7kZn2K7jR49WhNB5xwOu9XU1ERcnsj6wl+f7PoyZeo/\\npiq1aO382oh1TaX+bnuG32pqahJeT7alUodo7TB+/Hjftv/b5b9VatEL/nhB0vXMddn6LAENmgPH\\nuHTcEj1uZsxzz6lC7t/Gjct2S5k8dNttqqWloW+l0lLVmZNeK5j3lR/HTetykAS3y0BwRtJLl4FI\\nXQ3c5fkiWpeDeGLtY3h7upLpzgGh3RWy1bapdJmI1h93QQKd++JtP98nVjAmxDnnwEcfwerVvtyW\\n/GE1I7qtZmiJc7/kDymu89lnnXrGGCfXmGjcbjHuhGAlJc7jUUfZTGHBxP3STPiFIv8ALlHVtf5W\\nKTOqq6u1oSH2LLlu14BwwafSw7sMeBX8Oi/byRW3vnorU+dPRRBEhZLS0A9SR3tHl2UAba1tlJXH\\n7+HilvNaPtY6vG43Wp1TkUr9/VhPvNd1aAcd2sF3R32Xh/6/h1KpYk7Jhc+SiDSqanVGNpZhXo6b\\nheD2251RD9rbnb6106Y5fW6TtngxjBkDo0dDEbSf8V99vXPBYu/ezoWMEybAuLZ/wvHHw3HHwT//\\nme0qpsSP42YqAW0HMFxVV6VSgWxJ9MAcLXD1I6D1Y32ZsuD9BZzxuzP4rPWzbFfFpKi8pJyZZ8/k\\nm0cU5mh72fosWUCb/9w+iy0tTiYs5YtslixxgtmjjoKlS32rpylyr7ziRLbHH+/8ncf8OG7aRWEp\\nSvYCoHy4cCiSCYMnsO2mbXRoR9yyP5v2M26ddmuX5T+d+lNumXpL1NdEe86riorIFzpF225FRQUt\\nLS0pbdOPdSb6mljlvaxLRDonyjCm2LkZsAkTdg/tFfw4JYGZzKzLgfFVh3U5CGbfZh5FC0BTHQ/U\\n63ZySWlJKaWUxi03rXYa02qnAd6zZW75lHTQua1o2w0/NV1R5gTBvp2a7oDy0vL0viZW+WS2X2Dy\\n4bNkckO0jKxvV4xbQGvSwQLaENYKHmWq/12u9ZnNBC/7nEi7eAlk/JgIIVZZr8FUomMSey1vwVxx\\nfpZMctI+y5IFtCYdLKANYX1oTUbEGhPUS/Y20f6Q7va8jEXqdz/oZCW6vlzvb12srA9t/vG9z2y4\\nVavgkEPg4IPh3Xd9XLEpai++CKecAhMnOn/nMT+OmxbWm4zIdLbM7U7gZbupZDPTsV+WWTQms9I+\\ny5Kboe2If+2ByT/uLF719RnesGVoQ1grmKzwcuo8U1MEpzJtcV1dnW91Cp4tLZHyxpjUBU+H6zs3\\n4LAuBwXHze5PnercZzSotYA2hLWCyQovfVgT7eeaiQA4vE5uvbxMquFl3YnWxRiTB6wPbcFKR/9r\\nzxlfC2hDWCuYguHHhV5et+MGzeAtcI6Xdc1UNrqQWNuYvGEBbcFyZ/EqLXXuJ0xwlifbDSGhjK8F\\ntCFSaYWJwH/8qogpXl5OnefS6XU3cHbr5EfgnKlgvJCkMr1wMRCRU0XkHRFpEpGbIjx/kYgsF5E3\\nRWShiIzMRj0LSdQgxgLaghWp/3Uq3RASyvi6ZwqDEizFLOmAVlVfVtWdflbGFKd0nI73IwCO1bUh\\n1vPB5Szrmjhrn9SJSClwP3AaMAL4poiMCCv2HjBeVY8ApgGFMwdyElK9sCdmEGMBbUEL73+dSjeE\\naBnfiCxDG8JawRQkP4KiaBnA4OWxAudksq61tbU5lY3OhliZV/uR4NkYoElV16hqCzAbOCu4gKou\\nVNUtgYeLgAEZrmPO8OPCnphBjAW0RSWhoDRMQiNuWEAbwmYKMyYFfgdSdXV1NrZsDMHjCts4vDH1\\nBz4MerwW+GKM8t8B/pbWGuWwSMFooqMduEGMO5ZtSBBjAW1RSXbq5ODpl2++2cMLLKANkVAriMg3\\n01URY3JBtAzghAkTUsoMFnvWNR7LvGaPiHwFJ6C9McrzV4pIg4g0bNiwIbOVy5BUMmqumJk1C2iL\\njpdh4IK7uSR1lsAC2lDuaVAvN6AF+AdwaCKvy8Xb6NGj1RSnmpoaT+UAdT4iXZf7XR93W8E3r/Us\\nNF7bN1fbB2jQLB/fgHHAC0GPbwZujlDuSGA1MMzLegv5uLlwoepttzn3vvv8c1VQ7dYtDSs3+Wjh\\nQtXKStXSUuf+7LNVRZy3SWmp816M6/e/d15w/vlpr2+6+XHcTDSsHw2UA8tE5C4R2Sv5UDo2EfmC\\niLwkIu8G7ntFKHOAiMwXkRUi8raIXJeu+pjC4dfV8X5lD8P72tbU1MTta2vs4rE4FgNDRWSIiFQA\\nFwBzgguIyEDgOeBizdMpzP2U1okVLENrwgR3c9m1C+bO3T1oQWmpx7MElqENkVArqOqbqvpl4Erg\\nW8A7aeyGcBPwsqoOBV4OPA7XBvxQVUcAY4FrIlzJa0xC4o0zm+iMXokq9uGorHtG6lS1DbgWeAFY\\nCTyjqm+LyGQRmRwodgvQG3hARJaJSEOWqlv44gS0WZs61aRNvP9pcDcXkd2xqQhcfrlwo3fIAAAg\\nAElEQVTHH1YW0IZKNrUL7I0zLEwbMB84LNV0cdj63wH6Bf7uB7zj4TV/BiZ6WX8hnzozXSV7Wp8Y\\np79jPZcst54mf5EDXQ7SdbPjZpI6OlSdBJzzd5DwU88LF6a5+4NJu0j/02jlJk9WLS/f/faoqEjg\\n/z5zpvOiiy/2re7Z4sdxM5VxaLep6jXAMcA+wFIRuVtEqpJdZ5j9VLU58PdHwH6xCovIYOBo4F8+\\nbd8UEL8mLkjXxUvuet3srF0UZUwBEdmdRXOzagHhIyw8+WTqQ4iZ7Ar+n+7c6fxPIxk3DgYOTDI7\\nC5ahDZPwsF0iUo4TOI4Nug0OPH0NcIGIXKWqcyKvIWRd/wv0jfDU/wQ/UFUVkajj8wT68j4LTFHV\\n/8YodyVOdwkGDhwYr3rGdDn9na5ho2w4KmMKXEmJE4C0t+/ugkDX4b4g9SHETJq99ho0NkZ9+oJP\\nYAPO6WsUyh6G9/aAIUMil91YAm0dUCJw7hZghsd6LFrk3NtMYUCCAa2I1ANHARVAB/AGMBf4P+A1\\n4FOgBvijiHxfVR+MtT5VPSnGtj4WkX6q2iwi/YBPopQrxwlmf6uqz8XZ3kMEZsOprq62iKFIJdJH\\n0zKk2REc4BuT7+rr4RhKKaOtSz/a8DFLAWbOjDKercm+nTth4kTYsSNqkSHAPcEL2oHp0cve7T5Q\\n4OnALRHduyf4gsKUaIb2v8DtOMHrIlX9LEKZH4rIx8BPgJgBbRxzgEuBOwL3fw4vIM5530eBlap6\\nT/jzxkTiV6CUrouX7KIo58I4C2hNIXDHF93QVkoZ8K+F7XzxxNAy48aFZmGTGZTfZMhnnznBbLdu\\ncOWVUYs1N8Pzz0N7B5SWwDnnQL9+kcs2NED9IqcXrQiMGwvV1R7rs8cecPXVie9HAUoooFXVUzwW\\nfRUnEE3FHcAzIvId4APgPAAR2R94RFVPB74EXAy8KSLLAq/7iarOS3HbxsSVroDLAjljCkdnf0qc\\nbgavvdo1oA0XHuCaHNLS4tz37AkzovcN6AccHTTzV78Y/8/WerjxxN1Z+ZfvxhlJ2iQkXVPfvkHY\\nvOGJUtVNQJePvaquB04P/P1/gHUeMaYA1NbWhgxZ5l54V1NTY0G+yTvuNKa9eztBSvsOJ6D98rE2\\nFm1ea2117gMdnoOnqw3/EeL1h0myU+WaUGkJaFV1B07fWmOM8cQujDOFwu1m4Gbcpk+HbteXwudw\\nzCgLaPOam6GtqOjyf+4y5XECLCufOhvrwRhjjPFR+FBcmzbBnns5GdqGf7XHnUTBJlpIXtrbzg1o\\ny8u7/J8XLEjTNo0n6epyYIwxSbML40w+Cx+Ka8IE4F4noL3gG+283xo9o+dn1q/YZKTtgrocRPw/\\nm6yxDK0xJudYn1mTz9w+kdOmBQVVgbFn21vaY2b0LOuXvIy0XVCGNuL/2WSNZWiNMcYYn3XpExkI\\naPcob6e0zcno9e7tnB4PvhDIsn7Jy0jbBfWhBev7mkssoDXGGGPSLRDQ/m5WO39vcoLZKVO6nh63\\nK96Tl5G2CxvlwOQOC2iNMcaYdAsEtEeP7ODobziZ2WhT3FrWL3lpb7ugLgcQe9guk1kW0BpjjDHp\\nFgho3alvrWtBngrK0NoFfLnFAlpjjDEm3UoC12AHAlrrWpA/QrKwcYbtsv9j9lhAa4wxxqRbWIYW\\nrGtBPgjPwi67uYVhYMN25SALaI0xxph0ixDQmtwXnoX995utnQGtZdlziwW0xhhjTLpFCGjtgqLc\\nF56FHTE09KIwy7LnDgtojTHGmHQLC2hTvaDIguHMCM/CHvxG6Di0JndYQGuMMcakW1hAm8oFRYV4\\ndX26AnQ/1huShW0IjHIQyNCa3GEBrTHGGJNuPg7bVWhX16crQE/LelssQ5urLKA1xhhj0i0Q0L69\\nvJ05rzkBrHsqu3dv5x68BVyFdnV9ugL0tKzXAtqcZQGtMcYYk26BgPbHP2znhfbdGcMJExLPIka7\\nuj5f+9X6HaC77dC7dxoC/1brcpCrLKA1xhhj0i0Q0GpbO+0duzOGkFwWMfzq+nzuV+vn8Ffh7TB9\\nOmza5GOQbxnanGUBrTHGGJNugYC2W1k7pe2hGcNUs4j19VBbC7t2QUdHfvar9Wv4q/BuBps2wc03\\np77eTpahzVkW0BpjjDHpFghob/t5B2NaQzOGqWQn3YykG8yWlBRGv9pkpb1/sWVoc5YFtMYYY0y6\\nBQLaQ4e2c+hZoU+lkp10M5JuMHvSSU62NpnAONv9b/0aYivZHwietm8Bbc6ygNYYY4xJt5IS5/7c\\nc3cP4RVHh+4OVEskcpkbFa53Jx/rgIoFUPKVxKrWoXB0CxwdeNxWtrvK0bbrt/A6dFQkv+1xgRt1\\nadi+dTnIWRbQGmOMMel2wgkwZw60tTk3D0oCt3hl9ghe0JJ41bqsw1v1fOXHfmRs+1VVMGZMeitk\\nEmYBrTHGGJNu3/seTJrkpFw9+MUvoK4O2jugtARqauDHP05P1RYtgtNOc86ml5Q4F1R1aPTtBpev\\nqIC//Q3GjvWvDomsc9EiePVVOP741MontP2yMudmcor9R4wxxphMSKDf5Zcngt4BbS1QUuE8Dk0h\\n+mfsBJj3j91jt06ZEnu78+the6sTbO9qdR6PneBfHSZMgLEe+r7W18OJp3sfqixW+WS2b3KLBbTG\\nGFOARORU4FdAKfCIqt4R9rwEnj8d+By4TFWXZLyiJiI/x2b1uj13G0ccEXu76RpJINGL4xKdCSxe\\neb+GDjPZYQGtMcYUGBEpBe4HJgJrgcUiMkdVVwQVOw0YGrh9Efh14N7kiGwFWPG2m+lgO5pEA+tC\\nmzLYhMrZgFZEvgA8DQwG3gfOU9UtUcqWAg3AOlU9I1N1NMaYHDUGaFLVNQAiMhs4CwgOaM8CnlRV\\nBRaJSE8R6aeqzZmvrkmUlyGm0jkUV7LBtp91SjSwzpVA3KRHzga0wE3Ay6p6h4jcFHh8Y5Sy1wEr\\ngR6ZqpwxxuSw/sCHQY/X0jX7GqlMf8AC2hznZZrbZKbCTfdYtOmYntcNrOvr4fbb49fduhUUrngj\\ngmTTWcDMwN8zgbMjFRKRAcBXgUcyVC9jjCkaInKliDSISMOGDRuyXR1D5L6gyZQJ5gabU6c69/X1\\n6a33zp3w5JP+rDcTdTe5L5cD2v2CTn19BOwXpdx04MdA3LFQ7MBsjCkS64ADgh4PCCxLtAyq+pCq\\nVqtqdZ8+fXyvqEmc2xe0tDR6X1AvZYIlGgAnY8KE3aNdqcJjj/kTfGai7ib3ZTWgFZH/FZG3ItxC\\nJgYM9PHSCK8/A/hEVRu9bM8OzMaYIrEYGCoiQ0SkArgAmBNWZg5wiTjGAtus/2x+cPuCTpsW/bS9\\nlzLBEg2Ak633t78NEpiBq73dn+AzE3U3uS+rfWhV9aRoz4nIx+4FCiLSD/gkQrEvAWeKyOk4I+X1\\nEJGnVPVbaaqyMcbkPFVtE5FrgRdwhu16TFXfFpHJgecfBObhDNnVhDNs17ezVV+TOC99QRPpL5qp\\nC6YuuQRmzvQ20oDXPr12sZcBECf5mXtE5E5gU9BFYV9Q1ajzpIjIBOAGr6McVFdXa0NDgz+VNcaY\\nABFpVNXqbNcjHey4afzgdYQGvy8gM7nLj+NmLo9ycAfwjIh8B/gAOA9ARPbHGST89GxWzhhjjDGh\\nogWr4cvjBaeJTppgTM4GtKq6CTgxwvL1OKfJwpcvABakvWLGGGOM6SJaVjWZbKtNgmASlcujHBhj\\njDEmT0QbbSCZUQgSuajNHYPWhusqbjmboTXGGGNM/oiWVU022+qla4L1tTUuC2iNMcYYkzI3qxo+\\nYUI6RyGwvrbGZQGtMcYYY2JKZFpcd1iumTN3Z0zTNeWs9bU1LgtojTHGGBNVIqf1Y2VMEwmKvRo3\\nDqZPh2efha9/3bKzxcwCWmOMMcZElchp/WgZ03T1da2vhylTnPX+859wxBEW1BYrG+XAGGOMMVEl\\nMrVstNEJkhnpwIt0rdfkH8vQGmOMMSaqRC/qitRfNl19Xa0PrXFZQGuMMcaYmFK9qCtdIx2kcwQF\\nk18soDXGGGNM2qVrpIN0rdfkF+tDa4wxxhhj8poFtMYYY4wxJq+Jqma7DlkhIhuADzK0uX2AjRna\\nVjbY/uU32z9/DVLVPhncXsZk+LgJ9t7Md7Z/+SvvjptFG9Bmkog0qGp1tuuRLrZ/+c32z+SqQv/f\\n2f7lt0Lev3zcN+tyYIwxxhhj8poFtMYYY4wxJq9ZQJsZD2W7Amlm+5ffbP9Mrir0/53tX34r5P3L\\nu32zPrTGGGOMMSavWYbWGGOMMcbkNQto00BEviAiL4nIu4H7XjHKlorIUhH5SybrmAov+yciB4jI\\nfBFZISJvi8h12ahrIkTkVBF5R0SaROSmCM+LiMwIPL9cREZlo57J8rB/FwX2600RWSgiI7NRz2TE\\n27egcseISJuInJvJ+hlv7NiZf8dOO27m73ETCuvYaQFtetwEvKyqQ4GXA4+juQ5YmZFa+cfL/rUB\\nP1TVEcBY4BoRGZHBOiZEREqB+4HTgBHANyPU9zRgaOB2JfDrjFYyBR737z1gvKoeAUwjT/pQedw3\\nt9z/A17MbA1NAuzYmUfHTjtuAnl63ITCO3ZaQJseZwEzA3/PBM6OVEhEBgBfBR7JUL38Enf/VLVZ\\nVZcE/t6O88XTP2M1TNwYoElV16hqCzAbZz+DnQU8qY5FQE8R6ZfpiiYp7v6p6kJV3RJ4uAgYkOE6\\nJsvL/w7ge8CzwCeZrJxJiB078+vYacfN/D1uQoEdOy2gTY/9VLU58PdHwH5Ryk0Hfgx0ZKRW/vG6\\nfwCIyGDgaOBf6a1WSvoDHwY9XkvXLxEvZXJVonX/DvC3tNbIP3H3TUT6A+eQR9mhImXHziB5cOy0\\n42aofDpuQoEdO8uyXYF8JSL/C/SN8NT/BD9QVRWRLkNJiMgZwCeq2igiE9JTy+Slun9B69kL55fd\\nFFX9r7+1NOkgIl/BOTAfl+26+Gg6cKOqdohItutS1OzY6bBjZ2Ep0OMm5NGx0wLaJKnqSdGeE5GP\\nRaSfqjYHTq1EStN/CThTRE4H9gB6iMhTqvqtNFU5IT7sHyJSjnNA/q2qPpemqvplHXBA0OMBgWWJ\\nlslVnuouIkfinMY9TVU3ZahuqfKyb9XA7MABeR/gdBFpU9U/ZaaKxmXHzoI6dtpxk7w9bkKBHTut\\ny0F6zAEuDfx9KfDn8AKqerOqDlDVwcAFwD9y5YDsQdz9E+fd/yiwUlXvyWDdkrUYGCoiQ0SkAud/\\nMieszBzgksBVu2OBbUGnD3Nd3P0TkYHAc8DFqroqC3VMVtx9U9Uhqjo48Hn7I3B1Lh6QjR078+zY\\nacfN/D1uQoEdOy2gTY87gIki8i5wUuAxIrK/iMzLas384WX/vgRcDJwgIssCt9OzU934VLUNuBZ4\\nAecijGdU9W0RmSwikwPF5gFrgCbgYeDqrFQ2CR737xagN/BA4P/VkKXqJsTjvpn8YMfOPDp22nET\\nyNPjJhTesdNmCjPGGGOMMXnNMrTGGGOMMSavWUBrjDHGGGPymgW0xhhjjDEmr1lAa4wxxhhj8poF\\ntMYYY4wxJq9ZQGuMMcYYY/KaBbTGGGOMMSavWUBrjDHGGGPymgW0xhhjjDEmr1lAa4wxxhhj8poF\\ntMYYY4wxJq9ZQGuMMcYYY/KaBbTGGGOMMSavWUBrjDHGGGPymgW0xhhjjDEmr1lAa4wxBUhEThWR\\nd0SkSURuivD83iIyV0TeEJG3ReTb2ainMcb4QVQ123UwxhjjIxEpBVYBE4G1wGLgm6q6IqjMT4C9\\nVfVGEekDvAP0VdWWbNTZGGNSYRlaY4wpPGOAJlVdEwhQZwNnhZVRoEpEBNgL2Ay0ZbaaxhjjDwto\\njTGm8PQHPgx6vDawLNh9wKHAeuBN4DpV7chM9Ywxxl9l2a5Atuyzzz46ePDgbFfDGFNgGhsbN6pq\\nn2zXw4NTgGXACcBBwEsi8k9V/W9wIRG5ErgSoHv37qOHDx+e8YoaYwqbH8fNog1oBw8eTENDQ7ar\\nYYwpMCLyQbbrAKwDDgh6PCCwLNi3gTvUuZCiSUTeA4YDrwcXUtWHgIcAqqur1Y6bxhi/+XHctC4H\\nxhhTeBYDQ0VkiIhUABcAc8LK/Ac4EUBE9gMOAdZktJbGGOOTos3QGmNMoVLVNhG5FngBKAUeU9W3\\nRWRy4PkHgWnAEyLyJiDAjaq6MWuVNsaYFFhAa4wxBUhV5wHzwpY9GPT3euDkTNfLGGPSwbocGGOM\\nMcaYvGYBrTHGGM/q6+H22517Y4zJFdblwBhjjCeffQYnnggtLVBRAS+/DOPGZbtWxhhTxFPf2vAz\\n6bNz5042bNjAzp07aWuziYdySXl5Ofvuuy89evTIdlUKlog0qmp1tuuRjCVLlpxSVlZWo6p9iXAG\\n75NPNg1qb+/X+bhnT9h770zW0BiT11RBlV0tsGsndNsDulXAuubmlj59+jRHeVWHiHzU1tZWN2rU\\nqBeirdoytMZX27Zt4+OPP6ZPnz707duXsrIynJk1TbapKjt27GDdOmc4UgtqTbAlS5ac0q1bt/sG\\nDx7cUllZuaWkpKRLtuOtt1YMamk5lI4OKCmBYcNgr72yUVtjTN5pa4O33nLuAcqBdmAHtPft23b4\\n4YdHHGWlo6NDduzYsff7779/35IlS66NFtRaH1rjq40bNzJgwAB69epFeXm5BbM5RETYc8896d+/\\nP5988km2q2NyTFlZWc3gwYNbunfvviNSMAu7g9j+/S2YNcYkaMcOaGtDgXZKOm8dEjsULSkp0e7d\\nu+8YPHhwS1lZWU20cpahNb5qaWmhsrIy29UwMVRWVtLa2prtapgco6p9Kysrt8Qrt9deoYHsp5/C\\n9u1QVWUBrjEmho4OANq792D5jmEhZ3p4/624L6+srNwZ6A4VkQW0xneWlc1t9v8xUZREy8xG8+mn\\nsGoV1gXBGBNfIKAtKy9h2AGJ/xAOHJ+ipnMtoDXGGJOU7ds7v6Po6HAeW0BrjInIPViUlHQ50+OH\\nnO9DKyKnisg7ItIkIjfFKHeMiLSJyLmZrJ8xxhSrqionMwvOfVVVdutjjMlhQQFtOuR0hlZESoH7\\ngYnAWmCxiMxR1RURyv0/4MXM19IYY4rTXns53QysD60xJq40B7S5nqEdAzSp6hpVbQFmA2dFKPc9\\n4FnALt02abNy5UpEhJdeeiml9Xz/+9/njDPO8KlWu02fPp0jjjiCDvegYUwG7LUX9Ou3O5j99FNo\\nbnbuc0Ein9t0fDYz+bn06xgF1hauQjhe50xbuPtaWhqyePr06Zx99tmV7e3tKdQu9wPa/sCHQY/X\\nBpZ1EpH+wDnArzNYL1OEGhsbAaiuTn7M/NWrV/Pggw9SW1vrU612mzRpEhs2bGDmzJm+r9sYL9yL\\nxNatc+5zIaj1+rlN12czk59LP45RYG3hKpTjdc60RZQM7aRJk9i6dSv33Xdf7+RXnmBAKyJjRaRW\\nRP4uIstF5F0RqReRJ0Tk2yLSK5XKJGk6cKOqxv2ZIyJXikiDiDRs2LAhA1UzhaSxsZGDDjqIXr2S\\nf5tPnz6dkSNHpvyFE0llZSWXXHIJd911l+/rNsaLSBeJZZvXz226PpuZ/Fz6cYyC3GuLwYMHJxxI\\nFerxOq/bws3AhgW0lZWVfPWrX2279957ow7J5YWngFZELhWRN4GFwPXAnsC7wL+ALcAXgUeAdYHg\\ndkgqlQqyDjgg6PGAwLJg1cBsEXkfOBd4QETOjrQyVX1IVatVtbpPnz4+VdEUiyVLlnDMMccwa9Ys\\nRo0aRWVlJSNGjGD+/PmeXr9r1y6eeuopLrzwwpDlTU1NlJeXc8stt4Qsv+qqq6iqqiKRKZovuOAC\\nVqxYwcKFCz2/xhi/5OJFYl4+t+n+bGbqc5nqMQqsLVzR2gGsLYIl1BYx+tCefvrp7atXr97jpZde\\n6u59z8KoaswbsBxoxrno6mhAopTbG7gImAfsAM6Pt24P2y4D1gBDgArgDeCwGOWfAM71su7Ro0er\\n8d+KFSuyXYW06Ojo0KqqKh04cKCecsop+uyzz+qcOXP0kEMO0QEDBnhax4IFCxTQxYsXd3lu8uTJ\\nWlVVpRs3blRV1bq6Oq2oqNCXXnopoXq2t7drVVWVTp06NWa5Qv0/5QKgQVM89mXjtmzZsvdVtSHW\\n7e233464z9u3q65f79wH/51tXj+36f5sxvtcdnR0aGtra9xbW1tbyvsaT7bbIpJBgwZpTU2N5/Lp\\nPl6rWlsE89wWq1erLl6sGigX7I033vise/fu7dddd916jXEMChynIseA0Z7oLADXAXvEKxf2mpHA\\nKYm8Jsa6TgdWAauB/wksmwxMjlDWAtosK9RA6d///rcC+rWvfS1k+f3336+Afv7553HXcccdd6iI\\n6K5du7o8t379et1zzz31hhtu0IcfflhLSkr06aefTqquxx13nE6cODFmmUL9P+WCYgtot29XbWx0\\nvqcaG3MjkHV5/dxm4rMZ63M5f/58BeLexo8fn/K+xpPttogU3A8aNEinTp3qObhP9/Fa1doimOe2\\nePdd50CxeXOXp958883PRo0atf3YY4/dpkkGtHGH7VLVX8VN83Z9zRuBbGrKVHUeTtY3eNmDUcpe\\n5sc2jf+kLjdmp9KahCZC6rRkyRIAbrvttpDlGzdupEePHp3T/Z5//vmsXLmS0tJSysvLuf322znx\\nxBMBWL9+PT169KCioqLL+vv168eUKVO4++67aWtrY8aMGZx33nkhZaZNm8asWbNoamriueee4+yz\\nI/asoU+fPqxatSqp/TQmhMjo4IcjIhTZi/+/vXuPk6Mu8z3+eSbJ5MItYUCEBAQlyD2QzEIGYTMu\\nisB6uBzcVURgBUQOoLC7RyTuYiYb16i8BNZdEBB3hZVdliO44oqIRgMqEyQhhFsghCSQYKIhJIFc\\nJzPznD+qe9LT6Ut1d1V3Vc/3/Xr1q6era6qequ7+9dO/+l1gctxxeLyf21o+m1F8LqdMmcJTTz1V\\n9nj2KNGGI+yxlou30efiscce44Mf/OAuy2fNmsWsWbMGHk+bNo25c+cW3EaYc7F+/XouvPBClixZ\\nwujRo9lvv/247bbbOPTQQ8ueh2Y7F1D9d1eYc3HD3/8D//Ef/87yFa/y4De+wTkTJxbcTltbW+/y\\n5ctHFXwyhESPQyuSFAsWLODggw/m/e9//6DlCxcu5Nhjjx14fMcddzB27NiB50499VTefPNNWlpa\\n2LZtGyNHjiy6j4kTJ7J9+3ZOPvlkrrrqql2e//CHP8wFF1zAJZdcUjLW0aNHs3Xr1koOT6Qphf3c\\n1vLZjOJzufvuu3PccceVO5yS01aHPdZy8Tb6XBRK7s866yw++tGPcvnllw8sK5XchzkXZsa1117L\\nhz70IQC+9a1vcdlllw0khuXOAzTPuYDavrug+LnYtMk59X2H8umbbuSSbBJeZBzaUaNG9W/btq3q\\n2q9YElozO8Ddfx/HtiWdqq0ZTYoFCxYwefKu9VALFy7k7LN3Do2cLRAANm7cOGjdtrY2NmzYUHD7\\nc+bM4bOf/SwdHR389re/5dlnnx1U2ABMnTo1VKxvvfUW++yzT6h1RUpyX5D78MUXX5xy5JG71tNu\\n2pTMyRXCfm5r+WxG8bksVhOXr1RNXNhjLRdvo8/FHnvssUtP+tbWVg444IDQPezDnIuxY8cOJLMA\\nJ510EjfddNPA41LnAZrrXED1311Q+ly887bTecxhADjGjuEjYcyYgtvZsGHD8HHjxvWGOrAC4hqH\\ndl5M2xWpO3dn4cKFHH/88YOWr1+/ntdee22X5X/913/Ne9/7Xs477zweeOABWjK/Rg8//HB6enpY\\ntWrVoPWffvppzj333IHagYMOOojp06dXHe/y5ct3+TUuEoktW2DBgl1uu7+8gP1/H9wXer7gbdEi\\niPFKQiWf23p8Nkt9LrM1ceVud9xxR83HWk6jz0Wtqj0Xt9xyy6AEr9h5gOY9F5V+d0H5c7HH7kFl\\nVh8tbLbd6d3vwF0mVshauXJl6/ve975t1R5v1QmtmZ1V7AZU3QZCJGleffVVNm7cuMuv3IULFwLs\\nsvzmm29m2bJl3HvvvVx33XX09PQA8Kd/+qcA/O53vxtYd+nSpZxxxhmcdtpp/PM//zOtra3MmDGD\\nhx9+mMcff7ziWDds2MCSJUsG9iUSuaA3ce23HTtinXmhks9t3J/Ncp/LbE1cuVuxxKfSMqqURp+L\\nWlVzLmbOnMmyZcuYPXv2wLJC5wGa+1xU8t0F4c7F7plBuMxg9OjgVsjbb7/Na6+9NuqUU06pulCo\\npYb2h8C1BOPS5t8SMPqgSDSys6wUKhRGjhxJoUuwAKeffjrr16/nueeeA4IBsU844QR+/OMfA7Bm\\nzRpOO+00jjjiCO69996BX8MXXXQRhx9+ONdff33Fsf7kJz+htbWVc889t+L/FSlrzBiYPLmq26b3\\nT2ahTeZpJvMmmUusVXb2CqOSz23cn824P5fVllGFDLVz8ZWvfIWHH36Yn/70p4zJuRSefx6g+c9F\\nVrnvLqjgXGQ+4y0tVqxiFoC5c+cOGzFihH/yk59cX9XBBvuqejitl4GDizy3strt1uumYbviMZSH\\ng9qyZYsvW7Zs4PETTzzhY8eO9bdyhij5t3/7N99zzz198+bNVe9n2rRp/sMf/rDgc6effrp/6lOf\\nKruNofw6xY0hNmxXWL//fTBiz1NPuf/hqdeCP9asqXp7Uav1sxnF57KeSsU7VM5FV1eXn3DCCb5h\\nw4aCzyelvI5brN9dPT3BZ33hwpLn4qSTTuo9++yz13mZMqimcWiL/iP8PXBCkedmVLvdet2U0MZj\\nKCdK69at86lTp/pRRx3lkyZN8pNOOsnnzJkzaJ0dO3b44Ycf7jfeeGPF258xY4aPHz/eW1tbva2t\\nzcePH+8rV64ceH7hwoXe2trqr7zyStltDeXXKW5KaAvLHa92zVOvJy6hrfazGfL8qksAACAASURB\\nVOXnsh7Kxes+NM7F888/74C/733v80mTJvmkSZM8Py9ISnkdt1i/u7Zv9xmf+YyPf9e7Sp6LESNG\\n+HPPPfec15DQmnv4Sz5mNtndn666OjhB2tvbvZIpRSWcxYsXc8QRRzQ6jESbN28eTz/9NFdeeWWk\\n233kkUdYv349559/ftl19TrFx8wWuHv0k7/HbNGiRSsmTZr0Zql1io1yUEz+6AfZx21bV9L61h9g\\nwgR4d03Tt0cqjs9mJZ/LJNG5CCShvE6Kqs7F9u3w3HPQ2gp5I/dkPfLIIzzzzDPbr7/++ufLbW7R\\nokX7TJo06eBCz1Wa0G4EznH38BNDJ5QS2ngoUUoHvU7xUUIb2LQJliwJpm9vaYHDDssZ0mvVKliz\\nBsaPh/33rzV0EUmqbdvg+edh5Eg45piiqz3//PNbjj766MXlNlcqoa20U9h/AA+b2Xn5T5jZyWb2\\nmwq3JyIiTeidd4JkFoL7d97JeTI7QUAFFSoikmIlJgWJSkUJrbv/H2A2cJ+ZXQFgZkeb2Y+Bx4Fx\\n0YcoIiJps8ceOycEamkJHg9QQisyNNTxM17xTGHu/g9m9nvgNjM7H/gAsBK4BLgn4vhERCSFdt89\\naGZQcAYxJbQiQ0P2M560GloAMxsHTAT6gFMIZgWb6O7fc/f+iOMTEZEqmNnpZvaymS01s4IDZJpZ\\np5k9Y2YvvPnmm5H3ztp996CJ7C7T4SqhFRkaMp/xnl6Lcx4VoMKE1sy6gOXAVcA3CWpl24GbSvyb\\nDDGVdDSU+tPr0/zMbBhwK3AGcCRwvpkdmbfOWOA24Cx3P2rcuHF/6O/vj70aZdMmePudcOutXh3r\\nZGIiErMtW4Lvmx07jCVLavs8Z8qnohWnlTY5+BJwF/AP7r4GwMxWAg+a2X7Ap9x9R7XBSvq1tray\\ndevWQTOuSLJs3bqVESNGNDoMidcJwFJ3XwZgZvcBZwMv5qzzSeBBd38dYPjw4W9s3bp1r912221r\\nXEFlRz7Yp9/YE+jpcVpLrFdwhAQRSaT8YfoAtmyBMYBjA51Dq/0sb926dZSZrSn2fKVNDo5w9yuz\\nySyAu88BPghMAx6pLkxpFvvssw+rVq3irbfeYseOHaoNTBB3Z8uWLbzxxhu8613vanQ4Eq/xBH0b\\nslZlluU6DBhnZnPNbMHtt9/+2xUrVrRu3rx5dFw1tdmRD5xg8zt6CpcPJUdIEJHEyf4IfeMNePll\\nWLs2WL7b6OAz7hToHBpSf3+/bd68efSKFStae3t7ZxZbr6IaWnd/tcjyp83sZOBnFcYpTWavvfZi\\n5MiRrF27lnXr1tHb29vokCTHiBEj2G+//dhzzz0bHYo03nBgCnAqMPrb3/5291FHHTX7lFNOuczd\\n302mwmPz5s17bNmyZXcIfhRZDZ07tm+Hdetgm7/DFt6ib/M2hrGl6HruQXPb4cNhw4aqdysiMdu4\\ncfBndO3aYM6Ukb4N3nyT3uGbaNnHWbmy8P+vWbNmeF9f3z5FNt9vZmt6e3tnTp48uWieWfEoB8W4\\n+1IzOymq7Ul6jRo1igMPPLDRYYgMZW8AuR/CCZlluVYB69x9M7DZzB6/+uqr33H3ouV4FBPSdHfD\\num/+Gyc/cAlcfDF873tF15s7Fzo74bjjatqliMQk+zlta4Orr4YdmUanLS3wla/A9OMfgTPOgNNO\\ng58Vr/M88sgjn6t1QpqyCa2ZPQTMcPeF5dZ19z+Y2SjgSmCLu99eS3AiIlKVp4CJZnYIQSL7CYI2\\ns7l+BPyLmQ0HWoETgZvjDqyjA145agQ8AGtX97JvifU6OuKORkSq1d0Np54KPT3BzLZ//ddw001B\\nM6GRI4Mfo6zLXKUdHln9aVFh2tCuAOaZ2ZNm9nkzm5wpAAeY2QFmdo6ZfRdYDVwKPB19uCIiUo67\\n9wJXEzQDWwzc7+4vmNkV2Ulx3H0xQb+HZ4HfAXe5e9m51CvR3Q2zZwf3uctmzQ6+QubO6R30nIik\\nx9y5QTLb1xfcjx0Ljz8e1MzOmZP5Qdpbv4S27B7c/fNm9k/AtUAXsBfgZvY2sB0YS/Dr3ggKxWuB\\n77t7X1xBi4hIae7+MPBw3rLb8x7fCNwYx/7za2+yX3Bz58K2vuCrZ1jfDubOVU2sSBp1dgaf7exn\\nvLOzwJWVbEJbh5F1QqXMmc5gnzOzvwU6CC5NHQCMAtYBLwGPu/trcQUqIiLpkV97k01cOzvhmeHD\\noQdaW3qDy5IikjodHcEP1Wxb94I/TJNUQ5vL3XuAxzI3ERGRggrV3kDwpTf2H0fAF+ADJ/YyTrWz\\nIqlVtq17HRPaiqe+FRERKSdbezNrVk57uowjjgm+3DZt7OUjH4E772xQkCISq6UvBQntH9cnrIZW\\nREQkrKK1N5namlde3MGjL8KjjwaLL7+8frGJSLy6u+Hfv9HLbcDDPxvO+7vjbS9fdQ2tmb3PzH5l\\nZsvM7KbMcF3Z534XTXgiItJ0MgntcHZOvPLAA40KRkTiMHcuA00OtvcPDx7HqJYmB7cCDwJ/AewL\\n/MLMsjP0RtadzcxON7OXzWypmV1f4PkLzOxZM3vOzJ4ws0lR7VtERGJQIKE977xGBSMixRQaei+s\\nzk4YNTz4jHvL8Ng7gNbS5GA/d//nzN8XmtkM4OdmdhrBtL01M7NhBInzhwlmtXnKzB5y9xdzVlsO\\nTHP39WZ2BnAnwSgMIiKSRJkhfA49uJfTDguSWTU3EEmWQkPvQZlRDXJ0dMC7r+yFf4KzzxvO/jF3\\nAK0loR2d+8DdZ5pZH/AosHvhf6nYCcBSd18GYGb3AWcDAwmtuz+Rs/48gikeRUQkqTI1tO8a11tq\\nNkwRaaD8offuuQfuvnvXsaVLOWR8MBfu/gcme5SDV8zsz3IXuPtXCGaeObSmqHYaD6zMebwqs6yY\\nS4GfRrRvERGJQ3YIn+zE7yKSONmh94YNC+5h17Gly0rJsF0XAgvyF7r7TODoGrZbFTP7IEFC+8US\\n61xuZvPNbP7atWvrF5yINK2urq5Gh5A+2S+33t7S64lIw+QPvXfRRYMT3FBtYpM6sUKWmY109w3F\\nns9r41qLN4ADcx5PyCzLj+dY4C7gDHdfVyKuOwna2NLe3h5JO18RGdpmzpyppLZS2WkwldCKJFr+\\n0HtlZwbLl7Spb7PMrBO4G5hgZm8DzwJPAwsz9y+6e3+E8T0FTDSzQwgS2U8An8yL6SCC0RYudPcl\\nEe5bRKSg635+Hb9Y9ovgwWdh8h2TGxtQ2qiGViSVys4Mli/BNbS3AluAq4F9gOOBc4BrMs9vA8ZE\\nFZy795rZ1cDPgGHAv7r7C2Z2Reb524EvA23AbWYG0Ovu7VHFICKSa1vvNm584sadC/aHhWsWNi6g\\nNKqgDW13d4U1QiKSHAlOaA8B/sLdf5K70MzGApOB46IKLMvdHwYezlt2e87flwGXRb1fEZFCNvVs\\nAmCvkXvxy4t/yZQpU1iwYGd3gildUxoVWnqErKEtNGyQklqRHP39sGIFeG2tKJ9+Gp58Ek48ESZH\\necHpzTeD+wQmtIspMGlCpj3tLzM3EZGmlU1o9xy5J5P3nwyrCe4lvGx7uu3bYenSoqstegAO3A59\\n/TBse/C4Y98iK7/nPXVppyeSKBdcAPfdV/NmJmdusUlCQmtmpwLz3X0jcDNwOfDfcQcmIkNDV1dX\\nqjpVbe7ZDMDurcFw2zNmzGhkOOmUTTw3bICJE4uudkXmBkA/8M3MrZATT4R58yILUSQVFi0K7idM\\n2Dm2VhHbtsHWbTB6FIwatXP5+g3w1ls7H++9N4wbG2GM48bBGWdEuMHCwqTMPwfczF4l6KR1hJnd\\nD3zJ3Yv/tJYhK20JijRW2kYJ2LwjSGh3a90N0LBdVdlzT7j4YvjNb8quum0bbN0Ko0cP/hIe0N8P\\ny5fDs89GH6dI0vVn+uH//Odw+OFFVxvUfGfr4OY7L+U37fmfdDbtCZPQHglMydwmA3sDHwPOM7MV\\nDB7l4Gl3/2M8oUpa5CYoSm6l2WSbHGRraKUKZvC974VadVTmVlRPD4wcqRETZGjKJrQtpacVyJ/1\\na+7cnUlrdrzZtHe+LDuxgru/5O73uvvfuHunu+8FHA5cQDBcVhvwBYKOW6tjjVZSZ+bMmY0OQRKo\\nq6sLMyMzMsnA32n48ZNtcrDbiN0aHMnQ1N0Ns2cH90AwyjsE39QiQ032fV8moc2f9St/UoSODpg+\\nPb3JLFQ5U5i7L3H3+9z9C+7+Z+4+DjgMOD/a8CSMJCQBxRIUkUK6urpwdzzTMzf7dxLey+Vkmxyo\\nhrb+spdNb7ghuO/uZucXeX9/zT29RVInZA1t/qxfaU5ci6ll6ttB3H2pu98f1faaSdxf0kmoBc1P\\nUHKlqfZN4pfbHCWNsk0OVENbf4Uum2KmWloZurIJbfYzUEIz1MKWEllCK8UlIeGst7TWvkn8sp+H\\n7H3aRgkYaHLQqoS23opeNtXMYzJUhayhHQp0BlIqyW0Q05agSGMl4T0bVldXlzqFxWiX9rF5il42\\nzdZOKaGVoSZkG9qhQGcgJnEnnElug5gbg5JbgcKfh+x9Un6IlbNlxxZmfm8mL617CVCTg6gVbB9b\\nQMHLptkaWjU5kKFGNbQDhvQZiPNLNMkJZ1Sa6ViSqJnOb6HPQ/Y+LZ+Lj//g4/Bp+P6z3wdgj5F7\\nNDii5lKwfWxYanIgQ1UFbWjDKHeVJMmqTmjN7JdmNiHKYOotLW1by33ZN6oWNMz5S8s5TiKdu2TI\\n1i7/z2/+J1jwBvASrPjpikaG1XTKDStUkjqFyVAVYQ1t2KskSVXLGegExkQUR1OrNeEsl9gkpXYr\\nKXFIsmU/D2lpjpKtXT7siMMAWPzVxfh/Ojd13dTgyJpLTcMKqYZWhqoI29DWdJUkAYZsk4MFCxYA\\n9WnD10yJXqm2wdnEO8kd1pJuKJy7tA7b1dsfJEvDW8JMsCjVCDOsUMFLouoUJkNVhDW0NV0lSQAr\\nNG5oqH806wcOd/cl0YZUH+3t7b5gwYJB46YmaZrW3AQx14wZMxITo5kNOn/5j4stk3B07pLloJsP\\nYuXbK1lxzQreM/Y9RdczswXu3l7H0Oqmvb3d58+f37D9d+fPOZ+tyT3kEFixApYtC/4WGSr22AM2\\nbYKNG2HPPWveXHd3Y6bAjaLcHLI1tIXU0maxM+KfMmnpVDYUahSHGr12hamGtvGKXhJVDa0MVRF3\\nCkvz5AtDOqGNsg3fY489Ftm2KtWoBCRbW1wq8U5LO8kkSnJnv6FoR/8OAEYMG9HQOMzsUTObV2D5\\nMWa2w8wuyDw+3cxeNrOlZnZ9ie39iZn1mtnH4ow7CmUnVlCnMBlqNGzXgCF9BrJNDJJewzht2rSS\\nzzcqAQlzjpJ0HtMmjnPXiNejWd4DCaqh/S1wvJmNzC6woAC7DXjC3e81s2HArcAZwJHA+WZ2ZP6G\\nMut9HXi0LpFH4OKL4TOfyes4VkWnsDQPTyQyQBMrDBjyZ6CWS/udnZ0Fk+Gomx/k1/4mNUFQbWzy\\nFfvxU+qHXa3vt2ap8d3Rl6mhbWlsDS1BQtsKHJ+z7CJgKnBV5vEJwFJ3X+buPcB9wNkFtvU54AHg\\nj/GFG41s+9nvfAfuvnvwc5u3B5dbFy0Il9CmfXgikQGqoR2gM1BCuS/yuXPnFkyG58Y81sXMmTMT\\nV7OcpA51UrlSP+yaJSGtVbaGttFNDoB5QB9BAouZjQW+AfyLuz+fWWc8sDLnf1Zllg0ws/HAucC3\\n4w44CsXaz3Z3w5JXgxraKz/bFyo5TfvwRCIDIm5Dm2ZKaHPk1zA28ou8XMKatE5jtZ4rJcPxacSP\\nn6T94IpCtg1to5scuPsmYBGZhBb4R6AfqPQSyS3AF929v9RKZna5mc03s/lr166tON6oFGs/O3cu\\n9HrwZe47ekMlp2kfnkgEAPfgBpApa4eyWhLaDwOvRxVIEtTyZVuunWul8hPWbLKdTRybIUHI1Uy1\\ngHG9JtVut9IfP9nOfrUmpEn6wVWp/Dj7vZ/+TN43zBJRE/JbYKqZTQauAL7g7m/nPP8GcGDO4wmZ\\nZbnagfvMbAXwMeA2Mzsnf0fufqe7t7t7+7777hvlMVSk2MQLnZ3Ql/mRMXJ4X6jktKZJHESSIls7\\na6aEFnZ+0Qy125QpU7yQGTNmOLDLbcaMGQXXr4fgZSr+uFGxRXmu8o8pzeI6lii2W802wvxP/mue\\n+z9pfG3zY97eu93pwrmh/LEA8z3m8gv4y8zn7Xng8QLPDweWAYcQtLddBBxVYnvfAz5Wbr/Fys1G\\n2zjpZHfw5259rNGhiNRPT487uA8b1uhIahZFuakmB3myNTPB+W1MzVL+vkp1tmp021Xf+YWIu1c0\\n8UMzXpZOurg67uXWsFfy/k2y3OY9I0dnBhToT8z79LeZ+8OBq/OfdPfezPKfAYuB+939BTO7wsyu\\nqF+Y9bHnuKCG9ujDNQ6tDCFVtJ9t6tE9as2I03orVdNApnaGBtUsldtvbm1Y/rr1rK2lQC1ctees\\nkccRhbhq9qdNm9bwKwZh9pWNqdGx1qJU/Bu2bghqaKeXf39TnxravYDtwC1x7yv3ltQaWj/1VHdw\\nf/TRgUVPPOH+1a8G9yJNacuW4H0/alSo1Z94wn306KBCd/ToZH02oig3G55YNuqWXzDX68s4bHIQ\\nVv669UzCc/eVPa6oEtpG/ZiIQpSxF/rRUG/F3rPFPjPZWxrl/jDLPYa1m9cGCe115Y+rTgntN4HV\\nwF5x7yv3ltiE9iMfcQf3hx9292R/cYtEZtOm4H0/ZkyoH3Bf/Wrwmci2UvjqV+sXajlRlJuJb3JQ\\nbrYbC3wr8/yzmU4SFavXqAHVjANay7pRK7bvmTNn1hRP9rK0mhskT6n3bO5nJl8CLstXxXI6V2Tf\\ny1/7xtcA2G30bo0KCzMbY2YdZnYdcA1wpbtvbFhASZKdWCEzyLyG5ZIhIfN+76Ml1LjKzT66hxX7\\nMiq4stlU4HSC4WIOAEYDbwIvA48B/+3u6yMLLpjFZgnBiAqrgKeA8939xZx1ziQYHPxM4ETgn9z9\\nxHLbPm7ycT7nN3MKPrfPPvvw5ptvlvz/r3/j63zxui+GPJLKth1mndx1v3DdF7jxGzfu8twXrvtC\\nVTGGVSjOSmLP9/VvfD2S46j2tYnK17/xdYCqYyh2Hk466SQeeuih0NuI6hxk32Oltpf7uodZP5+Z\\nsffovWuOtRqlxtrNlo8rN67koFsOYvwe41n1N6tKbs/MFrh7e9RxmtlZwI8IRiuY7e63Rr2Pctrb\\n233+/Pn13m15Z58NDz0EP/whnHPOwMQJPT3BF7dGMpCmtGEDjBvHtpF7snvvRvr6gmR11iyYPr3w\\nv3R3Bz/wOjuT9ZmIotwMldCa2cXA/wWOAt4h6DG7FtgK7E3Qk/YwgjZd9wMz3X15LYFl9tsBdLn7\\nRzKPpwO4++ycde4A5rr7f2Yevwx0uvvqkts+wJzP1hqhiETl8smXc8f/uqOhMZjZQBKb+/fy9ct5\\n77fey8FjD2b5NaWLtrgS2iRIbEJ73nnw4IPwgx8Ef1P+izupX+wiob31FrS10bvHOPbsfSvVP+Ci\\nKDfLjhBuZs8C+wL3EEyv+IwXyILNbC/go8AFwItm9lfu/l+1BEfh2W7ya1+LzYhTMqEd1jKMvUbv\\nVXVgb617i73bKq9Ryv2/rVu2MnrM6F3WKba8kPx1C8VVyfYqUWi7xfZV6nxt3bKVrVu37rJ89OjR\\nVcWdu68wr1Mc56fa90dU24lq/9ltAaG3V+m++/r72Lh9I4+//nhV8cUld3SGpEyqIEVke3n3Vjb1\\nbZoTAJHsKAfDW1uY8zP9QAvT+eAaYFQlDXOBScBHam3gSzDY9105jy8kmN4xd53/AU7OeTwHaC+y\\nvcuB+cD8gw46KGRT5Z2i6DhGzJ18CsUSx34qFTaG7HqVdsYr1UEpqtiqjSHssRRar9T/5j8XdcfG\\nardX6flcvn6504UfdHPln8moFTu2F/74gtOFH/EvR5TdBnXoFNaoW2I7hZ1/vju4f//77l6+U1iS\\nO8eIhLZmTfAm3nffRkdSsyjKzYYXkCWDgw7gZzmPpwPT89a5g6Bdbfbxy8D+5bZdTcFcarisem6j\\nUo1KaKtJiKKItVhiW2y/cZyfarZZ6f+UWr+a/ZcbzaDc/1abTP9x0x+dLrzt620Vx1wvz6x+xunC\\nj7ntmLLrKqFtgE99Kvg6u/tudy+csOb2AtcoCNIUfv/74E2+336NjqRmUZSbSR/l4ClgopkdYmat\\nwCeA/F4xDwEXZUY7mAps9DLtZ6sV1fSscY9QkJ1sodGTFlQzckRUg/CX228Szk/SlBrNoJxaRgnZ\\nrTUYOWDzjs2hY61GLa9tb39wKXvEsBERRSORyo5ykGlykN+bu62NQb3AQVPfShOoYmKFplZrRlzo\\nBhwQ4bbOJBjp4FXg7zLLrgCuyPxtwK2Z55+jSHOD/Fs1NQ0UGHe1mKjHm61E/nbj2k8lSsUQ5lxV\\nctm80lrwas9PJc0BSq1HBTWbYdePevrhapvVhNHf3x+M8dqF9/b1VvS/lciPK+z7DnAmBPFxWair\\nDKqhrbfLLnMH9zvvHFiUWyOrJgbSlF5/PXhTT5jQ6EhqFkW5GVdC+3oc243yFrZgjrMd4VBKaGtt\\nZlDpMWT3F+cPi6jPa6Xbi2L/cUwoUs3/jvnHMU4X/s72d6rebzm1fC5+/dqvnS78A9/9QJj9KKGt\\nt89+1h3cb7ut4NNqYiBNacWK4H1fRZ+gpImi3Ky6yYGZnVXsBoyqdrtJE+eEC1FdXofSl9Cj3E+1\\n6n0pP3v5PMx+k3B+ciW9WUiYbVZqtxGZZgc90TY7iKppyY6+YJQDNTlIqLwmB/k6OtTEQJpLdzd8\\n+1+CiRVoSXrr0TqpNhMG+oBfAr8qcNtaa6Yd963WJgeFJGUu+3JxJkGYc1XL+YzrHMT5Glc6fXDU\\n76tGvm8OvuVgpwt/9a1XY9tH9nWq5vV7dOmjThf+oXs+FGY/qqGtt89/3h3cb7451OphpgkVSars\\nFYfDWl5xB996wHsbHVLNoig3a0loXwYOLvLcyloDi/tW6ygHhR7namRykIaENleYeMOsU+8fFNnt\\nx7HdRpg2bVpD9uvuftStRzld+LNrno1tH/nntZLz/JMlP3G68DO+f0aY/Sihrbe/+Rt38F+ccWPZ\\nJFXNDyTtsm3CJ/KyO/i6tonunu4falGUm7XUU/878K4iz91Vw3YTK/8yZRSjHsRxeTlpl9DrJc7m\\nIbUqF0MSRl147LHH6ravfPUY6aCWz0V2lANNrJBMb/wh6OX9i0f6Ss5lD8Hg8z090NcX3M+dG6w/\\ne3bp/xNJiuwoHiNaglEORu/WMjBZSHYkj6H4Xq4ooTWzydm/3f0r7v67Quu5ezTjW6VY2C/PqIYC\\nyxVFElTPRCrMuUpSkp5NPrPCJJ/lXuckJ+P1MGbEGAC27NgS2z7yz2Ul7ym1oU2211YFPzRavHcg\\nSS2m3JBeQzERkHTJtgm/9nNBG9rRu7UU/KE21FRa3fArMzvH3X8VSzQp0NXVNSg5ySY2M2bM2GWc\\n0zSbOXNm3Y4h7DinlYgiAc6O51tINvE0s4G/0yjs+zluUXYKK/W65a8Xlqa+TbaD3jscHoP32Ouc\\nOHwBf/5uYEHhdTtaYd6tsGABTJkS3B+9Hfr6Ydh2eOneYJ3ItbTAMcfs7MAmUoOODujYvR/+CWhp\\nGfihlp3OubOzwQE2gFXyZWxm3wb+CviUuz+Q99zJwNfc/eRII4xJe3u7z58/v6ZtmFlVX/z5SURW\\nVElE2C/0UtKeqEWh2DnIXV7qPFX7Okfx+lWjka/5x3/wce5/4X4O3ftQ2ka3lV1/1apVTJgwoeBz\\nTz75JCeeeGKk8b255U1eXf8qFxxzAd//398vua6ZLXD39kgDSIgoys1YzJoFX/5yo6Mo78IL4Z57\\nGh2FpFB3d1Dr2tYG69YFCWvHmEVw3HFw7LGwaNHAOp2d6RvJI4pys6KENrPTLwM3AJ9z99vN7Ghg\\nNvDnwGJ3P6qWgOql0oK5UJKRrc2qJgnIJg/5SUQjk9G4E+20CZPQhn29wr4mjUpmobEJbdfcLmY+\\nlvyWSjOmzaCrs6vkOkpoG+DFF+HKK+Gdd6r6902bg3/dYw/YfbeIYwPYtAmWLIGpU9WmQSqWbR+7\\nfXswOVhLC4wcCd23LWTSpycHSe3ChY0OsyYNSWgzO74MuA3oBj4ArARmAve4e38tAdVLpQVzoS/7\\nbAIYZUIbRVKRlG2kUbGkftq0aQU7TYVN9sOez4YmlQ1Mpvu9n0VrFrG9b3uo9Ts6OujOSQzuuusu\\nvvvd7+6y3qWXXspll10WSYwjh41k0rsn0WKlux4ooW0OkdZ2PflkkMz+yZ/A7wp2PREpavbsoI13\\nX9/OZcOGwXeuWMCnb22HyZODtjMpFkm5WemwCMA44OvAVqAf+A0wvNbhFup9q3T4GfKmvaWK4aHC\\n/B9VDtkU9ZBV1caRBmHPSfYcFloe5z6lsLDv8UafQzRsV+pFPrTXU0+5g/vkyZHEJ0NL9v3Y0hK8\\njVpagsfP3vVksKC9vdEh1iyKcrPSUQ66gOXAVcA3gUuAduCmSraTFsWGUoKdPwRy/w4zNFOh/8vf\\ndjVDNkXdSz5JowpELaqRJSp9fUo91+ghu9JgqI8EIfUTeY/xYcGwYoOq2ERCyo5q8JWvwB13BPdz\\n5sAxR2UuiGumsEAl2S/QQ9DU4N05y04FNgL/BYyoNcOu162WGtowy+u9vai30czKnZ9yNYGVzuhV\\naWx6/cordY7qPTNfPlRDm3qR19AuWuQO7kcfHUl8Iu7u/tvfBu+rjo5GR1KzKMrNStP6I9z9Sndf\\nk5MQzwE+CEwDHqk8pU63amsy46wBbeba1WpVUgtariZQNYKNV+o9rtdHqpE7uUK2RmzWrOC+5ja0\\nqqGVOPSrhnaQWjPinC/+Q4FXo9pe3LdKaxrqVevT6NqloYAKakDz141rKLUNQAAAFtlJREFUet16\\nT9sr8UE1tKkT+3S4ixe7g/thh0W8YRnS5s4N3lennNLoSGoWRbkZWVrv7kuBk6LaXtIkaZIBqZ/8\\nmsC42nGqfWj0dO4krNhnWcrW0PanYhAgSQvV0A5S9iyY2UNmdnyYjbn7H8xslJn9jZldUXt4kjTN\\nkCRU0iSjGY53qIpjWmlpTvnT4UY+y5KaHEgcsglt9v01xIVJ61cA88zsSTP7vJlNNrNBc/eZ2QFm\\ndo6ZfRdYDVwKPB19uNJozZAkRJWkxtVWWW2gReor8jaz+bI1aEpom1Ju++u6Ug3tIGXPgrt/HjgS\\n+B3QBTwFbDOzt8xstZltJZhY4UHgKOBa4Fh31+jRTUK1lIXFdV50vqunIdCkWh0dMH16TFOGqoa2\\naWVn8brhhuC+rkmtEtpBQp0Fd3/V3T8HvBv4M+BLwD3AjwjGoP0r4BB3n+rud7u7PrVNZObMmUoS\\nJBXUFlkSSQlt04qj/XXoGt/s+0kJLQDDy6+yk7v3AI9lbjKEZBOEoTolrohI1ZTQNq1s++uensHt\\nr6udOjlb45vdXskmMKqhHURnQQoqNUuaSBqoLbLUW9GaNSW0TatQ++tamiFUVOOrTmGDxJLQmtkB\\ncWxX6qfYpVslCZIWQ72ZgZmdbmYvm9lSM7u+wPMXmNmzZvacmT1hZpMaEWdS1Nqxp2QSo4S2qeW3\\nv66lGUJFI26ohnaQipocVGAecFBM25YGGupJgkgamNkw4Fbgw8Aq4Ckze8jdX8xZbTkwzd3Xm9kZ\\nwJ3AifWPtvEqusxbRKEkZmAbSmiHlGLNEMLI1viGaq6gNrSDVJ3QmtlZJZ4eVe12JXlUKyuSOicA\\nS919GYCZ3QecDQwktO7+RM7684AJdY0wQUomoyGVTGKU0A4pFSWlOXLb3U6fHuIfVEM7SC01tD8k\\n6BxWqGHlHjVsVxJGtbIiqTOeYDjFrFWUrn29FPhpoSfM7HLgcoCDDmrOC2+11KhllUxilNAOOR0d\\n5RPZ3AQWqrhKoDa0g9SS0C4FLnH3FflPmNnKXVcXEZGkMbMPEiS0Jxd63t3vJGiOQHt7e1MOcVJt\\njVqh7RT8XyW0kie/mctHPgLbtoF7BVcJVEM7SC1n4d+BdxV57q4atguAme1tZj83s1cy9+MKrHOg\\nmf3KzF40sxfM7Jpa9ysi6aArByW9ARyY83hCZtkgZnYsQXl9truvq1NsiaSJFaSecpu5bN8OP/5x\\nkMxC8HYJdZVAbWgHqfosuPtXis0G5u5RzI96PTDH3ScCczKP8/UCf+vuRwJTgavM7MgI9i0iCdcM\\n0zDH6ClgopkdYmatwCeAh3JXMLODCGZ4vNDdlzQgxqEjN+EoMI53w6ZOldiUe01zRzMw21nZagaX\\nXBLyh5VqaAep6iyY2cioAyngbODuzN93A+fkr+Duq9396czf7wCLCdqOiUhKqea1du7eC1wN/Iyg\\nXLzf3V8wsyvM7IrMal8G2oDbzOwZM5vfoHCHhiK1tA2dOlViEeY1zTZz+cxngnw0+ztnxAi46KKQ\\nO1Ib2kEqakNrZp0EyeUEM3sbeBZ4GliYuX/R3fsjim0/d1+d+XsNsF+Z2A4GjgeejGj/ItIAM2fO\\nLJrUdnV1DaqZzU72MWPGDCXCedz9YeDhvGW35/x9GXBZveMaslpagmS2rw+G7/zqLTZmaa3teSVG\\nX/oS/PrXRZ+esBIe3QoOsBX2/d/Aobuu15FZ94IdmXWB/feGQ68LGceaNcG9amiByjuF3QpsIfjl\\nvw9BAnkOkG27ug0YE3ZjZvYL4N0Fnvq73Afu7mZWtDOCme0OPABc6+5vl1iv6XvrijSzrq6ugcRV\\n0zBLqgwbBjt27FJDmz/CQltb7WPiSozeeSdoS1DCgQxuwM6azK3GdYs6+OAK/6E5VZrQHgL8hbv/\\nJHehmY0FJgPHVbIxd/9QsefM7A9mtr+7rzaz/YE/FllvBEEye6+7P1hmf03fW1ckjVTzKs2suxum\\n9A+jFXZJaPNHWIhiTFyJ0bZtwf1eewU9uYr45jfhRz8Kal6HtcCll8KFFxZe96GH4KabwfuDJge3\\n3AJHHx0ynlGjYMqUig6hWVWa0C4GRuQvdPcNwC8zt6g8BFwMfC1z/6P8FSz41vsusNjdb4pw3yJS\\nR9XUvGrCD0mDbHvK1T1BQvu77j5OOG3wOvnDfdU6Jq7EqKcnuB8zBk45pehqJw2HGx7d+TrO/jRB\\nG4MCXvgN/AbocxjWBz/eAEcX37QUUbbhhZmdamZ7ZR7eTOaSfR18Dfiwmb0CfCjzGDM7wMyy7cI+\\nAFwI/FmmU8MzZnZmneITkQZSza2kwUCNK0HHnSd+XXrormyN7axZam6QSDt2BPetrSVXq+R1zB3x\\nQD9iqhemhvbngJvZqwRDwRxhZvcDX3L3pXEFlhkT8dQCy38PnJn5+zcUnqlMRFJKNa/SDLKzQLW1\\nBUlK39Ygof3A1PJj0YaZZUoaJFtDm0loc2f7yn/Nwr6OUU3sMdSFSWiPBKZkbpOBvYGPAeeZ2QoG\\nj3LwtLsXbOsqIhKGal4l7fJngbrlFhj9t8NgE/zJZE2ukGrZGtoRI3Z5nWupUdePmNqVTWjd/SXg\\nJeDe7DIzO4wguc0mul8A9iLT/jmWSEVERFIgv2PXunWw+55BQrvgd308+mLpmrhStX5SWuznLqeG\\nVh34kqXSTmEAZGaVWQLcl11mZocSJLkiIiJDVv5QXJ2dwLeDup5PfryPV3uL1+hFWes31NTl3GUT\\n2hEjCr/O0jCRjcbr7kvd/f6oticiQ5eaHUiaFewQlJnNqX9H3y6TKOQqNtGClFeXc5fTKUwd+JJF\\n00uISOLkjkkrkkYdHTB9ek6Sk0loR43oG+jN3tYWjNGfOzWqerxXry7nLq9T2C6vszRMVU0ORERE\\npAKZhPae7/XzyPIgmb322l0vj6vHe/Xqcu5ymhxIsqiGVkQSoaurCzMbmCUs+7eaH0hTyCS0xx/b\\nx/TpQUexYpfHVetXvdjPXd44tN3du9ayS2OohlZEEqGa2cJEUqMlU3+UmfpWHYpSKqeGVh34kqXq\\nGloz+6WZTYgyGBERkaaUqaHNJrTqUJRSZYbtksappYa2ExgTURwiIgM0W5g0nbyEFjSYfloMGts2\\np8mBatmTRU0ORCRx1G5Wmk6BhFaSL79ZwXPX9PA+gBEj1IEvYZTQioiIxK1AQqsZwZIvv1nBKy/u\\nCBLanGG79NolgxJaERGRuOUltLV2KFIyXB/5zQref4iG7UoqJbQiIiJxy0toC3UoCpuYNmPv+rgS\\n9Fq3m9+s4JDfDJ5YQZJDCa2IiEjc8hLaWjoU1ZIMJ1FcCXpU2x3UrOBXmU5hqqFNHCW0IiIiccsk\\ntC8+18ePngwS2GzNX1vbziGfwiRczda7Pq4EPZbt9qiGNqmU0IqIiMQtk9BOv66Pn/TtrDHs7Ky8\\nFrFY7/q0tquNOkHPnoe2thgSfyW0iaWEVkREJG6ZhNZ7++jrHzwQfzW1iPm969PcrjbK4a/yz8Mt\\ntwTTDEeW5O9Qk4OkqiWh/TDwelSBiIiINK1MQts6vJ9hfYNrDGutRezuhq4u2L4d+vvT2a42quGv\\n8psZrFsH06fXvt0BqqFNrKoTWnefE2UgIiIiTSuT0N7yiXl83OGYY+DwN4Onnp4Jzz2Xs+zH4Tf7\\n0kvwzb+HUTvgTIcWgxHD4NzhlW2nWZw7HBYMg16H4XGch1deCe5VQ5s4anIgIiISt5EjAZhwz1f5\\ni7ynDs/cqnE48IPcBQ70ANdVucGUG3Q+4jwPo0fHtGGplhJaERGRuF17bdD+MtsGM4S31geXzNva\\nYO9xxdeZ1x00NWhpgakdxdcttZ9at1GrRsdQ0f7b2uCss+oXnIRSUUJrZue7+3/GFYyIiEhT6uiA\\nBx8Mvfqgzk2vFe/ktTcwLmd0g72raId6x2y4YR70AcMMPnMMHHRQ6Y5UUY+okB/DrD+PuO1rwvcv\\ntau0hvZuM/sMcJW7L44jIBERkaGukjFUa+1QlTts1vDh8K//Guy32GgJcYyoUO3QXZUm1sXWb7ax\\nfYeiShPaKcBtwDNm9s9Al7tvij4sERGRoaueCVbusFmvvw7f+U7pRDqOCQuqGbqr0sS61PpRDh0m\\njdFSycru/py7nwJcDnwKeNnMzo8lMhERqZqZnW5mL5vZUjO7vsDzZmbfyjz/rJlNbkScUlg2wZo1\\nqz5jynZ0BJfYL7ooSPaGDSueSGeT7VLr1BJD2GMtlFjXsn6l+5dkqapTmLvfbWb/DXwV+Hczuxy4\\n2t1fiDQ6ERGpmJkNA24lGC98FfCUmT3k7i/mrHYGMDFzOxH4duZeEiKqsVkr3We5msqk1GZWWout\\nZgXNrZZxaDcCV5nZXcA9wMKcZgjv1BqYme0N/BdwMLAC+Et3X19k3WHAfOANd/9orfsWEUm5E4Cl\\n7r4MwMzuA84GchPas4F73N2BeWY21sz2d/fV9Q9XKhWm7Wi1HbfCJNLVJttRdiarNLFOSiIu8ag4\\noTWzEcDxwNSc28GZp68CPmFm/8fdH6oxtuuBOe7+tczlsuuBLxZZ9xpgMbBnjfsUEWkG44GVOY9X\\nsWvta6F1xgNKaBMuTNvRajpuRT1yQTVxVyqbWHd3w+zZ5WNvRK231EdFbWjNrBt4G+gGvgkcRjAH\\nx8eBCcC7gPuAH5jZFTXGdjZwd+bvu4FzisQ0Afhz4K4a9yciInnM7HIzm29m89euXdvocIRwbUcr\\nbV+aTTZvuCG47+6ON+5t2+Cee6LZbj1il+SrKKElSGZnA6cBY9293d2vcff/5+6/d/e33f1vgb8H\\nvlRjbPvlXPpaA+xXZL1bCOYC6a9xfyIizeIN4MCcxxMyyypdB3e/M1PWt++7776RByqVC9Mpq9KO\\nW5UmwNXo7AyGBQNwD4YHiyL5rEfsknyVjnLwEXf/B3ef4+6bS6z6OEHhWJKZ/cLMni9wOztvv04w\\noV/+/38U+KO7LwgTv2oaRGSIeAqYaGaHmFkr8AkgvxnYQ8BFmdEOpgIb1X42HcKMgFDpKAlxjVyQ\\nH9OnPw1mweO+vmiSz3rELslnQa4Y8UbNRgMfcvcf17CNl4FOd19tZvsDc939/XnrzAYuBHqBUQRt\\naB9090+V2357e7vPnz+/2vBERAoyswXu3p6AOM4kuII1DPhXd//HbFMwd7/dzAz4F+B0YAvwaXcv\\nWSiq3Gxucbehze4jbDvaSuKpR+wSnyjKzVgS2iiY2Y3AupxOYXu7+3Ul1u8E/m/YUQ5UMItIHJKS\\n0MZB5aZEIewIDVF3IJPkiqLcrHrYrjr4GnC/mV0KvAb8JYCZHQDc5e5nNjI4ERERCSc/iS2XnMYx\\nG5k0t8QmtO6+Dji1wPLfA7sks+4+F5gbe2AiIiJSUKHa12pqWzUJglQqsQmtiIiIpEexxLWa2tZK\\nJkFQ+1kBJbQiIiISgWKJa7W1rWGaJqitrWQpoRUREZGaZRPX7duhpQXa2oLlcU45q7a2klXpxAoi\\nIiIyxGSnli01EUJHB9xyS5DM9vXBtdfuXL+jA6ZPjz7Z1Bi0kqUaWhERESmqksv669YFs4D19+9a\\nYxpHW9dsEv3AA3DeeaqdHcqU0IqIiEhRlVzWL9ZeNq62rt3dQU1wTw/8+tdwzDFKaocqNTkQERGR\\noiq5rF9syt1CSXEU4tqupI9qaEVERKSoSjt1FRqdIK5xZTVerWQpoRUREZGSwgyhVe7/4xjpIM4R\\nFCRdlNCKiIhI7GpNiuu9XUkXtaEVERERkVRTQisiIiIiqWbu3ugYGsLM1gKv1Wl3+wBv1mlfjaDj\\nSzcdX7Te4+771nF/dVPnchP03kw7HV96pa7cHLIJbT2Z2Xx3b290HHHR8aWbjk+SqtlfOx1fujXz\\n8aXx2NTkQERERERSTQmtiIiIiKSaEtr6uLPRAcRMx5duOj5JqmZ/7XR86dbMx5e6Y1MbWhERERFJ\\nNdXQioiIiEiqKaGNgZntbWY/N7NXMvfjSqw7zMwWmtn/1DPGWoQ5PjM70Mx+ZWYvmtkLZnZNI2Kt\\nhJmdbmYvm9lSM7u+wPNmZt/KPP+smU1uRJzVCnF8F2SO6zkze8LMJjUizmqUO7ac9f7EzHrN7GP1\\njE/CUdmZvrJT5WZ6y01orrJTCW08rgfmuPtEYE7mcTHXAIvrElV0whxfL/C37n4kMBW4ysyOrGOM\\nFTGzYcCtwBnAkcD5BeI9A5iYuV0OfLuuQdYg5PEtB6a5+zHALFLShirksWXX+zrwaH0jlAqo7ExR\\n2alyE0hpuQnNV3YqoY3H2cDdmb/vBs4ptJKZTQD+HLirTnFFpezxuftqd3868/c7BF884+sWYeVO\\nAJa6+zJ37wHuIzjOXGcD93hgHjDWzPavd6BVKnt87v6Eu6/PPJwHTKhzjNUK89oBfA54APhjPYOT\\niqjsTFfZqXIzveUmNFnZqYQ2Hvu5++rM32uA/YqsdwtwHdBfl6iiE/b4ADCzg4HjgSfjDasm44GV\\nOY9XseuXSJh1kqrS2C8FfhprRNEpe2xmNh44lxTVDg1RKjtzpKDsVLk5WJrKTWiysnN4owNIKzP7\\nBfDuAk/9Xe4Dd3cz22UoCTP7KPBHd19gZp3xRFm9Wo8vZzu7E/yyu9bd3442SomDmX2QoGA+udGx\\nROgW4Ivu3m9mjY5lSFPZGVDZ2VyatNyEFJWdSmir5O4fKvacmf3BzPZ399WZSyuFquk/AJxlZmcC\\no4A9zez77v6pmEKuSATHh5mNICiQ73X3B2MKNSpvAAfmPJ6QWVbpOkkVKnYzO5bgMu4Z7r6uTrHV\\nKsyxtQP3ZQrkfYAzzazX3f+7PiFKlsrOpio7VW6S2nITmqzsVJODeDwEXJz5+2LgR/kruPt0d5/g\\n7gcDnwB+mZQCOYSyx2fBu/+7wGJ3v6mOsVXrKWCimR1iZq0Er8lDees8BFyU6bU7FdiYc/kw6coe\\nn5kdBDwIXOjuSxoQY7XKHpu7H+LuB2c+bz8ArkxigSwqO1NWdqrcTG+5CU1WdiqhjcfXgA+b2SvA\\nhzKPMbMDzOzhhkYWjTDH9wHgQuDPzOyZzO3MxoRbnrv3AlcDPyPohHG/u79gZleY2RWZ1R4GlgFL\\nge8AVzYk2CqEPL4vA23AbZnXa36Dwq1IyGOTdFDZmaKyU+UmkNJyE5qv7NRMYSIiIiKSaqqhFRER\\nEZFUU0IrIiIiIqmmhFZEREREUk0JrYiIiIikmhJaEREREUk1JbQiIiIikmpKaEVEREQk1ZTQigBm\\ndqiZ7TCzf8hb/m0ze8fM2hsVm4hIUqnslKRQQisCuPtSgrm4rzWzNgAz+zJwCXCuu6dm9hcRkXpR\\n2SlJoZnCRDLMbH+C6RlvA14G7gDOd/f7GxqYiEiCqeyUJFANrUiGu68GbgE+B9wOfD63QDazG8xs\\niZn1m9k5jYpTRCRJVHZKEiihFRnsFWAk0O3ut+Y993PgdODxukclIpJsKjuloZTQimSY2akEl8q6\\ngQ+Y2bG5z7v7PHdf1pDgREQSSmWnJIESWhHAzCYDPyTo3NAJvA7MbmRMIiJJp7JTkkIJrQx5ZnYo\\n8FPgUeBz7t4DzATONLM/bWhwIiIJpbJTkkQJrQxpZvZugsJ4MXCBu/dnnroHeAn4WqNiExFJKpWd\\nkjTDGx2ASCO5+xrgvQWW9wFH1D8iEZHkU9kpSaNxaEVCMrMu4DJgX+AdYBsw1d1XNTIuEZEkU9kp\\n9aCEVkRERERSTW1oRURERCTVlNCKiIiISKopoRURERGRVFNCKyIiIiKppoRWRERERFJNCa2IiIiI\\npJoSWhERERFJNSW0IiIiIpJqSmhFREREJNX+P4n2qEoTF4TKAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def plot_predictions(\\n\",\n \" regressors, X, y, axes, \\n\",\n \" label=None, \\n\",\n \" style=\\\"r-\\\", \\n\",\n \" data_style=\\\"b.\\\", \\n\",\n \" data_label=None):\\n\",\n \" \\n\",\n \" x1 = np.linspace(axes[0], axes[1], 500)\\n\",\n \" \\n\",\n \" y_pred = sum(\\n\",\n \" regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\\n\",\n \" \\n\",\n \" plt.plot(X[:, 0], y, data_style, label=data_label)\\n\",\n \" plt.plot(x1, y_pred, style, linewidth=2, label=label)\\n\",\n \" if label or data_label:\\n\",\n \" plt.legend(loc=\\\"upper center\\\", fontsize=16)\\n\",\n \" plt.axis(axes)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11,11))\\n\",\n \"\\n\",\n \"plt.subplot(321)\\n\",\n \"plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\\\"$h_1(x_1)$\\\", style=\\\"g-\\\", data_label=\\\"Training set\\\")\\n\",\n \"plt.ylabel(\\\"$y$\\\", fontsize=16, rotation=0)\\n\",\n \"plt.title(\\\"Residuals and tree predictions\\\", fontsize=16)\\n\",\n \"\\n\",\n \"plt.subplot(322)\\n\",\n \"plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\\\"$h(x_1) = h_1(x_1)$\\\", data_label=\\\"Training set\\\")\\n\",\n \"plt.ylabel(\\\"$y$\\\", fontsize=16, rotation=0)\\n\",\n \"plt.title(\\\"Ensemble predictions\\\", fontsize=16)\\n\",\n \"\\n\",\n \"plt.subplot(323)\\n\",\n \"plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\\\"$h_2(x_1)$\\\", style=\\\"g-\\\", data_style=\\\"k+\\\", data_label=\\\"Residuals\\\")\\n\",\n \"plt.ylabel(\\\"$y - h_1(x_1)$\\\", fontsize=16)\\n\",\n \"\\n\",\n \"plt.subplot(324)\\n\",\n \"plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\\\"$h(x_1) = h_1(x_1) + h_2(x_1)$\\\")\\n\",\n \"plt.ylabel(\\\"$y$\\\", fontsize=16, rotation=0)\\n\",\n \"\\n\",\n \"plt.subplot(325)\\n\",\n \"plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\\\"$h_3(x_1)$\\\", style=\\\"g-\\\", data_style=\\\"k+\\\")\\n\",\n \"plt.ylabel(\\\"$y - h_1(x_1) - h_2(x_1)$\\\", fontsize=16)\\n\",\n \"plt.xlabel(\\\"$x_1$\\\", fontsize=16)\\n\",\n \"\\n\",\n \"plt.subplot(326)\\n\",\n \"plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\\\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\\\")\\n\",\n \"plt.xlabel(\\\"$x_1$\\\", fontsize=16)\\n\",\n \"plt.ylabel(\\\"$y$\\\", fontsize=16, rotation=0)\\n\",\n \"\\n\",\n \"#save_fig(\\\"gradient_boosting_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# 1st row: ensemble = only one tree: predictions match 1st tree.\\n\",\n \"# 2nd row: new tree trained on residual errors of 1st tree.\\n\",\n \"# 3rd row: \\\" \\\"\\n\",\n \"# result: ensemble predictions get better as trees are added.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* *learning_rate* param controls contribution of each tree. Low values (ex: 0.1) = need more trees in ensemble to fit training set, but predictions usually generalize better. (This is called **shrinkage**.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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RUWwpAhUFJSd7FTCCECielmca2hvNwEyUWL3N/+7t27admyJfXr15fE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\\nYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# two GBRT ensembles trained with low learning rate\\n\",\n \"\\n\",\n \"from sklearn.ensemble import GradientBoostingRegressor\\n\",\n \"\\n\",\n \"gbrt = GradientBoostingRegressor(\\n\",\n \" max_depth=2, \\n\",\n \" n_estimators=3, \\n\",\n \" learning_rate=0.1, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"gbrt.fit(X, y)\\n\",\n \"\\n\",\n \"gbrt_slow = GradientBoostingRegressor(\\n\",\n \" max_depth=2, \\n\",\n \" n_estimators=200, \\n\",\n \" learning_rate=0.1, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"gbrt_slow.fit(X, y)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11,4))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_predictions(\\n\",\n \" [gbrt], X, y, \\n\",\n \" axes=[-0.5, 0.5, -0.1, 0.8], \\n\",\n \" label=\\\"Ensemble predictions\\\")\\n\",\n \"plt.title(\\\"learning_rate={}, n_estimators={}\\\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_predictions(\\n\",\n \" [gbrt_slow], X, y, \\n\",\n \" axes=[-0.5, 0.5, -0.1, 0.8])\\n\",\n \"plt.title(\\\"learning_rate={}, n_estimators={}\\\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\\n\",\n \"\\n\",\n \"#save_fig(\\\"gbrt_learning_rate_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# left: not enough trees (underfits)\\n\",\n \"# right: too many trees (overfits)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* To find optimal number of trees - use early stopping method.\\n\",\n \"* *staged_predict* method: returns iterator\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0.05877146809545241,\\n\",\n \" 0.050146609664278821,\\n\",\n \" 0.042693525239940654,\\n\",\n \" 0.036758764317358611,\\n\",\n \" 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0.0026594592750579836,\\n\",\n \" 0.0026425819418378605,\\n\",\n \" 0.0026524409142755744,\\n\",\n \" 0.0026418897165154491,\\n\",\n \" 0.0026483360802177103,\\n\",\n \" 0.0026456393608631189,\\n\",\n \" 0.0026465080389023671,\\n\",\n \" 0.0026396693211148074,\\n\",\n \" 0.002649273120700455,\\n\",\n \" 0.002643721514468783,\\n\",\n \" 0.0026463988198929221,\\n\",\n \" 0.0026333618213948747,\\n\",\n \" 0.0026314011519099879,\\n\",\n \" 0.0026349113355268257,\\n\",\n \" 0.0026387528659342825,\\n\",\n \" 0.0026345585421650142,\\n\",\n \" 0.0026355886319374901,\\n\",\n \" 0.0026310345391991532,\\n\",\n \" 0.0026519658939712061,\\n\",\n \" 0.0026467700098620557,\\n\",\n \" 0.00264498239665715,\\n\",\n \" 0.0026475491456891486,\\n\",\n \" 0.0026474836942911913,\\n\",\n \" 0.0026530458155365681,\\n\",\n \" 0.0026478335004093052,\\n\",\n \" 0.0026564768881028435,\\n\",\n \" 0.0026574608795571115,\\n\",\n \" 0.0026537575609276061,\\n\",\n \" 0.0026559108292476983,\\n\",\n \" 0.0026528848367343987,\\n\",\n \" 0.0026533895549644779,\\n\",\n \" 0.0026520896622857252,\\n\",\n \" 0.0026416985817433059,\\n\",\n \" 0.0026497886163651938,\\n\",\n \" 0.0026430582537166087,\\n\",\n \" 0.0026548742317473117,\\n\",\n \" 0.002660275592603878,\\n\",\n \" 0.0026582571161537366,\\n\",\n \" 0.0026570823709750535,\\n\",\n \" 0.0026557538081706522,\\n\",\n \" 0.002675470519360824,\\n\",\n \" 0.0026762761989050578,\\n\",\n \" 0.0026742086578626454,\\n\",\n \" 0.0026957941482744232,\\n\",\n \" 0.0026964801899977998,\\n\",\n \" 0.0026939578807501376,\\n\",\n \" 0.0026959742963617757,\\n\",\n \" 0.0026949319702616616,\\n\",\n \" 0.0026988916344244736,\\n\",\n \" 0.0027169473218451121,\\n\",\n \" 0.0027148017926961689,\\n\",\n \" 0.0027192710134859655,\\n\",\n \" 0.0027358435370699618,\\n\",\n \" 0.0027346474658663323,\\n\",\n \" 0.0027351047440069571,\\n\",\n \" 0.0027459941366245631,\\n\",\n \" 0.0027441324932851491,\\n\",\n \" 0.002756368378237764]\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"\\n\",\n \"X_train, X_val, y_train, y_val = train_test_split(X, y)\\n\",\n \"\\n\",\n \"# train GRBR regressor with 120 trees\\n\",\n \"\\n\",\n \"gbrt = GradientBoostingRegressor(\\n\",\n \" max_depth=2, \\n\",\n \" n_estimators=120, \\n\",\n \" learning_rate=0.1, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"gbrt.fit(X_train, y_train)\\n\",\n \"\\n\",\n \"# measure MSE validation error at each stage\\n\",\n \"errors = [mean_squared_error(y_val, y_pred) for y_pred in gbrt.staged_predict(X_val)]\\n\",\n \"errors\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\\n\",\n \" learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\\n\",\n \" max_leaf_nodes=None, min_impurity_split=1e-07,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, n_estimators=79, presort='auto',\\n\",\n \" random_state=42, subsample=1.0, verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# train another GBRT ensemble using optimal #trees\\n\",\n \"\\n\",\n \"best_n_estimators = np.argmin(errors)\\n\",\n \"min_error = errors[best_n_estimators]\\n\",\n \"\\n\",\n \"gbrt_best = GradientBoostingRegressor(\\n\",\n \" max_depth=2, \\n\",\n \" n_estimators=best_n_estimators, \\n\",\n \" learning_rate=0.1, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"gbrt_best.fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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NZ+CIxp7nKJiDSG5kZPQWSwKSIiIiL1U7CZAgWbIiIiIqlRsJmCXr3A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\\nXGyamZmZWcf8H7UidUQFsTGTAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(11, 4))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.plot(errors, \\\"b.-\\\")\\n\",\n \"plt.plot([best_n_estimators, best_n_estimators], [0, min_error], \\\"k--\\\")\\n\",\n \"plt.plot([0, 120], [min_error, min_error], \\\"k--\\\")\\n\",\n \"plt.plot(best_n_estimators, min_error, \\\"ko\\\")\\n\",\n \"plt.text(best_n_estimators, min_error*1.2, \\\"Minimum\\\", ha=\\\"center\\\", fontsize=14)\\n\",\n \"plt.axis([0, 120, 0, 0.01])\\n\",\n \"plt.xlabel(\\\"Number of trees\\\")\\n\",\n \"plt.title(\\\"Validation error\\\", fontsize=14)\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\\n\",\n \"plt.title(\\\"Best model (55 trees)\\\", fontsize=14)\\n\",\n \"\\n\",\n \"#save_fig(\\\"early_stopping_gbrt_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Another method: actually stopping training early\\n\",\n \"* Implement via *warm_start=True* (tells Scikit to keep existing trees when fit() is called - allowing incremental training.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"59\\n\"\n ]\n }\n ],\n \"source\": [\n \"gbrt = GradientBoostingRegressor(\\n\",\n \" max_depth=2, \\n\",\n \" n_estimators=1, \\n\",\n \" learning_rate=0.1, \\n\",\n \" random_state=42, \\n\",\n \" warm_start=True)\\n\",\n \"\\n\",\n \"min_val_error = float(\\\"inf\\\")\\n\",\n \"error_going_up = 0\\n\",\n \"\\n\",\n \"# 120 estimators.\\n\",\n \"# stop training with validation error doesn't improve for\\n\",\n \"# five consecutive iterations\\n\",\n \"\\n\",\n \"for n_estimators in range(1, 120):\\n\",\n \" gbrt.n_estimators = n_estimators\\n\",\n \" gbrt.fit(X_train, y_train)\\n\",\n \" y_pred = gbrt.predict(X_val)\\n\",\n \" val_error = mean_squared_error(y_val, y_pred)\\n\",\n \"\\n\",\n \" if val_error < min_val_error:\\n\",\n \" min_val_error = val_error\\n\",\n \" error_going_up = 0\\n\",\n \" else:\\n\",\n \" error_going_up += 1\\n\",\n \" if error_going_up == 5:\\n\",\n \" break # early stopping\\n\",\n \" \\n\",\n \"print(gbrt.n_estimators)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Stacking\\n\",\n \"* Instead of using a voting function to aggregate an ensemble's predictor outputs, instead train a model to do the aggregation. (\\\"blending\\\".)\\n\",\n \"* Blender training: common approach = use a *holdout set*.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# todo: stacking implementation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch08-dimensionality-reduction.html", "content": "\n\n\nch08-dimensionality-reduction\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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Intro

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  • Dimesionality reduction is lossy. It may speed up training but can degrade result quality. Also makes pipelines more complex. Try using original data before considering dimensionality reduction.
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  • Very useful for visualization (2D, 3D representations more intuitive.)
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  • Two main approaches: projection, manifold learning.
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  • Three most popular techniques: PCA, Kernel PCA, LLE.
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Curse of Dimensionality

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  • Many things behave differently in high-D space.
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1) Most points in high-D hypercube will be very close to a border.

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2) Distances between random points much greater (very high probability of sparse matrix representation).

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  • In 2D: ~0.52
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  • In 3D: ~0.66
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  • In 1,000,000D: ~408 ~ sqrt(1000000/6)
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Approaches: Projection

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  • Most dataset features are concentrated in a few dimensions - not uniformly across all. Much learnable training can be found in low-D subspace.
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In [1]:
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import numpy as np\nimport numpy.random as rnd\n
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In [2]:
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# build a 3D dataset\n\nrnd.seed(4)\nm = 60\nw1, w2 = 0.1, 0.3\nnoise = 0.1\n\nangles = rnd.rand(m) * 3 * np.pi / 2 - 0.5\nX = np.empty((m, 3))\nX[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * rnd.randn(m) / 2\nX[:, 1] = np.sin(angles) * 0.7 + noise * rnd.randn(m) / 2\nX[:, 2] = X[:, 0] * w1 + X[:, 1] * w2 + noise * rnd.randn(m)\n\n# mean-normalize the data\nX = X - X.mean(axis=0)\n\n# apply PCA to reduce to 2D\nfrom sklearn.decomposition import PCA\n\npca = PCA(n_components = 2)\nX2D = pca.fit_transform(X)\n\n# recover 3D points projected on 2D plane\nX2D_inv = pca.inverse_transform(X2D)\n
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In [3]:
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# utility to draw 3D arrows\nfrom matplotlib.patches import FancyArrowPatch\nfrom mpl_toolkits.mplot3d import proj3d\n\nclass Arrow3D(FancyArrowPatch):\n    def __init__(self, xs, ys, zs, *args, **kwargs):\n        FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)\n        self._verts3d = xs, ys, zs\n\n    def draw(self, renderer):\n        xs3d, ys3d, zs3d = self._verts3d\n        xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)\n        self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))\n        FancyArrowPatch.draw(self, renderer)\n\n# express plane as function of x,y\naxes = [-1.8, 1.8, -1.3, 1.3, -1.0, 1.0]\n\nx1s = np.linspace(axes[0], axes[1], 10)\nx2s = np.linspace(axes[2], axes[3], 10)\nx1, x2 = np.meshgrid(x1s, x2s)\n\nC = pca.components_\nR = C.T.dot(C)\nz = (R[0, 2] * x1 + R[1, 2] * x2) / (1 - R[2, 2])\n
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In [4]:
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# plot 3D dataset, plane & projections\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure(figsize=(10, 10))\nax = fig.add_subplot(111, projection='3d')\n\nX3D_above = X[X[:, 2] > X2D_inv[:, 2]]\nX3D_below = X[X[:, 2] <= X2D_inv[:, 2]]\n\nax.plot(X3D_below[:, 0], X3D_below[:, 1], X3D_below[:, 2], "bo", alpha=0.5)\n\nax.plot_surface(x1, x2, z, alpha=0.2, color="k")\nnp.linalg.norm(C, axis=0)\nax.add_artist(Arrow3D([0, C[0, 0]],[0, C[0, 1]],[0, C[0, 2]], mutation_scale=15, lw=1, arrowstyle="-|>", color="k"))\nax.add_artist(Arrow3D([0, C[1, 0]],[0, C[1, 1]],[0, C[1, 2]], mutation_scale=15, lw=1, arrowstyle="-|>", color="k"))\nax.plot([0], [0], [0], "k.")\n\nfor i in range(m):\n    if X[i, 2] > X2D_inv[i, 2]:\n        ax.plot([X[i][0], X2D_inv[i][0]], [X[i][1], X2D_inv[i][1]], [X[i][2], X2D_inv[i][2]], "k-")\n    else:\n        ax.plot([X[i][0], X2D_inv[i][0]], [X[i][1], X2D_inv[i][1]], [X[i][2], X2D_inv[i][2]], "k-", color="#505050")\n    \nax.plot(X2D_inv[:, 0], X2D_inv[:, 1], X2D_inv[:, 2], "k+")\nax.plot(X2D_inv[:, 0], X2D_inv[:, 1], X2D_inv[:, 2], "k.")\nax.plot(X3D_above[:, 0], X3D_above[:, 1], X3D_above[:, 2], "bo")\nax.set_xlabel("$x_1$", fontsize=18)\nax.set_ylabel("$x_2$", fontsize=18)\nax.set_zlabel("$x_3$", fontsize=18)\nax.set_xlim(axes[0:2])\nax.set_ylim(axes[2:4])\nax.set_zlim(axes[4:6])\n\n#save_fig("dataset_3d_plot")\nplt.show()\n
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In [5]:
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# 2D projection equivalent:\nfig = plt.figure()\nax = fig.add_subplot(111, aspect='equal')\nax.plot(X2D[:, 0], X2D[:, 1], "k+")\nax.plot(X2D[:, 0], X2D[:, 1], "k.")\nax.plot([0], [0], "ko")\nax.arrow(0, 0, 0, 1, head_width=0.05, length_includes_head=True, head_length=0.1, fc='k', ec='k')\nax.arrow(0, 0, 1, 0, head_width=0.05, length_includes_head=True, head_length=0.1, fc='k', ec='k')\nax.set_xlabel("$z_1$", fontsize=18)\nax.set_ylabel("$z_2$", fontsize=18, rotation=0)\nax.axis([-1.5, 1.3, -1.2, 1.2])\nax.grid(True)\nplt.show()\n
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Approaches: Manifolds

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  • Manifolds = shapes that can be bent/twisted in higher-D space.
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  • ex: \"Swiss roll\" problem
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In [6]:
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# Swiss roll visualization:\nfrom sklearn.datasets import make_swiss_roll\nX, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)\n\naxes = [-11.5, 14, -2, 23, -12, 15]\n\nfig = plt.figure(figsize=(8, 6))\nax = fig.add_subplot(111, projection='3d')\n\nax.scatter(X[:, 0], X[:, 1], X[:, 2], c=t, cmap=plt.cm.hot)\nax.view_init(10, -70)\nax.set_xlabel("$x_1$", fontsize=18)\nax.set_ylabel("$x_2$", fontsize=18)\nax.set_zlabel("$x_3$", fontsize=18)\nax.set_xlim(axes[0:2])\nax.set_ylim(axes[2:4])\nax.set_zlim(axes[4:6])\n\n#save_fig("swiss_roll_plot")\nplt.show()\n
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In [7]:
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# "squashed" swiss roll visualization:\nplt.figure(figsize=(11, 4))\n\nplt.subplot(121)\nplt.scatter(X[:, 0], X[:, 1], c=t, cmap=plt.cm.hot)\nplt.axis(axes[:4])\nplt.xlabel("$x_1$", fontsize=18)\nplt.ylabel("$x_2$", fontsize=18, rotation=0)\nplt.grid(True)\n\nplt.subplot(122)\nplt.scatter(t, X[:, 1], c=t, cmap=plt.cm.hot)\nplt.axis([4, 15, axes[2], axes[3]])\nplt.xlabel("$z_1$", fontsize=18)\nplt.grid(True)\n\n#save_fig("squished_swiss_roll_plot")\nplt.show()\n
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In [8]:
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from matplotlib import gridspec\n\naxes = [-11.5, 14, -2, 23, -12, 15]\n\nx2s = np.linspace(axes[2], axes[3], 10)\nx3s = np.linspace(axes[4], axes[5], 10)\nx2, x3 = np.meshgrid(x2s, x3s)\n\nfig = plt.figure(figsize=(6, 5))\nax = plt.subplot(111, projection='3d')\n\npositive_class = X[:, 0] > 5\nX_pos = X[positive_class]\nX_neg = X[~positive_class]\n\nax.view_init(10, -70)\nax.plot(X_neg[:, 0], X_neg[:, 1], X_neg[:, 2], "y^")\nax.plot_wireframe(5, x2, x3, alpha=0.5)\nax.plot(X_pos[:, 0], X_pos[:, 1], X_pos[:, 2], "gs")\nax.set_xlabel("$x_1$", fontsize=18)\nax.set_ylabel("$x_2$", fontsize=18)\nax.set_zlabel("$x_3$", fontsize=18)\nax.set_xlim(axes[0:2])\nax.set_ylim(axes[2:4])\nax.set_zlim(axes[4:6])\n\n#save_fig("manifold_decision_boundary_plot1")\nplt.show()\n\nfig = plt.figure(figsize=(5, 4))\nax = plt.subplot(111)\n\nplt.plot(t[positive_class], X[positive_class, 1], "gs")\nplt.plot(t[~positive_class], X[~positive_class, 1], "y^")\nplt.axis([4, 15, axes[2], axes[3]])\nplt.xlabel("$z_1$", fontsize=18)\nplt.ylabel("$z_2$", fontsize=18, rotation=0)\nplt.grid(True)\n\n#save_fig("manifold_decision_boundary_plot2")\nplt.show()\n\nfig = plt.figure(figsize=(6, 5))\nax = plt.subplot(111, projection='3d')\n\npositive_class = 2 * (t[:] - 4) > X[:, 1]\nX_pos = X[positive_class]\nX_neg = X[~positive_class]\nax.view_init(10, -70)\nax.plot(X_neg[:, 0], X_neg[:, 1], X_neg[:, 2], "y^")\nax.plot(X_pos[:, 0], X_pos[:, 1], X_pos[:, 2], "gs")\nax.set_xlabel("$x_1$", fontsize=18)\nax.set_ylabel("$x_2$", fontsize=18)\nax.set_zlabel("$x_3$", fontsize=18)\nax.set_xlim(axes[0:2])\nax.set_ylim(axes[2:4])\nax.set_zlim(axes[4:6])\n\n#save_fig("manifold_decision_boundary_plot3")\nplt.show()\n\nfig = plt.figure(figsize=(5, 4))\nax = plt.subplot(111)\n\nplt.plot(t[positive_class], X[positive_class, 1], "gs")\nplt.plot(t[~positive_class], X[~positive_class, 1], "y^")\nplt.plot([4, 15], [0, 22], "b-", linewidth=2)\nplt.axis([4, 15, axes[2], axes[3]])\nplt.xlabel("$z_1$", fontsize=18)\nplt.ylabel("$z_2$", fontsize=18, rotation=0)\nplt.grid(True)\n\n#save_fig("manifold_decision_boundary_plot4")\nplt.show()\n\n# Lesson learned (below):\n# Unrolling a dataset to a lower dimension doesn't necessarily lead to\n# a simpler representation.\n
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PCA (Principal Component Analysis)

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  • Most popular DR algorithm
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  • 1) Finds hyperplane that lies closest to the data
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  • 2) Projects data onto it
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Preserving Variance

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  • Below: simple 2D dataset projected onto 3 different axes.
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In [9]:
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angle = np.pi / 5\nstretch = 5\nm = 200\n\nrnd.seed(3)\nX = rnd.randn(m, 2) / 10\nX = X.dot(np.array([[stretch, 0],[0, 1]])) # stretch\nX = X.dot([[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]]) # rotate\n\nu1 = np.array([np.cos(angle), np.sin(angle)])\nu2 = np.array([np.cos(angle - 2 * np.pi/6), np.sin(angle - 2 * np.pi/6)])\nu3 = np.array([np.cos(angle - np.pi/2), np.sin(angle - np.pi/2)])\n\nX_proj1 = X.dot(u1.reshape(-1, 1))\nX_proj2 = X.dot(u2.reshape(-1, 1))\nX_proj3 = X.dot(u3.reshape(-1, 1))\n\nplt.figure(figsize=(8,4))\nplt.subplot2grid((3,2), (0, 0), rowspan=3)\nplt.plot([-1.4, 1.4], [-1.4*u1[1]/u1[0], 1.4*u1[1]/u1[0]], "k-", linewidth=1)\nplt.plot([-1.4, 1.4], [-1.4*u2[1]/u2[0], 1.4*u2[1]/u2[0]], "k--", linewidth=1)\nplt.plot([-1.4, 1.4], [-1.4*u3[1]/u3[0], 1.4*u3[1]/u3[0]], "k:", linewidth=2)\nplt.plot(X[:, 0], X[:, 1], "bo", alpha=0.5)\nplt.axis([-1.4, 1.4, -1.4, 1.4])\nplt.arrow(0, 0, u1[0], u1[1], head_width=0.1, linewidth=5, length_includes_head=True, head_length=0.1, fc='k', ec='k')\nplt.arrow(0, 0, u3[0], u3[1], head_width=0.1, linewidth=5, length_includes_head=True, head_length=0.1, fc='k', ec='k')\nplt.text(u1[0] + 0.1, u1[1] - 0.05, r"$\\mathbf{c_1}$", fontsize=22)\nplt.text(u3[0] + 0.1, u3[1], r"$\\mathbf{c_2}$", fontsize=22)\nplt.xlabel("$x_1$", fontsize=18)\nplt.ylabel("$x_2$", fontsize=18, rotation=0)\nplt.grid(True)\n\nplt.subplot2grid((3,2), (0, 1))\nplt.plot([-2, 2], [0, 0], "k-", linewidth=1)\nplt.plot(X_proj1[:, 0], np.zeros(m), "bo", alpha=0.3)\nplt.gca().get_yaxis().set_ticks([])\nplt.gca().get_xaxis().set_ticklabels([])\nplt.axis([-2, 2, -1, 1])\nplt.grid(True)\n\nplt.subplot2grid((3,2), (1, 1))\nplt.plot([-2, 2], [0, 0], "k--", linewidth=1)\nplt.plot(X_proj2[:, 0], np.zeros(m), "bo", alpha=0.3)\nplt.gca().get_yaxis().set_ticks([])\nplt.gca().get_xaxis().set_ticklabels([])\nplt.axis([-2, 2, -1, 1])\nplt.grid(True)\n\nplt.subplot2grid((3,2), (2, 1))\nplt.plot([-2, 2], [0, 0], "k:", linewidth=2)\nplt.plot(X_proj3[:, 0], np.zeros(m), "bo", alpha=0.3)\nplt.gca().get_yaxis().set_ticks([])\nplt.axis([-2, 2, -1, 1])\nplt.xlabel("$z_1$", fontsize=18)\nplt.grid(True)\n\n#save_fig("pca_best_projection")\nplt.show()\n
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Principal Components

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  • PCA finds axis responsible for largest amount of variance in dataset.
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  • Also finds 2nd axis, responsible for next largest amount.
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  • If higher-D dataset, PCA also finds 3rd axis...
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  • Repeat for # of dimensions in the dataset.
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  • Each axis vector is called a principal component. (PC)

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  • PCs found using Singular Value Decomposition (SVD), a matrix factorization technique.

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  • SVD decomposes training set matrix X into dot product of three matrices.
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  • Note: PCA assumes data is centered around origin. Scikit PCA will adjust data for you if needed.
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In [10]:
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# use NumPy svd() to get principal components of training set,\n# then extract 1st two PCs.\n\nX_centered = X - X.mean(axis=0)\nU,s,V = np.linalg.svd(X_centered)\n\nc1, c2 = V.T[:,0], V.T[:,1]\nprint(c1,c2)\n
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[-0.79644131 -0.60471583] [-0.60471583  0.79644131]\n
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Projecting Training Data Down to d Dimensions

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  • Done by computing dot product of training data (X) by matrix containing the first d principal components (Wd).
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In [11]:
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# project training set onto plane defined by 1st two PCs.\n\nW2 = V.T[:, :2]\nX2D = X_centered.dot(W2)\nprint(X2D)\n
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[[ -8.96088137e-01   2.61576283e-02]\n [ -4.53603363e-02  -1.85948860e-01]\n [  1.38359166e-01  -3.11666166e-02]\n [  4.16315780e-02  -6.04371773e-02]\n [  2.18583744e-02  -4.58726693e-02]\n [  6.53868464e-01   1.03673047e-01]\n [ -4.45218566e-01   1.63002740e-01]\n [ -2.52100754e-02  -3.96098381e-02]\n [  2.74828447e-01  -1.47486328e-01]\n [ -4.89804685e-01  -1.19064333e-01]\n [  5.91772943e-01  -6.68825324e-03]\n [ -7.44460369e-01   9.37220434e-03]\n [  5.12230114e-01  -5.91117152e-02]\n [ -3.13266691e-01  -2.12641588e-02]\n [  3.83765553e-01  -1.35145070e-02]\n [ -3.77664930e-01   1.91087392e-01]\n [  6.22192127e-01  -4.81326634e-02]\n [  4.05843018e-01  -2.32002753e-01]\n [  4.62900292e-01  -9.12474313e-02]\n [ -5.62638042e-01  -2.36637544e-02]\n [  8.09046208e-01   8.31463215e-02]\n [  1.80719622e-01  -1.69142171e-01]\n [  2.98447518e-01  -5.11785151e-02]\n [  4.35729072e-01   1.35618235e-02]\n [  1.12339132e+00  -1.68707469e-03]\n [ -5.09329756e-01   7.59538223e-02]\n [ -5.57383436e-01   1.01605408e-01]\n [ -7.42300017e-01  -1.26114063e-01]\n [ -4.19950471e-01  -1.93589947e-01]\n [  3.04360690e-01  -1.83671045e-01]\n [ -5.27822154e-01   1.23668447e-01]\n [  9.38906116e-02   1.80883149e-01]\n [  3.35918853e-01   2.35494590e-02]\n [ -7.52638095e-02  -1.06632109e-01]\n [ -2.24065944e-01   1.90613293e-01]\n [  5.09402619e-01   1.02097673e-01]\n [  7.24520511e-02   1.79929048e-01]\n [ -2.44318685e-01   6.38817933e-02]\n [ -3.23087658e-01   1.94977998e-02]\n [  6.93739438e-01   1.55229402e-01]\n [  6.83626747e-01   3.96804666e-02]\n [ -3.06255432e-01  -8.88712253e-02]\n [ -7.60306544e-02   1.16609123e-01]\n [  1.28713987e-02  -8.72153282e-02]\n [  1.45854192e+00  -6.50410607e-02]\n [  2.95510472e-01  -4.40211613e-02]\n [  4.78043426e-01   4.92202601e-02]\n [  2.86468892e-01  -3.50652486e-03]\n [ -3.38708502e-01  -9.13086575e-02]\n [  1.44466110e-01   2.20308932e-01]\n [ -4.35369951e-01  -1.37167289e-01]\n [  3.76189231e-02   5.86533049e-02]\n [ -6.17964798e-01   3.26551523e-03]\n [  2.65720436e-01  -6.60652314e-02]\n [ -3.24171713e-01   2.58737230e-02]\n [  2.57613005e-01  -1.20787043e-02]\n [  2.05472262e-01   7.82147166e-02]\n [  3.42825581e-01   5.72823775e-02]\n [ -4.27742475e-01   4.10117883e-02]\n [  4.13138475e-01   1.44642428e-01]\n [  3.37079378e-01   5.11618950e-02]\n [  3.79209530e-01  -1.65052243e-01]\n [ -1.14507596e-01   2.76931986e-02]\n [  3.70774307e-02   2.97937650e-02]\n [  3.24636337e-01  -6.55164554e-02]\n [  7.89476581e-02   1.94034121e-01]\n [ -4.07272297e-01  -5.92029130e-02]\n [ -2.79337894e-01  -5.23406778e-02]\n [  2.25715170e-01   9.21073425e-02]\n [  2.65055409e-01  -1.60611082e-01]\n [  4.51907852e-01   1.97745374e-02]\n [ -6.76603521e-02   1.24160746e-01]\n [  3.55338456e-01   8.20568965e-02]\n [ -2.13673993e-01  -1.80131732e-02]\n [ -4.19919072e-01   4.17639004e-02]\n [  4.27746050e-01   1.17613622e-01]\n [  6.09137236e-01   2.02104565e-02]\n [ -3.16955986e-03   4.38048374e-02]\n [  3.61657526e-01   5.53222800e-03]\n [  6.71393848e-02   1.02545906e-01]\n [  3.96663191e-01   1.70419112e-02]\n [  1.32276395e-01  -1.25644673e-01]\n [ -1.33866070e+00  -3.39620117e-02]\n [  7.39140839e-01   1.57883864e-01]\n [  5.33339338e-01   4.97439820e-02]\n [ -4.31004868e-01  -7.25504028e-02]\n [  2.15484246e-01  -4.80950244e-02]\n [  6.70354778e-02  -1.59469510e-01]\n [  6.16925576e-01   3.30095454e-04]\n [ -5.21745049e-01   5.35816552e-02]\n [ -8.67032328e-01   5.25304916e-02]\n [  2.54714484e-01  -5.51554532e-03]\n [  1.01594493e+00  -7.32702485e-02]\n [  5.08443285e-01   3.91889488e-02]\n [ -3.23713333e-01  -6.14794166e-02]\n [  2.96394651e-01  -1.47039271e-01]\n [  6.22912229e-02   2.75349476e-02]\n [ -2.22242499e-01  -8.15775375e-02]\n [ -9.97596213e-01   9.98881775e-02]\n [  4.76677524e-02  -5.03263425e-02]\n [  1.59385630e-01   1.98770961e-02]\n [  7.23983658e-03   4.99150260e-02]\n [ -3.88330571e-01   1.29862055e-01]\n [ -5.74324601e-01  -2.17412761e-02]\n [ -1.94317864e-01  -4.14215200e-02]\n [  2.88462890e-01   1.98608595e-01]\n [  2.24621449e-01   2.05254821e-01]\n [  1.72893464e-01   3.03337593e-02]\n [ -5.45686322e-01  -7.71908926e-03]\n [ -1.97062110e-01  -2.69019260e-02]\n [  2.37006918e-01  -1.01833788e-02]\n [  3.20293812e-01   1.71329418e-01]\n [  7.91454892e-02   1.75373382e-01]\n [  1.34116467e+00   3.15311858e-02]\n [ -2.81345950e-01  -3.39245876e-02]\n [ -5.49606098e-01   5.37889594e-02]\n [  1.35299898e-01   4.77632442e-02]\n [ -1.40721018e+00  -3.07936334e-03]\n [ -1.49842206e-01  -4.57709563e-02]\n [ -6.50670013e-02  -1.28928620e-01]\n [  9.28880501e-02   2.49171453e-01]\n [ -5.35894030e-01   5.42510650e-02]\n [ -5.56858738e-01   1.77869868e-01]\n [ -3.02147552e-01   2.02159209e-01]\n [ -5.36183631e-01   6.74427775e-03]\n [ -8.55623983e-01  -2.52623485e-01]\n [  2.83998192e-01   3.39738443e-02]\n [  4.55220905e-01  -4.60837740e-03]\n [  3.19652268e-01  -5.73147775e-02]\n [ -1.35803712e+00   3.55141032e-02]\n [  2.31421394e-02   1.33432806e-01]\n [  1.15723020e-01   9.23933561e-02]\n [ -1.79851419e-01   1.60298502e-01]\n [  7.07110703e-01   9.29033427e-02]\n [ -5.97227204e-02  -1.15625175e-01]\n [ -5.31804111e-01  -9.70759692e-02]\n [ -8.11573484e-01   3.56003677e-02]\n [ -3.48886570e-01   2.08692440e-03]\n [ -4.49281442e-01  -1.94684913e-01]\n [ -3.70829130e-02  -6.22225918e-02]\n [ -1.42251513e-01  -6.31866218e-03]\n [ -3.07506144e-01  -8.88695185e-02]\n [ -9.25307571e-01   2.13517965e-01]\n [  1.03097886e-01   2.07573310e-03]\n [ -1.47464910e-01   1.24724802e-01]\n [ -9.46748876e-01  -1.39178787e-01]\n [  3.08673618e-01  -9.83295182e-02]\n [  5.58671697e-01  -1.50783004e-01]\n [ -1.79969356e-01  -1.18326957e-01]\n [ -7.54431603e-01   7.63370593e-02]\n [  5.15860621e-01  -9.07637328e-02]\n [  3.02119496e-01   1.47799162e-01]\n [  4.26806702e-01  -1.38986614e-01]\n [  2.28667680e-02   3.92283202e-02]\n [  3.24088211e-01   1.70213975e-01]\n [ -1.22209299e-01   4.41346966e-02]\n [  4.26702773e-01   6.42551604e-03]\n [  8.19816829e-02  -2.24146989e-01]\n [ -1.41063811e-01  -1.81097498e-01]\n [  1.64457703e-02  -1.45681492e-01]\n [ -1.01134103e+00   1.26993172e-02]\n [ -4.33573355e-01   8.41743009e-02]\n [  6.65856885e-01  -2.12785979e-01]\n [  1.61808196e-01   9.45467292e-02]\n [ -4.82157648e-02   7.89423282e-02]\n [ -4.66117068e-01  -1.57706328e-01]\n [ -1.49009196e-01   1.94946740e-01]\n [  2.88429562e-01  -1.95469647e-01]\n [  1.15854187e-01  -4.26347471e-02]\n [ -2.08314238e-01  -9.22559093e-02]\n [ -4.17242418e-02  -2.21272525e-01]\n [ -4.31285498e-01   1.51023258e-01]\n [ -6.46651297e-01  -1.63796812e-01]\n [ -1.54321959e-01  -3.00319930e-01]\n [ -1.42477982e-01  -8.04414843e-03]\n [  4.96009127e-01   4.62484050e-02]\n [ -7.20255309e-02   9.38505820e-02]\n [ -6.51879918e-02   2.35315556e-02]\n [  9.73713335e-01  -9.40602754e-02]\n [  2.36218522e-01   3.60724373e-02]\n [ -1.06876746e-03   1.51498555e-01]\n [  4.34435954e-01  -1.34060334e-02]\n [ -1.28076019e-01  -2.44578006e-02]\n [ -2.47584073e-01  -6.08927420e-02]\n [ -7.10391471e-01  -4.55521748e-02]\n [ -5.58836834e-01  -1.34202263e-02]\n [ -7.43699081e-01  -1.54131331e-01]\n [ -2.58481292e-01  -4.73208896e-02]\n [ -1.19242387e-01   1.18190473e-01]\n [  5.71157514e-01  -1.18785924e-01]\n [  4.97483707e-01   3.73368260e-02]\n [  9.41952705e-01   3.09293927e-02]\n [  6.44331385e-01  -9.94076651e-02]\n [  1.82692942e-01   4.33205574e-02]\n [  3.13123024e-01  -4.12117592e-02]\n [  2.04403918e-02  -2.54497816e-02]\n [  1.33781786e+00  -1.34015280e-02]\n [ -4.58399199e-02   1.10234115e-01]\n [ -1.02539769e+00   4.64829485e-02]\n [ -3.85549579e-02  -9.59123942e-02]]\n
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Scikit PCA

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In [12]:
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from sklearn.decomposition import PCA\npca = PCA(n_components = 2)\nX2D = pca.fit_transform(X)\n\nprint(pca.components_[0])\nprint(pca.components_.T[:,0])\n
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[-0.79644131 -0.60471583]\n[-0.79644131 -0.60471583]\n
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Explained Variance Ratio

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  • Very useful metric: proportion of dataset's variance along the axis of each PC component.
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# 95% of dataset variance explained by 1st axis.\nprint(pca.explained_variance_ratio_)\n
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[ 0.95369864  0.04630136]\n
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Choosing Right #Dimensions

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  • No need to choose arbitrary #dimensions. Instead pick d that cumulatively accounts for a sufficient amount, ex: 95%.
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In [14]:
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# find minimum d to preserve 95% of training set variance\npca = PCA()\npca.fit(X)\ncumsum = np.cumsum(pca.explained_variance_ratio_)\nd = np.argmax(cumsum >= 0.95) + 1\nprint(d)\n
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PCA for Compression

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  • Example applying PCA to MNIST dataset with 95% preservation = results in ~150 features (original = 28x28 = 784)
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In [15]:
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#MNIST compression:\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import fetch_mldata\n\n#mnist = fetch_mldata('MNIST original')\nmnist_path = "./mnist-original.mat"\n\nfrom scipy.io import loadmat\nmnist_raw = loadmat(mnist_path)\nmnist = {\n    "data": mnist_raw["data"].T,\n    "target": mnist_raw["label"][0],\n    "COL_NAMES": ["label", "data"],\n    "DESCR": "mldata.org dataset: mnist-original",\n    }\n\nX, y = mnist["data"], mnist["target"]\nX_train, X_test, y_train, y_test = train_test_split(X, y)\n\nX = X_train\n\npca = PCA()\npca.fit(X)\nd = np.argmax(np.cumsum(pca.explained_variance_ratio_) >= 0.95) + 1\nd\n
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Out[15]:
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154
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In [16]:
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pca = PCA(n_components=0.95)\nX_reduced = pca.fit_transform(X)\npca.n_components_\n
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Out[16]:
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154
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In [17]:
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# did you hit your 95% minimum?\nnp.sum(pca.explained_variance_ratio_)\n
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Out[17]:
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0.9503623084769206
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In [18]:
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# use inverse_transform to decompress back to 784 dimensions\nX_mnist = X_train\n\npca = PCA(n_components = 154)\nX_mnist_reduced = pca.fit_transform(X_mnist)\nX_mnist_recovered = pca.inverse_transform(X_mnist_reduced)\n
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In [19]:
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import matplotlib\nimport matplotlib.pyplot as plt\n\ndef plot_digits(instances, images_per_row=5, **options):\n    size = 28\n    images_per_row = min(len(instances), images_per_row)\n    images = [instance.reshape(size,size) for instance in instances]\n    n_rows = (len(instances) - 1) // images_per_row + 1\n    row_images = []\n    n_empty = n_rows * images_per_row - len(instances)\n    images.append(np.zeros((size, size * n_empty)))\n    for row in range(n_rows):\n        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n        row_images.append(np.concatenate(rimages, axis=1))\n    image = np.concatenate(row_images, axis=0)\n    plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n    plt.axis("off")\n\nplt.figure(figsize=(7, 4))\nplt.subplot(121)\nplot_digits(X_mnist[::2100])\nplt.title("Original", fontsize=16)\nplt.subplot(122)\nplot_digits(X_mnist_recovered[::2100])\nplt.title("Compressed", fontsize=16)\n#save_fig("mnist_compression_plot")\nplt.show()\n
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Incremental PCA

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  • PCA normally requires entire dataset in memory for SVD algorithm.
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  • Incremental PCA (IPCA) splits dataset into batches.
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In [20]:
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# split MNIST into 100 minibatches using Numpy array_split()\n# reduce MNIST down to 154 dimensions as before.\n# note use of partial_fit() for each batch.\n\nfrom sklearn.decomposition import IncrementalPCA\n\nn_batches = 100\ninc_pca = IncrementalPCA(n_components=154)\n\nfor X_batch in np.array_split(X_mnist, n_batches):\n    print(".", end="")\n    inc_pca.partial_fit(X_batch)\n\nX_mnist_reduced_inc = inc_pca.transform(X_mnist)\n
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In [21]:
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# alternative: Numpy memmap class (use binary array on disk as if it was in memory)\n\nfilename = "my_mnist.data"\n\nX_mm = np.memmap(\n    filename, dtype='float32', mode='write', shape=X_mnist.shape)\n\nX_mm[:] = X_mnist\ndel X_mm\n
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In [22]:
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X_mm = np.memmap(filename, dtype='float32', mode='readonly', shape=X_mnist.shape)\n\nbatch_size = len(X_mnist) // n_batches\ninc_pca = IncrementalPCA(n_components=154, batch_size=batch_size)\ninc_pca.fit(X_mm)\n
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Out[22]:
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IncrementalPCA(batch_size=525, copy=True, n_components=154, whiten=False)
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In [23]:
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rnd_pca = PCA(\n    n_components=154, \n    random_state=42, \n    svd_solver="randomized")\n\nX_reduced = rnd_pca.fit_transform(X_mnist)\n
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In [24]:
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import time\n\nfor n_components in (2, 10, 154):\n    print("n_components =", n_components)\n    regular_pca = PCA(\n        n_components=n_components)\n    inc_pca     = IncrementalPCA(\n        n_components=154, \n        batch_size=500)\n    rnd_pca     = PCA(\n        n_components=154, \n        random_state=42, \n        svd_solver="randomized")\n\n    for pca in (regular_pca, inc_pca, rnd_pca):\n        t1 = time.time()\n        pca.fit(X_mnist)\n        t2 = time.time()\n        print(pca.__class__.__name__, t2 - t1, "seconds")\n
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n_components = 2\nPCA 1.308387279510498 seconds\nIncrementalPCA 18.326093673706055 seconds\nPCA 3.998342514038086 seconds\nn_components = 10\nPCA 1.4705824851989746 seconds\nIncrementalPCA 16.598721742630005 seconds\nPCA 4.156355619430542 seconds\nn_components = 154\nPCA 4.129154682159424 seconds\nIncrementalPCA 16.597434043884277 seconds\nPCA 4.0131142139434814 seconds\n
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Randomized PCA

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  • Stochastic algorithm, quickly finds approximation of 1st d components. Dramatically faster.
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In [25]:
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rnd_pca = PCA(n_components=154, svd_solver="randomized")\n\nt1 = time.time()\nX_reduced = rnd_pca.fit_transform(X_mnist)\nt2 = time.time()\nprint(t2-t1, "seconds")\n
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4.414088487625122 seconds\n
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Kernel PCA

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  • Use kernel trick to map instances into higher-D feature spaces. This enables non-linear classification & regression with SVMs.
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  • Good at preserving clusters after projecton.
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# Below: Swiss roll reduced to 2D using 3 techniques:\n# 1) linear kernel (equiv to PCA)\n# 2) RBF kernel\n# 3) sigmoid kernel (logistic)\n\nfrom sklearn.decomposition import KernelPCA\n\nX, t = make_swiss_roll(\n    n_samples=1000, \n    noise=0.2, \n    random_state=42)\n\nlin_pca = KernelPCA(\n    n_components = 2, \n    kernel="linear", \n    fit_inverse_transform=True)\n\nrbf_pca = KernelPCA(\n    n_components = 2, \n    kernel="rbf", \n    gamma=0.0433, \n    fit_inverse_transform=True)\n\nsig_pca = KernelPCA(\n    n_components = 2, \n    kernel="sigmoid", \n    gamma=0.001, \n    coef0=1, \n    fit_inverse_transform=True)\n\ny = t > 6.9\n\nplt.figure(figsize=(11, 4))\n\nfor subplot, pca, title in (\n    (131, lin_pca, "Linear kernel"), \n    (132, rbf_pca, "RBF kernel, $\\gamma=0.04$"), \n    (133, sig_pca, "Sigmoid kernel, $\\gamma=10^{-3}, r=1$")):\n    \n    X_reduced = pca.fit_transform(X)\n    if subplot == 132:\n        X_reduced_rbf = X_reduced\n    \n    plt.subplot(subplot)\n    #plt.plot(X_reduced[y, 0], X_reduced[y, 1], "gs")\n    #plt.plot(X_reduced[~y, 0], X_reduced[~y, 1], "y^")\n    plt.title(title, fontsize=14)\n    plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot)\n    plt.xlabel("$z_1$", fontsize=18)\n    if subplot == 131:\n        plt.ylabel("$z_2$", fontsize=18, rotation=0)\n    plt.grid(True)\n\n#save_fig("kernel_pca_plot")\nplt.show()\n
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Selecting a Kernel & Hyperparameters

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  • Dimensionality reduction = prep for supervised learning task
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  • Can use grid search to select kernel & params
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from sklearn.model_selection import GridSearchCV\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import Pipeline\n\nclf = Pipeline([\n    ("kpca", KernelPCA(n_components=2)),\n    ("log_reg", LogisticRegression())])\n\nparam_grid = [{\n    "kpca__gamma": np.linspace(0.03, 0.05, 10),\n    "kpca__kernel": ["rbf", "sigmoid"]}]\n\ngrid_search = GridSearchCV(clf, param_grid, cv=3)\ngrid_search.fit(X, y)\n\n# best kernel & params?\nprint(grid_search.best_params_)\n
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{'kpca__gamma': 0.043333333333333335, 'kpca__kernel': 'rbf'}\n
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  • Another (unsupervised approach): select kernel & params with lowest reconstruction error. Not as easy as with linear PCA.
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In [28]:
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rbf_pca = KernelPCA(\n    n_components = 2, \n    kernel="rbf", \n    gamma=0.0433,\n    fit_inverse_transform=True) # perform reconstruction\n\nX_reduced  = rbf_pca.fit_transform(X)\nX_preimage = rbf_pca.inverse_transform(X_reduced)\n\n# return reconstruction pre-image error\nfrom sklearn.metrics import mean_squared_error\nmean_squared_error(X, X_preimage)\n
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Out[28]:
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32.786308795766082
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In [29]:
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times_rpca = []\ntimes_pca = []\nsizes = [1000, 10000,  20000,  30000, 40000, 50000, 70000, \n              100000, 200000, 500000]\n\nfor n_samples in sizes:\n\n    X = rnd.randn(n_samples, 5)\n\n    pca = PCA(\n        n_components = 2, \n        random_state=42, \n        svd_solver="randomized")\n\n    t1 = time.time()\n    pca.fit(X)\n    t2 = time.time()\n    times_rpca.append(t2 - t1)\n    \n    pca = PCA(n_components = 2)\n    \n    t1 = time.time()\n    pca.fit(X)\n    t2 = time.time()\n    times_pca.append(t2 - t1)\n\nplt.plot(sizes, times_rpca, "b-o", label="RPCA")\nplt.plot(sizes, times_pca, "r-s", label="PCA")\nplt.xlabel("n_samples")\nplt.ylabel("Training time")\nplt.legend(loc="upper left")\nplt.title("PCA and Randomized PCA time complexity ")\nplt.show()\n
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LLE (Locally Linear Embedding)

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  • Powerful nonlinear dimensionality reduction tool
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  • Manifold Learning; doesn't rely on projections.
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  • LLE measures how each instance relates to closest neighbors, then looks for low-D representation where local relations are best preserved.
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In [30]:
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# Use LLE to unroll a Swiss Roll.\n\nfrom sklearn.manifold import LocallyLinearEmbedding\n\nX, t = make_swiss_roll(\n    n_samples=1000, \n    noise=0.2, \n    random_state=41)\n\nlle = LocallyLinearEmbedding(\n    n_neighbors=10, \n    n_components=2, \n    random_state=42)\n\nX_reduced = lle.fit_transform(X)\n\nplt.title("Unrolled swiss roll using LLE", fontsize=14)\nplt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot)\nplt.xlabel("$z_1$", fontsize=18)\nplt.ylabel("$z_2$", fontsize=18)\nplt.axis([-0.065, 0.055, -0.1, 0.12])\nplt.grid(True)\n\n#save_fig("lle_unrolling_plot")\nplt.show()\n
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  • 1st: For each instance, LLE finds k nearest neighbors & tries to reconstruct instance as linear function of neighbors (weights such that squared distance is minimum).
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  • Weight matrix W now encodes all local linear relations between instances.
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  • 2nd: Map instances into d-dimensional space & preserve relationship data
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  • Scikit computational complexity:
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  • finding K nearest neighbors: O(m x log(m) x n x log(k))
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  • weight optimization: O(m x n x k^3)
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  • constructing low-d representations: O(d x m^2)
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MDS, Isomap, t-SNE, LDA

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from sklearn.manifold import MDS\nmds = MDS(n_components=2, random_state=42)\nX_reduced_mds = mds.fit_transform(X)\n
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from sklearn.manifold import Isomap\nisomap = Isomap(n_components=2)\nX_reduced_isomap = isomap.fit_transform(X)\n
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from sklearn.manifold import TSNE\ntsne = TSNE(n_components=2)\nX_reduced_tsne = tsne.fit_transform(X)\n
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nlda = LinearDiscriminantAnalysis(n_components=2)\nX_mnist = mnist["data"]\ny_mnist = mnist["target"]\nlda.fit(X_mnist, y_mnist)\nX_reduced_lda = lda.transform(X_mnist)\n
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/home/bjpcjp/anaconda3/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n  warnings.warn("Variables are collinear.")\n
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titles = ["MDS", "Isomap", "t-SNE"]\n\nplt.figure(figsize=(11,4))\n\nfor subplot, title, X_reduced in zip((131, 132, 133), titles,\n                                     (X_reduced_mds, X_reduced_isomap, X_reduced_tsne)):\n    plt.subplot(subplot)\n    plt.title(title, fontsize=14)\n    plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot)\n    plt.xlabel("$z_1$", fontsize=18)\n    if subplot == 131:\n        plt.ylabel("$z_2$", fontsize=18, rotation=0)\n    plt.grid(True)\n\n#save_fig("other_dim_reduction_plot")\nplt.show()\n
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\n\n\n" }, { "path": "ch08-dimensionality-reduction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\\n\",\n \"\\n\",\n \"* Dimesionality reduction is lossy. It may speed up training but can degrade result quality. Also makes pipelines more complex. Try using original data before considering dimensionality reduction.\\n\",\n \"* Very useful for visualization (2D, 3D representations more intuitive.)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Two main approaches: **projection**, **manifold learning**.\\n\",\n \"* Three most popular techniques: **PCA**, **Kernel PCA**, **LLE**.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Curse of Dimensionality\\n\",\n \"* Many things behave differently in high-D space.\\n\",\n \"\\n\",\n \"1) Most points in high-D hypercube will be very close to a border.\\n\",\n \"\\n\",\n \"2) Distances between random points much greater (very high probability of sparse matrix representation).\\n\",\n \"* In 2D: ~0.52\\n\",\n \"* In 3D: ~0.66\\n\",\n \"* In 1,000,000D: ~408 ~ sqrt(1000000/6)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Approaches: Projection\\n\",\n \"* Most dataset features are concentrated in a few dimensions - not uniformly across all. Much learnable training can be found in low-D subspace.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import numpy.random as rnd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# build a 3D dataset\\n\",\n \"\\n\",\n \"rnd.seed(4)\\n\",\n \"m = 60\\n\",\n \"w1, w2 = 0.1, 0.3\\n\",\n \"noise = 0.1\\n\",\n \"\\n\",\n \"angles = rnd.rand(m) * 3 * np.pi / 2 - 0.5\\n\",\n \"X = np.empty((m, 3))\\n\",\n \"X[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * rnd.randn(m) / 2\\n\",\n \"X[:, 1] = np.sin(angles) * 0.7 + noise * rnd.randn(m) / 2\\n\",\n \"X[:, 2] = X[:, 0] * w1 + X[:, 1] * w2 + noise * rnd.randn(m)\\n\",\n \"\\n\",\n \"# mean-normalize the data\\n\",\n \"X = X - X.mean(axis=0)\\n\",\n \"\\n\",\n \"# apply PCA to reduce to 2D\\n\",\n \"from sklearn.decomposition import PCA\\n\",\n \"\\n\",\n \"pca = PCA(n_components = 2)\\n\",\n \"X2D = pca.fit_transform(X)\\n\",\n \"\\n\",\n \"# recover 3D points projected on 2D plane\\n\",\n \"X2D_inv = pca.inverse_transform(X2D)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# utility to draw 3D arrows\\n\",\n \"from matplotlib.patches import FancyArrowPatch\\n\",\n \"from mpl_toolkits.mplot3d import proj3d\\n\",\n \"\\n\",\n \"class Arrow3D(FancyArrowPatch):\\n\",\n \" def __init__(self, xs, ys, zs, *args, **kwargs):\\n\",\n \" FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)\\n\",\n \" self._verts3d = xs, ys, zs\\n\",\n \"\\n\",\n \" def draw(self, renderer):\\n\",\n \" xs3d, ys3d, zs3d = self._verts3d\\n\",\n \" xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)\\n\",\n \" self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))\\n\",\n \" FancyArrowPatch.draw(self, renderer)\\n\",\n \"\\n\",\n \"# express plane as function of x,y\\n\",\n \"axes = [-1.8, 1.8, -1.3, 1.3, -1.0, 1.0]\\n\",\n \"\\n\",\n \"x1s = np.linspace(axes[0], axes[1], 10)\\n\",\n \"x2s = np.linspace(axes[2], axes[3], 10)\\n\",\n \"x1, x2 = np.meshgrid(x1s, x2s)\\n\",\n \"\\n\",\n \"C = pca.components_\\n\",\n \"R = C.T.dot(C)\\n\",\n \"z = (R[0, 2] * x1 + R[1, 2] * x2) / (1 - R[2, 2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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kgMEWeWZiZpQRDwq1/9qqtM0qVSCXNzcwiHw5idnYXT6VTti+VwOCRx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\\nerT/A7WG3uyaRvnhAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot 3D dataset, plane & projections\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(10, 10))\\n\",\n \"ax = fig.add_subplot(111, projection='3d')\\n\",\n \"\\n\",\n \"X3D_above = X[X[:, 2] > X2D_inv[:, 2]]\\n\",\n \"X3D_below = X[X[:, 2] <= X2D_inv[:, 2]]\\n\",\n \"\\n\",\n \"ax.plot(X3D_below[:, 0], X3D_below[:, 1], X3D_below[:, 2], \\\"bo\\\", alpha=0.5)\\n\",\n \"\\n\",\n \"ax.plot_surface(x1, x2, z, alpha=0.2, color=\\\"k\\\")\\n\",\n \"np.linalg.norm(C, axis=0)\\n\",\n \"ax.add_artist(Arrow3D([0, C[0, 0]],[0, C[0, 1]],[0, C[0, 2]], mutation_scale=15, lw=1, arrowstyle=\\\"-|>\\\", color=\\\"k\\\"))\\n\",\n \"ax.add_artist(Arrow3D([0, C[1, 0]],[0, C[1, 1]],[0, C[1, 2]], mutation_scale=15, lw=1, arrowstyle=\\\"-|>\\\", color=\\\"k\\\"))\\n\",\n \"ax.plot([0], [0], [0], \\\"k.\\\")\\n\",\n \"\\n\",\n \"for i in range(m):\\n\",\n \" if X[i, 2] > X2D_inv[i, 2]:\\n\",\n \" ax.plot([X[i][0], X2D_inv[i][0]], [X[i][1], X2D_inv[i][1]], [X[i][2], X2D_inv[i][2]], \\\"k-\\\")\\n\",\n \" else:\\n\",\n \" ax.plot([X[i][0], X2D_inv[i][0]], [X[i][1], X2D_inv[i][1]], [X[i][2], X2D_inv[i][2]], \\\"k-\\\", color=\\\"#505050\\\")\\n\",\n \" \\n\",\n \"ax.plot(X2D_inv[:, 0], X2D_inv[:, 1], X2D_inv[:, 2], \\\"k+\\\")\\n\",\n \"ax.plot(X2D_inv[:, 0], X2D_inv[:, 1], X2D_inv[:, 2], \\\"k.\\\")\\n\",\n \"ax.plot(X3D_above[:, 0], X3D_above[:, 1], X3D_above[:, 2], \\\"bo\\\")\\n\",\n \"ax.set_xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \"ax.set_ylabel(\\\"$x_2$\\\", fontsize=18)\\n\",\n \"ax.set_zlabel(\\\"$x_3$\\\", fontsize=18)\\n\",\n \"ax.set_xlim(axes[0:2])\\n\",\n \"ax.set_ylim(axes[2:4])\\n\",\n \"ax.set_zlim(axes[4:6])\\n\",\n \"\\n\",\n \"#save_fig(\\\"dataset_3d_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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6SeCgq8AmsMN52XK2UxNjY2a7wo/6gVeAXVGG6ZnFgtkoLJyUm2b99e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\\nT0SCocATkWAo8EQkGAo8EQnGfwLvwVCvZn+R8QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# 2D projection equivalent:\\n\",\n \"fig = plt.figure()\\n\",\n \"ax = fig.add_subplot(111, aspect='equal')\\n\",\n \"ax.plot(X2D[:, 0], X2D[:, 1], \\\"k+\\\")\\n\",\n \"ax.plot(X2D[:, 0], X2D[:, 1], \\\"k.\\\")\\n\",\n \"ax.plot([0], [0], \\\"ko\\\")\\n\",\n \"ax.arrow(0, 0, 0, 1, head_width=0.05, length_includes_head=True, head_length=0.1, fc='k', ec='k')\\n\",\n \"ax.arrow(0, 0, 1, 0, head_width=0.05, length_includes_head=True, head_length=0.1, fc='k', ec='k')\\n\",\n \"ax.set_xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n \"ax.set_ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n \"ax.axis([-1.5, 1.3, -1.2, 1.2])\\n\",\n \"ax.grid(True)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Approaches: Manifolds\\n\",\n \"* Manifolds = shapes that can be bent/twisted in higher-D space.\\n\",\n \"* ex: \\\"Swiss roll\\\" problem\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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IlLkngIodPPAiPKKrVwJb5fMVuFTqN7Q5Qyguq61fQmwVzSMldwkqyv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\\nMUGUmCoUCoVCMUGUmCoUCoVCMUH+f4V7HAdsNICRAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Swiss roll visualization:\\n\",\n \"from sklearn.datasets import make_swiss_roll\\n\",\n \"X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)\\n\",\n \"\\n\",\n \"axes = [-11.5, 14, -2, 23, -12, 15]\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(8, 6))\\n\",\n \"ax = fig.add_subplot(111, projection='3d')\\n\",\n \"\\n\",\n \"ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=t, cmap=plt.cm.hot)\\n\",\n \"ax.view_init(10, -70)\\n\",\n \"ax.set_xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \"ax.set_ylabel(\\\"$x_2$\\\", fontsize=18)\\n\",\n \"ax.set_zlabel(\\\"$x_3$\\\", fontsize=18)\\n\",\n \"ax.set_xlim(axes[0:2])\\n\",\n \"ax.set_ylim(axes[2:4])\\n\",\n \"ax.set_zlim(axes[4:6])\\n\",\n \"\\n\",\n \"#save_fig(\\\"swiss_roll_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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IG3AR8ExoPpPEipDdIvhKXqHOBihOcmciy4FhiP+MK1ccaBmHTXS3xd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\\nrkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# \\\"squashed\\\" swiss roll visualization:\\n\",\n \"plt.figure(figsize=(11, 4))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n \"plt.axis(axes[:4])\\n\",\n \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \"plt.ylabel(\\\"$x_2$\\\", fontsize=18, rotation=0)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.scatter(t, X[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n \"plt.axis([4, 15, axes[2], axes[3]])\\n\",\n \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"#save_fig(\\\"squished_swiss_roll_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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pAT1YGdbmSLhZypm1FQzB5BcznoWcnhFrRm5cIR2tpuJtYloHLy5A0c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C6XxfPP/yEAEuKn1grqcimx4/flEO1l0gimelPUTmxoOD/kHYgP0qKG\\nJhz44LZCjeqejXvKHo8R28nKfXpE7ykIpvPec9ibDoKZwud1Nc25XBbATUYPKq5CpdQ2qrIXKV5j\\nnBdsCgmoxsk9QPW8mQ4p26v6i/PqH5drNYVUdIaytdGoFVNR8DHahIaOHbsVo6NPFfiptYa69CRl\\nz+Kpp9ZgYkLVc0FeacWtHddOPIze3rdh/fpnwGOYLtSUcqDYTtaEGcVrshTarNKbZEIe/2r0oLiB\\n1nlapcb2XnttP9TiGgGmprKF30R1311CAqI3KMZCuScqUpz0ST5zMsgVuoV27969iY8po5RkjYs2\\nZHFHwDA4uAPt7VvBFuF/qQmvsi49SRlMsCiKxYuvKMSm5HpfSqcwPv4CilvTm508lHIirhGYC2Rv\\nUtzG61TDwwK90yGlnoMHN2J09Fmn2J4KXV0HI79LJrMQhDQDQCFcICOXy6Kh4fyCgjrwA2X8MXwt\\npja5s4r47g5t29kk1+qKNLbkSQ2k67nlHUEQTKGv76Pgi3AtoC49SRG5XDZf1hYFFyXQcSFFDA3d\\nj8WL3xTroVSiAiG+EVgRS5Zcia6ugwbNSL06u041XN7mc2+Sx+X6+j6amCMpQxxPEExBLE8cGnoA\\nq1ffFqmM4mMYH+8D0Areq8d0bJFbqaMThcehR7mz4rKnmXZC0NZTjONo6uLKzPlwb1RXLtS9kWRb\\nxwao1bUbjFzIMGZx3nldWnFVjkrQKWTVIXoLjSQrgHA1jMlQyt0kTeEEtg2W7+U0zpzZh1yOdZlU\\n1VqbMuAmjme0r7eIcLggXO/9Qv4zjyppQyZupZ5OxBcHc0KkmnXclUTcXNfFlTlqhWJV99tt9UPN\\nUdwq8u3dpk0U7e1/BkKaIp8eGrq/pG2U2MSL3kJjty4uWxubUEBX10FcdNEfoyjgRNDW9vHCNtR0\\nDL6VftOb7o3IkGUyi3D++VdBrLXu6NhmpNuI49Y17bJRIRLDBerPzygTPbprPX36x0Y6EQchTcpr\\nNF2rCNNv6yrAXItoW9JmiCszVCI0YYO69yTNW031aqb3KtNd+Wwy4bZbKZtkRS6XxcmT30bRy6MY\\nGrofq1d/Bc3NK7XHOHt2H06d+iGmpgaVW+kgmMlzNfWivirEVbfYsBT4byJvk8X3VXQn3bU2N1+C\\na689FXrdVmrOhajvUvM8NDaElbevxHev+q7xmCqojlmJ8kDV9akk5mrBm6x7IwkUs92/+tU6YRvG\\noJrsNlvTWoNMin7+eRaHW7fuh4XXjx5VdRucLdREi9nd/fuvQ3c3yz6yvjQ7ASBy/xhUVJ34yR8X\\n/7TRxQTYbxIfUw7HL20rgsRE0Esv/bmx9LO5+bLEvNA4pBnG4TQjE0/TtTRWBXnRqDWJOQ5vJAUs\\nW7ah0HI1rmlVrfUGjoM4IRkv7QAAhB5Y1l8milOnwl02RArQqlVfyHufHJkIgTyJqG8SpR/Tb/Lr\\nX781NqacNHEkJoJ0dKeRkccB/BIuNd/VlBmLM3Sl9tsBosUE4u9XiUZgtqj7mCSH6qHMZnegt7c7\\n1ZiIbiuTdItjG7c8enQ7RkYew5EjN4V4adnsjkgjLRmzs68V7oFMAXrxxZsQrVbaEbpnYjzXNi6X\\nNp1KHEN39wmcd941ICQcO7WJf+kI48yDjpLFdZVPNteRloGstLpOXIlt25K2UGZbni+1Bu9J5qF+\\nKMMq1TaIizu5VNzYbGlsjleMNQKnTu2CuDbyemd1SwOOYpZY3gIPD/9T5NP8mKVsKdPaeql+D17h\\nIfsItiEAFWFcdwzdvConvaUUJalyQEUZYk3dwloJ1c5i6+CNZB7qJABXqb6noAoUF3gvpR5ZhmlL\\nowu4q4xmNNYY5qWdOPE1LFq0xrgdHR7+idLbViPAmTP7tMfiMC0oLjFB02+iUjlinh0bp4g4Izw6\\negjZ7J3ghHFmJFWCvPeEVKRM/XbKYRhKUZIqB8S52rakDcc/c1DiR6pr7msFdbfd1qmxyFtCkebD\\nJ7SJjsKPzbdfL7/yNSz/6zBVo2dfT1npGqqJL3qRJhDSgE2bqLa/dnPzxVaUG47W1o2xn4m7nzYw\\nHYMZtW9AVjkqUpGaAHzAOgTQ1/cx8Ac7CKa0HD9xvujuVzkSEj37elIhjsdtz0uZw0NjQ0p+ZFTB\\nabhmVJPqzkjaPJjqpMGOwuqni10NDGxHEDBtQwK1VFrcCp62bJo6Yx0Fry7q6joolfotQnd3Fl1d\\nBy0pNwxxBqBUgQt+jOJvEo1rMaPGtnpc5SjqCT9qde7R0UNS5r5YaiiDt3c4e/Yx5f1asuRKK65k\\nNSFzMUU+Zqle6NmzjyF67+Tdx86SF9C0UFdG0vbBVEtiTQkxlGjgnR27yDHkfW5aFWXhJgJw2tsg\\nXca6oeGCkLfMW54CauoNEPa2gT3S98Pk6TgDENdjW4ZqByB6JLwnDYds1IoqR1GKk82DyAxuGDrC\\neHv7nyEIJtDaGq9RaUI5Ei42XmAamWsTWlo2RIoxCGkq7D6YytSjKGUBTRN1ZSRtH0x9fFIvUsDE\\nFMIPIO9zowKPK5a7aqK5+RLl601NK5UUG3vxiWFnkYpSBC7kHUC07jfsTaqMGvt95C3yjJXXq6rv\\nN+mJpvGAD35+MHVDWaqhS2OexiXlGMXMfgEtN+rGSLqoSpvikxxBMIMDB16P0dHnALDEhgyXPjfl\\nCqTr6DfLlm1QZmZN4hNhRLsMxk1okVPo8l2dgroc1+LepEm0hG91RY/YxustlmpyZNDRsS3yXduF\\n2FapvBzzotSYogm8pNaE/7gvW3iugAza2rZg2bIN6Oz8qfBbMym/WihNrBsjqaZiqOt2ZejEVimd\\nRl/fDcjlspidPRd6d3IW2PxkVGyinHDxOnSr+cTEUUvqzWFrik4ul0Vvb3fB8xsft/8uoDY8IyPq\\nuNbZs/tw7NitEfk7vjVOEge0i6G5LcRpJK2SopyZbZt4+tDYEC74CsGxV74OIMBvBu/DyMjj2mRX\\ntb3JuqEAqY3CNKamsrFUDPnBGh09hN7etwJgZXhHjnw24tUsSKmlrA4u5V8qlJ40uNuqIiKXy+Lp\\np9djejoLviYT0hiqZgq37Y1+X2V4Lrzww4XqKA5CmtDSshEjI0/GGmF+TuCm2GtoadmAycnoueQM\\nvm0TtLia9GpW2lQKouYp0/pnxQnNzXYN2SqJuvEku7oO5t17Av6zcLi683K8i5XthR+Oxgzw0Teq\\n6TQmmCpyRIWgUgxkWk2pbI4zMLA9byABMaYrVjO5Kv2Y1HhGRp60qvDh5wR2xl6nLbHd9nNxW/L5\\nbiCXNwHvXlnUPOXGktLZQrIL2JMo2VUO1I0nqSoP4+D0kMnJl2MVWqJUEADIgfW/mY7UfLftc/MK\\nkhg/V7GBtAjvcccpZvyj4NVMxeZj9ko/OjUeW4ienE5PUoTtA9rZuTu22CBJTbot9mzcg+ufvr7m\\njaxOOT9MxK8d1I0nqVLM5uD0EB4XMUGdNQV41lSOQw1+fjDUCCzt2m3AjbKRXvZ1OPY4qox/EcyT\\nEmk5Kq8qSd13HMLeqZrOlcTTtokzxsXccrks7niLmjoWh81Pbi7Mt1qGSTm/2vFHFerCSEb7rqgw\\nC13fFhETE0djz2f6oUWjyf9lkIlQgsiXCBZ8aYHyGKXAlZ+ox07jcfReZCMWL75CYAsUaTmVyGRG\\n1cxnIudMklSxXXxs6C8uPdtFnJk+U/j/NKhD8jxNC1ufAXr2AUdGFeesAWk0GXWx3XYpp4urqd24\\nMdyF0FUGzCUon3ZfZN1Wr719q1YLUXcc4FHjlpHRZlRe5LSyfQNHOWuaWZZ9vTG5Ihs723tj2/3R\\n5AHH9Wx3gYtory10GpNx39GJamx9hsUnH7yGeZaZzCJcc80AmptXptr5sVTUhSepK6draLgAciLH\\n1ZvhZXzLlm1Ad3c2djvoOsnSJJrrtnp9fR918pxEsq94HPH7TJo/ioaGC7TdKdlxyudJHDt2K6am\\nsrGenGjsbO6NC/Unbnz83KZChGrBJV7OE41AkRYkzn3+/pOb/wzNC8IaCbWGujCSqrhWd/eJvH6i\\nOpEj/limGBX3TmzimUmh2oonMZy6rR7z7OxjlMwAhvt2y8aNLx68xWsmsxDd3Vk0N1+iXLAymSWF\\nRabUTKb8e4V5msV6dDGL2tV1UGnsdDqRIpJw+1RjFM9tKmudC+Bz1hQvf9NXV6SyuJQbZTGShJAd\\nhJCThJDnhdf+ihDyG0LIofy/95Tj3LZwUWgxxaiOHt2OqaksbOKZaUJelVWQX1ctFqyaiD2Jtis5\\nM2J7YpMpqtpqlefNa53TWmTk34srsZtq74vj1c0JtzLWOI9YNUb53Auq6E1WQqj3D9pOFQRhOGrR\\nmyyXJ3kvgOsUr99BKb0y/293mc4di2jwPuxdiA+8LiDPvRPWSpShFn9gE9LaJuqOraut5irpAwM3\\nl5xt13lk/Hijo88KSuz62nvA3FjMpYw1LuSiumbVuTnX1jZp0trYavU5E9Lg4dri7csBOTYtLi5p\\n8XlLRVmMJKX0MQCny3HsNOCyPdJlg4t9YgLhGNXZLiRVbSlnCZiutnpgYHtB33Jo6H4MDNxcUrbd\\n5JEVa9H1jchExLUNTvfehK85DarTQ+94yPh+pds4mLC8CViUTwXkAigdlGqWboqodEzyM4SQ5/Lb\\n8dKXvYSw3R7pPK2wdxJG3INUSxO1nN3pdLXVrDqJx4BnMTT0QGJPVuU16mKKMlRliqLXUq57Y+O9\\ni5qiaWmLplXqmNb8FQnljQQYGLhZ+kQ8D7dSIJSWh3hKCLkUwI8ppevyf7cBOAXmX98KoJ1S+iea\\n724FsBUAVqxYsX7Xrl0pjmwYwJcB3AJgecxnvwLgZ4rXLwbwG+hoLMDlAO4OvbL5yc0hHlsa2LNx\\nDwCmSB33mdIRvm/nzp3D0qVLDZ+/A8BuhBM8DZATPlE0AHgvgM9ajEk8RwPY7/JKzDnCxy9exx0A\\nHgHwfsO5XeaOzZjVYzL9niY8sv4R7W9ie0xxvujmbGtja0lzeXkT8J2rgeYQDTgD4J/A7uswguCT\\nyGRGUfxtbeeEPXp6enoppVfFfa5iRtL2PRlr1qyh/f39qY2rv38bstk7I21PVXjiiQsxMzPsdPwl\\nS64MbZHKJVaQQcaKRynHs+J6wugg37e4lp+6Htg2kO+hCkxkZD3CXiKBfuFSH3/v3r3o7l6Dp55a\\njSCYDHH1ZPT1bcHQ0E60tW3B2rX32l+QAN19OTJaumLUno17Ir+Jy/yTy1jTUseXcdPlwHvao1U3\\nbW0fx9q194T6uHOYfpekIIRYGcmKkckJIe2UUq508IcAnjd9vhyIU1+RP8tbrGYyi/DWt+7HwYNv\\nRxBMRj5r6tGdloGUjV3SCZykblt13+T3ZcOrMnK6Raeh4QLnOmyx3wyHrC5kCxsiuFhBNDT0AFav\\nvi3RA6u6Lxd8heCLaxndJyl5XAeb+ZekmkZHLFfpBciGWleWODz8CHQ9mcpZZBCHclGAHgSwH8Aa\\nQsgrhJA/BfA3hJB/JYQ8B6AHwOfKcW4TXEry1AmAyjV1SgsinzJpJll333gcb2DgZqsAu04lXfe6\\nDi5K4fGwU1gP16HP5v9OBze+HolLETkqHetWldfqsuKyMd36DPCf9gM5qSArCMZx9OjNUFVqVfMZ\\nK4snSSn9I8XL3yrHuWzhor6iJxWHceGFH8a6dVENxFqCOEFtS+dE6O4b8M68V/o4RkZ+CZV3LnuY\\nacldcVFdNqYMgAAdHdsSehl6hXVR71KuQy/FmxSRy2Xx7pUoqRSRe4JplvKVs6snoFYConQ2RKkD\\nyrPNdkVdVNwAyWk/Jpw6tQu9vd0YHX22kBmVM5O1AldOJPcSjx69WXnfgLsi0nPy/UyLwiFmnqMc\\nVza2Eye+Vmil4Qa1Svrg4H2Fe6NWM0rHm2QGn/1/NUoRdR5okjCRKiuvewZUW272O4TvcxBMKjLf\\nlUXdGEkbSgd/GHWtQFUYHT0Qqu8tR5Imja2UKyeSGziduC3wy0h8ltIpnDjxDYyOPpdqQyzR2JoW\\nsL6+GxIc/W5tt0N+b1T9i0yv24LfI24sVKWIaSvwyEhCGk/DsHIlIPG+q3u+UwwPP+I8xjRRFypA\\nQDRgrmoZwB/Gjo5P4Zprwnkllt39JlRya3wrns3uSDX4nubD4cL7Ew1cEIzna67Dsc39+y/WnClA\\nX98NoUaL5XEAAAAgAElEQVRjqu2rbYZdNrYqeX+O8fEX8PTTV+G3f/vHibdn6uTeJcqEU3Oz7h7Y\\nQWXwuTf51ZfSWRxNyj1Jj1+uahzxGd279wfIZD6GIJhEEIzHCiOXE3XjScpQtSjVeT52epTM6Ji2\\nSy6cxbQD8cX2FZnY/tiiQHEQzES8zaNHt0NF0OYYHz+MwUH91t5lGy7HUbm8v6oiBgDOnestaXsv\\nni8IWKO4zs6fWlfDuJTSqRauJqEUMQ1jpEuwVKr00ATzHDfrlVYSdWkk9S1K1T+KvR5lkJpyi6j8\\nU0oQnU9E2+1vdEGYjnz+9On4bWbUa50ttHuNG4dNf25TnfWJE1/D009fZTy27r1wvJM1inOJPcYt\\nAOL540oRxRifDrVUwWWLOCOt0yutVtVNXRpJ2SAODGw3JjVMD6SMxkz6wXdXg6nyFmzpT6o2F6I3\\nqWqfm8ksLEii5UeAqN4k29rbjMOmPzf3jFXeJBD1KMVElEvTMYBlsm0e0NHRQ8hm74RpAXAxoqYY\\nnxir5HOjZ19PSNVelUQxzZ9aMcpsp6JeZKuBujOSqiyv2GeFQ/xR5BVfHWBmWEAY501GWpPLJjgu\\nPxAumW2mFSmHFaYlUdqocEV0EWkMbes3baLo7NwdOw7R04zrzx23eGWz35K2+I/nKSbFWm/gJovF\\n0E4b8vDhj4AvDqowhcqLlj1bV0aAbj7oqrFM88fGKJu26GnNcdVOZd7xJGsZam9hFlHhXf2PwrdD\\n/f3bMDj4rdCDRUgTrn3jJ0DfF+XsVVqSnk96237QAOv4x0v0ODKZRejs/CkAvXBFFEXDyhM1Cxde\\nFjsOcayENKKx8QKsX/+MMmhv+h3YsXM4fvxWrFr1BSVdqa/vowAOF87PhXcPHLgMlIZ1Dk0cUG6A\\nwyWR08qWFrIXTSktGMXiOJkRLUcFDhCu1nLt3x7XmTOO9hZnSOVKt2pzJIE69CR13sKSJVdGlMsb\\nGs43brOSKMWktdq6HMdlnHFUoaVL14PVSLMFoaNjG7q7TyCTWRj6jmhYuXc0PPwT4zhUHi+LCZp5\\nciaPcnBwh4bryQsEaCSpZCOtxq+Jh2pUNeNymCLqRe8oaG4ODt4TGiels8awTVo8XFfKWlJZPo44\\ng5xeo7r0UHeepG3Vh02Nc5IKkrQEBFyOwz0kG9qNyaAW62pp4fXBwXswOzumNayidxQEY7jooi14\\n9dUHQelUpOZdHxO8H6tXf0U77q6ugwUBDtmrDYKp/BZb19pWPdboZ6LGHAgwNPQACNH5GnKYIjo2\\ncQziOCmdKrkZWKVRqtEuZ0/yUlB3RtIGLkIYJii3JvsqE/xe3gR8cS0K/DJbYQvR8MuG9fDhLYhW\\nRMwYPUTZM5ANgXh/TTHBgYGbsXbtPdpxs1iqattvoZRkMPYq8RK5bzerQFJhAd70pntx8OBGTE8P\\nK64tLNgc+bbAmawHuISFKglvJBVIUuOsQqlbExVsDSwXTVDFurhRivMuZcOqpv5Mo7n5Ylx77auR\\nd3K5LJ56anXIM5Ah3l9uoJkM2tsgbmFtvEkVbCXbKJ2NDQfwa5Jbf+jB4p7j433o6PgUrr66WKCg\\ni6OK4O0b/u6j7NpcPDVbKT2b72RSjMrJ1yDGRMspAl0KvJGUUEsuv23FjVxVsbwJBdEE2UMSjZLJ\\nuxR71GSzO9DevrUQUBdByMJC7FGGDb9U9RAwGTT52qPepE0IQWU8db3Sm5svxooVHyoYr3gvMh68\\nx7jcw9sUG+/qOijsQg4Bu922sapkjI2B1RnVtPu/ixDnLQ+bmO5/NeCNpIRadflNkB8IcaIFwQyG\\nhqJb3Pb2rcaQgpjAoDSHgwffEaFJsfcmMTCwXSlCG2cIVNDJoAGI1PDGhRB0RjRc/lYUD1Z5vvK9\\nMSWJZDFe8Xfg2fTx8b4Cz9OEUnYbuuyz7nUXmMocXSGHhFQOSja7A6Ojz2Dduh/6ssRaQS25/Ekq\\nbVRVI6q+4iJRW9VnPNzpkCIIxqEry9SRrZM0t+IyaCrwGl55jNnsjtDrnHfoyjkcGNge2+KUX5Mq\\noy/eB9ce3uVWj3LRf4w7TloQQ0KAzkGZwujoAV+WWEtIo2tdWkiyYttuccfHX9CSunU0GGABLrpo\\ni6LKJR3ZsLh4n9ytsujpToVeD1Nz7FWImKqPvsWpCB3flt8H1x7e5VCPqmWEQ0I70NvbjZERlfoW\\nu4e+LHGeohp1tabtIOc1Ll58ReQ98cE9e1ZFGAeAWaV0GlC6bBgQb+BFKpLc05tty54NUXNc+HYy\\niVnV4lSE7j7z+5C0h3etQtzVpDGvRdHdIGDeYkvLxsI9B/aEyk59WeIchkkwYfDzg7ETynXC6YRN\\n+SQWPWF5S8gJzCqVdUqncPbsPgBAS8sGbU10EIzjwgs/pHh9rOSH3obo39V1UOnpUjol1XrPaj1l\\nFVxJzF1dB5Vbbn4fKtXDO02Y5qLo6Za65eZeZFF0l913MWxi21ajEvBGskTExb3itlGuE86FVqQn\\nMLOfnXuWXEKtpWUjALMXFAQzOHXq+8rXS33oTaEOcTFSe7pBKIQgw2SUXFXbOWyFjNOMc9ssqqbP\\nmBbZtKXT2pa0KUWDVa0bgHDYxNRWo9Kwym4TtgyeA6ATAfshpXRzaqOaI0iLdF4uqI1dmMCczX6r\\n8Dq/Br69VHMMdeUf02VNbomLUUvLBkxOHpGuja/37s3akjIabI1fqfFs2cjo6qdbG1tx+i9PG49V\\nCneXJ5RK3W7ruiWGu3GaxU0qCVsKUCOAP1G8/jkAbwPr6j4nwGkhl1/+9wXOWlLDVirpnE+2tOgZ\\n5EskxJGTH04VgZkJOWSU12DLMQTsemUnhZ06uT6WGdckLKmnl+b1uswBncdXqoCKbVbdNskkfk68\\nPt5f/KbLgfe2M9I8R9FbvNvY172SsDKSlNIxAKE2ZoSQvwEzkH9BKdXXi9UYRK1CzllLwn9Mg3TO\\ndSLj4NJg3vQ5/TaaG/r4a+CGQeQXAux+9PZ2A0DqnDaVOrncXkMG5zwGwaT1NZULuVwWzz/PNlq6\\ne1NtlXBXuPIlVdenI/WzxSka964WnMnkhBAC4O8BfBrApymlX0t9VGVCWKuQJS9OnPgG2tv/M847\\nr9PpWJUgnfNJmBY9hMf29u+/BDrBB/trGMbBgxsLnvixY7didPQAAKR6D1SL0YkTX4/9zdIqLU0D\\n5bo3KrgsqKWAG71SOJ269heHD18PwBw2qCScEjeEyZ3cBWAbgD/lBpIQ0kwIuZsQMkAIGSWEvEgI\\n+UwZxlsS1BSTIFGXvVoineugyrwz1WeTIo7tNewsxAiLlByGcJYy2Xj5/w8MRGXOAGr8zZImYsoB\\n3b1x6YXjgrQMZNodGuNYGRx8pwfsTPX8pcDakySELABwH4CPAPgYpfRB6TiDAH4PwACATgD/QggZ\\nopTuSnG8iWEiKo+Pv4DR0eecvEnVKshWcfda23JBLtsrSp2F4Vojy3uQ8Bghqw0vJnR4ltLVYxLH\\ny8Vox8aiAXyA1UTrOuhVo7RUVwIp05X4vRHFdm3GFCd2mzZK6aukgk3CSNzpAY9qf99Kw8qTJKxO\\n7LtggYKPSAYSlNIxSukXKaUvUUoDSukhAA8DuDb1ESdEHFH58OEPl7yy11LVhK7ZmcqLdPWA2XGK\\nbQpYbbh4bwOlNyl7T7LnWBxvUYw2CMYKxG6Rb0hIo5YOUkkv39Q7J0p6B4AAJ058C4ODbtVA5VCU\\nSnI+wD6h6Jp4lCXoaoVHGmskCevw9BCA9wHYTCl9yOI7jQD+LwDPlTzClBDXD2Vioh8jI4/XzA+T\\nFHxiqmJyTHMxCpfMdNGYzeRfidaGs3NORe6lzCkV/w63cp0SSg7DXRZtttCVLC1V9c6JL+/MCddR\\nO8aAI8642XquLh5udKc3g8HBZGGbtGGz3d4JZiDvBdBKCPmY9P7DlNLXpNf+AcAoaiiwID4gei0/\\nWlN8RxuKkGoi6gxKGv1CXNrrqnQYuSERVYiy2R2FcfLvcnAlGJP6ebUSMqw74jcg987hY9ILAQMu\\njIJKQYxD2iZkTFluF16lrvChFtS3jEYyn8l+d/7Pj+f/iQgAnCd9528BdAP4HWqnTFpRsId1Z2xl\\nRjl/GNeguGvMqZwxuSTyZ/KYoipE5mlC6ZSVIG6lodK9FI1eZ+fuSJxStUAHgV5uzgblUA2yhU0b\\nEZuQgK7wgZfKVhNGI0kppQDOtz0YIeTvALwTzECeKnFsZQHrKz2BtrYtePXV74W6AgKVWdnFFbbU\\noLsqoH/X24A3nBf+XFoGRceTNEEvG8YR55kGaGxsU6qfVwujo4eUNfBAcUFiyZnH0dv7tkLHR7Ux\\noKkIhMxlqDpfEtJUKJWtJlIT3SWE/D2A3wHQQymtndksQG7gpAvJJvW6XAm2qs/qspgucvy8oiFt\\nGkdS2GzR5Qy7/LC0tqb/sLAs/U3I5f7FeUFkXqQalE7hzJl9yOWOAqCFjo9r194T6SHECe9cGEM3\\njjTFbm2QVhWYC2qpK4CIVAQuCCGrAHwGwOUAXiaEnMv/U+v6Vwly9kxXh6zzuuK4baKwadLJ5Nps\\nvtxIg88XlzQD1N0IXXiOScbJsvT/6pw4Mamnc8WilpYNoYVhaOj+yNjCyapJoyanOLcqAd1c5lVi\\nKo5jqbAVDKk0UjGSlNLjlFJCKV1IKV0q/Ht3/LcrgzhBV66IY8qExin+iITZWqIDiXA1Jq7q3iqo\\nss1h+bBGNDW1h/p0uz4sPMvc2/s27bWpKUfUmWiuUk/n84dXNUXnGuvRw8fAtS+Ln6FahfdKQUfy\\nrhT9qFYLNOpGKs1W0FUHFe9QRq0aRhEuRi/umpN6maoWE2xLyjwp14dFNHh8a6sa58DAzUrKka0o\\nL79W3fjOnNlnqBJi3iTnU4a1LzlmceTITZHvlaO1A5cx0yHJXNbtnmx3VeJiCuypalcAEXXTCMw2\\nK6urnBD7n1SbepIUrtJucfXPtr28TccVMTT0AFavvs35oZCPJ7ef5V7myMgvwcnqlFKn2Jd4rbrx\\n9fdvQzZ7p7ZKCJgt8Cl5F0UZw8M/iryW5uJbzu26KQlpqhiqdZTFkySE7CCEnCSEPC+8tpwQ8nNC\\nyJH8f1vLcW4dbAnGKk+LGZdvg09qXYxseRNwx1uAVp3qpoRyTxB52+TiPfFqEX1ccNi5hww/rp6C\\nZRbGVXmtpq2t+L7IZRTJ6hw2orymaxU/EwRjWLQo2iKDjw1gFUNtbVsALJDGkcPoqH0NRq0k5+KQ\\nViOyaqBc2+17AVwnvbYdwC8opW8A8Iv831WFqkxO9TCwbWC046D8UPHub3+8yu78PAhu0z8kaYP4\\nobEhrLxd3a7TZNzULRLEay4qR8tJB9M2nFOwOjq2Kdsf6MakCxPovdL7C6WY0Va4AWTaka0oL6Ws\\n2Zd8fSo5N3ExVrXSEFv9ikgiuFItxAlWzAeUxUhSSh9DVOvoA2ACGcj/9w/KcW4XqMrkVJ6WisMm\\nP1S5XLbQ/e26lVFv0jb+o1txZ2+ZTZw1Hxobck6GjIxEWySIjbiAR7VJB51BkxchVexONSaTJ6fP\\nnM8KHROjLAaeaImLfakWl6GhB0IlrDYLkK67ogpcvKNW4DLn5kJc3hWE8cXLcGBCLgXwY0rpuvzf\\nZymlLfn/JwDO8L8V390KYCsArFixYv2uXeUQEhoGcAOAKQDNYJWUn87/zdEM4B/BlOHkB7EJwIMA\\nluf/vgPTwcNozABTAbA7C3z1pfz1gIAq4k8i9mzcU9LV9OzriTn+5QBeUrxzOYC7Fa/fAWA3WI12\\nA4D3Avhs4T1Kd4OQGek7vw/gkwjf1+9AvEfhYy4GIFe0qsZkGgvy51Rd21IAkyjWmUfPc+7cHVi6\\ndKnmffncMvj13af4jDxO3RhVCH/X9Nvu2bgHm5/cjDPTZ6yOLM8z03dbG1vx0DuiUg3xc620uQwA\\n586di/ldSkdPT08vpfSquM9VxUjm/z5DKY2NS65Zs4b29/enPj6ZrLxo0RswMRHum6J7nSGDjo5P\\nFSTIOCm48G5mUaFW2iYj6VqfLUM8x/Im4ItrgS8fBs7knSiX2FXc9ehaODQ0XIgVKz4Uuq+cIB53\\nzKRjUYEnUBoalmNmJlr4JSbr4iqHdNcKFAnwIyNPOre00OsHRL9rK5MWN890c8n0PdW8iTtPGnFS\\nl4qupCCEWBnJSma3hwgh7ZTSLCGkHcDJCp47BHWZXDTbSOkUJiaOaiZyMUOcRq20iYvm2slOjI1+\\n1dZ5ERB3PV1dB7F37w+QyXwsZLxmZ89hcFBdMZH0Hrl+T9yaz86eAyELQWnYwHI+pg10FTLi9ZkM\\nto4toQsTLFp0RaQ1he1v39rYavQoxXYhSUti52PMMQ6VNJIPA9gC4Lb8f6NchwpB9eAR0mgUnlU3\\n0SpKkKl4c/v6v4YND/+g5PHaxHm4J8p7GvPY6M7jRW/SFiaeYlFef6HCeE0CCHsZLKlzM86dO5SI\\nKOzKmQzHlaNGiCdeJidfxpvf/D3juU3HFo9nMvQ6mhQnnff2rsf09HDB8/7n4wN4+6NRT83GqD30\\njoci3pdJdCJJq4f5GHOMQ1mMJCHkQQCbAFxICHkFwC1gxnEXIeRPARwH8OFynNsGNg+e7AGYvtPV\\nddCwBbE3cKVg8PODWHn7Smx53VCh+1yGMG/yu1m3ZI+Jp9jfvy0vr79U42HLWy2K4eFHcO21yfRO\\nOjt3K7fbKm8wSgeKZr2ZotCPMTNzJp94sW84lZzkrualHj26HVNT2dCxNl4A3NkYXdjKYZzKccy5\\nwHt0RVmMJKX0jzRvvbMc53OFDVlZ9gDKxfrn8ZsklRSyJ7C8CXhXGzOOAOttvPmSRfj/PqiOqdlA\\nXCxYFpvL6+fQ3Z1Fc/PK0DaUxwsBKog3jCeW4nfx3tQZ5GLsGGDqPb2968ENF5+S8nWqtshdXQcL\\n8U7xmDZjD4KZkBqQrpUGX9hUYRLXsEulMVc4m66om7JEF4geQDa7A7293bH1wLYEchGlrrqyJ/CJ\\nS4EFkq0tVSBApx4uEr9V1CnXsj8dXLw3nSbhmTNFTUKm3lMcF9eFlq9TbHKmbjERL7hhKr3UtdJo\\nygBXaMQJq7HVnc/8R1vUTVmiC+S41ujoAW3ciT9QSZIkaXsF3RcAJGIkkwsEyIsFPx7DDE6c+AYu\\nvHBzJAkmf7YUySsXD17UJMxm7wQzhpmCzJqsAcnG99OC2IRYsig2ORObk9m2qVWT2Fnp5cUXfy5/\\nviK4B77wtnbr660k6jEWyeE9SQlMkv/rkbiWqt+GaERUBHIXmLxKG49zeROwKFzhhkxmEbq7s+js\\n3J1IiEJeLFRe2gsvfEixHY5+tlKSV9HmW0Hht1NrQE6HxCbEksUgmCg0OePNyWwrlljrBlXGbFYp\\nblGu+1Oq6ES5jzcXUPeepJyg0YmpqvptiEZkgSGWpIMYY5K9SjHeaEPduPH1Ki9SVMhm3tCqVV9Q\\nxttk2CRBAGB2VkU5USdMKiF5pSqnDIIpDAxs12hAUon+pa6KCQJ1plznTXZ27sb+/ZdAtaXW0c1G\\nRp50SuIps9P7wnMkieiEDmkq6s8l1L2RFONPq1Z9QSvJL2coZSPSWEiS2BPITRPUVcPvivNZPEsE\\npVM4e3YfJiePFsY/MzOmpKTIUNOkGHmaUops9m4AM849u8sNVTklEODUqR+BkEZNRp5ApcgjH0OG\\nyvDzRbehoQ26skMT3Wywyz6JV6rOoyux3PX4rggnz2oHdW0k5UD87OwYWARC7TWJnkNc1tW23YI8\\nKV1WadHr4C0b5GP092/DxMSRwvjE1qemGKEuYVI0ujOF10zH0pGpy4VlyzYoK6SCYEJb3RLfY4dB\\ntSDwxA6/Pi7LZjK6sgq7fH+q0TrBFaXMWx3CNf/21Kxyo66NpEzRGBq6H6YHRpzccVnXpO0WXFbp\\nuEmpqiziiEs8mMrpuNG1OZbsqZfLYHJjMzU1rCGRT6G7O4tjx74cKQqIK58UjyF7jvL1FWXZ1JBL\\nFQcGtmNk5LFQt8S5uJWNm7dxpZUsF8CSbSI1qxZQt0ZSRdFQI6PkxJVTLTkt9WmTGnvSjLNNNQ7n\\nGj7//GacO3cQrlv9JODGqqPjU4WyvnB9fqOxOoobP/U9a0RT04UFjiOHaidiUr9XCTwzndKi4LDt\\nbzHXKDlxoQE1NWtzZQYXg7o1kjYd/BiCqvfYSIq4BlxJFNZNwhC8Gocni0ZHD4ATKFy2+q5QVbZw\\n4rucjZbrrOXrUN8zxnE0Je7YTiSsDxknxBHWKZ116r2dJC5oI5RR6a6MgI6a9WjiAoS0UVcUoLg+\\nJQDQ0HABmPdobgqmgtiLpFxwiUuJauxLllwZed8142wS05U5lYyGAxS9gylwg6DTjEzalTGOzM5h\\nQ7ORFexFsVyR8qPeicQLM3OIXiRHmo3AVJ6mTaKnGlt9NaOk+l0SOerKSMp9SsSOfYQ0oa1tC2Zn\\nz0Hk1rnAdgV2Kd9KS+7etn2FCaYmYlFOZU57HJ0obZKujDrB27NnH3Oqs9ZBZYBzuSyefnq9kixu\\ncz72/SsRzX7PGtvKuqAUb7CSXMjlTdBQs2ZqZgdXN9tt0dM5ceLrykoRThwG1LxIW6j0HDn4RKvG\\ntiYpcrlsJL4oBtZtOZUixK2+a4MyETqPsbV1Y0RyzBU6AzwzM4bp6azyOyYNSXHM09NqpUCVCn4a\\ncIlhJuVWJuFRMn5vmJpFSBMofTe6uv7ZeszlRN14kuGHiSorRdjKXiQVy95k3JaQNwL7xKXRXjf0\\nFoo9G/eESL6VbDZfCo4duxWjowcKJG2x5pm/bxffLUKXLHGtPClnr2adAWax1WJFk4tnXlwQ1Ghu\\nvrjkcauQ1oJsM29d6EtvaWlQ/n6Ajq9cedSFJ6nqqKeuFAlD9ibjWqhysdvfXuam51jLvLhimR8Q\\nji8WA+txCSKAeVidnbsjFCCdt2brTZaTZaAzwMX/d098iYZXxbtceftKDO2O15OcSzsRwOydqlSk\\n9u//twqOzoy6MJI6T2fx4itw9dVsS/arX61TVNsEOHuWKcjEbQnFRmC8I4ZJ9kpELfPiVGV+DGGl\\nchuI2W++zVbF9mqlr7l8XTplcluDrlsQ2tu34qWX/hxvfvP3jMkVFYE7DUNZ7cVYvZOoHTJ5XWy3\\ndZ6O2JVu2bINhSQOByFNaGlhCjJxW8Jjx24t6DgSQc/xupXAG5ddmOblxKKUTLF8nKJ+pAy3wLpK\\nZozF5rKQOarlqvOOuy+jo4fw+OMt2r7XSTPmcd/v6/tooqTV0NhQKkkW10WaszjSgK6/e7TZavVQ\\nF55kUSz1mwg/kA0FjyWOJK2SAxsdfQbr1v0QnJPXqFhyFjU04ZH3VFaEnYcFxDYFrj1Y+HHUscYM\\ngP+Irq5/ti47VPWufvVVVqNr0xAsDejCJcVqnUHMzo6gr++Gwg5DhKlUUyxN1EH3fS54MTh4D1oV\\nquQmiAZO5HymZciStHhwga6/uyeTVwFq6arpgscS17JAJQfGdSYppcjNTCqNZKXUbzhEj41n65P0\\nYAFMZPQAPCYZF6cVxxRlEhSJ5uXeXpvCJXK99fj4CxgdfQ7nndcZOoZujvT1bcHQ0M5YMrjq+2JV\\nEKWziZu3iXAxbJmYzWQSA+nixZ49q+7vXkuJm7rYbgNMuoqTgjlsO+fpFK8BFDh5KgN5ZBTo2Vfe\\n5IIMWT2ceyhxBPDBwagCO+dWinzSIpg3aKPUrfZIZ8EXrThtxqQQt9cqT/bgwY0Ajirrrfv6brA+\\nh1ha6HINqsWjVF1SwM2wJdUY0IHHSXlRRZyyeUvLhhBXmRdxqHvBVwd1YyRN8aS4WJVMxBaNBufk\\n9exD5B9X5uFbn559PVYTJylUWXzxOmWEy+qKnrEM9SIxg+HhH1tRd2yy3+UQnRXDDipPlnmP/yO/\\nvQtjfPwFnD79i9jYrqq00GV88pzkuqRzFS7ybbpEVtqLZamoGyNpijm6VHvofljT6l+q7p8tdDFE\\ncfLxBYG3LJAJ4NlstNJIVZ0E/D6CYMxqgsuLTBolknGQww7R6hjOiT2myd4DL7zwn4zzotTSQtWc\\nbMwAH33jlVYc2mpnpUtFqYmwSqFuYpK6LS+nddhWe+h+2DRiSaXC5LHJKuWqFgLsc9FKI1UGEvg5\\nKG2QvmsXW6xE+CEadjCXEKowOzsCANp5EfYiC9+yFqoo9T7UMnXMBjrHZXDwPtSSVFrdeJI6uFZ7\\n6H5YXYe7OKS57eYem85TO3t2X8G7Gh8/rE3KqOqqo95WkGqlS1q0JX6saNhhAQCCtrYtkdh0FOGs\\nsG5e6EoIy1VaON+g0hNob/8zBMEExIquaqOujaSOo2V6UPnWU1YK+mJ/cr5a2ttu1eTr7j6BmZnT\\nQrVHIzo6timTMrJRUGUgAVZFI9+LpN6Rro1r0mPpSk6Hhh7Q0JpYIu+ii7aAkHDsRDcvmpsvUR6n\\nXKWFNphrOpMiwrzcR2smNlk3220VdBwt3ZaRCT38Ic6dexby9ly39amVSXv06HZMTRVFGdiDvwOU\\nkliPsKVlAyYnWUsEXkp34sSH0Nm5xilUoYNKvLYUcV5zomhWmagBxLrs6PuqeVGJsEFcyWqpPMa4\\nRbySVT2qvu7VrroCAEJpbQssrFmzhvb395fl2E89tQ4TE1E+lkpB+vDh69HcfBlOnrwPvA+OTRMs\\nW0JvOYUucrmspnMfu46Ojm1GjqPcziCTWYQgeADt7f9LUP5O3hBM5AoCjWCexGzJJPNcLosDBy6D\\nLNvGjwtQ7N9/KQBz5r34vSW45pqXakIIlqMUwngpfWlM500yl9XzbCGuueblst1vQkgvpfSquM/V\\n9Xa7pWUD2EPJsQDd3dmIh3D0KOtDcvIkj5MUhR7SoiyUixYEsBVanbgocj1dOI7ME7srEX1D3kqb\\nxGtLzXTq6s5FYV67JmAEF120BUEwUeBX1spWsBSU4iHqSOhx5HQdVPOMC8xUG3VjJHUPZ7gKJ8pz\\nyy3MWCkAACAASURBVOWyOHmS0zyiK2TalIW045Mqaa5i7K3I9XThOLK/98fSN1SxRZluZdOHJ4lB\\nMtWd83ACq8KasTgaKbSe4PzKWnh4q4nZW2YjgtD0ForZW9ShDFG1X8UV1hVscIGZaqJujKScGOjt\\nVStLyzy3o0dVNI8iKl126Aq1JziDkyfvS8Rx5P+Ai2JjmbJBVIlc2PbhSeO6xYqOzs7daGg4H8AP\\nQte1ePEViqOx7T+DuYrJQ404rrCquksUmKkm6iJxo0oMiEmMMMKK2UUvMoxSYnCVhNoT1G9B7a/n\\n7kgjMBGqWmkV3UoMbfz612/F2NghaVzJFiETB4+PZWTkCQCtEIUUdH27ZQTBjPX94sruALBu3Q9r\\nKqZpA5sGYq7fVaFUbdFyoS6MpLqrHcD4cNEtNH8oTV6k7cNbbXFUlSbi/v2vg3zdaXvEOtUf0wPQ\\n1XXQWlWIX4vuszoxiWz2TkGBqEg14d+3KaFkmLZ+gLmyOwCjYbUxKLbiu+LnbJI7JkNYSsWYy9w3\\nVeBU0xmZ90Yyrr92W9vHsXatWk7/9Gk1Kbih4QJce+0p7Tl1E661sRWn/5Lp5JWSlSxlZT927NZC\\nT5FyecM61R9CFoQ+p3oAVKpCOmMof9ZkNKPqSDzSFB5DZ+fuSJZVB5sHOKzszso+dYY1idHRSaWJ\\nsFG+T2oIxXlcSrYcKG8rjlIw742kuf8KxdDQ/Vi9+ivKSdvcfAlmZoaVr5ugm1hnpostI0wTN84I\\nJp3QldrO6MjcMj9RfgB0cmY6w6nazuv4lfoyxZnIueRYNSFNWLDgfMzMhBdGmwdYzrCryj7LjUqV\\nL5a6YxK9f53BrwbmvZGM3z7NYmDgZqU3qeJK2mwDbWCauDovUyXhr/uualVPup1xvXbdPV+y5Eqc\\nf343stk70dHxKaMh4+NateoLWsOpFvGNEtt16kiqe6DSHaV0Cs3NF+Paa1+NvXYRRS9SvOeB0ZtM\\nC6XsNiqJuSDSURUjSQg5BmAUbDmfsSF0JkVcYgAAhocfMR6D90menj5pLV6gQ5rbExNUD4jNdkZl\\nEG2EdUUkERMxtW/VGU5bEd+4bo7iPRC324QsxNKlVyZOtuh5muX3JiulPFUKzKTzYSu190qgmhSg\\nHkrpleU0kDK6ug6iu/tEROAgCMaNdI6jR7cX+iS7CquaUOkJq6PziEbNhraTFCYxEVP7VtlwHj16\\ns6Y2Wy3ia/JsgT2hexAeI9PYTEogZ16pyjgHVY+zuaAUby95D56difr+lAPzfrstw3XLyWhADwiv\\n2Eth1Rrits22tJ0kHlBcp8Dp6WENaR3Sa7M4ffrHsRlocaymGuu9e/dqx8gNnE0bDBVca7ttmBBJ\\nK1pEqEIyNlly+fs2SLJTGh09BOARcD5qvVKAKID/RQiZBXAnpfSuSp3YNYPGaEBhozo09ABWr75N\\n+8NVm/ajQ9y22YW2k+Tcuk6B4+N96Oj4FJYt2yDUcLO6chksPnhJiF2gqsHX/abJGp+xRE65H1jR\\noOgMkdhuQRl33Gfv+YnfdTFmSUU3bMNLfX0fA6eo1QIFqCoCF4SQ11FKf0MIuQjAzwF8hlL6mPD+\\nVgBbAWDFihXrd+3aVfExMgyD9f9V3aPfB2An1d+zr0f73p6NeyKvbX5ycygTnhThYw8DuAFMzKEZ\\nwHcALNe8z5HJ/xNL9xoAvBfnzn0CS5fmAHwZwC3SsVT4JACTKjGXa5M9xCYA/wjg04axfwXAzxD+\\nTYY1Y7sDzEt5P4DP4ty5c1i6dKnlGBcAeB+Azxo+I0M3DjNs5ozpM7ZQzb9S4Trfw3gJ7HcQofrN\\nS0dPT4+VwEXVVYAIIX8F4Byl9HbV++VUAYrD4cNbBFGLMBoaLrTOdiblRGaQKalRkxgYZ9dyPwCq\\n5EeGlXjMWLLkSoyN3YH29l3aTLUMlQcXPiffRkavd9GiN2Jy8piS2xlWOFqA7u5X0Ny8skAcF8cm\\nKs1wJaD9+/8tQjUx3QtZmUi8LoAqr9HmHsn3x0Zlx/QZ292MjWKPq2dYikLQr361DuPj4V1Bufi8\\nNasCRAhZQgg5j/8/gN8DEG1yXGbohF3F13VkcsBNWDVp4DtAYNXrJA7F8kq+hWFakuK1m5IbYrKr\\n2GFy2Cmho0sIheN/6gVhYuJFbZ25qhGXLtkUp0LPf/uRkcdi6UKq6yol6eXSZ8kGabIm0syUm5Su\\ncrlsvgd5GNUmlFcju90G4AlCyLMAfgXgJ5TSRys5AEbpWa9UcxE77AXBGABmGLq7s9qMcBwGPz8I\\negvFno17UjF6rlCVV8oyVKbMt9q47Iy8Zlp4ZGOhE6BYvPgKRfvaMMTzqRpxidnv8GejiSPgdOG7\\n/Ldvadlo1bBMbsnLOZGqazQJdaTJIJgLGBobUhpKXg0mgouSVLIts4yKJ24opQMA3lLp84oYGNiO\\n6WkmcCEG43Wla7UQPE4C8iWCtiVt+P7bVXJgehkqeQsZNS47wCpowpSbmRm1orjKWOgSaBMTRy0y\\n18xQ6RpxsbBCWPNzdnYsYpRZBdBOAJuVmf24B1NuySse16ZWXXWcyZkJ/Lfvt2vPmSb5WncsF1GK\\npLzfpDzeaqBupNI4ZO+Dq7kAqtI1Ne+uGtBN6DhKyPT0UMEjDqNRK0MlbyFVYqiyDmOx9UHYGxod\\nPYRs9hsRY9HZ+VN0d5/AsmUbQl76xo0TSi4rALS1bQl5uPqGW1FjODz8Ew3F6IXCNbs0hDOFCzi5\\n3aZdqnycpgxw3UpEWhTzHYgsbqGCS3Zb9uhKaQfBv5fUkIu7GWBPYX6w8E71UHc8yaj3MV3g69mW\\nrlUDcSu0Llh+4+uhobVMR7zow4evx+WX/33Io2puXq24J2pqTvH/i/dKpHPI7/P2tirPU6f1KVKv\\ndLX1qrHpygr37t2bqKY9rorHplZdd5wMgVWLYnlOyPXONskb+f00qGsu6kMmuFZ6lQt1ZSRVMSyA\\neZO6PtQcabv9lSIOX3G+mpQNhI0Zn5DifaB0Fq2tG3HllT+PZG3FB1LuTyISxXWB+LNn92Fy8ihU\\nJYqq+mmG8EIVV/4oZrFVqkCHD18P4KZENe02kmpyryTb4zRlkLhFsQgb3mXa4CGe0hNHw5HwR7UI\\n5XVlJPW9Xqa1sTCbiZ4ErsThpNj6DPvvr95zpVbQVozHifQLMZ5nS0IvfpcRxXWybP392zAxcaTw\\nWdn46ersbRYqmyohUXT3tdeOO8fC0poT4nEqZchKAb2Fxo7TRoglHtHEYLW8yboyksxDiSINQ5i2\\nShCHPNlUq7RN8zDT9fX3b9N60UWRYv2Krgu4My9SpB3xah1VMigqwJsENltnub9zZ+exkn+zcv3+\\n8xVi3FK+d7lcFsCjTuGPcqKuEjddXQcjPTRKoReIlJekPDfX7oiqLXopcaQ4GTGb7oX8vgIZtLVt\\nwbJlG/KNxsLZB7FLoSmpoaMS2SDu2NHPpNPITe6hlGT8pSRiVt6+Ej37epTNttLyUNPMrHNvc+Xt\\nKyPPjqqLZdoN91xQV0ZS52UkzVqLnMqkPLdq13ibGmatX38Q4hTR3S+ZOjUy8rhShELsUmja3vIW\\nvgMDNxvHrjJGcceOLgozzr+ZvvMm+/0HBm5OtGByPq38TxffEzsQJp1HYr11XAVPOWT9pqeHlM3h\\nouyJ6lGB6mq7nWYPDdkw8NYEnOcmZiZrTehUhMmoMB6lbkX/UOG1KHWKyc91d2edt0di8zWTajw/\\nrxwr7eo6GCorlGOhacwB+bzqHkqBkj2g244nEYVIahhVxQymY5Wz+EFkX4jN4WpJmbyuPEmdQThz\\nZp/z9kg2DCaeW7W9RdM2SVdp09m526pETLddT7o9ClcHzWq9SVH1O5vdEfHqdLuFUgnLstc4Ovqs\\noodSNDyhCseIHulcEMnlSGvbvbwJePdKaH+rWkFdGUmdQWhp2WC9PWI9u9+ObHaHNo7HeW61ANN2\\nzQSxREzsV60S6VUlfZJMeFUL36Gh+5XHEFW/udK3bjyisZLngCy6Gwc5c26ijvF7wA2pqpY8aRy7\\nmpnwwc8PJi5uEHHj6wEiXUY1Y4861I2RdKkrNoG1B30KQE77GRPPTYwjJZnolegJ4hK7NfEFXSe8\\nuoVv1JuM9o4pepNplbap5ovqvoyPHzbyJWVDKteS83knV9iI4HOFJ/lqwbvUjcGFtnbF+exZEVEL\\nZYgy6iYmqWPvuyhvFye2GkdGi7xEHdKMI5ULLnE77oGpeI2uE16nuiT3IFL1juHepIlgruuZYtvX\\nR53kaizEPHX3QEWFEmvJKZ21qrBJwzjWUuOtrc/oY66iYny1UReepM5bdM1269qN8q1onIGcK7D1\\nxkRvi29j1bJqasjemq5Vr/z62bOPIVoaGeDMGbVgB2De2tpKnMXdF1U4h1HOwm4iT+6I8+79HcBl\\ni7XDLwk2mXKT8ZQ92TRRqwlNEXVhJHXiBTacOo7igxP1YPiDVKrgQK1AfNhFEQrZS1MZHhehCPn7\\nNo3KAKClZUNETo2QJrS2qgU7zCGVqC5mOFs9ma/3D4+P80LjeLbqcEQxuVMYP4AvrNUepiTInEmV\\nsRPpRzrUwja/Gpj3221TBYZL/EonusC+w4zBXFgVXaEXGYjW1tpU0nCopMls6UKucUdzSCVc/nbk\\nyJ/j1KkfoOip0oiwhqwjOTr6jLbtrMqAqrblhACrlxJMbj+BhbfppdLSgGjsSlH9SQMrb19Z88/N\\nvDeSJm+RT2BZEEG1RdSLLtRmsDkNmA1ZtLaWUmody1Q1HZucfDkSM1TFC10qpEyLJIsThsvfTp36\\nJ8VRZnHkyE2Ynh7Em9/8vYiO5OjoASeepTjvnn56PaanTwGYBiGNsYmutLPapZDQ0zCuc8E7nffb\\nbRuvQ/QSRX1JEV1dB9HQcIHyHA0NF1g/uEm33uWIB+nAY4UDA2qF797et0NVW3v2bLTtgS6WKRsu\\nXqmjU4o3GQ9TGaBpkVSVv+kwPPyjUHVVWEcSIa6mLYriz2Hd0jcuu9Dq+3Jljqh8X+5En1wdNJ8x\\n7z3JOOMVjTVOa7d/Ov1CXcJBBd3WwkZZxXVrtLwJ2oyuCcwwPY6RkV+iSIwuZmUZBSo8Xi6rds01\\nrF2Re+vWaNtW2y25SXcwfpFUqbZHQSmjfInVVfIxXbxJnWxfbmYC714xgRdH4o9Rja2qqyfLnQJ3\\nj3E40dwtB+a9kYyDKtbIvUkd3cUEU3nZd6/6rvZ7Np0RXSfaja8Hzpx9DH/1o9X4yofHrb5TNEwU\\n0b44vOQOiArpRr1zF8NVPE5Y4zKOnhVnSON+M17+Jmti6hEV02Vwi63qZPsaHbQka3GrqvMq3cME\\nO2tCcBeog+12HNSxxunEMcbp6SHc8Zao/H7chJ69ZbakbYv8XV7ylSHAxgsmrLeCZsVtMSvbEKrC\\nEbPQcQR9OXsutmqQq1Ti6FmubReSXXcYmcwiXHjhh6Dypm2rtmS+LW8217MvnmubBtJUB0obo6OH\\nwHqj10ZjtLo3kp2duyP9VOL4fSZ84lLgLcuAT16WwuBKgFjylSFwenhFL48/vNG+M3r1HFcakE6w\\nN46elaaqk867ZXHo8GPCeuY8jDhvWgcX6lk9Qmz5UQv3xW+3U1YGelcbM07vagPufhk4o06IlxXc\\ni+QlX00ZWG0F1U2/GE8wk1lsdZ9c+8Xokj0qpXjVlj6t3063LX/qqXWRODQbF1uBdO0hTFD19a42\\nQ0K1i6kGPWh09JBSHb+a7Rvq2pNkD/TO1Cbs0aPbsSDvvS0gem9Srt9OWxw1qXCA2puiGB7+iTU3\\n0VVQt6VlAzgpW9y6b9w4ESKzr19/KNI5r5Q6bT4O4GhhPKos+fnnrwc3iLy6SqyiSeLpLFumvuZq\\n9pZWoRoxT+ZFhlFtb7KuPUmWtJlAR8e2RMFhuT/1yZPfLhgnlTfZs68H0FfOJYZMK0oqHCA+pGIi\\nIwjG0Nn509BKrtP7izNcYkJn1aovxJKyxQZl4+N9Su3IbPZOdHR8yuk3LPa4+XeMjPy7sntjMQMt\\n1l3vyPNBk7UWkOO17e1b8dJLf17I4rrwD2utkss0Ht11yW0cbOT5Ko26NZIuVRM6iA/8zMw5yNlK\\n7k3+zYvpjl2EuE3iE3H788AX1wJfPswMNBcRMIk8yBgY2I4gYLQXly2syRuSDYQo8qAiZasalCWh\\nCPFjiQtascfNMQDAiRNfz5c6Fo3Xs8++E9EM/yR0CRub+6OSWhONv0zpMe0u4ug/LgZX1UvJBNck\\no26s4pzk8nziIiuLJlcDdbvd1lVN2EJ+QGWlGoB5k91q/rkVkkxEegvFk5v/DFe2ZHDgg9tCoga2\\n+oVqD6r0LKNKwdtEyo7buietE1dnsmmBC0npLJ555h2Gnt7JEjZqqbUXUK4srkz4dvE8XbbauvCR\\nTQGE+LukJXOXNurSk4xmcYsPqAvPTazSaWiIEowB4FW97GQsksQodd6Vi9fFBB3CHpToLbFudjch\\nl/sXa887es/1JZ7iVlz10HBPL1mdeHi7rBsDoH8/SbIGMNOMKtE2tVx9uG1U1VVJoOVNwIPXFBOL\\n4j317RuqALmzoU5N243nVqzSCYKxAs9N/Fdp+TQbxaM4r2t4OKrryIzQfRgdfRZPP70ewHNOnrc9\\nD7HYTMtkUGwoQqpzB8EUZC1KVyRNJJhJ9EVvXfTMqok0Va1UhvTG1xf/v9rJGRPqxkiKbr2KgsFg\\nr04eBOFyNl3NdyWh2s4N/J+v4bf+J8GxV74eyydkddndmJ09F3o9k1mEtrYtCIIJHD78kXy9MZy2\\niCYeoix7xniIPzEaFBuKEL+m6K7BXj1bd/4k22ORRN/W9seK4zJDYbPVrUTSxrV7owq6bbdMU6vV\\n/jZAnWy35a3mihUfxvh4HxYvXouJiSOhh81m26NqecmrdJY3hZMmOugUmV29B7F+V+WtZQjw39eq\\nKUG9vW/D+vXPhNS4R0cPQF47xQ6AExP9oddLTejo1Lybmy/Gtde+GntcHXiiprn5ssg94ckASimy\\n2bugKg+Mg0m5yAY6bz0u/jYXxSR0XqSOplbtMkQZdWEkdS0/RVl9DpuJ+qY33YPe3reGXuNVOje+\\n2I7OZVDK8e/ZuAfXP309hsaGCs3ZOZJ6BuIEVHlrTRmgY5GaEjQ1lcXAwM1Yu/YeqVRO9rR01l4v\\nBmKLUriBcsZaFNTgO4eGhhdiPE53A8mPMTz8Y8zMnHF+sHO5LIJgTHo1g/XrD+K88zqB3eXfZsdl\\nvsvtqc6V/jZAHRhJU8JA7E/CP1t86PQ4fPgjkde4Z/Gedua9XbcS2Hm86E1mkGE8SQ3SIO5yg2Py\\nRuktFLlcFvv3XwJgFkND9+Liiz+HEye+UVhIRNpFnPCDizeZNsQQishxFPmXcf2/9+59AwBzc5nF\\ni6/A5OTRgt7oNdcMAKA4cOAy8CZkLguFOj4boK/vBlx99fNWxygVaaoHJdGW3PoM23J/52qgeUHy\\nZFglMO+NpDmjGM6I8ofOtI3K5bKYmIgSH7ln0bygCZROYVFDEw58sGiAqx2EFyF3JXz++fdjenpI\\nmSmOT7gUxUBM8mhpQwyhZLM78q+ycMrU1KsOHM+7Y7Oo/f3bMDFxJHQ8liEPt7S1XSh08dnx8cM1\\nE5OzKUnkISNucE1zXGVIb3w9cyiA2t1qA3WQuDFlFIGwmCx/6HQCsAAK/U5EENKUT2yMxSZHqg1V\\nb+tc7jhk+S9+X+Lu35IlVxY82KR9pJMgnK2fKowxCGZw6tT3kRbHU5UMy2Z35A1ztKWt6vtyqaPc\\nK4cnrrgyeS30SrLxDF28RzkJNLn9BDZfshCNPnFTfYgxL12S4OzZfQi3HogKwHLoAu4qMdZqrY6m\\nEjB1b2tEaDE8PqSLGco8NtmzS1LBZOuJ6niuDNH4aSm/g5rQPgU5bqvzJk26mjoxkOOfSX/badI5\\nTWvrbdp2ywLBaYqTlBsVN5KEkOsAfBXAAgDfpJTeVqlzyw/84cNbcPLkTixefIWGuBz+0dQB9wVg\\nRicqxppmINqm9pXDNOmfeELfGsAUu4uD7Nm59n3hxzAJrZoy1iaU8juoPWnVuaMtbeMI/JU0FDaE\\n71Ix+PlB7ZZbPk+tVteoUFEjSZir9Y8A3gXgFQC/JoQ8TCmNVrWXGeK289SpXQAaI59RxSx1bQcA\\nIJNZiGuueTl1LyDN1V7XggJgoYS1a+91PqbOsxsctE9o2FQEmTLWOpSaEFB50v392zA4+K3QGMSW\\nttyYL1x4GWQCv2j85pKhSBu1pnhkQqVjklcDeIlSOkDZ7PgugA9UeAwAoskLfZlcsRIgLj4XBPqK\\nHdt4UtuStpLIu3EwNTRThRJsoEvumO6H6Riq6gvRiAbBeEE6bf36Q4IY8ALIi105KjnslI4eD9Wm\\nq2Jutn3GPaoLQmnlyKmEkA8CuI5S+on83zcCuIZS+l+kz20FsBUAVqxYsX7Xrl0pj2QYwIcR3jZl\\nALweXBUmjMsB3K04xh8halybADwIYHnkKOfOncPSpUsTjThdfBJq2ovqOtUIX4vueADQDOA7UN2P\\nIoYB3IBwvbT8vTsA7AYj8TcAuBjAcQCrwDYlpoZe+utK9puYxksV73E0AHgvgM86ns8Opmsx0c/2\\nbNwTeW3zk5txZvqM8Xytja146B0PlXQeHSrxrPT09PRSSq+K+1xNJm4opXcBuAsA1qxZQ9MudGex\\nyChPra1tE9auvUf5HRlMx1BFRJ5BR8cvlDGl2inaP1LyEcLXwo6n3oZStLer7wcH+x4grtfi9xhX\\n82dCKegMiovZsdCxXLfXSX4T03gppZH3ipjBkiXH0dXldj5bGK/FoGOq+s7pTaeTDcLxPDrUzrNS\\neSP5GwBi/9WL869VFKdPq7eVKrkzHVhpojqAXw8xJRWSxthstq+2iZpKZEh14z1zZh9yuXBNea2Q\\npF0Sf3PhPJVEpY3krwG8gRByGZhxvB5sb1JRpNE/28eNokh6T+K+FxcLFlGJxIduvMzDDHvptUJr\\nqVR/7kr3Aa8EKmokKaUzhJD/AuBfwKLsOyilL8R8LXV4Aze3IP5eusxytdWrgfrOVs9nVDwmSSnd\\nDRaB9/BwRi0bIr/4zk/UZOLGw0MHb4g8Ko15X7vt4eHhUQq8kfTw8PAwwBtJDw8PDwO8kfTw8PAw\\nwBtJDw8PDwO8kfTw8PAwwBtJDw8PDwO8kfTw8PAwwBtJDw8PDwO8kfTw8PAwwBtJDw8PDwMqqkye\\nBISQUQD91R5HSrgQwKlqDyIlzJdrmS/XAfhrccUqSumKuA/NBYGLfhuJ9bkAQsjT/lpqC/PlOgB/\\nLeWC3257eHh4GOCNpIeHh4cBc8FI3lXtAaQIfy21h/lyHYC/lrKg5hM3Hh4eHtXEXPAkPTw8PKqG\\nmjaShJAFhJCDhJAfV3sspYAQ0kII+T4h5N8IIX2EkO5qjykpCCGfI4S8QAh5nhDyICFkYbXHZAtC\\nyA5CyElCyPPCa8sJIT8nhBzJ/7e1mmO0heZa/md+jj1HCPkhIaSlmmO0geo6hPf+ghBCCSEXVmNs\\nHDVtJAHcBKCv2oNIAV8F8Cil9E0A3oI5ek2EkNcB+HMAV1FK14F1vLy+uqNywr0ArpNe2w7gF5TS\\nNwD4Rf7vuYB7Eb2WnwNYRyntBPAigJsrPagEuBfR6wAh5BIAvwfg3ys9IBk1ayQJIRcDeC+Ab1Z7\\nLKWAELIMwAYA3wIASukUpfRsdUdVEhoALCKENABYDOBElcdjDUrpYwBOSy9/AMB9+f+/D8AfVHRQ\\nCaG6FkrpzyilM/k/DwC4uOIDc4TmNwGAOwD83wCqnjSpWSMJ4O/AblJQ7YGUiMsAvArgnnzo4JuE\\nkCXVHlQSUEp/A+B2sNU9C2CEUvqz6o6qZLRRSrP5/x8E0FbNwaSIPwHw02oPIgkIIR8A8BtK6bPV\\nHgtQo0aSEPI+ACcppb3VHksKaADwNgBfp5S+FcAY5s6WLoR8vO4DYIa/A8ASQsjHqjuq9EAZ1aPq\\nnkupIIT8dwAzAL5d7bG4ghCyGMBfAvh/qj0Wjpo0kgD+A4D3E0KOAfgugN8hhDxQ3SElxisAXqGU\\nPpX/+/tgRnMu4ncBvEwpfZVSOg3gIQDvqPKYSsUQIaQdAPL/PVnl8ZQEQsjHAbwPwEfp3OT3/RbY\\nIvxs/vm/GMAzhJCV1RpQTRpJSunNlNKLKaWXgiUG/jeldE56LJTSQQD/hxCyJv/SOwEcruKQSsG/\\nA3g7IWQxIYSAXcucTEIJeBjAlvz/bwHwoyqOpSQQQq4DC1G9n1I6Xu3xJAGl9F8ppRdRSi/NP/+v\\nAHhb/jmqCmrSSM5DfAbAtwkhzwG4EsBfV3k8iZD3hr8P4BkA/wo2f2qmMiIOhJAHAewHsIYQ8goh\\n5E8B3AbgXYSQI2Ce8m3VHKMtNNfyDwDOA/BzQsghQsg3qjpIC2iuo6bgK248PDw8DPCepIeHh4cB\\n3kh6eHh4GOCNpIeHh4cB3kh6eHh4GOCNpIeHh4cB3kh6eHh4GOCNpIeHh4cB3kh6eHh4GOCNpMec\\nAiGkiRAylRdjVf17qNpj9JhfmAt9tz08RDSCyYDJ+ByYcMgjlR2Ox3yHL0v0mPMghPwNgP8K4C8o\\npX9b7fF4zC94T9JjziKvRPT3AD4N4NOU0q9VeUge8xA+JukxJ0EI4QpE2wD8qWggCSEfJoQ8QQg5\\nl9ck9PBIDO9Jesw5EEIWgPWj+QiAj1FKH5Q+cgZMNqwNLFbp4ZEY3kh6zCkQQhoBfAfA+wF8hFIa\\nyWZTSn+e/+ycaOrlUdvwRtJjzoAQ0gwm+vu7ADZTSn9S5SF51AG8kfSYS9gJ1r/lXgCtiiZkD1NK\\nX6v4qDzmNbyR9JgTyGey353/8+P5fyICsNYFHh6pwhtJjzmBfOe/86s9Do/6gzeSHvMO+ex3Y/4f\\nIYQsBLOzueqOzGMuwhtJj/mIGwHcI/w9AeA4gEurMhqPOQ1flujh4eFhgK+48fDw8DDAG0kPvj5X\\nYwAAADdJREFUDw8PA7yR9PDw8DDAG0kPDw8PA7yR9PDw8DDAG0kPDw8PA7yR9PDw8DDAG0kPDw8P\\nA/5/6DQiWF6ovO8AAAAASUVORK5CYII=\\n\",\n 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1ODefPm5VSgy2jUfNB6A5Bz5syZ/OdV9XVtxTZ03NAhuneqHqrSdSwl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\\nBEEQAoEEmSAIQiCQIBMEQQiE/wNjQf62aQCMDAAAAABJRU5ErkJggg==\\n\",\n 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66MTFTXf/z4VQqLtzjt303lbvCbJ5nKWG45xGb+6J64p71BZ2eIZ565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\\nxx/DAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from matplotlib import gridspec\\n\",\n \"\\n\",\n \"axes = [-11.5, 14, -2, 23, -12, 15]\\n\",\n \"\\n\",\n \"x2s = np.linspace(axes[2], axes[3], 10)\\n\",\n \"x3s = np.linspace(axes[4], axes[5], 10)\\n\",\n \"x2, x3 = np.meshgrid(x2s, x3s)\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(6, 5))\\n\",\n \"ax = plt.subplot(111, projection='3d')\\n\",\n \"\\n\",\n \"positive_class = X[:, 0] > 5\\n\",\n \"X_pos = X[positive_class]\\n\",\n \"X_neg = X[~positive_class]\\n\",\n \"\\n\",\n \"ax.view_init(10, -70)\\n\",\n \"ax.plot(X_neg[:, 0], X_neg[:, 1], X_neg[:, 2], \\\"y^\\\")\\n\",\n \"ax.plot_wireframe(5, x2, x3, alpha=0.5)\\n\",\n \"ax.plot(X_pos[:, 0], X_pos[:, 1], X_pos[:, 2], \\\"gs\\\")\\n\",\n \"ax.set_xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \"ax.set_ylabel(\\\"$x_2$\\\", fontsize=18)\\n\",\n \"ax.set_zlabel(\\\"$x_3$\\\", fontsize=18)\\n\",\n \"ax.set_xlim(axes[0:2])\\n\",\n \"ax.set_ylim(axes[2:4])\\n\",\n \"ax.set_zlim(axes[4:6])\\n\",\n \"\\n\",\n \"#save_fig(\\\"manifold_decision_boundary_plot1\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(5, 4))\\n\",\n \"ax = plt.subplot(111)\\n\",\n \"\\n\",\n \"plt.plot(t[positive_class], X[positive_class, 1], \\\"gs\\\")\\n\",\n \"plt.plot(t[~positive_class], X[~positive_class, 1], \\\"y^\\\")\\n\",\n \"plt.axis([4, 15, axes[2], axes[3]])\\n\",\n \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n \"plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"#save_fig(\\\"manifold_decision_boundary_plot2\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(6, 5))\\n\",\n \"ax = plt.subplot(111, projection='3d')\\n\",\n \"\\n\",\n \"positive_class = 2 * (t[:] - 4) > X[:, 1]\\n\",\n \"X_pos = X[positive_class]\\n\",\n \"X_neg = X[~positive_class]\\n\",\n \"ax.view_init(10, -70)\\n\",\n \"ax.plot(X_neg[:, 0], X_neg[:, 1], X_neg[:, 2], \\\"y^\\\")\\n\",\n \"ax.plot(X_pos[:, 0], X_pos[:, 1], X_pos[:, 2], \\\"gs\\\")\\n\",\n \"ax.set_xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \"ax.set_ylabel(\\\"$x_2$\\\", fontsize=18)\\n\",\n \"ax.set_zlabel(\\\"$x_3$\\\", fontsize=18)\\n\",\n \"ax.set_xlim(axes[0:2])\\n\",\n \"ax.set_ylim(axes[2:4])\\n\",\n \"ax.set_zlim(axes[4:6])\\n\",\n \"\\n\",\n \"#save_fig(\\\"manifold_decision_boundary_plot3\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(5, 4))\\n\",\n \"ax = plt.subplot(111)\\n\",\n \"\\n\",\n \"plt.plot(t[positive_class], X[positive_class, 1], \\\"gs\\\")\\n\",\n \"plt.plot(t[~positive_class], X[~positive_class, 1], \\\"y^\\\")\\n\",\n \"plt.plot([4, 15], [0, 22], \\\"b-\\\", linewidth=2)\\n\",\n \"plt.axis([4, 15, axes[2], axes[3]])\\n\",\n \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n \"plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"#save_fig(\\\"manifold_decision_boundary_plot4\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# Lesson learned (below):\\n\",\n \"# Unrolling a dataset to a lower dimension doesn't necessarily lead to\\n\",\n \"# a simpler representation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### PCA (Principal Component Analysis)\\n\",\n \"* Most popular DR algorithm\\n\",\n \"* 1) Finds hyperplane that lies closest to the data\\n\",\n \"* 2) Projects data onto it\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Preserving Variance\\n\",\n \"* Below: simple 2D dataset projected onto 3 different axes.\\n\",\n \"* Projection on solid line preserves the maximum variance. (Therefore less likely to lose information.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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f0WyM0338ynn35KeHg4x48fx3UuNTM1zZL4eJg1C0pLZdEXf3+Drl23\\nc/ToXtq3v5EBAy5g1ixVkb2v0Wg0Z4MW/RaIUgqlFJ999hl9+vThjTfe8LZJmgYkPl4WW7n88hJy\\ncj4mL+8XHnigJ5Mn+xMRgRZ8jUZzzmjRb8EopcjJyeHVV1/FUdtqGpoWx+bNJ/nkk7cIDQ3ld7/7\\nHcHBwYSESKKfRqPRnCt6TL8FM2nSJP7xj38wZcoULBb9r2wt/Otf/2LlygzGjbuGCy/sU7HdbpdE\\nP41GozlXtFK0cH7/+98DcPDgQdavX8+NN97oZYtaNtWr4VWuftfYlJSUMHPmTL777js++OBLvvii\\nJ9nZksFvt0tW/+23N40tGo2mdaJFvxWQnp7OwIEDKS0tpV+/fvTv39/bJrVI3NXwwsKqVr+bNavx\\nhf/EiROMGzeOLl26sH79eoKDg+nZs2oH5Pbb9Xi+RqM5P7TotwI6d+7Mb3/7W77//nsKCgq8bU6L\\npXo1PPdzYmLjiu23337LPffcwyOPPMKsWbMqKi7Gx2uR12g0DYsW/VbCiy++iFKKwMBAXanvHDl8\\nWDz8ytSUPNdQQwAul4t58+bx6quv8pe//IU//elP5268RqPR1AOdvd9KCAoKIjAwkN27d3PxxRez\\nd+9eb5vU4oiO9pS7dVM9ea76gjiVF8A5G3Jycrj++utZtmwZ69evZ+jQoed/AxqNRlMHWvRbGQsW\\nLOCnn37ihRde0B7/WTJpkoh4dja4XJ6/J03ytKk8BGAyybPDAQ8+CDNmwNy5dXcAkpOTGT58ONHR\\n0axYsYKVK1fy6KOPEhUVha+vL8HBwfTu3ZtbbrmFFStWNOo9azSatoUO77cynnnmGfbt28dVV12l\\nV+M7S9zV8GpLnqs+BJCRIWvdOxwwblzdyX///ve/mTlzJi+//DLDhg1j5MiR7Ny5s0qbkpIS8vLy\\n2LNnD1arlQkTJjTSHWs0mraGFv1WRkBAAMuWLSMpKYnS0lLS09Pp3r27t81qMdSVPFd9QZxdu8Tj\\n79jR4/nD6cl/paWlzJw5kxUrVvD999/TtWtX4uPjOXLkSEUbs9nMgAEDiI6OJj09nS1btjTCHWo0\\nmraMFv1WyvHjxxkxYgTFxcVs3rwZPz8/b5vUKqi8IE5ICJw4ARaLiH5SkmcpXLf4Axw9epTJkycT\\nGRnJhg0bCAkJ4bHHHqsi+OHh4XzzzTdVxvbT0tLYtGlTxet9+/bx3HPPsWbNGnbu3FkxfJOXl0dg\\nYGCj3rdGo2kd6DH9VkpYWBhlZWWkpqby888/e9ucVoN7CCAsDI4eFbHv2hV274aiIhF8ux0OHJCx\\n/e+//57hw4dz3XXXkZiYSEhICABLliypct577rnntGS+Ll268Otf/7ridUpKCu+88w47duzQ+Roa\\njeacaJWefmZmJsXFxfj6tt31xn18fPjoo49QShGvJ3s3KJWHAJKT4dZbQSmw2Qzy8srIzS0mJOQI\\nV1yxGrt9JsuWLeOyyy6rco7U1NQqrwcNGlTndbt06cKcOXMYNWoUTz/9NOvWrWuwe9JoNG2DVin6\\ndrud2NhY/vSnP3HXXXcRFBTkbZO8gltIioqKmD9/PjNnztRh4AaisLCQHTt2sG3bNvLyLuDUKRf5\\n+WaczixgJ5AJdOWdd149TfDPleHDhzN8+HAA5rvHGDQajeYsaJXh/bi4OL7++ms2bNhAbGwsb731\\nlrdN8iozZszg8ccfZ/bs2d42pcWxZYuLBx88xWWXHeKSS5IYP/4hevfuTVBQEMOHD2fGjBkcOPAZ\\ndnsSTudnwI/ACSCE8PAipk+fXuN5Y2JiqrzeunVro9+LRqPRtEpPH8TL/fjjj9mzZw+ZmZkA5Ofn\\nk5ubS+fOnb1sXdMyZ84cvvnmG/r27ettU1oUycnwhz8cZtWqJYAdiAWmAhcBW4BEIKX8eVb5UXYg\\nBAhj1qwITKaa+9XXXXcd27dvr3j95ptvct111zFkyJCKbcePH2f9+vVcffXVDX5vGo2mbdJqRd9N\\nr1696NWrFwDr1q1j8uTJTJkyhYcffpjY2FgvW9c0DBw4kEOHDhEcHAxI+dcziZHGQ2IiDBrUjczM\\nzuzc6QMMBAwgrPwxC5iPCP98YBLQDThEdPR3PPTQkjOcGR566CHef/990tPTAclDGT58OAMHDqRr\\n164cP36cTZs2ccstt7Rp0d+zZ0++Umq3t+1oIDoAWd42ogFoLfcBreteetenUasX/cpceuml7N69\\nm7///e+MGDGCK6+8kkceeYQBAwZ427RGJzg4mPz8fGbPnk1xcTHvv/++t01q9kghHsVVV13F3r1b\\ncDiKgWLEk88pbzUJEX33Q3jqqYWYzeYznjssLIyVK1cyadIkdu8WTXM6nWzZskXPz6/KbsMwhnnb\\niIZAKbWhNdxLa7kPaH33Up92bc7dCw8P55lnnmH//v0MGDCARx55xNsmNRnp6eksXLiQhQsX6ml8\\n5SQnS+ncyiV03ds2b4blyyEvL4CoqP6I4PviEXw74tlXpXv37tx88811Xrtfv35s2bKFt99+m5Ej\\nR9K5c2dsNhsBAQHExsbym9/8pl7n0Wg0mvriVU9fKXUl8HfADLxjGMa8avsTgC+AA+WbEg3DeKoh\\nrh0SElJF8E+ePMn06dOZOXMmCQkJrbKEba9evXjppZcwm82MHj3a2+Z4HffiOWFhnsVz/vIXMAzo\\n0QNGjIBVq+DHHyEw0B/x8BXgLpgTAhw67byPPPIIVqu1Xjb4+vpyxx13EBcXR0JCQq1ty8rKsJev\\nCFRWVlax/eTJkxVTVPXsDI1GUxte8/SVUmbgdWAi0A+4SSnVr4amPxmGMbj80SCCXxNBQUFcf/31\\n3H333YwZM4Yvv/yyVRZAufvuu7nzzjtRSrX5ed41LZ5z4gRkZsrfnTpBQgI4HCdJSdmPCP42ZDpe\\nKDKun1jlnJ07d+Z3v/tdo9j7888/Ex4eTnh4OGvWrKnY3r17d8LDw7n//vsb5bpepjVNvWkt99Ja\\n7gPa4L14M7w/AthnGEaqYRilwMfAtd4yxsfHh+nTp7Njxw5mzpzJY489xuDBg8nJyan74BbIfffd\\nx8iRI/nkk0+8bcp5UVN4vr4cPiyldCtTUiIPN6tWfcq+fa8Ba4FbgX1AVyAbTxKfh4cffhibzXb2\\nN6KpEcMwWs2Pcmu5l9ZyH9A278Wb4f0uwJFKr48CI2toN1oplQykAbMMw9heQxuUUr8Hfg8QERFB\\nUlLSORsWHh7Oiy++SEpKSkVS1bZt2+jTp0+NYdv8/Pzzul5jUJdNbmH64IMPiIiI8Lo958L+/QEs\\nWtSVwEAHAQEOtm2zsHathSlTjtCjR0Gd9rhcB9i+3UpQkKNie0mJFM0/cOAUx44do0uXLrRrF4vd\\nfpDU1AP8+td7GDYsGAgGflflnBaLhYEDB/Luu+v56acOHD/uS0REMWPHZtXLnvq8Pz/88EOt+5vb\\n51Cj0TQvlLdC2EqpycCVhmHcUf76VmCkYRj3V2oTDLgMw8hXSv0K+LthGD3rOvewYcOMDRvqlchY\\nLwzD4Prrr2fjxo3MmjWLO+64g4CAgIr9SUlJdY7HNjV12WQYBitXrmT8+PFNkr/QGO/R3LlVV7wD\\nz+u5c+u2p127hIox/ZAQ2L8f1q8Hu72EEyc2IXPxS5Aw/ulefXXmzZvHxIl/rnJOu11sOtNSu5Xt\\naerPkFJqY2vJXNZoNPXDm+H9NCRO6iaqfFsFhmHkGoaRX/73fwGrUqpD05koKKVYsmQJS5Ys4aef\\nfiI2NpZnn322RYf+lVIV67T/5z//4eOPP/ayRWdPTeH5kBDZXh8qL56TnAwpKeDnt40TJ94rb3E5\\n4EN1wf/kk09O87jDwsK49957a8wTCAuT/AGNRqPxNt4U/fVAT6VUjFLKB/gtsLRyA6VUpCp3Q5VS\\nIxB7Tza5peVccMEFLF68mKSkJPbu3cuBAwfqPqiZk5SUxM0338xdd91VZanXlkB0tHjSlbHbZXt9\\niY+XqMCgQS5SU98lOfkz4DiwHPiq/G+P4B85coQpU6aQkJDAH/7wh4rtf/zjHwkKCjrvjohGo9E0\\nJl4b0zcMw6GUuh/5dTUD7xmGsV0pdXf5/gXAZOAepZQDKAJ+azSDlPq+ffuycOHCitcvv/wyiYmJ\\nzJo1i+izUZxmQEJCAtdccw3BwcEtbrqXe237rCxZ5jYzExwOGDRIEvuio6VNXYsMHjt2jKee+hpJ\\nK6mMZx5+ZGQkaWlpVSoZ/vWvf+Xrr7/m2LFjPPDAA4Bcs/qQw9l2RDQajaax8GpxHsMw/msYRi/D\\nMHoYhvFs+bYF5YKPYRivGYbR3zCMQYZhXGgYxpraz+gdbrvtNvz8/BgyZAgzZsyoqLDWElBK8emn\\nn/LBBx8QFhZGSeXU9WZG9Ux9gGuukbB8Zib4+UFZmby2WkV858+vPaM/MTGxfC2GQ8i8+8rIPPy5\\nc+dy7Nix00oX+/v7889//pM//OEPhJWr/KRJct3sbHC5PH9PmtQw74FGo9GcD22uIl9j0K5dO557\\n7jn27dtHTEwMY8eO5c033/S2WfXGx8cHwzB49913iYmJOW2t9+aAu5BOdrankM78+fDddzKXfsoU\\nCAqCjh0hOBh27657PH3KlCnccMMN5a8SgRjgCmTm6BVADB9+eCNPPPHEGe268MILmVspa7BynsDR\\no/JcVxKfRqPRNBVtqvZ+YxMWFsZjjz3GzJkzKSwsBGDHjh1kZ2czZswYL1tXO0opVq5cybFjx3js\\nscf48MMPvWZLcrII9eHDnhD9G2+IkJeWyhh5nz4iqKtWwa9/LcfZ7SL4+fmwa5fntTvU7j5vaqqD\\njAwfVq7cWasdEydexYABdX9FqtfYj4/XIt9QhIaGGnFxcd42o0EoKCioMuunpdJa7gNa171s3Lgx\\nyzCM8LraadFvBAIDAyvGx9PS0rj77ruJiopizpw5TJgwodmW+H399deJioqq1bNtbGoqjTtnDmzd\\nKhXygoOhqAjWroULL5SSuXa7dAays2HvXigsBKVE/G02aN8eFi+GpUuhrOwEH3/8JhdddDVVV8mb\\nhFR73kyvXr256aabyM6WToIWcO8RERFBQ06/9SbNcWrvudBa7gNa170opU6vCV4DWvQbmfHjx7N7\\n924++eQTHnroIWw2G48//jjXXuu14oNnpH379syfPx+APXv2oJSiZ886yyI0KJWnvIE8Z2bK+LhS\\n8vDzk31btojwp6bCvn1gNkNBeQ0cpSSpr6xM2s+bBz4+61i79r8AmEy5SFU99yp53YAjTJ58I/37\\n9wckorBli+QPVI466E6ARqNpqegx/SbAYrFwyy23kJyczOOPP87BgwcBKZDjcDhqP9gLfPvttwwe\\nPJipU6c2iX2VE/SWLIHi4qr7c3Jk29698sjLEw//5Em4917o0kUiAAUFMjfeYpFnEOE2DINNm06x\\ndu3XFef85ZdfqLpK3iFmzPhTheCDFOs5cOD0PIKzKfWr0Wg0zQkt+k2IyWTi2muvrZjfvWXLFuLi\\n4njjjTcoKirysnUehg0bRvv27bHb7Rw7dqxRr1U9Qc9mk3H6jAzZn5Eh4m6xQEyMbNu7Fw4elG2J\\nidLmiisk/O/nJ+fw9xdvv6DARXq6A8MIBeYgIf0biI0dhTs7PzIyks2bn6CsLKhK1n1KCvTv71mI\\nZ+tW2LABHnxQC79Go2mZaNH3IkOGDOHjjz/mm2++ITY2lueff57c3Fxvm0VoaCjLly9n8+bNdO3a\\nte4DzoPqFeyGDpXtmzeL+G7eLFn5ISEi8pGREsZ3ueDii0WcDxyQ8H5IiIi9yyWL5pSWOiksdCFl\\nICh/tgG9SEsbCsRw992RHDt2jMGDTadl3cfEQFycdCrWrpVcgg4dpANwJo//fBYA0mg0msZGi76X\\nufDCC1m6dCnLly9ny5YtDBkypFmE/Pv164efnx8pKSlMmzaN0tLSRrlO9Qp2EREwbpwk5h09Ks8T\\nJsCll4oXn5Ymwt6+vXj2YWEwYABs3w6dO3s6ByUlZbhcLjwfcQdQhiyPqygp8WHSpAt58817Kq7t\\nrs733nvyPHiwJAnu2gW+vnL9khKZFljTVMAzTSvUwq/RaJoLOpGvmRAfH89HH32E3W7HYrFgGAbP\\nP/88U6dOpUuXLl6xyeVyMWXKFHbu3Em3bt146qmnGuS8lafkpaaKkFbOF/T1hWuvFeGtvKhOZKSI\\nsNUq3vyyPObJAAAgAElEQVSXX0rHwDBEkIODoXfvMvbt2wz0RLx6AANwlf/tBErx8bERElL76oLu\\nin8nToiHX1QkuQVDh9ZcWremJET3dp38p9FomgPa029mhJS7vcXFxWRkZDBw4EDuuusu9u/f3+S2\\nmEwmFixYwODBgysVsTk/qnvDnTtL6Hzv3por2FWvcOfjI9n8mZkyru+eIp+fD9u35/DVV0ORFZpf\\nBrYChYjoq4qHv38QNpurztK47kI7HTtKqV8/Pxg9WqIRNZXW1XX3NRpNc0eLfjPFz8+Pl156id27\\nd9OxY0dGjhzJLbfcQoY7w62JGDduHBs3bmTQoEEUFxdTXD21/iypPobfq5dMu0tLq7mCXfUKd0OG\\niKdfXCwdAJCEPZPpOKtX70Sm4IFU2DsBuHMkTChlxWr1QSno0qWoxtK4NZX6feUVGDZMavqHh5+5\\ntG5DLACk0Wg0jYkW/WZOeHg4Tz/9NKmpqQwZMoSgoCCAJl3W12QysXnzZoYOHcpjjz12Vse6RfS5\\n53ozd67Me6/uDcfFQWysZyy9eii88lj7ggXQr58IvcsFVqvByZNbyczcCfjimYKXAvwfsAxIw2Yz\\nYbGYMJslVN+tW2GNttY0Jg/1K62r6+5rNJrmjh7TbyEEBwcza9asitdTpkzB6XQyZ84cLr300kav\\n8ldaWsru3bs5fPgws2fPpmPHjnUeU7m6Xnh4CXv3wi+/yNS3bt2klK57nP5svOHBg+HYMSgrK2Xz\\nZvcaTL5AMbJwjpsUIiOfJi3tblJSTBW2hITA9u0G8+dXFe/axuRr6oxUxx2VqFxC+Pbb9Xi+RqNp\\nPmjRb6F89dVXfPTRR9x3332EhoYyZ84crr766tNWgmsoRo4cyT/+8Q/Gjh1bL8GHqiJ64ICVQ4dE\\ncHNy5LFmjWTeWywijrVROfnPxwfy8k6wc2cWIvYAwcBeJKwvPPnkkzz++OOn2QIQFOQgJKRqkt3h\\nw+LhV+Zsx+R13X2NRtOc0aLfQrFarUybNo2pU6fy+eefM3fuXAoKCrjpppsa7Zp33HEHAPn5+Xz9\\n9dfceOONtbavLKKHD/vj6wuhoRKaDw2VrPj0dBkzr00ok5PhL3+R9tnZcORIFsXFDiQTPxjx8H8A\\nFpQf8ThXX30/Llc4ycly7voIenS0Z6aAGz0mr9FoWhNa9Fs4ZrOZyZMnc8MNN5TPS4ePPvqIjRs3\\nMmrUKGw2Wx1nODtKS0sZPnw4u3bt4ttvv+Wyyy47Y9vKIpqfbyEkRBLwIiNlOVyXS8bI3YJf0+p6\\n8fHw5ptSfMdkcrJ3byaSjW9FpuFtQhbNAbgbmMCwYbF07myuGJO/5hqZGvjLL5KJ37evtK4u6O4p\\neiAdArtd7K8rCqHRaDQtBZ3I10pQSlUs8dqjRw9Wr15Njx49eOmllyhwr0LTAPj4+HDzzTcTGBhI\\nZmZmrW0rJ7YFBDjIyIBDh2Q8PilJhNwturUVtvnlFzCZCtix4zBSZKek/NERWTTnHmAWoaGXM3x4\\nT0wmM7/8IoV9nE54+mmZGmixyLDCzz/D0aO+pyXZVZ8pcKaEPY1Go2mpaE+/FTJy5Eiee+45goOD\\n+dvf/sbf/vY35s2bx4wZMxrk/I888gi33XYb3bp1q7Vd5cS24mIThw5JXXybTcL6hw97RLe2JLqM\\njAwyMg4CPZCPrAXx8kuRRXN+zcSJgRw71gs/Pxk+ANi5Uwr3lJXJ1MDgYKmud+IEZGXZePHFmmcK\\naJHXaDStlXp5+kopP6XUUaXUYaWUrdq+d5RSTqXUbxvHRM25MnToUD799FNWrVpFnz59AMjLyzvv\\nuf5Wq5Vu3bpht9uZPn06X3311RnbxseLsDscJrp1k7H8wkLIzZXs/ZQUaVdTYZvgYIMXXviEjIzP\\ngHDAHxF8d3hfAbH06zeAYcN6VQwfgFT1y8iAHTvg1Cmp3rdhg4Tsw8Ml8qDFXaPRtDXqJfqGYRQB\\nTwBdgXvd25VSfwNuBx4wDOPjRrFQc9706dOH0aNHA7KkbL9+/bjvvvsqlvitjdoWkPnggw9YuHAh\\nd95552kLBSUnwz33SDGdCRPgxAkbgYGyiE3//jJlr6DAk0hXvbBNXl4ezzzzdwoLdyAJeu4piX5I\\neV0FWOnY8Rb69fPBbpdORHGxlMs9eVKq6BmG1Oo/eFAeJpNcJyPDV9fE12g0bY6zGdNfCGwHHlVK\\nBSql/gg8AjxhGMYbjWGcpuEZP348O3fuJCgoiAsuuIBp06axc+fOGtvWtYDMPffcw9VXX83zzz9f\\nUTTIfdxf/iLj9larjKuXlJjZv1/K5YJ44pmZnjH9yuP/27fv5MUX3wXC8EzB80PG8x2AGZPJSkRE\\nCBaLia1bZRhg82ZPDf/0dFmQ55JLpHNhNsuwQkaGdARiYgpOWzBHo9FoWjv1HtM3DMOplHoEKXH2\\nBXAJ8KphGA2zCoumyYiIiGDevHn8+c9/5vXXX+fhhx9m2bJlp7WrawEZs9nMsmXLSE6GJ5+EXbsK\\n6NMngIwMWe42N1eEPT9fhN/t2fft61k4xz2m7x7//+1vF7FzpxUYBOQgZXUvBIKQPqqB1aowDDOZ\\nmTJ+b7NJVj5Ixb/x40Xw4+PFs9+2Ta5nt4sdnTqBn59T18TXaDRtjrNK5DMM40ul1GbgUuBj4A+V\\n95eP978GXIYMwh5DOgavNoy5moYkLCyM//u//6t4nZmZyZ133slDDz3E2LFjT5vbfvw4rFsnYfIl\\nS6Rm/uWXQ2JiGevXryAtbSdBQffyzTf+2O0ixkVF7qMlPG+3S9a+0yni7/a2e/YsYtAgf2AAMAup\\nrGcH4pCPmwt3Ep/DoTAMOc7PT57T06U+/oUXSuckIsIzXdAd0jcMKexTUgJbt4Zy1VWN8a5qNBpN\\n8+WsRF8p9RvEBQPIMwz3T2+V82UAE4BUIB5YrpQ6bhjGovM1VtO4BAcHc/XVVzN9+nSCgsbgcLzI\\n2rXtiIhQdOwI//ufTLczDNizR8T/v/+FAQMsZGV1oaioMz/8cIqSEn+cTknYczikvfuT4u8v4+6d\\nOokof/01fP99Dj/9NLbcikmIRz8ICEX6jmbARECAlaIimd/vxr3MrsUC69fDbbdJNOGPf5ShiKws\\nCem7XBIV8PGRe5BoQdO9txqNRtMcqLfoK6UmAP8CPgfKgBlKqZcMw6gYEDYMowCovCLLFqXUUuAi\\nQIt+M8dms3HHHXcwdOjv+OMfj7B161pKSnpisfQgOdlCXp6Ip80m3nNxsYTs8/MV3bv3paQkl6Cg\\ncE6cEE++rEzaVV4WICxMsukDAmRcf9eu45w4YQW+A44iQq/wTM1rB5gwmUwoJZ69u+yAxSKdCqUg\\nKAjy8jwFd9zDBQ8+KDaHhootLpeM7wcElFFa2rTvr0aj0Xibeom+UmokklH1M3ALEAXcAPwNuK6W\\n46zAWDwl0zQtgKVLLQwZEsMll3Rn69YMcnIs7N0LDocLPz+Fj4+ouFJSAKewEEJCbEA4EvwpQilf\\nfH1NFcJqtboICTGRkSHC7+trsGZNMg5HF6R+vnvefSTi2WcDxZhMVlwuEy6XhOXL6w8B0nEoK5Po\\ngWFIJ6JyBb34eFm9zzCkg+IeCjAMOHjQrMvrajSaNked2ftKqX7Af4E9wHWGYZQYhrEfeBe4Vik1\\nppbDXwPykAiBpoXgnjOvlGLw4E4kJEBIiAG4KCrKp6SkhOojO0VFIqb796eTl3eE0tISYmKMikRA\\nf38ngYEi3Pn5TlatOojD0R6ZfmcCfJAZoVakLxpKu3bdcLk8H1HDkAiCv78IePfu0Lu3dD4KC2H0\\n6NMr6EVHQ5cunql8hiHRAIvF0EveajSaNketoq+UigaWI27XRMMwKk/GfhooAp4/w7EvAqPKj9OB\\n1BZE9TnzAOHhCpvNgtXqT2mpk9zcfIqKyrBYXPj5SXnd3buhfftwzOaDWCylpKYa2O0iyCaTi9xc\\n8PUtITf3FOLdt0em4pkQLz8U90fSYvElO9szLqCUjN+bTNC+vZTWDQ2VkH7fvvD++/DppzWvcW+x\\nSG0A9zRBw4Bbbz2ki/NoNJo2R63hfcMwDiPuV0370pESaaehlHoZyeC/1DCMrPM1UtP4VF7sxmaD\\nI0egRw/PwjMxMSK6Bw6YKS31x+l04nQW4+fnQ2ioifBwEfdDh3ywWMZjNpuwWsFmM3A4FCUlFlyu\\nDIqKfkSS9ALw9DmdSHgfTCaFYZjw9RXPXCkReh8fCeebzSLeDz0kj7qoXArY11fm7U+aBKdO6Y+l\\nRqNpezR47X2l1CvIHKtLDMOodUUWpdSVwN+RQdx3DMOYV22/Kt//K6AQ+J1hGJsa2ua2jrsIT1iY\\nTNGz20VsS0pk4ZnoaHj2WWn7xhuyAI5SZszmgIqa9p99dpC8vHBMJl8Mw4xSBnl5DvLyyggK8qW0\\n1KCs7DCwEzgJ9AUCEY+/CLAREOBPWZnC6aRC9J1O8dR9fGRbYKB0Ps6GmurpJyWd33um0Wg0LZEG\\nFX2lVDfgAWQJtAPKk7b9k2EYE6u1NQOvA+ORtO31SqmlhmHsqNRsItCz/DESeLP8WdOA1FSEJzZW\\nnufOrdp2wQJPVODDDyEtTUQ/Lq4TBw9mkJERAIThcqnyqXUm7PZT+Pn5U1YWUH6WE+UPX8CfkBB/\\nHI6euFwSWejQQYrtbNwo0QN/fxH74mLJ/L/yyqZ5XzSa5sDRozIddd06WTAKZAGpyZNl6uv69TK0\\nduiQTKM9doyKBFqlZDgrKEi+z8nJsv/8GFt3kzNgMkm0zuWSDr17W3i45OeUlMg0Wx8fGbabOBEG\\nDZL3YM8eucfcXPldKCiQ3wb3ezF8+PneV9ugQUXfMIxDeIqk18UIYJ9hGKkASqmPgWuByqJ/LfCv\\n8noAvyilQpVSnQzDOO+PraaqeHfuDP36SVEbEPGtqWJd5ahA584SFVi7FoqLbXTu3I2sLBcOh8Lh\\ncKGUVM4Df5xOAwnoXIGIfTFQxAUXRDNmTBeKi2HVKrnG2LHi1aeny5fbnX1vsch4frt2TfHuaDTe\\n5+hR+OILEcKffpLvhdkswv3UU7LeRNeuUnVy+3b5zvj6SufYLaoBAVKrIicHr9emcLmq1tlwbzt+\\nXFa/9PeXDkphoXT6c3KkENjw4bB3r3RqiotlbQ13Jc70dPlNmjVLC3998ObSul2AI5VeH+V0L76m\\nNl2QSn+a86Am8V6zRjLgIyI8890rt09MlB8gHx9ZSKdfPzlGKel9nzgBLpep3LswYRhgsSgcjjJK\\nS11AOpK45wt0IDIykpgYK6WlMvUuIUF+lEpLxYNxd0J27xZ7QkLEG9Dz6zVthfXrJWH1++9FDIOD\\nxRsuKpLvwc6dMtyVlyflrgMC5LtiNst3yS2yLpf3Bb8u3MtgKyWRPYdDRD4uTn5/ysokCpiSIm1C\\nQ6UDUFwskYLFi7Xo1wdvin6DopT6PfB7kNrySU04aJufn9+k16sPddm0cGE3CgqsmEwOQkKsHD0a\\nAhisWeMgLq6A/HwLU6YcISmpgP37A1i0qCuBgQ5yc0Pw8TFYudJE//52unWDQ4f8yc0NxGx2YTZb\\nUMrA6TThdILD4U7Ss2AyBREdDSUl7QkJ8cUwikhJKSQsrIwOHYopKzMTEVHM2LFZ9OhRwMGD3cjK\\nstK9u6PC7qwsC0FBZSQlHWrU96epaW72NBdOnjyJUqcHD9PS0ujcuTNz587lySefbLX73313KV99\\n9Q7wR2RITLjkkmux2fzYti21fGnrGKA/UABEERQUhMtlpqyshOLiwvKjgqtdxYwk0Z6J2vY7z/P4\\nmveXljo4diyLuLiOKGXl+PEcUlNXIznjhUA+MJLo6CgsFiv796exfv1mYDPQneefvw2o//u7cOFC\\nLrnkkjPu9/b//1z314Y6vZJu06CUGgXMNQzjivLXjwIYhvG3Sm3+ASQZhvGf8te7gYS6wvvDhg0z\\nNmzY0Gi2VycpKYmEhIQmu159qMumGTMkac9UnkCfkSFeQ3o63HKLZLi7k9/mzvXUsU9KEi+jsFC8\\ni7Aw8fztdrj4Yli6VNparQbFxbk4HD5IiocV+UHyA9Lp2jUMk6ljeVuIjJRr2u1y/KxZcm13NMI9\\ni8C973yn2zW3/5k37FFKbTQMY1iTXvQs6d27t7F7925vm9EgnMv/+PPP5buWmCjPbk/fapXomtUq\\n34WUFNixQ0L67oWlHA7x8P385Ln6NNxzxy34DY+PjwzhuT19pcTTDwz0VPhMSZH76dhRnrt1E08/\\nKAiee+7srtfcfgfOh/p+n73p6a8HeiqlYoA04LfAzdXaLAXuLx/vHwnY9Xh+w5CXB+++KyFBpURU\\nIyPhsss8yXvukP5771FRAjcw0LNynskkXzx3Ys2aNXKewsIy8vPtSHa+ASh8fByUlpaWb4viyJEi\\nOnY0cDjkR2znTsXrr8vUwF695Lpz58I118Brr0nCYJcucP/95y/4Gk1LYfhwGVKLj4eVK0X4zGYR\\n/+JiGdOPjJQEt8BA6bQHBsqYvnvs3N2xdyf1NVfctTgMQ35ffHxk2rC7Y+Me0w8NlTH9nBz5/fH1\\nldfTp3v7DloGXhN9wzAcSqn7keI/ZuA9wzC2K6XuLt+/AKkE+CtgHxLb0f/WBmDxYkm+y8uTHxEQ\\nT7+sTL5Eycmybf588RhKSuQHpLhYet8ZGZ6FdPLz5UfH5ZLkorCwU+TlZeDv3x2n0wZAcbGBy2VF\\nqu6ZcNfVP3myAEnyc6KUorTUzMGDqqKgT3KyRA4GDYJx48RTWbpUOgVa+DVtgagouPZaGdsvLvZk\\n73fqBA884Mned0cB3Nn7gYHSrnGy98+d88ne9/WVe6yevd+5s87ePxu8OqZvGMZ/EWGvvG1Bpb8N\\n4L6mtqu189prkhDjconwG4bHG4iN9Sx3GxYGW7fKFzIrSxKH0tI8iXQ2m/yoSMawAWRz+PBu4uLi\\nsdn8OXJEvpx+fjbKypxIiN/taphwOk2YTGAymTGMYvLzXSjlIjc3kJwcU41TCUHs06KvaStERcnj\\n+uvPvL8pSUr6qclD4lrQG446a+9rWh/uufUul0x/69BBxtFKSjxT9dz19+122RcVJe1LSz0r5/n6\\nyjQ6l8tFXl4RhYUuLrjgAgICAsjLk2iAxSKFfKSGvhMR/WIkcKNwuRz4+Cj8/PwJCPCltLSMzEw7\\nwcGuChsqc6aphBqNRqOpGy36bZAuXSivgy/CDCL4QUGeqXru+vshITJelpUl4UXxzCVE53SC0+nA\\n4XBiGDagPYcO+WAyyfkdDs+8f/mouQAHsjLzMaQyXxllZXaUApvNQvv27enTJ4ihQ0107eri++83\\nkJeXV2F79amEGo1Go6k/WvTbIPffL6Jss8k4fkGBePD9+kl2/KRJ8sjOljGztDRp4xZ8yQg2cDiK\\nKSwsQykTJpO5YsndQ4ckEhAQIOft2xf8/Jz4+Pgggn8EyMW92I7T6SI8PJ9OnaTzkZdnISMDevYs\\n5eRJJ6+99iFffvkVBw/aK+zTaDQazdnTaubpa2qn8oI60dFw332wbJlMvzOZJGt+xAjPVL3kZBHt\\nFSvkeKtVkvzy8iQxLz+/BKXysNnaYbWacblkyMDplM5ESIh0KDIz3Yk6xZw6FYRSDkpKrIjHfwSp\\ntxTHwYM+hIb606WLiTFj5BwrVvjy9NMj+d//+rN8+S4++WQBl11mJzLyj0BHr72XGo1G01LRot8G\\nqL6gTnY2pKbCK6/IfndnoKb2kZGSSVtSItm1O3YUs3NnEUoFEBLSHofDRM+e4tmbzTKtzzAkkjBk\\niFTTc08zmjABDhxoz8aNGUAOsvjOCWRyxiDM5jVcdNFUdu2SML6PD3z7LSxYEMgLLwwjJyeOt99+\\nm8DAQJKT4aOPijhxwo/o6Kp1BTQajUZTM1r02wCJiTK+vnWrZ5y+c2dZMa+wUMQ9Lw/efhtefFGm\\n+wwZIpn8oaESDcjOhsREJ04n+Pj4ExFhJSxMVdTEj46WBTFARN/HR4T+vfdEjJOSNtKuXQLTpkGf\\nPnHs2nWikoV2oAsnT+5ixYpcwsODCQ6W6377rXRC4uMhNDSU2bNnV3RKvvvuK2y2YoYMSSA1tQuz\\nZikt/BqNRlMLWvTbAFu2iGfv50eFmKakSGLexIkyb/fbbyWkHhQkhT1+/lnC++HhsGKFQW6uTLXz\\n8bECZtLTJWzfvr149Skp0L8/HDkixw8dKvkAL7/sTrzrQGqqdAasVhsRETEcPx4GrAFKkfpMgzlw\\nYAddu45EKYVScv7qU/TcU/lmzLierVu38sMPidhsETgcg/nww141lm3VaDQajU7kaxPk5IhA+/l5\\nKuu5M+xDQmTJTptNsvndRTOKiuDrr2HTJge5uUWAgVIKl8uM2SzinZ0t7RMSpDOxb590IubNk3PZ\\nbJ7hhA8+6IbTKRGEkhLo1q07MnVvCBAGvAp0AOCXX/5HUZF0SgYPPn2Knnsqn9lsZujQodx///2M\\nHt2fb77ZyeLFi5vwndVoNJqWhRb9NkBoqIhzUZGE3ouK5HVoqIT73Zn8paXSQbBY5FFQ4CI11Y5S\\nVqxWE76+poppflariHJIiEzLu+IK8e7nzhWv311Ux2SSZ6dTcfSo5AiMGiUdj379hgI2YD7wObAS\\nMCgttVFQkMXo0dJ5qD5Fzz2d0I3JZCIqagAPPHAtk8pT+//973/z7rvvlpf+1Wg0Gg1o0W8TDB4M\\nAweK0ObmyvPAgXDJJeKF+/mJgOfkuMvrGpSWlmEYDvz8AgErZWWKoiIpu1tW5qnkFx4u16g8f77m\\nojplZGbK35GREh24/HIfxo2zAynlrd4E9gA/sn3765jNjhqn6LmnE7ojDe6/b7hBYTbLQiCxsbEs\\nWrSIuLg4XnnlFQoLC9FoNJq2jhb9Vsr+/QHMnSur6WVkiNgPGgS//rU8m81wzz2yoI17AYuiIlDK\\noLS0BIfDha+vCaVsZ1yko107yc7fu5cq4lzdEwfo0KEEq1XaHTsmQwdffQX9+l0CDChvlYJ4/dlA\\nV95++/kaV9SLj5eV9sLCpCZ3WNjpK++NHj2a5cuX89lnn5GUlERMTAz//Oc/z/dt1Wg0mhaNTuRr\\nhSQnw6JFXenTR8bU7XbxyktLRSSjo+H226Xt0qVw+eUi+AcOGJSVGYCZ7t1NFBSYycyUPAClPKt2\\nKSUJf336wIkTUrznlVc8ojtgADz9tEQEwsPFBosFHntMEga/+04S9C67THIDpkxZz6JFwxHRdz/E\\n3m3buhEff8tp9xgfX78pesOHDycxMZHt27eTm5sLQG5uLtnZ2ef1Hms0Gk1LRIt+KyQxEQIDHVUW\\nqgkOhp07ZRpeRgY884yIb1GRCG9BgRPIwWIJwmazUlgo4Xw3lQXfZpNIQUKCbD961CPA7pXx+veX\\nzkBmpgwbXH99FpMnR5KSAldd5Vk8R/Bl/PgFrFx50Wn3MnXqVK677joCAgLO6z3p379/xd+rV69m\\n2rRpJCUlMWvWLLp27Xpe59ZoNJqWghb9VsjhwxAQ4Kh4vX27CHxhIWzbJh50aalk6StlUFQk8Xuz\\nOZTgYHOVpXRluzy7l9MtKZEhATi9Fn7llfF69ZJt2dlw8GBAhW3VVwULCYGoqDFnvJ/AwECMBlwI\\n/Fe/+hXvv/8+v/zyC4MHD+b666/nz3/+Mz179mywa2g0Gk1zRI/pt0Kio6GgQOrXf/klLFki3rY7\\nAa+w0C36Bg5HuQuPCZfLTEGBrLoXEyNb/f1F7B2ePkRF8Z116yApSeoAzJ0rXv6ZVsY7fty3wrbq\\n4/3ujkNtIfe33nrrvN6T6rRv354XXniBvXv30rVrV2bPnt2g59doNJrmiBb9FkByMhVJeW5xrY1J\\nkyA93Zcff5TFb9weuntcXkL1RvlDVTwMQ9rabNI2OBi6d/fM61dKnq1WSQz84QdZiOfIEVi0CG69\\nVToVNYl6RERxhW01Zd5PmiQV9xYsWFDjPd11113Yq5+4AWjXrh1PPPEES5YsAeD48eNcf/31rFmz\\npsGvpdFoNN5Gi34zx11yNjvbU+hm/vzahT8+HsLDSwgOlil27rr4JhN4xF6wWk1VKtiFhMCVV0qd\\n/UsvFWEOCJDleDt1kgS+oCDpCPj6SgTh0CFP4Z+tW2H//tNFfezYrArbasu8v+uuu854X6HuMYVG\\nJCQkhCuvvJKpU6eSkJDAihUrGnRoQaPRaLyJHtNvprhXxfviCwmlDxniKXQD8OabUhTHvWpe9QVn\\nyspMXHGFCHJuroTzwcDpdCJ9PQWYMAzPkrkg2fZhYZ7s/jlz5Hh3wp/ZLHX7MzJE1P39pd3Jk9Ct\\nm1yna1c5h9u222+HU6cKKmyrK/M+Pz+fwMDAGvf9v//3/3jooYfO4R2tH76+vtx1113cfvvtfPzx\\nx8ycORM/Pz+SkpLOaJNGo9G0FLSn3wyp7N0bhjzWrhWhBUmwW7mydu8/IqIYu132W61gNrsoKysD\\nwGRSmEwKS3mXz2oV7717dwnTz53rEea//lXm9kuynYz3+/l5Ev1OnoSsLLFt3z7ZV1Ii53jvPc+5\\nzoaAgAA++OCDGvfNmjWLrKysszvhOWCxWJg6dSrbtm3jpZdeqhD8NWvWVLyPGo1G09LQot8MqZwB\\nHxoqYXNfX9i1S/Zv2SLz3CuXuQ0Lk+PcjB2bRXa2ZNAHBORRWpqNxaIIDTXTpYtizBi48UYYPVqS\\n9qKiROBrKoSzYIGce+JE8eyLiiTkD5ID4HTKc0GBLLbj43P+78HUqVPPOE0v3F0GsAkwmUyMHTsW\\nAKfTyZNPPkmvXr148803KXZPb9BoNJoWghb9ZkjlDPi+fcWjNgxIT5dKdjt3isi6PX+Q9ocPe5L+\\n3n+/O9u3u1i+/ATHjx+jd28frrrKyh/+oHjxRejXD3bskND9gAFSD/+//605UdA91HD4MIwcKZ58\\nu3YSHXDj4yMPX1/ppDQEtXn0c+fObZiLnAVms5nly5fz0Ucf8fXXXxMbG8sLL7xAXl5ek9ui0Wg0\\n504rF64AACAASURBVIIW/WZI5WltERHijRcWUlG7vmdP8a4rh/ztdhHdOXPgs89gy5Zgtm3LpqCg\\nmGuvjWLEiCCeekrG/pculQz9a66RTsWGDZJ17x4qmDNHSvTOmCHPc+Z4hhJsNvH0zWZP7f2OHSXT\\n32SCceOkU9AQ+Pr68vnnn9e478knnyQ9Pb1hLnSWjBo1iqVLl/LNN9+wadMmjhw54hU7NBqN5mzR\\niXzNkEmTZIwexIN3J9D96lci+MePw5o1EgH45hsJuVut0kE4cgSyskooKDhJQIA/vr5RHD5sYswY\\nT/g/LEyEedUqz5DB7t1y7pISyb7PzJSV85Yvl2hAVJRnKKFzZzlm4EDpAPj5eZ59fSXLv6G47rrr\\niIqK4ujRo6ft69Kli1cz6+Pj4/nPf/5T8frOO+8kJCSEP/3pT3R2v0kajUbTjNCefjOkpmltMTHQ\\no4fsj4iQKXXZ2TJlLjxcQvTr10Nu7klOnswhONifDh1CsdlMHD3qCf8fPiydhbVrPUvtms2QmipR\\ng127JGxfWioiX1oqr92dA5BV+06eFPEvKhIbiopkWl9Nq+KdL6mpqWfcN3PmzIa92HnwxBNP4HQ6\\nGTBgAHfffXetdms0Go030KLfTImPr5oBP3hw1aI3J06I6A4eLEvkdu9eRlFRLhkZZXTqFIZS/uTk\\nSGJdfr5k1kdHy2PLFvHI/fzk4XTK865dnmu4cwrcz5Wv7esL48dLkmBsrCQbxsZKpKCmVfHOF6vV\\nyvLly2vc9/LLL3PgwIGGveA5EhUVxUsvvcTu3bvp0KEDI0aM4P333/e2WRqNRlOBDu83Uyonz0VH\\niye/dKnsCwkR0bdYZKW7nJwcPvnkE3x9L8PHJxZ/fxOZmc6KKXk2G/zyi3jgvXrBv/8tiXiGIV68\\n3S5eek6OtM3NhQsukGP79pVSu8HBMk3PbhdvvjHEvTYmTJjAgAEDSElJOW1fbGxssyqgEx4ezjPP\\nPMPs2bMplQIJbNu2jeLiYoYPH+5l6zQaTVtGe/rNkJqq8C1dKol37pB/x47SEcjP38fbb79NfHw8\\nEyf2IDLSRGEh+Pi4sFhkrD8mRrLz3XoZGgoHDsg4vs0mXrt7dtyQIRAXJ3kELpc8x8XJ9uRkqbiX\\nmysdkrrKATc0W7ZsOeO+GTNmNKEl9SMkJKRieuGhQ4e44YYbGD9+PD/88EOz6qRoNJq2g/b0myGV\\n5+mD5zklRUL9AFu2uJg2bRv79q1j8uQphIZ2Iztb5to/8wz4+RUTFWWlTx+IjBQB37JFxu579646\\nZm+xyDa39149yvDss3LN+fOl6l5IiKcgUFN6/GazmdWrV3PRRacvwfv+++/z8MMP06dPn6Yx5iy5\\n+uqrmTBhAh9++CF33313Rc3/K6+80tumNWv27NmTr5Ta7W07GogOQONXlmp8Wst9QOu6l971aaRF\\nvxlypuVnDx+Wv0+dOsWcOVMxmzty992vkp0dVFE6Nz5eOgfbttkZONAzkd5ul/B9t27SiSgrk1Xy\\nsrPFc58/3yPeNZXJnTu35o5IYmLThvnHjBnDRRddxOrVq0/b17dvX1wuV5W1BJoTPj4+TJ8+ndtu\\nu43ExET27dsHgGG4yyNramC3YRjDvG1EQ6CU2tAa7qW13Ae0vnupTzst+s0QWWbWI6zgWX5206ZN\\nTJ48meuuu44vvngOq9V62vGTJsHatRb27IG0NJl+Z7XKOL4scyuh/YgIOWdWlgwf9Op1ZgGvqyPS\\nlKxatQqTqeaRqcmTJ/PZZ581sUVnh9ls5sYbb6x4vXbtWqZNm8bcuXO59dZbsdlsXrROo9G0Zrwy\\npq+UaqeUWqmU2lv+HHaGdgeVUtuUUlvq24tpDZxp+VmTaQlXXHEF8+bN48UXX6xR8EGEe9SoLLZv\\nF8F3T+k7dUqy+Hfu9GTvl5RIfkD1Mr7VqVwwyI27I9LUKKXYsKHmj0NiYmKtY//NkVGjRjF79mwW\\nL15MXFwcL7/8MgUFBXUfqNFoNGeJtxL5HgG+MwyjJ/Bd+eszcYlhGINbSwimPlSfpx8UVEZBwZP8\\n5z+P8uOPPzJlypQ6z3HwYAAJCTBlikzp69lThH/7dsn8d1fWKy6WGQB1ee1n6og09Jz8+nLBBRdw\\n1VVX1bhvyJAhLSpRTinFoEGD+Oabb1iyZAmrV69m2LBhuFwub5vWHHjL2wY0IK3lXlrLfUAbvBdv\\nif61wD/L//4ncJ2X7Gi2uOfpP/HEIb75ZhQmUwrr1q2jX79+9Tr++HHfijn2bnr0kEz+jh0lpO/n\\nJ1n9kZF1e+01FQxq6ml71Vm2bNkZ911++eVNaEnDccEFF7B48WLWrl2LyWTC5XLxwgsvcPz4cW+b\\n5hUMw2g1P8qt5V5ay31A27wXb4l+hGEYx8r/zgAiztDOAL5VSm1USv2+aUxrPixfvpyRI0dy8803\\ns2jRIoIqr3BTB+6ldStjt0sxn1degWHDYNAg6QDU12uvXjDIm4IP4iFv3769xn3ff/89a9eubWKL\\nGo7Q0FAACgsLOXjwIH379v3/7d15dBR1uv/x90MSElmEOAgaYmSRZXCBASJcUS4oo8JBNmUUUUEQ\\nxBGcq/gbdXRGzxyVUVHv+JOZ30QQxA10hh3uyDIEUEchyCJcCYjIpoIIIbIlhHx/f3QnJiFLJ+nu\\n6qQ/r3PqUNX1ra6nOl39UNvzZcKECezevdvjyESkJrNQnQY1s+XABaXMehx4wznXuEjbI865s67r\\nm1lz59x+M2sKLAMmOOdWl7G+scBYgGbNmnWZNWtWMDYjIMeOHSvsbz0Y8vPzeeutt1i4cCFPPPEE\\nHTt2rPR7fP65sWhROxo0yKN+/TyOH4/l2LFYfvWrvbRufZydO+uzZk0TDhxIoFmzU1xzzSFatw7d\\ndeRgf0ZF7dq1i8OHDxdOnzhxgpiYGOLj4+lSUGUojPFURUXxHD58mPfff58lS5Zw1VVXMW7cOBqV\\nPJVTSb17914fTZfNRCSESb/clfqeu+3lnPvWzC4E0p1z5T5jaGZPAcecc5Mrev+uXbu6sm70CoX0\\n9HR69epVpWVLPhPfp89R/vSn4WRlZfHee+9VueOW9PR0zjuvV7H3HjLEu6Pz6nxGFXHOlXk3f+fO\\nnVm/fn1Y46mKQOM5cuQIaWlp/OY3vyEhIYGjR49WOfmbmZK+SJTx6vT+AmCEf3wEML9kAzOrb2YN\\nC8aB64Gza7DWYCUr723f/j19+66gUaNrWLlyZbV7aou00/GhYmaFz7yX9Nlnn/Gvf/0rzBGFTmJi\\nIo888ggJCQmAr+hP3759S61bICJSkldJ/0/AL81sB9DHP42ZJZnZEn+bZsCHZrYJWAssds7905No\\nQ6Ro5b3Nmzcyb950rr76Utq0eaTMx/GqY/NmX/IfNcr3b7jL6IZS69atuf/++0udd91115GXlxfm\\niMJj+fLlDBkyhBEjRtCzZ0/++c9/1qgnF0QkvDxJ+s65H5xz1znn2jjn+jjnDvtf/8Y5188//pVz\\nrqN/uNQ594wXsYbSnj1Qv34eCxcu5MMPP2TkyJGkprYLScGb0ur5T55cuxL/q6++Wua8du0CqlBZ\\n48THxzNmzBgyMzMZN24cDz/8MPPnn3XiTEQEUIc7nmrUKItp0/7OiRMnGDNmDE2bNg1ZwZuiZxXq\\n1PlpvLyCPDXR3r17S339q6++KvcRv5ouNjaW22+/nc2bN3PTTTcBMHPmTGbMmMHp06c9jk5EIoXK\\n8Hpk6dKlzJz5LC1aTKFHjw7ExVnho3OjRwd/fZFURjeUkpOT+d3vfsezzz571rwBAwZw6tSpWl3m\\ntugNjS1btuSpp57iySef5Le//S2jRo3inHPO8TC6ymvcuLG75JJLvA4jKI4fP079gu4sa7Dash1Q\\nu7Zl/fr1h5xz51fUTkk/zPLz85k0aRJTpkzhH/94h/POu7TYHfYFneYEW3n1/GubZ555ptSkD5CU\\nlMQPP/wQ5oi8cc0117BixQo++eQTJk2axNNPP83kyZMZPny416EFrFmzZmWWXK5pIu2JkaqqLdsB\\ntWtbzCygIh5K+mGUlZXFXXfdxQ8//MC6deto3rw5EJ676ocM8V3DB98R/tGjoTurEAkOHDhAs2Zn\\n13w6fPgws2bN4oILSishUTt1796d+fPn8/nnn3PixAkAjpas3CQiUUHX9MNk06ZNdO3alZYtW7Jy\\n5crChB8ukVhGN5SaNm3KpEmTSp03bNiwqKxrf/nll9OtWzcAVq1a5XE0IuIFHemHwcyZM5k4cSKv\\nvPIKw4YNO2t+0QI9deuCma/3u2AX1Lniitqb5Evz6KOP8thjj5U6b8OGDVx77bVnvZ6Tk0NMTAyx\\nsbV71xgwYIDXIYiIB3SkH0IZGbl07bqQCRPqMXToFi69tPSEX/AoXVwcrFoF6em+8dr4WF24FS3P\\nW9LUqVOLTe/Zs4drrrmG7du3hzosERFPKOmHyNKl33Hjjcs4csS4997+xMU1KzWBF32ULjMTzj3X\\nN2Rm1t7H6sIpMTGx1Of3jx49ypgxY8jOzgZ8T1N07tyZdevWsW3btoDfPycnh+nTp9O/f3+Sk5NJ\\nSEjg3HPPpV27dgwfPpylS5cGbVtERKqrdp/D9IivStpGOnXqxXXXdcHMqFfPN2/OnOKn2Is+Snf0\\nqC/hF4xD7XysLtzuv/9+xo8fX+y1gqP5Ro0a8fTTT/P73/++sJJdoEk/MzOTwYMH88UXXxR7PScn\\nhx9//JHt27cTFxfH9ddfH4StEBGpPh3pB1F+fj7PPvssd955J1dffTvXXtsVMyucX1oCT0kpnuBP\\nnfINBX2o1NbH6sKt4Ii+QGpqauH4E088Uax0bWZmZoXvl5WVxS9/+ctiCT8mJoaOHTty00030aVL\\nF2JiYoIQuYhI8CjpB0lWVhaDBw9m4cKFrFu3ju7dk0rtz75kAh8y5Kf+7Nu1g+xs39CuXeD93EvF\\nGjZsyIwZMwqnz5w5U2bbQI70X3zxxWLV/5KTk1m7di0bN25kwYIFZGRksHv3bm6++ebCNgsXLmT4\\n8OG0bduWc889l8TERFJTU5k+fXpUPk0gIuGnpB8EO3fuJDU1lZSUFFatWkVycnKxZJ6fX3YCL/oo\\n3enT8J//Cb16+cZr+2N14TZixIjC3uk++OCDMttt27atwk5r5s2bV2z6xRdfpHPnzsVea968eWFJ\\nXIApU6bwzjvvsGPHDn788UeysrLIyMhg1KhRvPLKK5XdHBGRStM1/Wp66623mDhxIlOmTClW6awg\\nmQdSbS/aHqXzSk5ODsOHD2fatGn07NmzzC53s7Oz+e6777jwwgvLfK+vvvqq2HTPnj0rXH9CQgIP\\nPvggo0ePplWrVixevJhhw4aRl5fHggULOHjwIE2bNq3cRomIVIKSfhXl5uby4IMPsnTpUl566aVS\\nS5sqmUeOPXv2MHToUNauXQtAgwYNym2fmZlZbtKvijfffJOGDRsWTt9yyy288cYbLFq0COccO3fu\\nVNIXkZDS6f0AlOyHftmy7+jZsyf79+8nIyODVq1aeR2ilGP58uV07ty5MOEHoqLr+i1btiw2vXr1\\n6grfs2jCL3Dq1KnC8XBXaRSR6KOkX4GS/dBv3ryXAQNW063bGObMmUOjgtvsJSLl5eXx3nvvlVuk\\npzQVJf1BgwYVm544cSIbNmwo9tqBAwdYtGhRme+xevXqwksMXbp0IUWPaYhIiCnpV6CgeE7jxo6P\\nP/6QpUtnc+ON3UlMHF2sG1OJTLGxsaSlpbFhwwb69OkT8HIVPbY3ceJEkpKSCqf37dtHamoqv/jF\\nLxgwYADdunUjOTmZv//976Uuv27dOgYNGkR+fj7NmzfnkUceCTg2EZGqUtaqwJ49EB9/itmzZ7N5\\n8wEuu2w8u3alMG+eyuPWJB07dmTp0qUsWbKEDh06VNi+oiP9xMREli1bRrt27QpfO3PmDBs3bmTh\\nwoWsXbuWvLy8Upf9+OOP6dOnD0eOHCEpKYkVK1Zw/vkVdoMtIlJtSvoViI8/QFrabGJikrjggsE4\\nl0DduhAfr7r4NY2Z0bdvXzZt2sTFF19cate7BXbv3l3YDW1ZOnTowMaNG3nttdfo168fSUlJxMfH\\nU79+fVq1asWtt97K7bffXmyZVatWccMNN5CdnU2LFi1Ys2ZNsf84iIiEku7eL8fbb7/Nu+/+jcsu\\nm87p060peHQ7JweuusrXI96cOb7n6qXmiI2NpUmTJuzYsYMXXniByZMnc/LkyWJtnHPs2LGDjh07\\nlvteCQkJ3HPPPdxzzz0VrnfZsmUMHDiQkydP0rZtW1asWEFyQQ1mEZEw0JF+KXJzc5kwYQJPPvkk\\nq1e/yl/+0prcXMjNhXPO8SX8Zs1UF7+ma9iwIX/84x/ZsWMHI0eOLFYyGQIrx1sZzzzzTOF/LrZv\\n385FF12EmWFm9O7du1jFQBGRUFDSL2Hfvn306tWL3bt3k5GRwRVXXMEVV8DAgT9Vyys4K6y6+LVD\\n8+bNmT59Op999hnXXXdd4evTpk3zMCoRkeBT0i9i5cqVpKam0r9/f+bNm0fjxo0L5wVaVldqrk6d\\nOrFs2TKmTp0K+E7H//vf/w7a+6enp+OcK3VYuXIlI0eODNq6RERKo2v6+K7fvvDCC7z00ku89dZb\\npT7aVV5Z3fT08McsoWFmjB49mm3btvHhhx/SokULr0MSEQmaqE/62dnZ3H333ezdu5e1a9eWWyBF\\nZXWjx6RJk4iJicHMOHHiBPXq1fM6JBGRaovq0/tbt24lNTWVpk2bsmbNGlVEk0KxsbGYGXPnzqVl\\ny5ak63SOiNQCUXuk/+677/LAAw8wefJkRowYUWqbzZuLn84fMkRH+tFm06ZNHDx4kMcee4yPP/74\\nrDv8RURqkqhL+rm5uTz88MMsXryYZcuW0alTp1LbFdTcT0z01dw/csQ3rf7to8vjjz9OTk4OEydO\\nVMIXkRovqk7vf/PNN/Tu3Ztdu3aRkZFRZsKHn2ruJyZCnTo/jc+ZE8aAxXNxcXFMmjSJJk2asH//\\nfj799FOvQxIRqbKoSfqrVq2ia9eu9O3bl/nz55OYmFhu+z17fMV3ilIxnui1ZcsWLrvsMm6++Way\\nsrK8DkdEpEo8SfpmNtTMtppZvpl1LafdjWaWaWZfmtmjVVmXc47Jkydz6623MmPGDJ544omAesdL\\nSfEV3ylKxXiiV/v27Wnfvj1mxtdff+11OCLVtm8fvPYadO/uKyluFtjQu/c1Abc18/VTkpwMd90F\\nU6dCWhrMnetbv4SfV0f6W4AhwOqyGphZDDAF6At0AIaZWcXdoxWRnZ3N0KFDmT17Np9++inXX399\\nwMuqGI8UFRsby+zZs9myZUu5l4VEaoJ9+2DGDPjb32DtWjh9OnTrys2F77+H99+H11+HmBg4cQLm\\nz1fi94InSd8594VzrqLC5lcCXzrnvnLO5QKzgIGBrmPr1q1ceeWV/OxnP2PNmjVcfPHFlYqxoBhP\\nYqLvi5mYqJv4ol1KSgqNGjVi165dDB06lEOHDnkdUq23fft2zIz169cDMHbsWMyMsWPHArB+/frC\\n/gsKdOnSBTMjLS0NgLS0NMyMLl26FLYpWCZa33fdOjh0CNav/wHnzgBn/EudKTKUNk2J18tqU/y1\\n3NwznDqVxYYNR9i5Ew4e/JLx44dz0UWDauXn68X7BiqS795vDuwtMr0P6BbIgkeOHKFXr148//zz\\n3H333VUOQMV4pDQTJkxg8eLFxMTEMGvWLK/DEam077/3HYFDfBjXeoa8vHiOHoXzzwf4EbggjOsX\\nAHMF/cUG+43NllP6X/Rx59x8f5t04GHnXEYpy98C3Oicu8c/fSfQzTk3voz1jQXGAsTExHT561//\\nSps2bYKyLRU5duwYDRo0CMu6AhVpMdWmeL799lv+8Ic/8MADD3D55Zd7Hk9V9e7de71zrsx7aiJB\\nu3btXLB7O/RKeno6vSKkH+65c2HVKt8p/pL3LlXsDBBT6XXWrw8NGsCoUfAf/wHZ2VCvHgweXOm3\\nCppI+ptUl5kFtD+H7EjfOXd2AfvK2Q9cVGQ62f9aWetLA9IAOnXq5MaMGVPN1QcuEr84kRZTbYvn\\ntttuw8zIy8vj+PHjNCr5qEeY4xGpjNRU2LoVLrkEPvsMQnTsV6huXThzBlq1gtatfQk/K8vXc6mE\\nVyQ/srcOaGNmLc2sLnAbsCCQBWNjI/mqhdQGZsbOnTvp0aMHd9xxB6E6YyYSCsnJMHIk3HsvXHkl\\nxMWFbl116/pO5w8d6jvKP3PGd4Q/cKAvDgkvT7KjmQ0G/i9wPrDYzDY6524wsyRgqnOun3Muz8zG\\nAx/gO5f0unNuqxfxipQmPj6ezMxM1q9fz6ZNm3RXv9QoyckwZoxvqIz09DU6K1WDeZL0nXNzgbml\\nvP4N0K/I9BJgSRhDEwlYcnIyM2fOJCkpSQlfRGqESD69LxLxBgwYQNeuXcnNzWXatGk6zS8iEU0X\\nv0WqyTlHv379WLFiBbm5udx3331ehyQiUiod6YtUk5lx7733UrduXXJ9Dz+LiEQkHemLBMHQoUPp\\n1q0bKeqcQUQimI70RYIkJSWFkydP8tBDD/HnP//Z63BERM6iI32RIFq9ejUvv/wyCQkJ3HTTTbRq\\n1crrkERECinpiwTRDTfcwK9//Wvat29PixYtvA5HRKQYJX2RIJsyZUrh+O7duyvdw6OISKjomr5I\\nCOTn5zNu3Djatm3L5s2bvQ5HRARQ0hcJiTp16lCnTh1yc3N54403vA5HRATQ6X2RkHn++efp0aMH\\nt99+u9ehiIgAOtIXCZkGDRowfPhwzIx58+aRkZHhdUgiEuWU9EVC7J133mHw4MHcddddnDp1yutw\\nRCSKKemLhNjgwYNp3749l156qZK+iHhK1/RFQuycc87ho48+IjExETPDOYeZeR2WiEQhHemLhMF5\\n550H+E71d+vWjePHj3sckYhEIyV9kTDJy8vjueeeY926dTz99NNehyMiUUhJXyRM4uLimDlzJnff\\nfTePPPKI1+GISBRS0hcJo44dO/L666/TuHFjvv76a44ePep1SCISRZT0RTwwf/58Lr/8ch566CGv\\nQxGRKKKkL+KBtm3bcvr0adLT08nKyvI6HBGJEkr6Ih74+c9/zqJFi9i0aRONGzf2OhwRiRJK+iIe\\n6dOnDw0aNGD79u28+eabXocjIlFAxXlEPHTixAl69OjBoUOH6Nu3L7fccovXIYlILaYjfREP1atX\\nj6eeeoqUlBQuvvhir8MRkVpOR/oiHrvvvvto06YNqampnD59mtjYWJXpFZGQMOec1zEEnZl9D+wO\\n4yqbAIfCuL5ARFpMiqd8XsRzsXPu/DCvs1LM7Ecg0+s4giTSvnNVVVu2A2rXtrRzzjWsqFGtPNIP\\n9w+ZmWU457qGc50VibSYFE/5Ii2eCJJZWz6X2vI3ri3bAbVvWwJpp2v6IiIiUUJJX0REJEoo6QdH\\nmtcBlCLSYlI85Yu0eCJFbfpcasu21JbtgCjcllp5I5+IiIicTUf6IiIiUUJJvwrMbKiZbTWzfDMr\\n885PM7vRzDLN7EszezTEMZ1nZsvMbIf/38Qy2n1tZp+b2cZA7/asZBzlbrP5vOKfv9nMOgc7hkrG\\n08vMjvo/j41m9ocQx/O6mR00sy1lzA/r51MTmNkLZrbN/3nMNbMa2VlBoL8bkSycv2mhVNF+WFOY\\n2UVmttLM/tf/3fpNhQs55zRUcgB+DrQD0oGuZbSJAXYCrYC6wCagQwhjeh541D/+KPBcGe2+BpqE\\nKIYKtxnoB/wPYEB34NMQfiaBxNMLWBTG705PoDOwpYz5Yft8asoAXA/E+sefK+u7HelDIL8bkTyE\\n+zctxNtS7n5YUwbgQqCzf7whsL2iv4mO9KvAOfeFc66igiFXAl86575yzuUCs4CBIQxrIPCGf/wN\\nYFAI11WWQLZ5IDDT+XwCNDazCz2MJ6ycc6uBw+U0CefnUyM455Y65/L8k58AyV7GU1UB/m5Esojb\\nn6oqgP2wRnDOfeuc+8w//iPwBdC8vGWU9EOnObC3yPQ+KvhjVFMz59y3/vHvgGZltHPAcjNbb2Zj\\ngxxDINsczs8l0HVd5T91/D9mdmmIYglUuL83Nc0ofGdCJPz03YxgZtYC+AXwaXntamVFvmAws+XA\\nBaXMetw5Nz/c8UD5MRWdcM45MyvrsYyrnXP7zawpsMzMtvn/1xutPgNSnHPHzKwfMA9o43FMUSeQ\\n/c3MHgfygLfDGVtlROLvhtR+ZtYA+AfwX8657PLaKumXwTnXp5pvsR+4qMh0sv+1KisvJjM7YGYX\\nOue+9Z8OPljGe+z3/3vQzObiO2UXrKQfyDYH/XOpTjxFdxDn3BIz+4uZNXHOeVWPO5yfT8SoaH8z\\ns5FAf+A657+AGYmC8LsRyaLyuxnpzCwOX8J/2zk3p6L2Or0fOuuANmbW0szqArcBC0K4vgXACP/4\\nCOCsowozq29mDQvG8d0gFcy7VwPZ5gXAXf671LsDR4tclgi2CuMxswvM36WdmV2Jb5/4IUTxBCKc\\nn0+NYGY3Ar8FBjjnTngdTxQL92+aVMD/2zUN+MI591JAC3l992FNHIDB+K5n5QAHgA/8rycBS4q0\\n64fvbsqd+E7vhTKmnwErgB3AcuC8kjHhu+t2k3/YGoqYSttmYBwwzj9uwBT//M8J8V3MAcQz3v9Z\\nbMJ3k9hVIY7nXeBb4LT/OzTay8+nJgzAl/iuJW/0D//P65iquB2l/m7UpCGcv2kh3o6z9kOvY6ri\\ndlyN7z6tzUX2j37lLaOKfCIiIlFCp/dFRESihJK+iIhIlFDSFxERiRJK+iIiIlFCSV9ERCRKKOmL\\niIhECSV9ERGRKKGkLyIiEiWU9CUozOwcM9tnZnvMLL7EvKlmdsbMbvMqPhEJnJnVNbNcM3NlDBXW\\neJfIpA53JCiccyfN7ElgKvBr4GUAM5uEr9Ts/c65WR6GKCKBi8PXjXFJDwKdgYXhDUeCRWV4P3cZ\\nIwAAAgtJREFUJWjMLAZfDfum+Or834Mv+T/pnPujl7GJSPWY2fPA/wEmukA7d5GIo6QvQWVm/fEd\\nBfwL6A286px7wNuoRKSq/D25vQLcD4x3zv3F45CkGnRNX4LKObcI2ABcC8wGflOyjZn9ysw+NLNj\\nZvZ1mEMUkQCZWR0gDd8lu9FFE77245pJSV+CysxuBTr6J390pZ9KOgK8CjwetsBEpFL8l+tmAiOB\\nO5xz00s00X5cA+lGPgkaM7se34/EXHz9VI8ys5edc18UbeecW+ZvPyj8UYpIRcwsDngHGADc6pw7\\n62597cc1k470JSjMrBswB/gIGA48AeQDk7yMS0Qqx//I7RygPzCktIQvNZeO9KXazKwDsATYDgxy\\nzuUAO81sGjDOzHo45z7yNEgRCdRMfAl/BpBoZneUmL/AOZcd9qgkKHT3vlSLmaXgO7rPAXo45w4U\\nmZcEfAlscM71KGXZQcB/O+dahClcESmH/079o0DDMprkAw2dcyeKLKP9uAbRkb5Ui3NuD3BRGfO+\\nAeqFNyIRqSr/jbfneh2HhI6SvoSd/67gOP9gZpaA7/cmx9vIRCRQ2o9rJiV98cKdQNHHf04Cu4EW\\nnkQjIlWh/bgG0jV9ERGRKKFH9kRERKKEkr6IiEiUUNIXERGJEkr6IiIiUUJJX0REJEoo6YuIiEQJ\\nJX0REZEooaQvIiISJf4/XUoZIF5jhhgAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"angle = np.pi / 5\\n\",\n \"stretch = 5\\n\",\n \"m = 200\\n\",\n \"\\n\",\n \"rnd.seed(3)\\n\",\n \"X = rnd.randn(m, 2) / 10\\n\",\n \"X = X.dot(np.array([[stretch, 0],[0, 1]])) # stretch\\n\",\n \"X = X.dot([[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]]) # rotate\\n\",\n \"\\n\",\n \"u1 = np.array([np.cos(angle), np.sin(angle)])\\n\",\n \"u2 = np.array([np.cos(angle - 2 * np.pi/6), np.sin(angle - 2 * np.pi/6)])\\n\",\n \"u3 = np.array([np.cos(angle - np.pi/2), np.sin(angle - np.pi/2)])\\n\",\n \"\\n\",\n \"X_proj1 = X.dot(u1.reshape(-1, 1))\\n\",\n \"X_proj2 = X.dot(u2.reshape(-1, 1))\\n\",\n \"X_proj3 = X.dot(u3.reshape(-1, 1))\\n\",\n \"\\n\",\n \"plt.figure(figsize=(8,4))\\n\",\n \"plt.subplot2grid((3,2), (0, 0), rowspan=3)\\n\",\n \"plt.plot([-1.4, 1.4], [-1.4*u1[1]/u1[0], 1.4*u1[1]/u1[0]], \\\"k-\\\", linewidth=1)\\n\",\n \"plt.plot([-1.4, 1.4], [-1.4*u2[1]/u2[0], 1.4*u2[1]/u2[0]], \\\"k--\\\", linewidth=1)\\n\",\n \"plt.plot([-1.4, 1.4], [-1.4*u3[1]/u3[0], 1.4*u3[1]/u3[0]], \\\"k:\\\", linewidth=2)\\n\",\n \"plt.plot(X[:, 0], X[:, 1], \\\"bo\\\", alpha=0.5)\\n\",\n \"plt.axis([-1.4, 1.4, -1.4, 1.4])\\n\",\n \"plt.arrow(0, 0, u1[0], u1[1], head_width=0.1, linewidth=5, length_includes_head=True, head_length=0.1, fc='k', ec='k')\\n\",\n \"plt.arrow(0, 0, u3[0], u3[1], head_width=0.1, linewidth=5, length_includes_head=True, head_length=0.1, fc='k', ec='k')\\n\",\n \"plt.text(u1[0] + 0.1, u1[1] - 0.05, r\\\"$\\\\mathbf{c_1}$\\\", fontsize=22)\\n\",\n \"plt.text(u3[0] + 0.1, u3[1], r\\\"$\\\\mathbf{c_2}$\\\", fontsize=22)\\n\",\n \"plt.xlabel(\\\"$x_1$\\\", fontsize=18)\\n\",\n \"plt.ylabel(\\\"$x_2$\\\", fontsize=18, rotation=0)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot2grid((3,2), (0, 1))\\n\",\n \"plt.plot([-2, 2], [0, 0], \\\"k-\\\", linewidth=1)\\n\",\n \"plt.plot(X_proj1[:, 0], np.zeros(m), \\\"bo\\\", alpha=0.3)\\n\",\n \"plt.gca().get_yaxis().set_ticks([])\\n\",\n \"plt.gca().get_xaxis().set_ticklabels([])\\n\",\n \"plt.axis([-2, 2, -1, 1])\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot2grid((3,2), (1, 1))\\n\",\n \"plt.plot([-2, 2], [0, 0], \\\"k--\\\", linewidth=1)\\n\",\n \"plt.plot(X_proj2[:, 0], np.zeros(m), \\\"bo\\\", alpha=0.3)\\n\",\n \"plt.gca().get_yaxis().set_ticks([])\\n\",\n \"plt.gca().get_xaxis().set_ticklabels([])\\n\",\n \"plt.axis([-2, 2, -1, 1])\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot2grid((3,2), (2, 1))\\n\",\n \"plt.plot([-2, 2], [0, 0], \\\"k:\\\", linewidth=2)\\n\",\n \"plt.plot(X_proj3[:, 0], np.zeros(m), \\\"bo\\\", alpha=0.3)\\n\",\n \"plt.gca().get_yaxis().set_ticks([])\\n\",\n \"plt.axis([-2, 2, -1, 1])\\n\",\n \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"#save_fig(\\\"pca_best_projection\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Principal Components\\n\",\n \"* PCA finds axis responsible for largest amount of variance in dataset.\\n\",\n \"* Also finds 2nd axis, responsible for next largest amount.\\n\",\n \"* If higher-D dataset, PCA also finds 3rd axis...\\n\",\n \"* Repeat for # of dimensions in the dataset.\\n\",\n \"* Each axis vector is called a **principal component**. (PC)\\n\",\n \"\\n\",\n \"* PCs found using **Singular Value Decomposition (SVD)**, a matrix factorization technique.\\n\",\n \"* SVD decomposes training set matrix X into dot product of three matrices.\\n\",\n \"* Note: PCA assumes data is centered around origin. Scikit PCA will adjust data for you if needed.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-0.79644131 -0.60471583] [-0.60471583 0.79644131]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# use NumPy svd() to get principal components of training set,\\n\",\n \"# then extract 1st two PCs.\\n\",\n \"\\n\",\n \"X_centered = X - X.mean(axis=0)\\n\",\n \"U,s,V = np.linalg.svd(X_centered)\\n\",\n \"\\n\",\n \"c1, c2 = V.T[:,0], V.T[:,1]\\n\",\n \"print(c1,c2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Projecting Training Data Down to d Dimensions\\n\",\n \"* Done by computing dot product of training data (X) by matrix containing the first d principal components (Wd).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n 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-1.38986614e-01]\\n\",\n \" [ 2.28667680e-02 3.92283202e-02]\\n\",\n \" [ 3.24088211e-01 1.70213975e-01]\\n\",\n \" [ -1.22209299e-01 4.41346966e-02]\\n\",\n \" [ 4.26702773e-01 6.42551604e-03]\\n\",\n \" [ 8.19816829e-02 -2.24146989e-01]\\n\",\n \" [ -1.41063811e-01 -1.81097498e-01]\\n\",\n \" [ 1.64457703e-02 -1.45681492e-01]\\n\",\n \" [ -1.01134103e+00 1.26993172e-02]\\n\",\n \" [ -4.33573355e-01 8.41743009e-02]\\n\",\n \" [ 6.65856885e-01 -2.12785979e-01]\\n\",\n \" [ 1.61808196e-01 9.45467292e-02]\\n\",\n \" [ -4.82157648e-02 7.89423282e-02]\\n\",\n \" [ -4.66117068e-01 -1.57706328e-01]\\n\",\n \" [ -1.49009196e-01 1.94946740e-01]\\n\",\n \" [ 2.88429562e-01 -1.95469647e-01]\\n\",\n \" [ 1.15854187e-01 -4.26347471e-02]\\n\",\n \" [ -2.08314238e-01 -9.22559093e-02]\\n\",\n \" [ -4.17242418e-02 -2.21272525e-01]\\n\",\n \" [ -4.31285498e-01 1.51023258e-01]\\n\",\n \" [ -6.46651297e-01 -1.63796812e-01]\\n\",\n \" [ -1.54321959e-01 -3.00319930e-01]\\n\",\n \" [ -1.42477982e-01 -8.04414843e-03]\\n\",\n \" [ 4.96009127e-01 4.62484050e-02]\\n\",\n \" [ -7.20255309e-02 9.38505820e-02]\\n\",\n \" [ -6.51879918e-02 2.35315556e-02]\\n\",\n \" [ 9.73713335e-01 -9.40602754e-02]\\n\",\n \" [ 2.36218522e-01 3.60724373e-02]\\n\",\n \" [ -1.06876746e-03 1.51498555e-01]\\n\",\n \" [ 4.34435954e-01 -1.34060334e-02]\\n\",\n \" [ -1.28076019e-01 -2.44578006e-02]\\n\",\n \" [ -2.47584073e-01 -6.08927420e-02]\\n\",\n \" [ -7.10391471e-01 -4.55521748e-02]\\n\",\n \" [ -5.58836834e-01 -1.34202263e-02]\\n\",\n \" [ -7.43699081e-01 -1.54131331e-01]\\n\",\n \" [ -2.58481292e-01 -4.73208896e-02]\\n\",\n \" [ -1.19242387e-01 1.18190473e-01]\\n\",\n \" [ 5.71157514e-01 -1.18785924e-01]\\n\",\n \" [ 4.97483707e-01 3.73368260e-02]\\n\",\n \" [ 9.41952705e-01 3.09293927e-02]\\n\",\n \" [ 6.44331385e-01 -9.94076651e-02]\\n\",\n \" [ 1.82692942e-01 4.33205574e-02]\\n\",\n \" [ 3.13123024e-01 -4.12117592e-02]\\n\",\n \" [ 2.04403918e-02 -2.54497816e-02]\\n\",\n \" [ 1.33781786e+00 -1.34015280e-02]\\n\",\n \" [ -4.58399199e-02 1.10234115e-01]\\n\",\n \" [ -1.02539769e+00 4.64829485e-02]\\n\",\n \" [ -3.85549579e-02 -9.59123942e-02]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# project training set onto plane defined by 1st two PCs.\\n\",\n \"\\n\",\n \"W2 = V.T[:, :2]\\n\",\n \"X2D = X_centered.dot(W2)\\n\",\n \"print(X2D)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Scikit PCA\\n\",\n \"* Uses SVD decomposition as before.\\n\",\n \"* You can access each PC using *components_* variable. (\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-0.79644131 -0.60471583]\\n\",\n \"[-0.79644131 -0.60471583]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.decomposition import PCA\\n\",\n \"pca = PCA(n_components = 2)\\n\",\n \"X2D = pca.fit_transform(X)\\n\",\n \"\\n\",\n \"print(pca.components_[0])\\n\",\n \"print(pca.components_.T[:,0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Explained Variance Ratio\\n\",\n \"* Very useful metric: proportion of dataset's variance along the axis of each PC component.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.95369864 0.04630136]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 95% of dataset variance explained by 1st axis.\\n\",\n \"print(pca.explained_variance_ratio_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Choosing Right #Dimensions\\n\",\n \"* No need to choose arbitrary #dimensions. Instead pick d that cumulatively accounts for a sufficient amount, ex: 95%.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"# find minimum d to preserve 95% of training set variance\\n\",\n \"pca = PCA()\\n\",\n \"pca.fit(X)\\n\",\n \"cumsum = np.cumsum(pca.explained_variance_ratio_)\\n\",\n \"d = np.argmax(cumsum >= 0.95) + 1\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### PCA for Compression\\n\",\n \"* Example applying PCA to MNIST dataset with 95% preservation = results in ~150 features (original = 28x28 = 784)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"154\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#MNIST compression:\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.datasets import fetch_mldata\\n\",\n \"\\n\",\n \"#mnist = fetch_mldata('MNIST original')\\n\",\n \"mnist_path = \\\"./mnist-original.mat\\\"\\n\",\n \"\\n\",\n \"from scipy.io import loadmat\\n\",\n \"mnist_raw = loadmat(mnist_path)\\n\",\n \"mnist = {\\n\",\n \" \\\"data\\\": mnist_raw[\\\"data\\\"].T,\\n\",\n \" \\\"target\\\": mnist_raw[\\\"label\\\"][0],\\n\",\n \" \\\"COL_NAMES\\\": [\\\"label\\\", \\\"data\\\"],\\n\",\n \" \\\"DESCR\\\": \\\"mldata.org dataset: mnist-original\\\",\\n\",\n \" }\\n\",\n \"\\n\",\n \"X, y = mnist[\\\"data\\\"], mnist[\\\"target\\\"]\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y)\\n\",\n \"\\n\",\n \"X = X_train\\n\",\n \"\\n\",\n \"pca = PCA()\\n\",\n \"pca.fit(X)\\n\",\n \"d = np.argmax(np.cumsum(pca.explained_variance_ratio_) >= 0.95) + 1\\n\",\n \"d\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"154\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pca = PCA(n_components=0.95)\\n\",\n \"X_reduced = pca.fit_transform(X)\\n\",\n \"pca.n_components_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.9503623084769206\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# did you hit your 95% minimum?\\n\",\n \"np.sum(pca.explained_variance_ratio_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# use inverse_transform to decompress back to 784 dimensions\\n\",\n \"X_mnist = X_train\\n\",\n \"\\n\",\n \"pca = PCA(n_components = 154)\\n\",\n \"X_mnist_reduced = pca.fit_transform(X_mnist)\\n\",\n \"X_mnist_recovered = pca.inverse_transform(X_mnist_reduced)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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YqKiiCEYK+FxWJBQ0MDNm7cCEC1En0+H7xeL9LS0gAAI0aMQGFhIaZM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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"def plot_digits(instances, images_per_row=5, **options):\\n\",\n \" size = 28\\n\",\n \" images_per_row = min(len(instances), images_per_row)\\n\",\n \" images = [instance.reshape(size,size) for instance in instances]\\n\",\n \" n_rows = (len(instances) - 1) // images_per_row + 1\\n\",\n \" row_images = []\\n\",\n \" n_empty = n_rows * images_per_row - len(instances)\\n\",\n \" images.append(np.zeros((size, size * n_empty)))\\n\",\n \" for row in range(n_rows):\\n\",\n \" rimages = images[row * images_per_row : (row + 1) * images_per_row]\\n\",\n \" row_images.append(np.concatenate(rimages, axis=1))\\n\",\n \" image = np.concatenate(row_images, axis=0)\\n\",\n \" plt.imshow(image, cmap = matplotlib.cm.binary, **options)\\n\",\n \" plt.axis(\\\"off\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(7, 4))\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_digits(X_mnist[::2100])\\n\",\n \"plt.title(\\\"Original\\\", fontsize=16)\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_digits(X_mnist_recovered[::2100])\\n\",\n \"plt.title(\\\"Compressed\\\", fontsize=16)\\n\",\n \"#save_fig(\\\"mnist_compression_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Incremental PCA\\n\",\n \"* PCA normally requires entire dataset in memory for SVD algorithm.\\n\",\n \"* **Incremental PCA (IPCA)** splits dataset into batches.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"....................................................................................................\"\n ]\n }\n ],\n \"source\": [\n \"# split MNIST into 100 minibatches using Numpy array_split()\\n\",\n \"# reduce MNIST down to 154 dimensions as before.\\n\",\n \"# note use of partial_fit() for each batch.\\n\",\n \"\\n\",\n \"from sklearn.decomposition import IncrementalPCA\\n\",\n \"\\n\",\n \"n_batches = 100\\n\",\n \"inc_pca = IncrementalPCA(n_components=154)\\n\",\n \"\\n\",\n \"for X_batch in np.array_split(X_mnist, n_batches):\\n\",\n \" print(\\\".\\\", end=\\\"\\\")\\n\",\n \" inc_pca.partial_fit(X_batch)\\n\",\n \"\\n\",\n \"X_mnist_reduced_inc = inc_pca.transform(X_mnist)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# alternative: Numpy memmap class (use binary array on disk as if it was in memory)\\n\",\n \"\\n\",\n \"filename = \\\"my_mnist.data\\\"\\n\",\n \"\\n\",\n \"X_mm = np.memmap(\\n\",\n \" filename, dtype='float32', mode='write', shape=X_mnist.shape)\\n\",\n \"\\n\",\n \"X_mm[:] = X_mnist\\n\",\n \"del X_mm\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"IncrementalPCA(batch_size=525, copy=True, n_components=154, whiten=False)\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_mm = np.memmap(filename, dtype='float32', mode='readonly', shape=X_mnist.shape)\\n\",\n \"\\n\",\n \"batch_size = len(X_mnist) // n_batches\\n\",\n \"inc_pca = IncrementalPCA(n_components=154, batch_size=batch_size)\\n\",\n \"inc_pca.fit(X_mm)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"rnd_pca = PCA(\\n\",\n \" n_components=154, \\n\",\n \" random_state=42, \\n\",\n \" svd_solver=\\\"randomized\\\")\\n\",\n \"\\n\",\n \"X_reduced = rnd_pca.fit_transform(X_mnist)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"n_components = 2\\n\",\n \"PCA 1.308387279510498 seconds\\n\",\n \"IncrementalPCA 18.326093673706055 seconds\\n\",\n \"PCA 3.998342514038086 seconds\\n\",\n \"n_components = 10\\n\",\n \"PCA 1.4705824851989746 seconds\\n\",\n \"IncrementalPCA 16.598721742630005 seconds\\n\",\n \"PCA 4.156355619430542 seconds\\n\",\n \"n_components = 154\\n\",\n \"PCA 4.129154682159424 seconds\\n\",\n \"IncrementalPCA 16.597434043884277 seconds\\n\",\n \"PCA 4.0131142139434814 seconds\\n\"\n ]\n }\n ],\n \"source\": [\n \"import time\\n\",\n \"\\n\",\n \"for n_components in (2, 10, 154):\\n\",\n \" print(\\\"n_components =\\\", n_components)\\n\",\n \" regular_pca = PCA(\\n\",\n \" n_components=n_components)\\n\",\n \" inc_pca = IncrementalPCA(\\n\",\n \" n_components=154, \\n\",\n \" batch_size=500)\\n\",\n \" rnd_pca = PCA(\\n\",\n \" n_components=154, \\n\",\n \" random_state=42, \\n\",\n \" svd_solver=\\\"randomized\\\")\\n\",\n \"\\n\",\n \" for pca in (regular_pca, inc_pca, rnd_pca):\\n\",\n \" t1 = time.time()\\n\",\n \" pca.fit(X_mnist)\\n\",\n \" t2 = time.time()\\n\",\n \" print(pca.__class__.__name__, t2 - t1, \\\"seconds\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Randomized PCA\\n\",\n \"* Stochastic algorithm, quickly finds approximation of 1st d components. Dramatically faster.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4.414088487625122 seconds\\n\"\n ]\n }\n ],\n \"source\": [\n \"rnd_pca = PCA(n_components=154, svd_solver=\\\"randomized\\\")\\n\",\n \"\\n\",\n \"t1 = time.time()\\n\",\n \"X_reduced = rnd_pca.fit_transform(X_mnist)\\n\",\n \"t2 = time.time()\\n\",\n \"print(t2-t1, \\\"seconds\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Kernel PCA\\n\",\n \"* Use kernel trick to map instances into higher-D feature spaces. This enables non-linear classification & regression with SVMs.\\n\",\n \"* Good at preserving clusters after projecton.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Lf70PbOrZVSf1Rk3Q5FROQn4B6lVM4e8nVFr4JUU5Vgk58DRVm/j8qE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\\n/h8RHYNe61yPswAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Below: Swiss roll reduced to 2D using 3 techniques:\\n\",\n \"# 1) linear kernel (equiv to PCA)\\n\",\n \"# 2) RBF kernel\\n\",\n \"# 3) sigmoid kernel (logistic)\\n\",\n \"\\n\",\n \"from sklearn.decomposition import KernelPCA\\n\",\n \"\\n\",\n \"X, t = make_swiss_roll(\\n\",\n \" n_samples=1000, \\n\",\n \" noise=0.2, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"lin_pca = KernelPCA(\\n\",\n \" n_components = 2, \\n\",\n \" kernel=\\\"linear\\\", \\n\",\n \" fit_inverse_transform=True)\\n\",\n \"\\n\",\n \"rbf_pca = KernelPCA(\\n\",\n \" n_components = 2, \\n\",\n \" kernel=\\\"rbf\\\", \\n\",\n \" gamma=0.0433, \\n\",\n \" fit_inverse_transform=True)\\n\",\n \"\\n\",\n \"sig_pca = KernelPCA(\\n\",\n \" n_components = 2, \\n\",\n \" kernel=\\\"sigmoid\\\", \\n\",\n \" gamma=0.001, \\n\",\n \" coef0=1, \\n\",\n \" fit_inverse_transform=True)\\n\",\n \"\\n\",\n \"y = t > 6.9\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11, 4))\\n\",\n \"\\n\",\n \"for subplot, pca, title in (\\n\",\n \" (131, lin_pca, \\\"Linear kernel\\\"), \\n\",\n \" (132, rbf_pca, \\\"RBF kernel, $\\\\gamma=0.04$\\\"), \\n\",\n \" (133, sig_pca, \\\"Sigmoid kernel, $\\\\gamma=10^{-3}, r=1$\\\")):\\n\",\n \" \\n\",\n \" X_reduced = pca.fit_transform(X)\\n\",\n \" if subplot == 132:\\n\",\n \" X_reduced_rbf = X_reduced\\n\",\n \" \\n\",\n \" plt.subplot(subplot)\\n\",\n \" #plt.plot(X_reduced[y, 0], X_reduced[y, 1], \\\"gs\\\")\\n\",\n \" #plt.plot(X_reduced[~y, 0], X_reduced[~y, 1], \\\"y^\\\")\\n\",\n \" plt.title(title, fontsize=14)\\n\",\n \" plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n \" plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n \" if subplot == 131:\\n\",\n \" plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n \" plt.grid(True)\\n\",\n \"\\n\",\n \"#save_fig(\\\"kernel_pca_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Selecting a Kernel & Hyperparameters\\n\",\n \"* Dimensionality reduction = prep for supervised learning task\\n\",\n \"* Can use grid search to select kernel & params\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'kpca__gamma': 0.043333333333333335, 'kpca__kernel': 'rbf'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import GridSearchCV\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.pipeline import Pipeline\\n\",\n \"\\n\",\n \"clf = Pipeline([\\n\",\n \" (\\\"kpca\\\", KernelPCA(n_components=2)),\\n\",\n \" (\\\"log_reg\\\", LogisticRegression())])\\n\",\n \"\\n\",\n \"param_grid = [{\\n\",\n \" \\\"kpca__gamma\\\": np.linspace(0.03, 0.05, 10),\\n\",\n \" \\\"kpca__kernel\\\": [\\\"rbf\\\", \\\"sigmoid\\\"]}]\\n\",\n \"\\n\",\n \"grid_search = GridSearchCV(clf, param_grid, cv=3)\\n\",\n \"grid_search.fit(X, y)\\n\",\n \"\\n\",\n \"# best kernel & params?\\n\",\n \"print(grid_search.best_params_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Another (unsupervised approach): select kernel & params with **lowest reconstruction error**. Not as easy as with linear PCA.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"32.786308795766082\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"rbf_pca = KernelPCA(\\n\",\n \" n_components = 2, \\n\",\n \" kernel=\\\"rbf\\\", \\n\",\n \" gamma=0.0433,\\n\",\n \" fit_inverse_transform=True) # perform reconstruction\\n\",\n \"\\n\",\n \"X_reduced = rbf_pca.fit_transform(X)\\n\",\n \"X_preimage = rbf_pca.inverse_transform(X_reduced)\\n\",\n \"\\n\",\n \"# return reconstruction pre-image error\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"mean_squared_error(X, X_preimage)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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v3nfmjenM3Jx+hYfhZVpo2g1NGtjB2jVK2iRIi7HztGKbJ7S9BCCfsr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\\nxgTEEoYxxpiAWMIwxhgTEEsYxhhjAvL/x9+/WzbNkDMAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"times_rpca = []\\n\",\n \"times_pca = []\\n\",\n \"sizes = [1000, 10000, 20000, 30000, 40000, 50000, 70000, \\n\",\n \" 100000, 200000, 500000]\\n\",\n \"\\n\",\n \"for n_samples in sizes:\\n\",\n \"\\n\",\n \" X = rnd.randn(n_samples, 5)\\n\",\n \"\\n\",\n \" pca = PCA(\\n\",\n \" n_components = 2, \\n\",\n \" random_state=42, \\n\",\n \" svd_solver=\\\"randomized\\\")\\n\",\n \"\\n\",\n \" t1 = time.time()\\n\",\n \" pca.fit(X)\\n\",\n \" t2 = time.time()\\n\",\n \" times_rpca.append(t2 - t1)\\n\",\n \" \\n\",\n \" pca = PCA(n_components = 2)\\n\",\n \" \\n\",\n \" t1 = time.time()\\n\",\n \" pca.fit(X)\\n\",\n \" t2 = time.time()\\n\",\n \" times_pca.append(t2 - t1)\\n\",\n \"\\n\",\n \"plt.plot(sizes, times_rpca, \\\"b-o\\\", label=\\\"RPCA\\\")\\n\",\n \"plt.plot(sizes, times_pca, \\\"r-s\\\", label=\\\"PCA\\\")\\n\",\n \"plt.xlabel(\\\"n_samples\\\")\\n\",\n \"plt.ylabel(\\\"Training time\\\")\\n\",\n \"plt.legend(loc=\\\"upper left\\\")\\n\",\n \"plt.title(\\\"PCA and Randomized PCA time complexity \\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### LLE (Locally Linear Embedding)\\n\",\n \"* Powerful nonlinear dimensionality reduction tool\\n\",\n \"* Manifold Learning; doesn't rely on projections.\\n\",\n \"* LLE measures how each instance relates to closest neighbors, then looks for low-D representation where local relations are best preserved.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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/P168lKCXiakyHSQiKRIDs390+0VKPRaNzRprm9nLmzZuFP8XLLpAU5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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Use LLE to unroll a Swiss Roll.\\n\",\n \"\\n\",\n \"from sklearn.manifold import LocallyLinearEmbedding\\n\",\n \"\\n\",\n \"X, t = make_swiss_roll(\\n\",\n \" n_samples=1000, \\n\",\n \" noise=0.2, \\n\",\n \" random_state=41)\\n\",\n \"\\n\",\n \"lle = LocallyLinearEmbedding(\\n\",\n \" n_neighbors=10, \\n\",\n \" n_components=2, \\n\",\n \" random_state=42)\\n\",\n \"\\n\",\n \"X_reduced = lle.fit_transform(X)\\n\",\n \"\\n\",\n \"plt.title(\\\"Unrolled swiss roll using LLE\\\", fontsize=14)\\n\",\n \"plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n \"plt.ylabel(\\\"$z_2$\\\", fontsize=18)\\n\",\n \"plt.axis([-0.065, 0.055, -0.1, 0.12])\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"#save_fig(\\\"lle_unrolling_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* 1st: For each instance, LLE finds k nearest neighbors & tries to reconstruct instance as linear function of neighbors (weights such that squared distance is minimum). \\n\",\n \"* Weight matrix W now encodes all local linear relations between instances.\\n\",\n \"* 2nd: Map instances into d-dimensional space & preserve relationship data\\n\",\n \"* Scikit computational complexity: \\n\",\n \"- finding K nearest neighbors: O(m x log(m) x n x log(k))\\n\",\n \"- weight optimization: O(m x n x k^3)\\n\",\n \"- constructing low-d representations: O(d x m^2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### MDS, Isomap, t-SNE, LDA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.manifold import MDS\\n\",\n \"mds = MDS(n_components=2, random_state=42)\\n\",\n \"X_reduced_mds = mds.fit_transform(X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.manifold import Isomap\\n\",\n \"isomap = Isomap(n_components=2)\\n\",\n \"X_reduced_isomap = isomap.fit_transform(X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.manifold import TSNE\\n\",\n \"tsne = TSNE(n_components=2)\\n\",\n \"X_reduced_tsne = tsne.fit_transform(X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/bjpcjp/anaconda3/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\\n\",\n \" warnings.warn(\\\"Variables are collinear.\\\")\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\\n\",\n \"lda = LinearDiscriminantAnalysis(n_components=2)\\n\",\n \"X_mnist = mnist[\\\"data\\\"]\\n\",\n \"y_mnist = mnist[\\\"target\\\"]\\n\",\n \"lda.fit(X_mnist, y_mnist)\\n\",\n \"X_reduced_lda = lda.transform(X_mnist)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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1Py/QcfMHfKlLRBJQKl5bJNrZWo3mxvG8jK4q833NDwDdU0CluXLmXq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\\nzLJyAWpmZmZmWbkANTMzM7Os/g9LCgvLAbjJFQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"titles = [\\\"MDS\\\", \\\"Isomap\\\", \\\"t-SNE\\\"]\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11,4))\\n\",\n \"\\n\",\n \"for subplot, title, X_reduced in zip((131, 132, 133), titles,\\n\",\n \" (X_reduced_mds, X_reduced_isomap, X_reduced_tsne)):\\n\",\n \" plt.subplot(subplot)\\n\",\n \" plt.title(title, fontsize=14)\\n\",\n \" plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=t, cmap=plt.cm.hot)\\n\",\n \" plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n \" if subplot == 131:\\n\",\n \" plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n \" plt.grid(True)\\n\",\n \"\\n\",\n \"#save_fig(\\\"other_dim_reduction_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch09-tensorflow-setup.html", "content": "\n\n\nch09-tensorflow-setup\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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Intro

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  • Graphs defined in Python, executed in C++
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  • Open-sourced 2015. Windows, Linux, macOS, iOS, Android
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  • Python API: tensorflow.contrib.learn (trains NNs)
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  • Simpler API: tensorflow.contrib.slim (simplifies building NNs)
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  • Other high-level APIs: Keras, Pretty Tensor.
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  • Libraries: Caffe, DeepLearning4J, H2O, MXNet, Theano, Torch
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  • TensorBoard visualization tool
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  • Cloud service
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  • Resources: home page, GitHub, StackOverflow
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Installation & Test

$ cd

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$ source env/bin/activate (if using virtualenv)

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$ pip3 install --upgrade tensorflow (or tensorflow-gpu for GPU support)

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$ python3 -c 'import tensorflow; print(tensorflow.version)'

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In [41]:
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!python3 -c 'import tensorflow; print(tensorflow.__version__)'\n
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In [42]:
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import numpy as np\n
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First Graph

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In [43]:
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# create your first graph\nimport tensorflow as tf\nx = tf.Variable(3, name="x")\ny = tf.Variable(4, name="y")\nf = x*x*y + y + 2\n\n# run graph by opening a session\n\nsess = tf.Session()\nsess.run(x.initializer)\nsess.run(y.initializer)\nresult = sess.run(f)\nprint(result)\nsess.close()\n
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In [44]:
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# for repeated session "runs"\n\nwith tf.Session() as sess:\n    x.initializer.run()\n    y.initializer.run()\n    result = f.eval()\n\nprint(result)\n
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In [45]:
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# use global_variables_initializer() to set up initialization\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n    init.run() # actually initialize all the variables\n    result = f.eval()\nprint(result)\n
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In [46]:
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# interactive sessions (from within Jupyter or Python shell)\n# interactive sesions are auto-set as default sessions\n\nsess = tf.InteractiveSession()\ninit.run()\nresult = f.eval()\nprint(result)\nsess.close()\n
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Managing Graphs

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# any created node = added to default graph\nx1 = tf.Variable(1)\nx1.graph is tf.get_default_graph()\n
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In [48]:
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# handling multiple graphs\n\ngraph = tf.Graph()\nwith graph.as_default():\n    x2 = tf.Variable(2)\n\nx2.graph is graph, x2.graph is tf.get_default_graph()\n
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Node Lifecycles

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In [49]:
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# TF finds node's dependencies & evaluates them first\n\nw = tf.constant(3)\nx = w + 2\ny = x + 5\nz = x * 3\n\n# previous eval results = NOT reused. above code evals w & x twice.\n\nwith tf.Session() as sess:\n    print(y.eval())\n    print(z.eval())\n    \n# a more efficient evaluation call:\nwith tf.Session() as sess:\n    y_val, z_val = sess.run([y,z])\n    print(y_val)\n    print(z_val)\n
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Linear Regression with TF

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Below: Linear Regression on 2D arrays (California Housing dataset)

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In [50]:
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# (never used Numpy c_ operator before. Had to check it out.)\na = np.ones((6,1))\nb = np.zeros((6,4))\nc = np.ones((6,2))\nnp.c_[a,b,c]\n
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Out[50]:
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array([[ 1.,  0.,  0.,  0.,  0.,  1.,  1.],\n       [ 1.,  0.,  0.,  0.,  0.,  1.,  1.],\n       [ 1.,  0.,  0.,  0.,  0.,  1.,  1.],\n       [ 1.,  0.,  0.,  0.,  0.,  1.,  1.],\n       [ 1.,  0.,  0.,  0.,  0.,  1.,  1.],\n       [ 1.,  0.,  0.,  0.,  0.,  1.,  1.]])
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In [51]:
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from sklearn.datasets import fetch_california_housing\nimport numpy as np\n\nhousing = fetch_california_housing()\nm, n = housing.data.shape\n\n# add bias feature, x0 = 1\nhousing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]\n\nprint(m,n,housing_data_plus_bias.shape)\n
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20640 8 (20640, 9)\n
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In [52]:
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tf.reset_default_graph()\n\nX = tf.constant(\n    housing_data_plus_bias,        \n    dtype=tf.float64, name="X")\n\nprint("X shape: ",X.shape)\n\n# housing.target = 1D array. Reshape to col vector to compute theta.\n# reshape() accepts -1 = "unspecified" for a dimension.\n\ny = tf.constant(\n    housing.target.reshape(-1, 1), \n    dtype=tf.float64, name="y")\n\nXT = tf.transpose(X)\n\nprint("XT shape: ",XT.shape)\n\n# normal equation: theta = (XT * X)^-1 * XT * y\n\ntheta = tf.matmul(\n            tf.matmul(\n                tf.matrix_inverse(\n                    tf.matmul(XT, X)), \n                    XT), \n                y)\n\n# TF doesn't immediately run the code. It creates nodes that will run with eval().\n# TF will auto-run on GPU if available.\n\nwith tf.Session() as sess:\n    result = theta.eval()\n\nprint("theta: \\n",result)\n
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X shape:  (20640, 9)\nXT shape:  (9, 20640)\ntheta: \n [[ -3.69419202e+01]\n [  4.36693293e-01]\n [  9.43577803e-03]\n [ -1.07322041e-01]\n [  6.45065694e-01]\n [ -3.97638942e-06]\n [ -3.78654265e-03]\n [ -4.21314378e-01]\n [ -4.34513755e-01]]\n
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In [53]:
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# compare to pure NumPy\nX = housing_data_plus_bias\ny = housing.target.reshape(-1, 1)\n\ntheta_numpy = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\n\nprint("theta: \\n",theta_numpy)\n
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theta: \n [[ -3.69419202e+01]\n [  4.36693293e-01]\n [  9.43577803e-03]\n [ -1.07322041e-01]\n [  6.45065694e-01]\n [ -3.97638942e-06]\n [ -3.78654265e-03]\n [ -4.21314378e-01]\n [ -4.34513755e-01]]\n
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In [54]:
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# compare to Scikit\nfrom sklearn.linear_model import LinearRegression\nlin_reg = LinearRegression()\n\nlin_reg.fit(\n    housing.data, \n    housing.target.reshape(-1, 1))\n\nprint("theta: \\n",np.r_[\n    lin_reg.intercept_.reshape(-1, 1), \n    lin_reg.coef_.T])\n
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theta: \n [[ -3.69419202e+01]\n [  4.36693293e-01]\n [  9.43577803e-03]\n [ -1.07322041e-01]\n [  6.45065694e-01]\n [ -3.97638942e-06]\n [ -3.78654265e-03]\n [ -4.21314378e-01]\n [ -4.34513755e-01]]\n
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Batch Gradient Descent (instead of Normal Equation):

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In [55]:
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# normalize input features first - otherwise training = much slower.\n\nfrom sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\n\nscaled_housing_data = scaler.fit_transform(\n    housing.data)\n\nscaled_housing_data_plus_bias = np.c_[\n    np.ones((m, 1)), \n    scaled_housing_data]\n\nimport pandas as pd\npd.DataFrame(scaled_housing_data_plus_bias).info()\n
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<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20640 entries, 0 to 20639\nData columns (total 9 columns):\n0    20640 non-null float64\n1    20640 non-null float64\n2    20640 non-null float64\n3    20640 non-null float64\n4    20640 non-null float64\n5    20640 non-null float64\n6    20640 non-null float64\n7    20640 non-null float64\n8    20640 non-null float64\ndtypes: float64(9)\nmemory usage: 1.4 MB\n
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In [56]:
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print("mean (axis=0): \\n",scaled_housing_data_plus_bias.mean(axis=0))\nprint("mean (axis=1): \\n",scaled_housing_data_plus_bias.mean(axis=1))\nprint("mean (w/bias): \\n",scaled_housing_data_plus_bias.mean())\nprint("data shape:    \\n",scaled_housing_data_plus_bias.shape)\n
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mean (axis=0): \n [  1.00000000e+00   6.60969987e-17   5.50808322e-18   6.60969987e-17\n  -1.06030602e-16  -1.10161664e-17   3.44255201e-18  -1.07958431e-15\n  -8.52651283e-15]\nmean (axis=1): \n [ 0.38915536  0.36424355  0.5116157  ..., -0.06612179 -0.06360587\n  0.01359031]\nmean (w/bias): \n 0.111111111111\ndata shape:    \n (20640, 9)\n
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Manual gradient computation

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# batch gradient step:\n# theta(next) = theta - learning_rate * MSE(theta)\n\ntf.reset_default_graph()\n\nn_epochs = 1000\nlearning_rate = 0.01\n\nX = tf.constant(\n    scaled_housing_data_plus_bias, \n    dtype=tf.float32, name="X")\n\ny = tf.constant(\n    housing.target.reshape(-1, 1), \n    dtype=tf.float32, name="y")\n\ntheta = tf.Variable( # tf.random_uniform = generates random tensor\n    tf.random_uniform([n+1, 1], -1.0, 1.0, seed=42), \n    name="theta")\n\ny_pred = tf.matmul(\n    X, theta, name="predictions")\n\nerror       = y_pred - y\nmse         = tf.reduce_mean(tf.square(error), name="mse")\ngradients   = 2/m * tf.matmul(tf.transpose(X), error)\ntraining_op = tf.assign(theta, theta - learning_rate * gradients)\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n    sess.run(init)\n\n    for epoch in range(n_epochs):\n        if epoch % 100 == 0: # do every 100th epoch:\n            print("Epoch", epoch, "MSE =", mse.eval())\n        sess.run(training_op)\n    \n    best_theta = theta.eval()\n\nprint("Best theta: \\n",best_theta)\n
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Epoch 0 MSE = 2.75443\nEpoch 100 MSE = 0.632222\nEpoch 200 MSE = 0.57278\nEpoch 300 MSE = 0.558501\nEpoch 400 MSE = 0.549069\nEpoch 500 MSE = 0.542288\nEpoch 600 MSE = 0.537379\nEpoch 700 MSE = 0.533822\nEpoch 800 MSE = 0.531243\nEpoch 900 MSE = 0.529371\nBest theta: \n [[  2.06855226e+00]\n [  7.74078071e-01]\n [  1.31192386e-01]\n [ -1.17845096e-01]\n [  1.64778158e-01]\n [  7.44080753e-04]\n [ -3.91945168e-02]\n [ -8.61356616e-01]\n [ -8.23479712e-01]]\n
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Using autodiff

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In [58]:
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tf.reset_default_graph()\n\nn_epochs = 1000\nlearning_rate = 0.01\n\nX = tf.constant(\n    scaled_housing_data_plus_bias, \n    dtype=tf.float32, name="X")\n\ny = tf.constant(\n    housing.target.reshape(-1, 1), \n    dtype=tf.float32, name="y")\n\ntheta = tf.Variable(\n    tf.random_uniform([n + 1, 1], -1.0, 1.0, \n                      seed=42), name="theta")\n\ny_pred = tf.matmul(\n    X, theta, name="predictions")\n\nerror       = y_pred - y\nmse         = tf.reduce_mean(tf.square(error), name="mse")\n\n# AutoDiff to the rescue\n# creates list of ops, one/variable, to find gradients per variable\ngradients   = tf.gradients(mse, [theta])[0]\n#\n\ntraining_op = tf.assign(theta, theta - learning_rate * gradients)\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n    sess.run(init)\n\n    for epoch in range(n_epochs):\n        if epoch % 100 == 0:\n            print("Epoch", epoch, "MSE =", mse.eval())\n        sess.run(training_op)\n    \n    best_theta = theta.eval()\n\nprint("Best theta: \\n", best_theta)\n
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Epoch 0 MSE = 2.75443\nEpoch 100 MSE = 0.632222\nEpoch 200 MSE = 0.57278\nEpoch 300 MSE = 0.558501\nEpoch 400 MSE = 0.549069\nEpoch 500 MSE = 0.542288\nEpoch 600 MSE = 0.537379\nEpoch 700 MSE = 0.533822\nEpoch 800 MSE = 0.531243\nEpoch 900 MSE = 0.529371\nBest theta: \n [[  2.06855249e+00]\n [  7.74078071e-01]\n [  1.31192386e-01]\n [ -1.17845066e-01]\n [  1.64778143e-01]\n [  7.44078017e-04]\n [ -3.91945094e-02]\n [ -8.61356676e-01]\n [ -8.23479772e-01]]\n
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Four ways to run autodiff - see Appendix D

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Using a predefined optimizer (Gradient Descent)

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In [59]:
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tf.reset_default_graph()\n\nn_epochs = 1000\nlearning_rate = 0.01\n\nX = tf.constant(\n    scaled_housing_data_plus_bias, \n    dtype=tf.float32, name="X")\n\ny = tf.constant(\n    housing.target.reshape(-1, 1), \n    dtype=tf.float32, name="y")\n\ntheta = tf.Variable(\n    tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), \n    name="theta")\n\ny_pred = tf.matmul(\n    X, theta, name="predictions")\n\nerror       = y_pred - y\nmse         = tf.reduce_mean(tf.square(error), name="mse")\n\n#####\noptimizer   = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\ntraining_op = optimizer.minimize(mse)\n#####\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n    sess.run(init)\n\n    for epoch in range(n_epochs):\n        if epoch % 100 == 0:\n            print("Epoch", epoch, "MSE =", mse.eval())\n        sess.run(training_op)\n    \n    best_theta = theta.eval()\n\nprint("Best theta:\\n", best_theta)\n
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Epoch 0 MSE = 2.75443\nEpoch 100 MSE = 0.632222\nEpoch 200 MSE = 0.57278\nEpoch 300 MSE = 0.558501\nEpoch 400 MSE = 0.549069\nEpoch 500 MSE = 0.542288\nEpoch 600 MSE = 0.537379\nEpoch 700 MSE = 0.533822\nEpoch 800 MSE = 0.531243\nEpoch 900 MSE = 0.529371\nBest theta:\n [[  2.06855249e+00]\n [  7.74078071e-01]\n [  1.31192386e-01]\n [ -1.17845066e-01]\n [  1.64778143e-01]\n [  7.44078017e-04]\n [ -3.91945094e-02]\n [ -8.61356676e-01]\n [ -8.23479772e-01]]\n
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Using a predefined optimizer (Momentum)

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In [60]:
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tf.reset_default_graph()\n\nn_epochs = 1000\nlearning_rate = 0.01\n\nX = tf.constant(\n    scaled_housing_data_plus_bias, \n    dtype=tf.float32, name="X")\n\ny = tf.constant(\n    housing.target.reshape(-1, 1), \n    dtype=tf.float32, name="y")\n\ntheta = tf.Variable(\n    tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), \n    name="theta")\n\ny_pred      = tf.matmul(X, theta, name="predictions")\nerror       = y_pred - y\nmse         = tf.reduce_mean(tf.square(error), name="mse")\n#####\noptimizer   = tf.train.MomentumOptimizer(\n    learning_rate=learning_rate, \n    momentum=0.25)\n#####\ntraining_op = optimizer.minimize(mse)\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n    sess.run(init)\n\n    for epoch in range(n_epochs):\n        sess.run(training_op)\n    \n    best_theta = theta.eval()\n\nprint("Best theta:\\n", best_theta)\n
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Best theta:\n [[  2.06855392e+00]\n [  7.94067979e-01]\n [  1.25333667e-01]\n [ -1.73580602e-01]\n [  2.18767926e-01]\n [ -1.64708309e-03]\n [ -3.91250364e-02]\n [ -8.85289013e-01]\n [ -8.50607991e-01]]\n
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Training & Data Feeds

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In [61]:
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A = tf.placeholder(tf.float32, shape=(None, 3))\nB = A + 5\n\nwith tf.Session() as sess:\n    B_val_1 = B.eval(\n        feed_dict={A: [[1, 2, 3]]})\n    \n    B_val_2 = B.eval(\n        feed_dict={A: [[4, 5, 6], [7, 8, 9]]})\n    \nprint(B_val_1, "\\n", B_val_2)\n
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[[ 6.  7.  8.]] \n [[  9.  10.  11.]\n [ 12.  13.  14.]]\n
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In [62]:
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# definition phase: change X,y to placeholder nodes\ntf.reset_default_graph()\n\nn_epochs = 1000\nlearning_rate = 0.01\n\n##########\n\nX = tf.placeholder(tf.float32, shape=(None, n+1), name="X")\ny = tf.placeholder(tf.float32, shape=(None, 1),   name="y")\n\n##########\n\ntheta = tf.Variable(\n    tf.random_uniform([n+1, 1], -1.0, 1.0, seed=42), \n    name="theta")\n\ny_pred = tf.matmul(\n    X, theta, name="predictions")\n\nerror  = y_pred - y\n\nmse = tf.reduce_mean(tf.square(error), name="mse")\n\noptimizer = tf.train.GradientDescentOptimizer(\n    learning_rate=learning_rate)\n\ntraining_op = optimizer.minimize(mse)\n\ninit = tf.global_variables_initializer()\n
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In [63]:
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# execution phase: fetch minibatches one-by-one. \n# use feed_dict to provide values to dependent nodes\n\nimport numpy.random as rnd\n\nn_epochs = 10\nbatch_size = 100\nn_batches = int(np.ceil(m / batch_size))\n\nprint("m: ",m,"\\n","n_batches: ",n_batches,"\\n")\n\ndef fetch_batch(epoch, batch_index, batch_size):\n    \n    rnd.seed(epoch * n_batches + batch_index)\n    indices = rnd.randint(m, size=batch_size)\n    \n    X_batch = scaled_housing_data_plus_bias[indices]\n    y_batch = housing.target.reshape(-1, 1)[indices]\n    \n    return X_batch, y_batch\n\nwith tf.Session() as sess:\n    sess.run(init)\n\n    for epoch in range(n_epochs):\n        for batch_index in range(n_batches):\n            \n            X_batch, y_batch = fetch_batch(\n                epoch, batch_index, batch_size)\n            \n            sess.run(\n                training_op, \n                feed_dict={X: X_batch, y: y_batch})\n\n    best_theta = theta.eval()\n    \nprint("Best theta: \\n",best_theta)\n
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m:  20640 \n n_batches:  207 \n\nBest theta: \n [[ 2.07001591]\n [ 0.82045609]\n [ 0.1173173 ]\n [-0.22739051]\n [ 0.31134021]\n [ 0.00353193]\n [-0.01126994]\n [-0.91643935]\n [-0.87950081]]\n
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Model Save/Restore

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In [64]:
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tf.reset_default_graph()\n\nn_epochs = 1000\nlearning_rate = 0.01\n\nX = tf.constant(\n    scaled_housing_data_plus_bias, \n    dtype=tf.float32, name="X")\n\ny = tf.constant(\n    housing.target.reshape(-1, 1), \n    dtype=tf.float32, name="y")\n\ntheta = tf.Variable(\n    tf.random_uniform([n+1, 1], -1.0, 1.0, seed=42), \n    name="theta")\n\ny_pred = tf.matmul(\n    X, theta, name="predictions")\n\nerror = y_pred - y\n\nmse = tf.reduce_mean(\n    tf.square(error), \n    name="mse")\n\noptimizer = tf.train.GradientDescentOptimizer(\n    learning_rate=learning_rate)\n\ntraining_op = optimizer.minimize(mse)\n\ninit = tf.global_variables_initializer()\n\nsaver = tf.train.Saver()\n# can specify which vars to save:\n# saver = tf.train.Saver({"weights": theta})\n
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In [65]:
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with tf.Session() as sess:\n    sess.run(init)\n\n    for epoch in range(n_epochs):\n        if epoch % 100 == 0:\n            print("Epoch", epoch, "MSE =", mse.eval())\n            save_path = saver.save(sess, "/tmp/my_model.ckpt")\n        sess.run(training_op)\n    \n    best_theta = theta.eval()\n    save_path = saver.save(sess, "my_model_final.ckpt")\n\nprint("Best theta:\\n",best_theta)\n\n# model restoration:\n# 1) create Saver at end of construction phase\n# 2) call saver.restore() at start of execution\n
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Epoch 0 MSE = 2.75443\nEpoch 100 MSE = 0.632222\nEpoch 200 MSE = 0.57278\nEpoch 300 MSE = 0.558501\nEpoch 400 MSE = 0.549069\nEpoch 500 MSE = 0.542288\nEpoch 600 MSE = 0.537379\nEpoch 700 MSE = 0.533822\nEpoch 800 MSE = 0.531243\nEpoch 900 MSE = 0.529371\nBest theta:\n [[  2.06855249e+00]\n [  7.74078071e-01]\n [  1.31192386e-01]\n [ -1.17845066e-01]\n [  1.64778143e-01]\n [  7.44078017e-04]\n [ -3.91945094e-02]\n [ -8.61356676e-01]\n [ -8.23479772e-01]]\n
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In [66]:
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!cat checkpoint\n
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model_checkpoint_path: "my_model_final.ckpt"\r\nall_model_checkpoint_paths: "/tmp/my_model.ckpt"\r\nall_model_checkpoint_paths: "my_model_final.ckpt"\r\n
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Visualization - inside Jupyter

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In [67]:
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from IPython.display import clear_output, Image, display, HTML\n\ndef strip_consts(graph_def, max_const_size=32):\n    """Strip large constant values from graph_def."""\n    strip_def = tf.GraphDef()\n    for n0 in graph_def.node:\n        n = strip_def.node.add() \n        n.MergeFrom(n0)\n        if n.op == 'Const':\n            tensor = n.attr['value'].tensor\n            size = len(tensor.tensor_content)\n            if size > max_const_size:\n                tensor.tensor_content = b"<stripped %d bytes>"%size\n    return strip_def\n\ndef show_graph(graph_def, max_const_size=32):\n    """Visualize TensorFlow graph."""\n    if hasattr(graph_def, 'as_graph_def'):\n        graph_def = graph_def.as_graph_def()\n        \n    strip_def = strip_consts(graph_def, max_const_size=max_const_size)\n    \n    code = """\n        <script>\n          function load() {{\n            document.getElementById("{id}").pbtxt = {data};\n          }}\n        </script>\n        <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>\n        <div style="height:600px">\n          <tf-graph-basic id="{id}"></tf-graph-basic>\n        </div>\n    """.format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))\n\n    # original was width=1200px, height=620px\n    iframe = """\n        <iframe seamless style="width:1200px;height:620px;border:0" srcdoc="{}"></iframe>\n    """.format(code.replace('"', '&quot;'))\n    display(HTML(iframe))\n    \n
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In [68]:
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show_graph(tf.get_default_graph())\n
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Visualization - using TensorBoard

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  • Start TensorBoard: $ tensorboard --logdir tf_logs/ (starts on localhost:6006)
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In [69]:
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tf.reset_default_graph()\n\n# Need a logging directory for TB data\n# with timestamps to avoid mixing runs together\n\nfrom datetime import datetime\nnow         = datetime.utcnow().strftime("%Y%m%d%H%M%S")\n\nroot_logdir = "tf_logs"\nlogdir      = "{}/run-{}/".format(root_logdir, now)\n\nn_epochs = 1000\nlearning_rate = 0.01\n\nX = tf.placeholder(\n    tf.float32, \n    shape=(None, n + 1), \n    name="X")\n\ny = tf.placeholder(\n    tf.float32, \n    shape=(None, 1), \n    name="y")\n\ntheta = tf.Variable(\n    tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), \n    name="theta")\n\ny_pred = tf.matmul(\n    X, theta, name="predictions")\n    \nerror = y_pred - y\n\nmse = tf.reduce_mean(\n    tf.square(error), \n    name="mse")\n\noptimizer = tf.train.GradientDescentOptimizer(\n    learning_rate=learning_rate)\n\ntraining_op = optimizer.minimize(mse)\n\ninit = tf.global_variables_initializer()\n\nmse_summary = tf.summary.scalar('MSE', mse)\n\n# FileWriter - creates logdir if not already present,\n# then writes graph def to a binary logfile.\n\nsummary_writer = tf.summary.FileWriter(\n    logdir, \n    tf.get_default_graph())\n
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In [70]:
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n_epochs = 10\nbatch_size = 100\nn_batches = int(np.ceil(m / batch_size))\n\nwith tf.Session() as sess:\n    sess.run(init)\n\n    for epoch in range(n_epochs):\n        for batch_index in range(n_batches):\n            \n            X_batch, y_batch = fetch_batch(\n                epoch, \n                batch_index, \n                batch_size)\n            \n            # evaluate mse_summary on periodic basis,\n            # eg every 10 minibatches.\n            # adds summary for addition to events file.\n            \n            if batch_index % 10 == 0:\n                summary_str = mse_summary.eval(\n                    feed_dict={X: X_batch, y: y_batch})\n                \n                step = epoch * n_batches + batch_index\n                \n                summary_writer.add_summary(\n                    summary_str, \n                    step)\n                \n            sess.run(\n                training_op, \n                feed_dict={X: X_batch, y: y_batch})\n\n    best_theta = theta.eval()\n\nsummary_writer.flush()\nsummary_writer.close()\nprint("Best theta:")\nprint(best_theta)\n
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Best theta:\n[[ 2.07001591]\n [ 0.82045609]\n [ 0.1173173 ]\n [-0.22739051]\n [ 0.31134021]\n [ 0.00353193]\n [-0.01126994]\n [-0.91643935]\n [-0.87950081]]\n
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In [81]:
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#!ls tf_logs/run*\n
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Name Scopes

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  • Graphs can contain thousands of nodes. name scopes group related nodes to aid visualization.
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In [82]:
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tf.reset_default_graph()\n\nnow = datetime.utcnow().strftime("%Y%m%d%H%M%S")\nroot_logdir = "tf_logs"\nlogdir = "{}/run-{}/".format(root_logdir, now)\n\nn_epochs = 1000\nlearning_rate = 0.01\n\nX = tf.placeholder(\n    tf.float32, \n    shape=(None, n + 1), \n    name="X")\n\ny = tf.placeholder(\n    tf.float32, \n    shape=(None, 1), \n    name="y")\n\ntheta = tf.Variable(\n    tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), \n    name="theta")\n\ny_pred = tf.matmul(\n    X, theta, \n    name="predictions")\n\n##### Name Scope\nwith tf.name_scope('loss') as scope:\n    error = y_pred - y\n    mse = tf.reduce_mean(tf.square(error), name="mse")\n#####\n\noptimizer = tf.train.GradientDescentOptimizer(\n    learning_rate=learning_rate)\n\ntraining_op = optimizer.minimize(mse)\n\ninit = tf.global_variables_initializer()\n\nmse_summary = tf.summary.scalar(\n    'MSE', mse)\n\nsummary_writer = tf.summary.FileWriter(\n    logdir, tf.get_default_graph())\n
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In [83]:
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n_epochs = 10\nbatch_size = 100\nn_batches = int(np.ceil(m / batch_size))\n\nwith tf.Session() as sess:\n    sess.run(init)\n\n    for epoch in range(n_epochs):\n        for batch_index in range(n_batches):\n            \n            X_batch, y_batch = fetch_batch(\n                epoch, \n                batch_index, \n                batch_size)\n            \n            if batch_index % 10 == 0:\n                \n                summary_str = mse_summary.eval(\n                    feed_dict={X: X_batch, y: y_batch})\n                \n                step = epoch * n_batches + batch_index\n                \n                summary_writer.add_summary(\n                    summary_str, \n                    step)\n                \n            sess.run(\n                training_op, \n                feed_dict={X: X_batch, y: y_batch})\n\n    best_theta = theta.eval()\n\nsummary_writer.flush()\nsummary_writer.close()\nprint("Best theta:")\nprint(best_theta)\n
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Best theta:\n[[ 2.07001591]\n [ 0.82045609]\n [ 0.1173173 ]\n [-0.22739051]\n [ 0.31134021]\n [ 0.00353193]\n [-0.01126994]\n [-0.91643935]\n [-0.87950081]]\n
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In TensorBoard:

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Modularity

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  • ex: create graph, adds two ReLU nodes
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In [84]:
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# UGLY\n\ntf.reset_default_graph()\n\nn_features = 3\nX = tf.placeholder(\n    tf.float32, \n    shape=(None, n_features), \n    name="X")\n\nw1 = tf.Variable(\n    tf.random_normal(\n        (n_features, 1)), \n    name="weights1")\n\nw2 = tf.Variable(\n    tf.random_normal(\n        (n_features, 1)), \n    name="weights2")\n\nb1 = tf.Variable(\n    0.0, name="bias1")\nb2 = tf.Variable(\n    0.0, name="bias2")\n\nlinear1 = tf.add(\n    tf.matmul(X, w1), b1, name="linear1")\n\nlinear2 = tf.add(\n    tf.matmul(X, w2), b2, name="linear2")\n\nrelu1 = tf.maximum(\n    linear1, 0, name="relu1")\n\nrelu2 = tf.maximum(\n    linear2, 0, name="relu2")\n\noutput = tf.add_n([relu1, relu2], name="output")\n
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In [85]:
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# better -- you can create functions that build ReLUs!\n\ntf.reset_default_graph()\n\ndef relu(X):\n    w_shape = int(\n        X.get_shape()[1]), 1\n    \n    w = tf.Variable(\n        tf.random_normal(w_shape), \n        name="weights")\n    \n    b = tf.Variable(\n        0.0, \n        name="bias")\n    \n    linear = tf.add(\n        tf.matmul(X, w), \n        b, \n        name="linear")\n    \n    return tf.maximum(linear, 0, name="relu")\n\nn_features = 3\n\nX = tf.placeholder(\n    tf.float32, \n    shape=(None, n_features), \n    name="X")\n\nrelus = [relu(X) for i in range(5)]\n\noutput = tf.add_n(\n    relus, \n    name="output")\n\nsummary_writer = tf.summary.FileWriter(\n    "logs/relu1", \n    tf.get_default_graph())\n
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Sharing Variables

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In [86]:
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# better, with name scopes\n\ntf.reset_default_graph()\n\ndef relu(X):\n    with tf.name_scope("relu"):\n        \n        w_shape = int(\n            X.get_shape()[1]), 1\n        \n        w = tf.Variable(\n            tf.random_normal(w_shape), name="weights")\n        \n        b = tf.Variable(\n            0.0, name="bias")\n        \n        linear = tf.add(\n            tf.matmul(X, w), b, name="linear")\n        \n        return tf.maximum(\n            linear, 0, name="max")\n\nn_features = 3\n\nX = tf.placeholder(\n    tf.float32, \n    shape=(None, n_features), \n    name="X")\n\nrelus = [relu(X) for i in range(5)]\n\noutput = tf.add_n(\n    relus, name="output")\n\nsummary_writer = tf.summary.FileWriter(\n    "logs/relu2", \n    tf.get_default_graph())\n\nsummary_writer.close()\n
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relu1  relu2  relu6\r\n
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In [88]:
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tf.reset_default_graph()\n\ndef relu(X, threshold):\n    with tf.name_scope("relu"):\n        w_shape = int(X.get_shape()[1]), 1\n        w = tf.Variable(tf.random_normal(w_shape), name="weights")\n        b = tf.Variable(0.0, name="bias")\n        linear = tf.add(tf.matmul(X, w), b, name="linear")\n        return tf.maximum(linear, threshold, name="max")\n\nthreshold = tf.Variable(0.0, name="threshold")\nX = tf.placeholder(tf.float32, shape=(None, n_features), name="X")\nrelus = [relu(X, threshold) for i in range(5)]\noutput = tf.add_n(relus, name="output")\n
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In [89]:
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tf.reset_default_graph()\n\ndef relu(X):\n    with tf.name_scope("relu"):\n        if not hasattr(relu, "threshold"):\n            relu.threshold = tf.Variable(0.0, name="threshold")\n        w_shape = int(X.get_shape()[1]), 1\n        w = tf.Variable(tf.random_normal(w_shape), name="weights")\n        b = tf.Variable(0.0, name="bias")\n        linear = tf.add(tf.matmul(X, w), b, name="linear")\n        return tf.maximum(linear, relu.threshold, name="max")\n\nX = tf.placeholder(tf.float32, shape=(None, n_features), name="X")\nrelus = [relu(X) for i in range(5)]\noutput = tf.add_n(relus, name="output")\n
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In [90]:
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tf.reset_default_graph()\n\ndef relu(X):\n    with tf.variable_scope("relu", reuse=True):\n        threshold = tf.get_variable("threshold", shape=(), initializer=tf.constant_initializer(0.0))\n        w_shape = int(X.get_shape()[1]), 1\n        w = tf.Variable(tf.random_normal(w_shape), name="weights")\n        b = tf.Variable(0.0, name="bias")\n        linear = tf.add(tf.matmul(X, w), b, name="linear")\n        return tf.maximum(linear, threshold, name="max")\n\nX = tf.placeholder(tf.float32, shape=(None, n_features), name="X")\nwith tf.variable_scope("relu"):\n    threshold = tf.get_variable("threshold", shape=(), initializer=tf.constant_initializer(0.0))\nrelus = [relu(X) for i in range(5)]\noutput = tf.add_n(relus, name="output")\n\nsummary_writer = tf.summary.FileWriter("logs/relu6", tf.get_default_graph())\nsummary_writer.close()\n
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\n\n\n" }, { "path": "ch09-tensorflow-setup.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\\n\",\n \"* Graphs defined in Python, executed in C++\\n\",\n \"* Open-sourced 2015. Windows, Linux, macOS, iOS, Android\\n\",\n \"* Python API: **tensorflow.contrib.learn** (trains NNs)\\n\",\n \"* Simpler API: **tensorflow.contrib.slim** (simplifies building NNs)\\n\",\n \"* Other high-level APIs: **[Keras](http://keras.io)**, **[Pretty Tensor](https://github.com/google/prettytensor/)**.\\n\",\n \"* Libraries: **Caffe**, **DeepLearning4J**, **H2O**, **MXNet**, **Theano**, **Torch**\\n\",\n \"* **TensorBoard** visualization tool\\n\",\n \"* [Cloud service](https://cloud.google.com/ml)\\n\",\n \"* Resources: [home page](https://www.tensorflow.org/), [GitHub](https://github.com/jtoy/awesome-tensorflow), [StackOverflow](http://stackoverflow.com/)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Installation & Test\\n\",\n \"\\n\",\n \"$ cd \\n\",\n \"\\n\",\n \"$ source env/bin/activate (if using virtualenv)\\n\",\n \"\\n\",\n \"$ pip3 install --upgrade tensorflow (or tensorflow-gpu for GPU support)\\n\",\n \"\\n\",\n \"$ python3 -c 'import tensorflow; print(tensorflow.__version__)'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1.0.0\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!python3 -c 'import tensorflow; print(tensorflow.__version__)'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### First Graph\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"42\\n\"\n ]\n }\n ],\n \"source\": [\n \"# create your first graph\\n\",\n \"import tensorflow as tf\\n\",\n \"x = tf.Variable(3, name=\\\"x\\\")\\n\",\n \"y = tf.Variable(4, name=\\\"y\\\")\\n\",\n \"f = x*x*y + y + 2\\n\",\n \"\\n\",\n \"# run graph by opening a session\\n\",\n \"\\n\",\n \"sess = tf.Session()\\n\",\n \"sess.run(x.initializer)\\n\",\n \"sess.run(y.initializer)\\n\",\n \"result = sess.run(f)\\n\",\n \"print(result)\\n\",\n \"sess.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"42\\n\"\n ]\n }\n ],\n \"source\": [\n \"# for repeated session \\\"runs\\\"\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" x.initializer.run()\\n\",\n \" y.initializer.run()\\n\",\n \" result = f.eval()\\n\",\n \"\\n\",\n \"print(result)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"42\\n\"\n ]\n }\n ],\n \"source\": [\n \"# use global_variables_initializer() to set up initialization\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run() # actually initialize all the variables\\n\",\n \" result = f.eval()\\n\",\n \"print(result)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"42\\n\"\n ]\n }\n ],\n \"source\": [\n \"# interactive sessions (from within Jupyter or Python shell)\\n\",\n \"# interactive sesions are auto-set as default sessions\\n\",\n \"\\n\",\n \"sess = tf.InteractiveSession()\\n\",\n \"init.run()\\n\",\n \"result = f.eval()\\n\",\n \"print(result)\\n\",\n \"sess.close()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Managing Graphs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# any created node = added to default graph\\n\",\n \"x1 = tf.Variable(1)\\n\",\n \"x1.graph is tf.get_default_graph()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(True, False)\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# handling multiple graphs\\n\",\n \"\\n\",\n \"graph = tf.Graph()\\n\",\n \"with graph.as_default():\\n\",\n \" x2 = tf.Variable(2)\\n\",\n \"\\n\",\n \"x2.graph is graph, x2.graph is tf.get_default_graph()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Node Lifecycles\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"15\\n\",\n \"10\\n\",\n \"15\\n\"\n ]\n }\n ],\n \"source\": [\n \"# TF finds node's dependencies & evaluates them first\\n\",\n \"\\n\",\n \"w = tf.constant(3)\\n\",\n \"x = w + 2\\n\",\n \"y = x + 5\\n\",\n \"z = x * 3\\n\",\n \"\\n\",\n \"# previous eval results = NOT reused. above code evals w & x twice.\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" print(y.eval())\\n\",\n \" print(z.eval())\\n\",\n \" \\n\",\n \"# a more efficient evaluation call:\\n\",\n \"with tf.Session() as sess:\\n\",\n \" y_val, z_val = sess.run([y,z])\\n\",\n \" print(y_val)\\n\",\n \" print(z_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Linear Regression with TF\\n\",\n \"* TF ops take any number of inputs & produce any number of outputs\\n\",\n \"* Constants & variables = source ops (no inputs)\\n\",\n \"* Inputs & outputs = multidimensional \\\"tensors\\\" = **NumPy ndarrays** in Python API. Typically floats, can also be strings.\\n\",\n \"\\n\",\n \"#### Below: Linear Regression on 2D arrays (California Housing dataset)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1., 0., 0., 0., 0., 1., 1.],\\n\",\n \" [ 1., 0., 0., 0., 0., 1., 1.],\\n\",\n \" [ 1., 0., 0., 0., 0., 1., 1.],\\n\",\n \" [ 1., 0., 0., 0., 0., 1., 1.],\\n\",\n \" [ 1., 0., 0., 0., 0., 1., 1.],\\n\",\n \" [ 1., 0., 0., 0., 0., 1., 1.]])\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# (never used Numpy c_ operator before. Had to check it out.)\\n\",\n \"a = np.ones((6,1))\\n\",\n \"b = np.zeros((6,4))\\n\",\n \"c = np.ones((6,2))\\n\",\n \"np.c_[a,b,c]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"20640 8 (20640, 9)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.datasets import fetch_california_housing\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"housing = fetch_california_housing()\\n\",\n \"m, n = housing.data.shape\\n\",\n \"\\n\",\n \"# add bias feature, x0 = 1\\n\",\n \"housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]\\n\",\n \"\\n\",\n \"print(m,n,housing_data_plus_bias.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"X shape: (20640, 9)\\n\",\n \"XT shape: (9, 20640)\\n\",\n \"theta: \\n\",\n \" [[ -3.69419202e+01]\\n\",\n \" [ 4.36693293e-01]\\n\",\n \" [ 9.43577803e-03]\\n\",\n \" [ -1.07322041e-01]\\n\",\n \" [ 6.45065694e-01]\\n\",\n \" [ -3.97638942e-06]\\n\",\n \" [ -3.78654265e-03]\\n\",\n \" [ -4.21314378e-01]\\n\",\n \" [ -4.34513755e-01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"X = tf.constant(\\n\",\n \" housing_data_plus_bias, \\n\",\n \" dtype=tf.float64, name=\\\"X\\\")\\n\",\n \"\\n\",\n \"print(\\\"X shape: \\\",X.shape)\\n\",\n \"\\n\",\n \"# housing.target = 1D array. Reshape to col vector to compute theta.\\n\",\n \"# reshape() accepts -1 = \\\"unspecified\\\" for a dimension.\\n\",\n \"\\n\",\n \"y = tf.constant(\\n\",\n \" housing.target.reshape(-1, 1), \\n\",\n \" dtype=tf.float64, name=\\\"y\\\")\\n\",\n \"\\n\",\n \"XT = tf.transpose(X)\\n\",\n \"\\n\",\n \"print(\\\"XT shape: \\\",XT.shape)\\n\",\n \"\\n\",\n \"# normal equation: theta = (XT * X)^-1 * XT * y\\n\",\n \"\\n\",\n \"theta = tf.matmul(\\n\",\n \" tf.matmul(\\n\",\n \" tf.matrix_inverse(\\n\",\n \" tf.matmul(XT, X)), \\n\",\n \" XT), \\n\",\n \" y)\\n\",\n \"\\n\",\n \"# TF doesn't immediately run the code. It creates nodes that will run with eval().\\n\",\n \"# TF will auto-run on GPU if available.\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" result = theta.eval()\\n\",\n \"\\n\",\n \"print(\\\"theta: \\\\n\\\",result)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"theta: \\n\",\n \" [[ -3.69419202e+01]\\n\",\n \" [ 4.36693293e-01]\\n\",\n \" [ 9.43577803e-03]\\n\",\n \" [ -1.07322041e-01]\\n\",\n \" [ 6.45065694e-01]\\n\",\n \" [ -3.97638942e-06]\\n\",\n \" [ -3.78654265e-03]\\n\",\n \" [ -4.21314378e-01]\\n\",\n \" [ -4.34513755e-01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# compare to pure NumPy\\n\",\n \"X = housing_data_plus_bias\\n\",\n \"y = housing.target.reshape(-1, 1)\\n\",\n \"\\n\",\n \"theta_numpy = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\\n\",\n \"\\n\",\n \"print(\\\"theta: \\\\n\\\",theta_numpy)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"theta: \\n\",\n \" [[ -3.69419202e+01]\\n\",\n \" [ 4.36693293e-01]\\n\",\n \" [ 9.43577803e-03]\\n\",\n \" [ -1.07322041e-01]\\n\",\n \" [ 6.45065694e-01]\\n\",\n \" [ -3.97638942e-06]\\n\",\n \" [ -3.78654265e-03]\\n\",\n \" [ -4.21314378e-01]\\n\",\n \" [ -4.34513755e-01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# compare to Scikit\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"lin_reg = LinearRegression()\\n\",\n \"\\n\",\n \"lin_reg.fit(\\n\",\n \" housing.data, \\n\",\n \" housing.target.reshape(-1, 1))\\n\",\n \"\\n\",\n \"print(\\\"theta: \\\\n\\\",np.r_[\\n\",\n \" lin_reg.intercept_.reshape(-1, 1), \\n\",\n \" lin_reg.coef_.T])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Batch Gradient Descent (instead of Normal Equation):\\n\",\n \"* Could use TF; let's use Scikit first.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 20640 entries, 0 to 20639\\n\",\n \"Data columns (total 9 columns):\\n\",\n \"0 20640 non-null float64\\n\",\n \"1 20640 non-null float64\\n\",\n \"2 20640 non-null float64\\n\",\n \"3 20640 non-null float64\\n\",\n \"4 20640 non-null float64\\n\",\n \"5 20640 non-null float64\\n\",\n \"6 20640 non-null float64\\n\",\n \"7 20640 non-null float64\\n\",\n \"8 20640 non-null float64\\n\",\n \"dtypes: float64(9)\\n\",\n \"memory usage: 1.4 MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"# normalize input features first - otherwise training = much slower.\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"scaler = StandardScaler()\\n\",\n \"\\n\",\n \"scaled_housing_data = scaler.fit_transform(\\n\",\n \" housing.data)\\n\",\n \"\\n\",\n \"scaled_housing_data_plus_bias = np.c_[\\n\",\n \" np.ones((m, 1)), \\n\",\n \" scaled_housing_data]\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"pd.DataFrame(scaled_housing_data_plus_bias).info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"mean (axis=0): \\n\",\n \" [ 1.00000000e+00 6.60969987e-17 5.50808322e-18 6.60969987e-17\\n\",\n \" -1.06030602e-16 -1.10161664e-17 3.44255201e-18 -1.07958431e-15\\n\",\n \" -8.52651283e-15]\\n\",\n \"mean (axis=1): \\n\",\n \" [ 0.38915536 0.36424355 0.5116157 ..., -0.06612179 -0.06360587\\n\",\n \" 0.01359031]\\n\",\n \"mean (w/bias): \\n\",\n \" 0.111111111111\\n\",\n \"data shape: \\n\",\n \" (20640, 9)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"mean (axis=0): \\\\n\\\",scaled_housing_data_plus_bias.mean(axis=0))\\n\",\n \"print(\\\"mean (axis=1): \\\\n\\\",scaled_housing_data_plus_bias.mean(axis=1))\\n\",\n \"print(\\\"mean (w/bias): \\\\n\\\",scaled_housing_data_plus_bias.mean())\\n\",\n \"print(\\\"data shape: \\\\n\\\",scaled_housing_data_plus_bias.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Manual gradient computation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 0 MSE = 2.75443\\n\",\n \"Epoch 100 MSE = 0.632222\\n\",\n \"Epoch 200 MSE = 0.57278\\n\",\n \"Epoch 300 MSE = 0.558501\\n\",\n \"Epoch 400 MSE = 0.549069\\n\",\n \"Epoch 500 MSE = 0.542288\\n\",\n \"Epoch 600 MSE = 0.537379\\n\",\n \"Epoch 700 MSE = 0.533822\\n\",\n \"Epoch 800 MSE = 0.531243\\n\",\n \"Epoch 900 MSE = 0.529371\\n\",\n \"Best theta: \\n\",\n \" [[ 2.06855226e+00]\\n\",\n \" [ 7.74078071e-01]\\n\",\n \" [ 1.31192386e-01]\\n\",\n \" [ -1.17845096e-01]\\n\",\n \" [ 1.64778158e-01]\\n\",\n \" [ 7.44080753e-04]\\n\",\n \" [ -3.91945168e-02]\\n\",\n \" [ -8.61356616e-01]\\n\",\n \" [ -8.23479712e-01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# batch gradient step:\\n\",\n \"# theta(next) = theta - learning_rate * MSE(theta)\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_epochs = 1000\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"X = tf.constant(\\n\",\n \" scaled_housing_data_plus_bias, \\n\",\n \" dtype=tf.float32, name=\\\"X\\\")\\n\",\n \"\\n\",\n \"y = tf.constant(\\n\",\n \" housing.target.reshape(-1, 1), \\n\",\n \" dtype=tf.float32, name=\\\"y\\\")\\n\",\n \"\\n\",\n \"theta = tf.Variable( # tf.random_uniform = generates random tensor\\n\",\n \" tf.random_uniform([n+1, 1], -1.0, 1.0, seed=42), \\n\",\n \" name=\\\"theta\\\")\\n\",\n \"\\n\",\n \"y_pred = tf.matmul(\\n\",\n \" X, theta, name=\\\"predictions\\\")\\n\",\n \"\\n\",\n \"error = y_pred - y\\n\",\n \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\\n\",\n \"gradients = 2/m * tf.matmul(tf.transpose(X), error)\\n\",\n \"training_op = tf.assign(theta, theta - learning_rate * gradients)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(init)\\n\",\n \"\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" if epoch % 100 == 0: # do every 100th epoch:\\n\",\n \" print(\\\"Epoch\\\", epoch, \\\"MSE =\\\", mse.eval())\\n\",\n \" sess.run(training_op)\\n\",\n \" \\n\",\n \" best_theta = theta.eval()\\n\",\n \"\\n\",\n \"print(\\\"Best theta: \\\\n\\\",best_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Using autodiff\\n\",\n \"* automatically finds gradients. Note the different gradients assignment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 0 MSE = 2.75443\\n\",\n \"Epoch 100 MSE = 0.632222\\n\",\n \"Epoch 200 MSE = 0.57278\\n\",\n \"Epoch 300 MSE = 0.558501\\n\",\n \"Epoch 400 MSE = 0.549069\\n\",\n \"Epoch 500 MSE = 0.542288\\n\",\n \"Epoch 600 MSE = 0.537379\\n\",\n \"Epoch 700 MSE = 0.533822\\n\",\n \"Epoch 800 MSE = 0.531243\\n\",\n \"Epoch 900 MSE = 0.529371\\n\",\n \"Best theta: \\n\",\n \" [[ 2.06855249e+00]\\n\",\n \" [ 7.74078071e-01]\\n\",\n \" [ 1.31192386e-01]\\n\",\n \" [ -1.17845066e-01]\\n\",\n \" [ 1.64778143e-01]\\n\",\n \" [ 7.44078017e-04]\\n\",\n \" [ -3.91945094e-02]\\n\",\n \" [ -8.61356676e-01]\\n\",\n \" [ -8.23479772e-01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_epochs = 1000\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"X = tf.constant(\\n\",\n \" scaled_housing_data_plus_bias, \\n\",\n \" dtype=tf.float32, name=\\\"X\\\")\\n\",\n \"\\n\",\n \"y = tf.constant(\\n\",\n \" housing.target.reshape(-1, 1), \\n\",\n \" dtype=tf.float32, name=\\\"y\\\")\\n\",\n \"\\n\",\n \"theta = tf.Variable(\\n\",\n \" tf.random_uniform([n + 1, 1], -1.0, 1.0, \\n\",\n \" seed=42), name=\\\"theta\\\")\\n\",\n \"\\n\",\n \"y_pred = tf.matmul(\\n\",\n \" X, theta, name=\\\"predictions\\\")\\n\",\n \"\\n\",\n \"error = y_pred - y\\n\",\n \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\\n\",\n \"\\n\",\n \"# AutoDiff to the rescue\\n\",\n \"# creates list of ops, one/variable, to find gradients per variable\\n\",\n \"gradients = tf.gradients(mse, [theta])[0]\\n\",\n \"#\\n\",\n \"\\n\",\n \"training_op = tf.assign(theta, theta - learning_rate * gradients)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(init)\\n\",\n \"\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" if epoch % 100 == 0:\\n\",\n \" print(\\\"Epoch\\\", epoch, \\\"MSE =\\\", mse.eval())\\n\",\n \" sess.run(training_op)\\n\",\n \" \\n\",\n \" best_theta = theta.eval()\\n\",\n \"\\n\",\n \"print(\\\"Best theta: \\\\n\\\", best_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Four ways to run autodiff - see Appendix D\\n\",\n \"* reverse-mode (default): best for many inputs, few outputs\\n\",\n \"* symbolic diff: high accuracy\\n\",\n \"* forward mode: high accuracy\\n\",\n \"* numerical diff: low accuracy, but trivial to implement\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Using a predefined optimizer (Gradient Descent)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 0 MSE = 2.75443\\n\",\n \"Epoch 100 MSE = 0.632222\\n\",\n \"Epoch 200 MSE = 0.57278\\n\",\n \"Epoch 300 MSE = 0.558501\\n\",\n \"Epoch 400 MSE = 0.549069\\n\",\n \"Epoch 500 MSE = 0.542288\\n\",\n \"Epoch 600 MSE = 0.537379\\n\",\n \"Epoch 700 MSE = 0.533822\\n\",\n \"Epoch 800 MSE = 0.531243\\n\",\n \"Epoch 900 MSE = 0.529371\\n\",\n \"Best theta:\\n\",\n \" [[ 2.06855249e+00]\\n\",\n \" [ 7.74078071e-01]\\n\",\n \" [ 1.31192386e-01]\\n\",\n \" [ -1.17845066e-01]\\n\",\n \" [ 1.64778143e-01]\\n\",\n \" [ 7.44078017e-04]\\n\",\n \" [ -3.91945094e-02]\\n\",\n \" [ -8.61356676e-01]\\n\",\n \" [ -8.23479772e-01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_epochs = 1000\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"X = tf.constant(\\n\",\n \" scaled_housing_data_plus_bias, \\n\",\n \" dtype=tf.float32, name=\\\"X\\\")\\n\",\n \"\\n\",\n \"y = tf.constant(\\n\",\n \" housing.target.reshape(-1, 1), \\n\",\n \" dtype=tf.float32, name=\\\"y\\\")\\n\",\n \"\\n\",\n \"theta = tf.Variable(\\n\",\n \" tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), \\n\",\n \" name=\\\"theta\\\")\\n\",\n \"\\n\",\n \"y_pred = tf.matmul(\\n\",\n \" X, theta, name=\\\"predictions\\\")\\n\",\n \"\\n\",\n \"error = y_pred - y\\n\",\n \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\\n\",\n \"\\n\",\n \"#####\\n\",\n \"optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\\n\",\n \"training_op = optimizer.minimize(mse)\\n\",\n \"#####\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(init)\\n\",\n \"\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" if epoch % 100 == 0:\\n\",\n \" print(\\\"Epoch\\\", epoch, \\\"MSE =\\\", mse.eval())\\n\",\n \" sess.run(training_op)\\n\",\n \" \\n\",\n \" best_theta = theta.eval()\\n\",\n \"\\n\",\n \"print(\\\"Best theta:\\\\n\\\", best_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Using a predefined optimizer (Momentum)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best theta:\\n\",\n \" [[ 2.06855392e+00]\\n\",\n \" [ 7.94067979e-01]\\n\",\n \" [ 1.25333667e-01]\\n\",\n \" [ -1.73580602e-01]\\n\",\n \" [ 2.18767926e-01]\\n\",\n \" [ -1.64708309e-03]\\n\",\n \" [ -3.91250364e-02]\\n\",\n \" [ -8.85289013e-01]\\n\",\n \" [ -8.50607991e-01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_epochs = 1000\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"X = tf.constant(\\n\",\n \" scaled_housing_data_plus_bias, \\n\",\n \" dtype=tf.float32, name=\\\"X\\\")\\n\",\n \"\\n\",\n \"y = tf.constant(\\n\",\n \" housing.target.reshape(-1, 1), \\n\",\n \" dtype=tf.float32, name=\\\"y\\\")\\n\",\n \"\\n\",\n \"theta = tf.Variable(\\n\",\n \" tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), \\n\",\n \" name=\\\"theta\\\")\\n\",\n \"\\n\",\n \"y_pred = tf.matmul(X, theta, name=\\\"predictions\\\")\\n\",\n \"error = y_pred - y\\n\",\n \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\\n\",\n \"#####\\n\",\n \"optimizer = tf.train.MomentumOptimizer(\\n\",\n \" learning_rate=learning_rate, \\n\",\n \" momentum=0.25)\\n\",\n \"#####\\n\",\n \"training_op = optimizer.minimize(mse)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(init)\\n\",\n \"\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" sess.run(training_op)\\n\",\n \" \\n\",\n \" best_theta = theta.eval()\\n\",\n \"\\n\",\n \"print(\\\"Best theta:\\\\n\\\", best_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training & Data Feeds\\n\",\n \"* Goal: modify previous code for Minibatch gradient descent\\n\",\n \"* Best practice: *placeholder* nodes (no computation, just data output)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 6. 7. 8.]] \\n\",\n \" [[ 9. 10. 11.]\\n\",\n \" [ 12. 13. 14.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"A = tf.placeholder(tf.float32, shape=(None, 3))\\n\",\n \"B = A + 5\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" B_val_1 = B.eval(\\n\",\n \" feed_dict={A: [[1, 2, 3]]})\\n\",\n \" \\n\",\n \" B_val_2 = B.eval(\\n\",\n \" feed_dict={A: [[4, 5, 6], [7, 8, 9]]})\\n\",\n \" \\n\",\n \"print(B_val_1, \\\"\\\\n\\\", B_val_2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# definition phase: change X,y to placeholder nodes\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_epochs = 1000\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"##########\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, shape=(None, n+1), name=\\\"X\\\")\\n\",\n \"y = tf.placeholder(tf.float32, shape=(None, 1), name=\\\"y\\\")\\n\",\n \"\\n\",\n \"##########\\n\",\n \"\\n\",\n \"theta = tf.Variable(\\n\",\n \" tf.random_uniform([n+1, 1], -1.0, 1.0, seed=42), \\n\",\n \" name=\\\"theta\\\")\\n\",\n \"\\n\",\n \"y_pred = tf.matmul(\\n\",\n \" X, theta, name=\\\"predictions\\\")\\n\",\n \"\\n\",\n \"error = y_pred - y\\n\",\n \"\\n\",\n \"mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\\n\",\n \"\\n\",\n \"optimizer = tf.train.GradientDescentOptimizer(\\n\",\n \" learning_rate=learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(mse)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"m: 20640 \\n\",\n \" n_batches: 207 \\n\",\n \"\\n\",\n \"Best theta: \\n\",\n \" [[ 2.07001591]\\n\",\n \" [ 0.82045609]\\n\",\n \" [ 0.1173173 ]\\n\",\n \" [-0.22739051]\\n\",\n \" [ 0.31134021]\\n\",\n \" [ 0.00353193]\\n\",\n \" [-0.01126994]\\n\",\n \" [-0.91643935]\\n\",\n \" [-0.87950081]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# execution phase: fetch minibatches one-by-one. \\n\",\n \"# use feed_dict to provide values to dependent nodes\\n\",\n \"\\n\",\n \"import numpy.random as rnd\\n\",\n \"\\n\",\n \"n_epochs = 10\\n\",\n \"batch_size = 100\\n\",\n \"n_batches = int(np.ceil(m / batch_size))\\n\",\n \"\\n\",\n \"print(\\\"m: \\\",m,\\\"\\\\n\\\",\\\"n_batches: \\\",n_batches,\\\"\\\\n\\\")\\n\",\n \"\\n\",\n \"def fetch_batch(epoch, batch_index, batch_size):\\n\",\n \" \\n\",\n \" rnd.seed(epoch * n_batches + batch_index)\\n\",\n \" indices = rnd.randint(m, size=batch_size)\\n\",\n \" \\n\",\n \" X_batch = scaled_housing_data_plus_bias[indices]\\n\",\n \" y_batch = housing.target.reshape(-1, 1)[indices]\\n\",\n \" \\n\",\n \" return X_batch, y_batch\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(init)\\n\",\n \"\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" for batch_index in range(n_batches):\\n\",\n \" \\n\",\n \" X_batch, y_batch = fetch_batch(\\n\",\n \" epoch, batch_index, batch_size)\\n\",\n \" \\n\",\n \" sess.run(\\n\",\n \" training_op, \\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \"\\n\",\n \" best_theta = theta.eval()\\n\",\n \" \\n\",\n \"print(\\\"Best theta: \\\\n\\\",best_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Model Save/Restore\\n\",\n \"* Use a *saver* node once construction is complete.\\n\",\n \"* call **save()** method - pass it session and filepath info.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_epochs = 1000\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"X = tf.constant(\\n\",\n \" scaled_housing_data_plus_bias, \\n\",\n \" dtype=tf.float32, name=\\\"X\\\")\\n\",\n \"\\n\",\n \"y = tf.constant(\\n\",\n \" housing.target.reshape(-1, 1), \\n\",\n \" dtype=tf.float32, name=\\\"y\\\")\\n\",\n \"\\n\",\n \"theta = tf.Variable(\\n\",\n \" tf.random_uniform([n+1, 1], -1.0, 1.0, seed=42), \\n\",\n \" name=\\\"theta\\\")\\n\",\n \"\\n\",\n \"y_pred = tf.matmul(\\n\",\n \" X, theta, name=\\\"predictions\\\")\\n\",\n \"\\n\",\n \"error = y_pred - y\\n\",\n \"\\n\",\n \"mse = tf.reduce_mean(\\n\",\n \" tf.square(error), \\n\",\n \" name=\\\"mse\\\")\\n\",\n \"\\n\",\n \"optimizer = tf.train.GradientDescentOptimizer(\\n\",\n \" learning_rate=learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(mse)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"saver = tf.train.Saver()\\n\",\n \"# can specify which vars to save:\\n\",\n \"# saver = tf.train.Saver({\\\"weights\\\": theta})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 0 MSE = 2.75443\\n\",\n \"Epoch 100 MSE = 0.632222\\n\",\n \"Epoch 200 MSE = 0.57278\\n\",\n \"Epoch 300 MSE = 0.558501\\n\",\n \"Epoch 400 MSE = 0.549069\\n\",\n \"Epoch 500 MSE = 0.542288\\n\",\n \"Epoch 600 MSE = 0.537379\\n\",\n \"Epoch 700 MSE = 0.533822\\n\",\n \"Epoch 800 MSE = 0.531243\\n\",\n \"Epoch 900 MSE = 0.529371\\n\",\n \"Best theta:\\n\",\n \" [[ 2.06855249e+00]\\n\",\n \" [ 7.74078071e-01]\\n\",\n \" [ 1.31192386e-01]\\n\",\n \" [ -1.17845066e-01]\\n\",\n \" [ 1.64778143e-01]\\n\",\n \" [ 7.44078017e-04]\\n\",\n \" [ -3.91945094e-02]\\n\",\n \" [ -8.61356676e-01]\\n\",\n \" [ -8.23479772e-01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"with tf.Session() as sess:\\n\",\n \" sess.run(init)\\n\",\n \"\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" if epoch % 100 == 0:\\n\",\n \" print(\\\"Epoch\\\", epoch, \\\"MSE =\\\", mse.eval())\\n\",\n \" save_path = saver.save(sess, \\\"/tmp/my_model.ckpt\\\")\\n\",\n \" sess.run(training_op)\\n\",\n \" \\n\",\n \" best_theta = theta.eval()\\n\",\n \" save_path = saver.save(sess, \\\"my_model_final.ckpt\\\")\\n\",\n \"\\n\",\n \"print(\\\"Best theta:\\\\n\\\",best_theta)\\n\",\n \"\\n\",\n \"# model restoration:\\n\",\n \"# 1) create Saver at end of construction phase\\n\",\n \"# 2) call saver.restore() at start of execution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"model_checkpoint_path: \\\"my_model_final.ckpt\\\"\\r\\n\",\n \"all_model_checkpoint_paths: \\\"/tmp/my_model.ckpt\\\"\\r\\n\",\n \"all_model_checkpoint_paths: \\\"my_model_final.ckpt\\\"\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!cat checkpoint\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Visualization - inside Jupyter\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from IPython.display import clear_output, Image, display, HTML\\n\",\n \"\\n\",\n \"def strip_consts(graph_def, max_const_size=32):\\n\",\n \" \\\"\\\"\\\"Strip large constant values from graph_def.\\\"\\\"\\\"\\n\",\n \" strip_def = tf.GraphDef()\\n\",\n \" for n0 in graph_def.node:\\n\",\n \" n = strip_def.node.add() \\n\",\n \" n.MergeFrom(n0)\\n\",\n \" if n.op == 'Const':\\n\",\n \" tensor = n.attr['value'].tensor\\n\",\n \" size = len(tensor.tensor_content)\\n\",\n \" if size > max_const_size:\\n\",\n \" tensor.tensor_content = b\\\"\\\"%size\\n\",\n \" return strip_def\\n\",\n \"\\n\",\n \"def show_graph(graph_def, max_const_size=32):\\n\",\n \" \\\"\\\"\\\"Visualize TensorFlow graph.\\\"\\\"\\\"\\n\",\n \" if hasattr(graph_def, 'as_graph_def'):\\n\",\n \" graph_def = graph_def.as_graph_def()\\n\",\n \" \\n\",\n \" strip_def = strip_consts(graph_def, max_const_size=max_const_size)\\n\",\n \" \\n\",\n \" code = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \" \\\"\\\"\\\".format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))\\n\",\n \"\\n\",\n \" # original was width=1200px, height=620px\\n\",\n \" iframe = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\\"\\\"\\\".format(code.replace('\\\"', '"'))\\n\",\n \" display(HTML(iframe))\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph(tf.get_default_graph())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Visualization - using TensorBoard\\n\",\n \"\\n\",\n \"* Start **TensorBoard**: $ tensorboard --logdir tf_logs/ (starts on localhost:6006)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"# Need a logging directory for TB data\\n\",\n \"# with timestamps to avoid mixing runs together\\n\",\n \"\\n\",\n \"from datetime import datetime\\n\",\n \"now = datetime.utcnow().strftime(\\\"%Y%m%d%H%M%S\\\")\\n\",\n \"\\n\",\n \"root_logdir = \\\"tf_logs\\\"\\n\",\n \"logdir = \\\"{}/run-{}/\\\".format(root_logdir, now)\\n\",\n \"\\n\",\n \"n_epochs = 1000\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"X = tf.placeholder(\\n\",\n \" tf.float32, \\n\",\n \" shape=(None, n + 1), \\n\",\n \" name=\\\"X\\\")\\n\",\n \"\\n\",\n \"y = tf.placeholder(\\n\",\n \" tf.float32, \\n\",\n \" shape=(None, 1), \\n\",\n \" name=\\\"y\\\")\\n\",\n \"\\n\",\n \"theta = tf.Variable(\\n\",\n \" tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), \\n\",\n \" name=\\\"theta\\\")\\n\",\n \"\\n\",\n \"y_pred = tf.matmul(\\n\",\n \" X, theta, name=\\\"predictions\\\")\\n\",\n \" \\n\",\n \"error = y_pred - y\\n\",\n \"\\n\",\n \"mse = tf.reduce_mean(\\n\",\n \" tf.square(error), \\n\",\n \" name=\\\"mse\\\")\\n\",\n \"\\n\",\n \"optimizer = tf.train.GradientDescentOptimizer(\\n\",\n \" learning_rate=learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(mse)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"mse_summary = tf.summary.scalar('MSE', mse)\\n\",\n \"\\n\",\n \"# FileWriter - creates logdir if not already present,\\n\",\n \"# then writes graph def to a binary logfile.\\n\",\n \"\\n\",\n \"summary_writer = tf.summary.FileWriter(\\n\",\n \" logdir, \\n\",\n \" tf.get_default_graph())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best theta:\\n\",\n \"[[ 2.07001591]\\n\",\n \" [ 0.82045609]\\n\",\n \" [ 0.1173173 ]\\n\",\n \" [-0.22739051]\\n\",\n \" [ 0.31134021]\\n\",\n \" [ 0.00353193]\\n\",\n \" [-0.01126994]\\n\",\n \" [-0.91643935]\\n\",\n \" [-0.87950081]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n_epochs = 10\\n\",\n \"batch_size = 100\\n\",\n \"n_batches = int(np.ceil(m / batch_size))\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(init)\\n\",\n \"\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" for batch_index in range(n_batches):\\n\",\n \" \\n\",\n \" X_batch, y_batch = fetch_batch(\\n\",\n \" epoch, \\n\",\n \" batch_index, \\n\",\n \" batch_size)\\n\",\n \" \\n\",\n \" # evaluate mse_summary on periodic basis,\\n\",\n \" # eg every 10 minibatches.\\n\",\n \" # adds summary for addition to events file.\\n\",\n \" \\n\",\n \" if batch_index % 10 == 0:\\n\",\n \" summary_str = mse_summary.eval(\\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \" \\n\",\n \" step = epoch * n_batches + batch_index\\n\",\n \" \\n\",\n \" summary_writer.add_summary(\\n\",\n \" summary_str, \\n\",\n \" step)\\n\",\n \" \\n\",\n \" sess.run(\\n\",\n \" training_op, \\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \"\\n\",\n \" best_theta = theta.eval()\\n\",\n \"\\n\",\n \"summary_writer.flush()\\n\",\n \"summary_writer.close()\\n\",\n \"print(\\\"Best theta:\\\")\\n\",\n \"print(best_theta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"#!ls tf_logs/run*\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Name Scopes\\n\",\n \"* Graphs can contain thousands of nodes. *name scopes* group related nodes to aid visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"now = datetime.utcnow().strftime(\\\"%Y%m%d%H%M%S\\\")\\n\",\n \"root_logdir = \\\"tf_logs\\\"\\n\",\n \"logdir = \\\"{}/run-{}/\\\".format(root_logdir, now)\\n\",\n \"\\n\",\n \"n_epochs = 1000\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"X = tf.placeholder(\\n\",\n \" tf.float32, \\n\",\n \" shape=(None, n + 1), \\n\",\n \" name=\\\"X\\\")\\n\",\n \"\\n\",\n \"y = tf.placeholder(\\n\",\n \" tf.float32, \\n\",\n \" shape=(None, 1), \\n\",\n \" name=\\\"y\\\")\\n\",\n \"\\n\",\n \"theta = tf.Variable(\\n\",\n \" tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), \\n\",\n \" name=\\\"theta\\\")\\n\",\n \"\\n\",\n \"y_pred = tf.matmul(\\n\",\n \" X, theta, \\n\",\n \" name=\\\"predictions\\\")\\n\",\n \"\\n\",\n \"##### Name Scope\\n\",\n \"with tf.name_scope('loss') as scope:\\n\",\n \" error = y_pred - y\\n\",\n \" mse = tf.reduce_mean(tf.square(error), name=\\\"mse\\\")\\n\",\n \"#####\\n\",\n \"\\n\",\n \"optimizer = tf.train.GradientDescentOptimizer(\\n\",\n \" learning_rate=learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(mse)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"mse_summary = tf.summary.scalar(\\n\",\n \" 'MSE', mse)\\n\",\n \"\\n\",\n \"summary_writer = tf.summary.FileWriter(\\n\",\n \" logdir, tf.get_default_graph())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best theta:\\n\",\n \"[[ 2.07001591]\\n\",\n \" [ 0.82045609]\\n\",\n \" [ 0.1173173 ]\\n\",\n \" [-0.22739051]\\n\",\n \" [ 0.31134021]\\n\",\n \" [ 0.00353193]\\n\",\n \" [-0.01126994]\\n\",\n \" [-0.91643935]\\n\",\n \" [-0.87950081]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n_epochs = 10\\n\",\n \"batch_size = 100\\n\",\n \"n_batches = int(np.ceil(m / batch_size))\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(init)\\n\",\n \"\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" for batch_index in range(n_batches):\\n\",\n \" \\n\",\n \" X_batch, y_batch = fetch_batch(\\n\",\n \" epoch, \\n\",\n \" batch_index, \\n\",\n \" batch_size)\\n\",\n \" \\n\",\n \" if batch_index % 10 == 0:\\n\",\n \" \\n\",\n \" summary_str = mse_summary.eval(\\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \" \\n\",\n \" step = epoch * n_batches + batch_index\\n\",\n \" \\n\",\n \" summary_writer.add_summary(\\n\",\n \" summary_str, \\n\",\n \" step)\\n\",\n \" \\n\",\n \" sess.run(\\n\",\n \" training_op, \\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \"\\n\",\n \" best_theta = theta.eval()\\n\",\n \"\\n\",\n \"summary_writer.flush()\\n\",\n \"summary_writer.close()\\n\",\n \"print(\\\"Best theta:\\\")\\n\",\n \"print(best_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### In TensorBoard:\\n\",\n \"\\n\",\n \"![Collapsed Name Scope](pics/collapsed-namescope-tensorboard.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Modularity\\n\",\n \"* ex: create graph, adds two ReLU nodes\\n\",\n \"* output: result if >0, 0 otherwise\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# UGLY\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_features = 3\\n\",\n \"X = tf.placeholder(\\n\",\n \" tf.float32, \\n\",\n \" shape=(None, n_features), \\n\",\n \" name=\\\"X\\\")\\n\",\n \"\\n\",\n \"w1 = tf.Variable(\\n\",\n \" tf.random_normal(\\n\",\n \" (n_features, 1)), \\n\",\n \" name=\\\"weights1\\\")\\n\",\n \"\\n\",\n \"w2 = tf.Variable(\\n\",\n \" tf.random_normal(\\n\",\n \" (n_features, 1)), \\n\",\n \" name=\\\"weights2\\\")\\n\",\n \"\\n\",\n \"b1 = tf.Variable(\\n\",\n \" 0.0, name=\\\"bias1\\\")\\n\",\n \"b2 = tf.Variable(\\n\",\n \" 0.0, name=\\\"bias2\\\")\\n\",\n \"\\n\",\n \"linear1 = tf.add(\\n\",\n \" tf.matmul(X, w1), b1, name=\\\"linear1\\\")\\n\",\n \"\\n\",\n \"linear2 = tf.add(\\n\",\n \" tf.matmul(X, w2), b2, name=\\\"linear2\\\")\\n\",\n \"\\n\",\n \"relu1 = tf.maximum(\\n\",\n \" linear1, 0, name=\\\"relu1\\\")\\n\",\n \"\\n\",\n \"relu2 = tf.maximum(\\n\",\n \" linear2, 0, name=\\\"relu2\\\")\\n\",\n \"\\n\",\n \"output = tf.add_n([relu1, relu2], name=\\\"output\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# better -- you can create functions that build ReLUs!\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"def relu(X):\\n\",\n \" w_shape = int(\\n\",\n \" X.get_shape()[1]), 1\\n\",\n \" \\n\",\n \" w = tf.Variable(\\n\",\n \" tf.random_normal(w_shape), \\n\",\n \" name=\\\"weights\\\")\\n\",\n \" \\n\",\n \" b = tf.Variable(\\n\",\n \" 0.0, \\n\",\n \" name=\\\"bias\\\")\\n\",\n \" \\n\",\n \" linear = tf.add(\\n\",\n \" tf.matmul(X, w), \\n\",\n \" b, \\n\",\n \" name=\\\"linear\\\")\\n\",\n \" \\n\",\n \" return tf.maximum(linear, 0, name=\\\"relu\\\")\\n\",\n \"\\n\",\n \"n_features = 3\\n\",\n \"\\n\",\n \"X = tf.placeholder(\\n\",\n \" tf.float32, \\n\",\n \" shape=(None, n_features), \\n\",\n \" name=\\\"X\\\")\\n\",\n \"\\n\",\n \"relus = [relu(X) for i in range(5)]\\n\",\n \"\\n\",\n \"output = tf.add_n(\\n\",\n \" relus, \\n\",\n \" name=\\\"output\\\")\\n\",\n \"\\n\",\n \"summary_writer = tf.summary.FileWriter(\\n\",\n \" \\\"logs/relu1\\\", \\n\",\n \" tf.get_default_graph())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Sharing Variables\\n\",\n \"* Simplest option: define it first, then share it as parameter to all functions that need it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# better, with name scopes\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"def relu(X):\\n\",\n \" with tf.name_scope(\\\"relu\\\"):\\n\",\n \" \\n\",\n \" w_shape = int(\\n\",\n \" X.get_shape()[1]), 1\\n\",\n \" \\n\",\n \" w = tf.Variable(\\n\",\n \" tf.random_normal(w_shape), name=\\\"weights\\\")\\n\",\n \" \\n\",\n \" b = tf.Variable(\\n\",\n \" 0.0, name=\\\"bias\\\")\\n\",\n \" \\n\",\n \" linear = tf.add(\\n\",\n \" tf.matmul(X, w), b, name=\\\"linear\\\")\\n\",\n \" \\n\",\n \" return tf.maximum(\\n\",\n \" linear, 0, name=\\\"max\\\")\\n\",\n \"\\n\",\n \"n_features = 3\\n\",\n \"\\n\",\n \"X = tf.placeholder(\\n\",\n \" tf.float32, \\n\",\n \" shape=(None, n_features), \\n\",\n \" name=\\\"X\\\")\\n\",\n \"\\n\",\n \"relus = [relu(X) for i in range(5)]\\n\",\n \"\\n\",\n \"output = tf.add_n(\\n\",\n \" relus, name=\\\"output\\\")\\n\",\n \"\\n\",\n \"summary_writer = tf.summary.FileWriter(\\n\",\n \" \\\"logs/relu2\\\", \\n\",\n \" tf.get_default_graph())\\n\",\n \"\\n\",\n \"summary_writer.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"relu1 relu2 relu6\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!ls logs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"def relu(X, threshold):\\n\",\n \" with tf.name_scope(\\\"relu\\\"):\\n\",\n \" w_shape = int(X.get_shape()[1]), 1\\n\",\n \" w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")\\n\",\n \" b = tf.Variable(0.0, name=\\\"bias\\\")\\n\",\n \" linear = tf.add(tf.matmul(X, w), b, name=\\\"linear\\\")\\n\",\n \" return tf.maximum(linear, threshold, name=\\\"max\\\")\\n\",\n \"\\n\",\n \"threshold = tf.Variable(0.0, name=\\\"threshold\\\")\\n\",\n \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n \"relus = [relu(X, threshold) for i in range(5)]\\n\",\n \"output = tf.add_n(relus, name=\\\"output\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"def relu(X):\\n\",\n \" with tf.name_scope(\\\"relu\\\"):\\n\",\n \" if not hasattr(relu, \\\"threshold\\\"):\\n\",\n \" relu.threshold = tf.Variable(0.0, name=\\\"threshold\\\")\\n\",\n \" w_shape = int(X.get_shape()[1]), 1\\n\",\n \" w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")\\n\",\n \" b = tf.Variable(0.0, name=\\\"bias\\\")\\n\",\n \" linear = tf.add(tf.matmul(X, w), b, name=\\\"linear\\\")\\n\",\n \" return tf.maximum(linear, relu.threshold, name=\\\"max\\\")\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n \"relus = [relu(X) for i in range(5)]\\n\",\n \"output = tf.add_n(relus, name=\\\"output\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"def relu(X):\\n\",\n \" with tf.variable_scope(\\\"relu\\\", reuse=True):\\n\",\n \" threshold = tf.get_variable(\\\"threshold\\\", shape=(), initializer=tf.constant_initializer(0.0))\\n\",\n \" w_shape = int(X.get_shape()[1]), 1\\n\",\n \" w = tf.Variable(tf.random_normal(w_shape), name=\\\"weights\\\")\\n\",\n \" b = tf.Variable(0.0, name=\\\"bias\\\")\\n\",\n \" linear = tf.add(tf.matmul(X, w), b, name=\\\"linear\\\")\\n\",\n \" return tf.maximum(linear, threshold, name=\\\"max\\\")\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, shape=(None, n_features), name=\\\"X\\\")\\n\",\n \"with tf.variable_scope(\\\"relu\\\"):\\n\",\n \" threshold = tf.get_variable(\\\"threshold\\\", shape=(), initializer=tf.constant_initializer(0.0))\\n\",\n \"relus = [relu(X) for i in range(5)]\\n\",\n \"output = tf.add_n(relus, name=\\\"output\\\")\\n\",\n \"\\n\",\n \"summary_writer = tf.summary.FileWriter(\\\"logs/relu6\\\", tf.get_default_graph())\\n\",\n \"summary_writer.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch10-neural-nets.html", "content": "\n\n\nch10-neural-nets\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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Intro - Perceptrons

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  • Simplest ANN architecture
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  • Uses linear threshold unit (LTU) - returns weight sum of inputs, applies step function to sum, outputs result
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  • Single LTU can be used for simple linear binary classification
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  • Perceptron = single layer of LTUs, each one connected to all inputs
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  • Percepton training based on Hebb's Rule. (basically, connection weight between two neurons goes up when they have same output.)
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  • Linear decision boundary, so Perceptrons not capable of learning complex patterns.
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In [1]:
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# Perceptron with Iris dataset (Scikit)\n\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\niris = load_iris()\n\nX = iris.data[:, (2, 3)]  # petal length, petal width\ny = (iris.target == 0).astype(np.int)\n
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from sklearn.linear_model import Perceptron\n\nper_clf = Perceptron(random_state=42)\nper_clf.fit(X, y)\n\ny_pred = per_clf.predict([[2, 0.5]])\nprint(y_pred)\n
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  • Perceptron learning algo very similar go SGD.
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  • Perceptrons do provide class probability (like Logistic Regression classsifier). They simply make predictions based on hard threshold.
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  • Some limitations can be eliminated with stacked Perceptrons.
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In [3]:
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import matplotlib.pyplot as plt\n\na = -per_clf.coef_[0][0] / per_clf.coef_[0][1]\nb = -per_clf.intercept_ / per_clf.coef_[0][1]\n\naxes = [0, 5, 0, 2]\n\nx0, x1 = np.meshgrid(\n        np.linspace(axes[0], axes[1], 500).reshape(-1, 1),\n        np.linspace(axes[2], axes[3], 200).reshape(-1, 1),\n    )\n\nX_new = np.c_[\n    x0.ravel(), \n    x1.ravel()]\n\ny_predict = per_clf.predict(X_new)\n\nzz = y_predict.reshape(x0.shape)\n\nplt.figure(figsize=(10, 4))\nplt.plot(X[y==0, 0], X[y==0, 1], "bs", label="Not Iris-Setosa")\nplt.plot(X[y==1, 0], X[y==1, 1], "yo", label="Iris-Setosa")\n\nplt.plot(\n    [axes[0], \n     axes[1]], \n    [a * axes[0] + b, \n     a * axes[1] + b], \n    "k-", linewidth=3)\n\nfrom matplotlib.colors import ListedColormap\ncustom_cmap = ListedColormap(['#9898ff', '#fafab0'])\n\nplt.contourf(x0, x1, zz, cmap=custom_cmap, linewidth=5)\nplt.xlabel("Petal length", fontsize=14)\nplt.ylabel("Petal width", fontsize=14)\nplt.legend(loc="lower right", fontsize=14)\nplt.axis(axes)\n\n#save_fig("perceptron_iris_plot")\nplt.show()\n
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MLPs and Backpropagation

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  • MLP contains one input layer, at least one hidden layer (LTU based), and one output layer (LTU based).
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  • Backpropagation intro'd in 1986 paper. Can be described as Gradient Descent using reverse-mode autodiff.
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  • For each training instance:
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    • Measure output error & how much each node in last hidden layer contributed to it.
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    • Measure how much each node in previous hidden layer contributed to this hidden layer.
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    • Repeat until input layer is reached (backward pass).
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    • Adjust each connection weight to reduce the error.
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  • To make algorithm work, MLP architecture changed to use logistic function delta(z) = 1/(1+exp(-z)) instead of step function.
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  • Backpropagation can also use hyperbolic tangent or ReLU functions if desired.
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# Activation functions\n\ndef logit(z):\n    return 1 / (1 + np.exp(-z))\n\ndef relu(z):\n    return np.maximum(0, z)\n\ndef derivative(f, z, eps=0.000001):\n    return (f(z + eps) - f(z - eps))/(2 * eps)\n
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z = np.linspace(-5, 5, 200)\n\nplt.figure(figsize=(11,4))\n\nplt.subplot(121)\nplt.plot(z, np.sign(z), "r-", linewidth=2, label="Step")\nplt.plot(z, logit(z), "g--", linewidth=2, label="Logit")\nplt.plot(z, np.tanh(z), "b-", linewidth=2, label="Tanh")\nplt.plot(z, relu(z), "m-.", linewidth=2, label="ReLU")\nplt.grid(True)\nplt.legend(loc="center right", fontsize=14)\nplt.title("Activation functions", fontsize=14)\nplt.axis([-5, 5, -1.2, 1.2])\n\nplt.subplot(122)\nplt.plot(z, derivative(np.sign, z), "r-", linewidth=2, label="Step")\nplt.plot(0, 0, "ro", markersize=5)\nplt.plot(0, 0, "rx", markersize=10)\nplt.plot(z, derivative(logit, z), "g--", linewidth=2, label="Logit")\nplt.plot(z, derivative(np.tanh, z), "b-", linewidth=2, label="Tanh")\nplt.plot(z, derivative(relu, z), "m-.", linewidth=2, label="ReLU")\nplt.grid(True)\n#plt.legend(loc="center right", fontsize=14)\nplt.title("Derivatives", fontsize=14)\nplt.axis([-5, 5, -0.2, 1.2])\n\n#save_fig("activation_functions_plot")\nplt.show()\n
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In [6]:
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# activation functions, continued\ndef heaviside(z):\n    return (z >= 0).astype(z.dtype)\n\ndef sigmoid(z):\n    return 1/(1+np.exp(-z))\n\ndef mlp_xor(x1, x2, activation=heaviside):\n    return activation(\n        -activation(x1 + x2 - 1.5) + activation(x1 + x2 - 0.5) - 0.5)\n
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In [7]:
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x1s = np.linspace(-0.2, 1.2, 100)\nx2s = np.linspace(-0.2, 1.2, 100)\nx1, x2 = np.meshgrid(x1s, x2s)\n\nz1 = mlp_xor(x1, x2, activation=heaviside)\nz2 = mlp_xor(x1, x2, activation=sigmoid)\n\nplt.figure(figsize=(10,4))\n\nplt.subplot(121)\nplt.contourf(x1, x2, z1)\nplt.plot([0, 1], [0, 1], "gs", markersize=20)\nplt.plot([0, 1], [1, 0], "y^", markersize=20)\nplt.title("Activation function: heaviside", fontsize=14)\nplt.grid(True)\n\nplt.subplot(122)\nplt.contourf(x1, x2, z2)\nplt.plot([0, 1], [0, 1], "gs", markersize=20)\nplt.plot([0, 1], [1, 0], "y^", markersize=20)\nplt.title("Activation function: sigmoid", fontsize=14)\nplt.grid(True)\nplt.show()\n
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MLP Training

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  • MLP often used for classification - each output corresponding to distinct binary class (ex: urgent/not-urgent, spam/not-spam, ...)
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  • If exclusive classes, output layer often uses shared softmax function.
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DNN Training with \"plain\" TF

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  • Use mini-batch gradient descent on MNIST dataset
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  • Specify #inputs, #outputs, #hidden neurons in each layer
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In [18]:
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import tensorflow as tf\n\ntf.reset_default_graph()\n\nn_inputs = 28*28  # MNIST\nn_hidden1 = 300\nn_hidden2 = 100\nn_outputs = 10\nlearning_rate = 0.01\n\n# placeholders for training data & targets\n# X,y only partially defined due to unknown #instances in training batches\n\nX = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")\ny = tf.placeholder(tf.int64,   shape=(None),           name="y")\n
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In [20]:
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# now need to create two hidden layers + one output layer\n\n'''\nNo need to define your own. TF shortcuts:\nfully_connected()\n'''\n\ndef neuron_layer(X, n_neurons, name, activation=None):\n    \n    # define a name scope to aid readability\n    with tf.name_scope(name):\n        \n        n_inputs = int(X.get_shape()[1])\n        \n        # create weights matrix. 2D (#inputs, #neurons)\n        # randomly initialized w/ truncated Gaussian, stdev = 2/sqrt(#inputs)\n        # aids convergence speed\n        \n        stddev = 1 / np.sqrt(n_inputs)\n        init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev)       \n        W = tf.Variable(init, name="weights")\n        \n        # create bias variable, initialized to zero, one param per neuron\n        b = tf.Variable(tf.zeros([n_neurons]), name="biases")\n        \n        # Z = X dot W + b\n        Z = tf.matmul(X, W) + b\n        \n        # return relu(z), or simply z\n        if activation=="relu":\n            return tf.nn.relu(Z)\n        else:\n            return Z\n
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with tf.name_scope("dnn"):\n    hidden1 = neuron_layer(X,       n_hidden1, "hidden1", activation="relu")\n    hidden2 = neuron_layer(hidden1, n_hidden2, "hidden2", activation="relu")\n    \n    # logits = NN output before going thru softmax activation\n    logits =  neuron_layer(hidden2, n_outputs, "output")\n\nwith tf.name_scope("loss"):\n    \n    # sparse_softmax_cross_entropy_with_logits() -- TF routine, handles corner cases for you.\n    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n        labels=y, \n        logits=logits)\n    \n    # use reduce_mean() to find mean cross-entropy over all instances.\n    loss = tf.reduce_mean(\n        xentropy, \n        name="loss")\n\n# use GD to handle cost function, ie minimize loss\nwith tf.name_scope("train"):\n    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n    training_op = optimizer.minimize(loss)\n\n# use accuracy as performance measure.\n\nwith tf.name_scope("eval"):        # verify whether highest logit corresponds to target class\n    correct = tf.nn.in_top_k(      # using in_top_k(), returns 1D tensor of booleans\n        logits, y, 1)\n    \n    accuracy = tf.reduce_mean(             # recast booleans to float & find avg.\n        tf.cast(correct, tf.float32))      # this gives overall accuracy number.\n\ninit = tf.global_variables_initializer()   # initializer node\nsaver = tf.train.Saver()                   # to save trained params to disk\n
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Execution Phase

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  • Load MNIST using TF helpers (fetch, auto-scale, shuffle, provide minibatch function)
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# load MNIST\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets("/tmp/data/")\n\nn_epochs = 20\nbatch_size = 50\n
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Extracting /tmp/data/train-images-idx3-ubyte.gz\nExtracting /tmp/data/train-labels-idx1-ubyte.gz\nExtracting /tmp/data/t10k-images-idx3-ubyte.gz\nExtracting /tmp/data/t10k-labels-idx1-ubyte.gz\n
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# Train\nwith tf.Session() as sess:\n\n    init.run() # initialize all variables\n    \n    for epoch in range(n_epochs):\n        for iteration in range(mnist.train.num_examples // batch_size):\n\n            # use next_batch() to fetch data\n            X_batch, y_batch = mnist.train.next_batch(batch_size)\n\n            sess.run(\n                training_op, \n                feed_dict={X: X_batch, y: y_batch})\n\n        acc_train = accuracy.eval(\n            feed_dict={X: X_batch, y: y_batch})\n\n        acc_test = accuracy.eval(\n            feed_dict={X: mnist.test.images,\n                       y: mnist.test.labels})\n\n        print(epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)\n\n        save_path = saver.save(sess, "./my_model_final.ckpt")\n
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0 Train accuracy: 0.94 Test accuracy: 0.8753\n1 Train accuracy: 0.9 Test accuracy: 0.9087\n2 Train accuracy: 0.94 Test accuracy: 0.9212\n3 Train accuracy: 0.88 Test accuracy: 0.9239\n4 Train accuracy: 0.88 Test accuracy: 0.9335\n5 Train accuracy: 0.96 Test accuracy: 0.9365\n6 Train accuracy: 0.92 Test accuracy: 0.9415\n7 Train accuracy: 0.94 Test accuracy: 0.9449\n8 Train accuracy: 0.96 Test accuracy: 0.9459\n9 Train accuracy: 0.94 Test accuracy: 0.9497\n10 Train accuracy: 1.0 Test accuracy: 0.9539\n11 Train accuracy: 0.94 Test accuracy: 0.9563\n12 Train accuracy: 0.98 Test accuracy: 0.958\n13 Train accuracy: 0.98 Test accuracy: 0.9584\n14 Train accuracy: 0.94 Test accuracy: 0.9614\n15 Train accuracy: 1.0 Test accuracy: 0.9622\n16 Train accuracy: 0.96 Test accuracy: 0.9639\n17 Train accuracy: 0.92 Test accuracy: 0.9635\n18 Train accuracy: 0.98 Test accuracy: 0.9654\n19 Train accuracy: 0.92 Test accuracy: 0.9667\n
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Using in production

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with tf.Session() as sess:\n\n    # load from disk\n    saver.restore(sess, save_path) #"my_model_final.ckpt")\n    \n    # grab images you want to classify\n    X_new_scaled = mnist.test.images[:20]\n    \n    Z = logits.eval(feed_dict={X: X_new_scaled})\n    print(np.argmax(Z, axis=1))\n    print(mnist.test.labels[:20])\n
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[7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4]\n[7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\n
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Parameter Tuning

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  • Way too many parameters - Grid Search approach not time-effective.
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  • 1st option: randomized search.
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  • 2nd option: Oscar
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Number of hidden layers

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Number of neurons per hidden layer

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  • Determined by input dimensions. Ex: MNIST requires 28x28 inputs, 10 outputs
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Activation functions

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\n\n\n" }, { "path": "ch10-neural-nets.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro - Perceptrons\\n\",\n \"* Simplest ANN architecture\\n\",\n \"* Uses *linear threshold unit (LTU)* - returns weight sum of inputs, applies *step function* to sum, outputs result\\n\",\n \"* Single LTU can be used for simple linear binary classification\\n\",\n \"* Perceptron = single layer of LTUs, each one connected to all inputs\\n\",\n \"* Percepton training based on *Hebb's Rule*. (basically, connection weight between two neurons goes up when they have same output.)\\n\",\n \"* Linear decision boundary, so Perceptrons not capable of learning complex patterns.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Perceptron with Iris dataset (Scikit)\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"from sklearn.datasets import load_iris\\n\",\n \"iris = load_iris()\\n\",\n \"\\n\",\n \"X = iris.data[:, (2, 3)] # petal length, petal width\\n\",\n \"y = (iris.target == 0).astype(np.int)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import Perceptron\\n\",\n \"\\n\",\n \"per_clf = Perceptron(random_state=42)\\n\",\n \"per_clf.fit(X, y)\\n\",\n \"\\n\",\n \"y_pred = per_clf.predict([[2, 0.5]])\\n\",\n \"print(y_pred)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Perceptron learning algo very similar go SGD.\\n\",\n \"* Perceptrons do provide class probability (like Logistic Regression classsifier). They simply make predictions based on hard threshold.\\n\",\n \"* Some limitations can be eliminated with stacked Perceptrons.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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VKlMNatG0lExOVmHJs2zSI6uj4nTmi8ioiI3LoCPJ2ASGry5w/hyy9f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\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"a = -per_clf.coef_[0][0] / per_clf.coef_[0][1]\\n\",\n \"b = -per_clf.intercept_ / per_clf.coef_[0][1]\\n\",\n \"\\n\",\n \"axes = [0, 5, 0, 2]\\n\",\n \"\\n\",\n \"x0, x1 = np.meshgrid(\\n\",\n \" np.linspace(axes[0], axes[1], 500).reshape(-1, 1),\\n\",\n \" np.linspace(axes[2], axes[3], 200).reshape(-1, 1),\\n\",\n \" )\\n\",\n \"\\n\",\n \"X_new = np.c_[\\n\",\n \" x0.ravel(), \\n\",\n \" x1.ravel()]\\n\",\n \"\\n\",\n \"y_predict = per_clf.predict(X_new)\\n\",\n \"\\n\",\n \"zz = y_predict.reshape(x0.shape)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 4))\\n\",\n \"plt.plot(X[y==0, 0], X[y==0, 1], \\\"bs\\\", label=\\\"Not Iris-Setosa\\\")\\n\",\n \"plt.plot(X[y==1, 0], X[y==1, 1], \\\"yo\\\", label=\\\"Iris-Setosa\\\")\\n\",\n \"\\n\",\n \"plt.plot(\\n\",\n \" [axes[0], \\n\",\n \" axes[1]], \\n\",\n \" [a * axes[0] + b, \\n\",\n \" a * axes[1] + b], \\n\",\n \" \\\"k-\\\", linewidth=3)\\n\",\n \"\\n\",\n \"from matplotlib.colors import ListedColormap\\n\",\n \"custom_cmap = ListedColormap(['#9898ff', '#fafab0'])\\n\",\n \"\\n\",\n \"plt.contourf(x0, x1, zz, cmap=custom_cmap, linewidth=5)\\n\",\n \"plt.xlabel(\\\"Petal length\\\", fontsize=14)\\n\",\n \"plt.ylabel(\\\"Petal width\\\", fontsize=14)\\n\",\n \"plt.legend(loc=\\\"lower right\\\", fontsize=14)\\n\",\n \"plt.axis(axes)\\n\",\n \"\\n\",\n \"#save_fig(\\\"perceptron_iris_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### MLPs and Backpropagation\\n\",\n \"* MLP contains one input layer, at least one hidden layer (LTU based), and one output layer (LTU based).\\n\",\n \"* Backpropagation intro'd in [1986 paper](https://goo.gl/Wl7Xyc). Can be described as Gradient Descent using reverse-mode autodiff.\\n\",\n \"* For each training instance:\\n\",\n \" * Find output of each node in each consecutive layer (forward pass).\\n\",\n \" * Measure output error & how much each node in last hidden layer contributed to it.\\n\",\n \" * Measure how much each node in *previous* hidden layer contributed to *this* hidden layer.\\n\",\n \" * Repeat until *input* layer is reached (backward pass).\\n\",\n \" * Adjust each connection weight to reduce the error.\\n\",\n \"* To make algorithm work, MLP architecture changed to use logistic function delta(z) = 1/(1+exp(-z)) instead of step function.\\n\",\n \"* Backpropagation can also use hyperbolic tangent or ReLU functions if desired.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Activation functions\\n\",\n \"\\n\",\n \"def logit(z):\\n\",\n \" return 1 / (1 + np.exp(-z))\\n\",\n \"\\n\",\n \"def relu(z):\\n\",\n \" return np.maximum(0, z)\\n\",\n \"\\n\",\n \"def derivative(f, z, eps=0.000001):\\n\",\n \" return (f(z + eps) - f(z - eps))/(2 * eps)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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kNtvr66u3dz+auvuiWvbEI5QTciWIkvqW7JE6BpOdMZ4CRhHCCaZCI5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\\nAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"z = np.linspace(-5, 5, 200)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11,4))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.plot(z, np.sign(z), \\\"r-\\\", linewidth=2, label=\\\"Step\\\")\\n\",\n \"plt.plot(z, logit(z), \\\"g--\\\", linewidth=2, label=\\\"Logit\\\")\\n\",\n \"plt.plot(z, np.tanh(z), \\\"b-\\\", linewidth=2, label=\\\"Tanh\\\")\\n\",\n \"plt.plot(z, relu(z), \\\"m-.\\\", linewidth=2, label=\\\"ReLU\\\")\\n\",\n \"plt.grid(True)\\n\",\n \"plt.legend(loc=\\\"center right\\\", fontsize=14)\\n\",\n \"plt.title(\\\"Activation functions\\\", fontsize=14)\\n\",\n \"plt.axis([-5, 5, -1.2, 1.2])\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.plot(z, derivative(np.sign, z), \\\"r-\\\", linewidth=2, label=\\\"Step\\\")\\n\",\n \"plt.plot(0, 0, \\\"ro\\\", markersize=5)\\n\",\n \"plt.plot(0, 0, \\\"rx\\\", markersize=10)\\n\",\n \"plt.plot(z, derivative(logit, z), \\\"g--\\\", linewidth=2, label=\\\"Logit\\\")\\n\",\n \"plt.plot(z, derivative(np.tanh, z), \\\"b-\\\", linewidth=2, label=\\\"Tanh\\\")\\n\",\n \"plt.plot(z, derivative(relu, z), \\\"m-.\\\", linewidth=2, label=\\\"ReLU\\\")\\n\",\n \"plt.grid(True)\\n\",\n \"#plt.legend(loc=\\\"center right\\\", fontsize=14)\\n\",\n \"plt.title(\\\"Derivatives\\\", fontsize=14)\\n\",\n \"plt.axis([-5, 5, -0.2, 1.2])\\n\",\n \"\\n\",\n \"#save_fig(\\\"activation_functions_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# activation functions, continued\\n\",\n \"def heaviside(z):\\n\",\n \" return (z >= 0).astype(z.dtype)\\n\",\n \"\\n\",\n \"def sigmoid(z):\\n\",\n \" return 1/(1+np.exp(-z))\\n\",\n \"\\n\",\n \"def mlp_xor(x1, x2, activation=heaviside):\\n\",\n \" return activation(\\n\",\n \" -activation(x1 + x2 - 1.5) + activation(x1 + x2 - 0.5) - 0.5)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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5pO3ivz25+Fa9eWhWOdm7hzA4vSPTohkzBmwUWLbwWWhWcnloK66egb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\\nUhgTERERydH/D++Sw+hSouxDAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1s = np.linspace(-0.2, 1.2, 100)\\n\",\n \"x2s = np.linspace(-0.2, 1.2, 100)\\n\",\n \"x1, x2 = np.meshgrid(x1s, x2s)\\n\",\n \"\\n\",\n \"z1 = mlp_xor(x1, x2, activation=heaviside)\\n\",\n \"z2 = mlp_xor(x1, x2, activation=sigmoid)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10,4))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.contourf(x1, x2, z1)\\n\",\n \"plt.plot([0, 1], [0, 1], \\\"gs\\\", markersize=20)\\n\",\n \"plt.plot([0, 1], [1, 0], \\\"y^\\\", markersize=20)\\n\",\n \"plt.title(\\\"Activation function: heaviside\\\", fontsize=14)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.contourf(x1, x2, z2)\\n\",\n \"plt.plot([0, 1], [0, 1], \\\"gs\\\", markersize=20)\\n\",\n \"plt.plot([0, 1], [1, 0], \\\"y^\\\", markersize=20)\\n\",\n \"plt.title(\\\"Activation function: sigmoid\\\", fontsize=14)\\n\",\n \"plt.grid(True)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### MLP Training\\n\",\n \"* MLP often used for classification - each output corresponding to distinct binary class (ex: urgent/not-urgent, spam/not-spam, ...)\\n\",\n \"* If exclusive classes, output layer often uses shared **softmax** function.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### DNN Training with \\\"plain\\\" TF\\n\",\n \"* Use **mini-batch gradient descent** on MNIST dataset\\n\",\n \"* Specify #inputs, #outputs, #hidden neurons in each layer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_inputs = 28*28 # MNIST\\n\",\n \"n_hidden1 = 300\\n\",\n \"n_hidden2 = 100\\n\",\n \"n_outputs = 10\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"# placeholders for training data & targets\\n\",\n \"# X,y only partially defined due to unknown #instances in training batches\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n \"y = tf.placeholder(tf.int64, shape=(None), name=\\\"y\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# now need to create two hidden layers + one output layer\\n\",\n \"\\n\",\n \"'''\\n\",\n \"No need to define your own. TF shortcuts:\\n\",\n \"fully_connected()\\n\",\n \"'''\\n\",\n \"\\n\",\n \"def neuron_layer(X, n_neurons, name, activation=None):\\n\",\n \" \\n\",\n \" # define a name scope to aid readability\\n\",\n \" with tf.name_scope(name):\\n\",\n \" \\n\",\n \" n_inputs = int(X.get_shape()[1])\\n\",\n \" \\n\",\n \" # create weights matrix. 2D (#inputs, #neurons)\\n\",\n \" # randomly initialized w/ truncated Gaussian, stdev = 2/sqrt(#inputs)\\n\",\n \" # aids convergence speed\\n\",\n \" \\n\",\n \" stddev = 1 / np.sqrt(n_inputs)\\n\",\n \" init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev) \\n\",\n \" W = tf.Variable(init, name=\\\"weights\\\")\\n\",\n \" \\n\",\n \" # create bias variable, initialized to zero, one param per neuron\\n\",\n \" b = tf.Variable(tf.zeros([n_neurons]), name=\\\"biases\\\")\\n\",\n \" \\n\",\n \" # Z = X dot W + b\\n\",\n \" Z = tf.matmul(X, W) + b\\n\",\n \" \\n\",\n \" # return relu(z), or simply z\\n\",\n \" if activation==\\\"relu\\\":\\n\",\n \" return tf.nn.relu(Z)\\n\",\n \" else:\\n\",\n \" return Z\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n \" hidden1 = neuron_layer(X, n_hidden1, \\\"hidden1\\\", activation=\\\"relu\\\")\\n\",\n \" hidden2 = neuron_layer(hidden1, n_hidden2, \\\"hidden2\\\", activation=\\\"relu\\\")\\n\",\n \" \\n\",\n \" # logits = NN output before going thru softmax activation\\n\",\n \" logits = neuron_layer(hidden2, n_outputs, \\\"output\\\")\\n\",\n \"\\n\",\n \"with tf.name_scope(\\\"loss\\\"):\\n\",\n \" \\n\",\n \" # sparse_softmax_cross_entropy_with_logits() -- TF routine, handles corner cases for you.\\n\",\n \" xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\\n\",\n \" labels=y, \\n\",\n \" logits=logits)\\n\",\n \" \\n\",\n \" # use reduce_mean() to find mean cross-entropy over all instances.\\n\",\n \" loss = tf.reduce_mean(\\n\",\n \" xentropy, \\n\",\n \" name=\\\"loss\\\")\\n\",\n \"\\n\",\n \"# use GD to handle cost function, ie minimize loss\\n\",\n \"with tf.name_scope(\\\"train\\\"):\\n\",\n \" optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n \" training_op = optimizer.minimize(loss)\\n\",\n \"\\n\",\n \"# use accuracy as performance measure.\\n\",\n \"\\n\",\n \"with tf.name_scope(\\\"eval\\\"): # verify whether highest logit corresponds to target class\\n\",\n \" correct = tf.nn.in_top_k( # using in_top_k(), returns 1D tensor of booleans\\n\",\n \" logits, y, 1)\\n\",\n \" \\n\",\n \" accuracy = tf.reduce_mean( # recast booleans to float & find avg.\\n\",\n \" tf.cast(correct, tf.float32)) # this gives overall accuracy number.\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer() # initializer node\\n\",\n \"saver = tf.train.Saver() # to save trained params to disk\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Execution Phase\\n\",\n \"* Load MNIST using TF helpers (fetch, auto-scale, shuffle, provide minibatch function)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n ]\n }\n ],\n \"source\": [\n \"# load MNIST\\n\",\n \"\\n\",\n \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\")\\n\",\n \"\\n\",\n \"n_epochs = 20\\n\",\n \"batch_size = 50\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 Train accuracy: 0.94 Test accuracy: 0.8753\\n\",\n \"1 Train accuracy: 0.9 Test accuracy: 0.9087\\n\",\n \"2 Train accuracy: 0.94 Test accuracy: 0.9212\\n\",\n \"3 Train accuracy: 0.88 Test accuracy: 0.9239\\n\",\n \"4 Train accuracy: 0.88 Test accuracy: 0.9335\\n\",\n \"5 Train accuracy: 0.96 Test accuracy: 0.9365\\n\",\n \"6 Train accuracy: 0.92 Test accuracy: 0.9415\\n\",\n \"7 Train accuracy: 0.94 Test accuracy: 0.9449\\n\",\n \"8 Train accuracy: 0.96 Test accuracy: 0.9459\\n\",\n \"9 Train accuracy: 0.94 Test accuracy: 0.9497\\n\",\n \"10 Train accuracy: 1.0 Test accuracy: 0.9539\\n\",\n \"11 Train accuracy: 0.94 Test accuracy: 0.9563\\n\",\n \"12 Train accuracy: 0.98 Test accuracy: 0.958\\n\",\n \"13 Train accuracy: 0.98 Test accuracy: 0.9584\\n\",\n \"14 Train accuracy: 0.94 Test accuracy: 0.9614\\n\",\n \"15 Train accuracy: 1.0 Test accuracy: 0.9622\\n\",\n \"16 Train accuracy: 0.96 Test accuracy: 0.9639\\n\",\n \"17 Train accuracy: 0.92 Test accuracy: 0.9635\\n\",\n \"18 Train accuracy: 0.98 Test accuracy: 0.9654\\n\",\n \"19 Train accuracy: 0.92 Test accuracy: 0.9667\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Train\\n\",\n \"with tf.Session() as sess:\\n\",\n \"\\n\",\n \" init.run() # initialize all variables\\n\",\n \" \\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" for iteration in range(mnist.train.num_examples // batch_size):\\n\",\n \"\\n\",\n \" # use next_batch() to fetch data\\n\",\n \" X_batch, y_batch = mnist.train.next_batch(batch_size)\\n\",\n \"\\n\",\n \" sess.run(\\n\",\n \" training_op, \\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \"\\n\",\n \" acc_train = accuracy.eval(\\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \"\\n\",\n \" acc_test = accuracy.eval(\\n\",\n \" feed_dict={X: mnist.test.images,\\n\",\n \" y: mnist.test.labels})\\n\",\n \"\\n\",\n \" print(epoch, \\\"Train accuracy:\\\", acc_train, \\\"Test accuracy:\\\", acc_test)\\n\",\n \"\\n\",\n \" save_path = saver.save(sess, \\\"./my_model_final.ckpt\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"### Using in production\\n\",\n \"* Now trained - you can use the NN to predict.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4]\\n\",\n \"[7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"with tf.Session() as sess:\\n\",\n \"\\n\",\n \" # load from disk\\n\",\n \" saver.restore(sess, save_path) #\\\"my_model_final.ckpt\\\")\\n\",\n \" \\n\",\n \" # grab images you want to classify\\n\",\n \" X_new_scaled = mnist.test.images[:20]\\n\",\n \" \\n\",\n \" Z = logits.eval(feed_dict={X: X_new_scaled})\\n\",\n \" print(np.argmax(Z, axis=1))\\n\",\n \" print(mnist.test.labels[:20])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Parameter Tuning\\n\",\n \"* Way too many parameters - Grid Search approach not time-effective.\\n\",\n \"* 1st option: [randomized search](https://goo.gl/QFjMKu).\\n\",\n \"* 2nd option: [Oscar](http://oscar.calldesk.ai/)\\n\",\n \"* Start with common defaults to restrict search space.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"### Number of hidden layers\\n\",\n \"* Deep nets have **much better parameter efficiency** than shallow ones. (They can model complex functions with much fewer neurons.)\\n\",\n \"* Largely due to hierarchical nature of most data modeling probs\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"### Number of neurons per hidden layer\\n\",\n \"* Determined by input dimensions. Ex: MNIST requires 28x28 inputs, 10 outputs\\n\",\n \"* Try increasing # of layers before # neurons/layer.\\n\",\n \"* Simple trick: pick model w/ excessive layers & neurons, use early stopping, regularization, dropout, etc. to prevent overfit.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"### Activation functions\\n\",\n \"* Defaults:\\n\",\n \" * use ReLU in hidden layers. Faster & helps avoid GD getting stuck on local plateaus.\\n\",\n \" * use Softmax in output layer (for classification; none needed for regression.) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch11-DNN-training.html", "content": "\n\n\nch11-DNN-training\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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Vanishing & Exploding Gradients

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  • gradients get smaller as algorithm progresses to lower layers. Eventually GD leaves lower weights virtually unchanged. so training never converges.
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  • gradients can also grow out of control (often seen in RNNs).
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  • Significant paper - using combo of logistic sigmoid activiation with random weight initialization (normal, mean=0, stdev=1) -- output variance was >> input variance.
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  • logistic activation: function saturates at 0 or 1 with derivative very close to 0 ==> so backpropagation has no gradient to use.\n\"sigmoid\"
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Xavier & He Initialization

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  • For signals to flow properly in both directions, each layer's output variance should equal its input variance.
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  • Recommends initializing connection weights with random settings using #ins, #outs\n\"init
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  • Default: fully_connected() function uses Xavier initialization w/ uniform distribution. Change to He initialization by using variance_scaling_initializer() function
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In [8]:
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import tensorflow as tf\nfrom tensorflow.contrib.layers import fully_connected\n\nn_inputs = 28*28\nn_hidden1 = 300\n\nX = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")\n\nhe_init = tf.contrib.layers.variance_scaling_initializer()\nhidden1 = fully_connected(X, n_hidden1, weights_initializer=he_init, scope="h1")\n
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Non-Saturating Activation Functions

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  • ReLU activations suffer from dying ReLU problem (they stop emitting anything other than zero).
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  • Workaround: the leaku ReLU. Alpha defines leakage; typical set to 0.01.\n\"leaku
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  • Also: randomized leaky ReLU (RReLU) (randomized alpha)
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  • Also: parametric leaky RuLE (PReLU) (alpha can be modified during backprop)
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  • Also: exponential linear unit (ELU). Allows negative values when z<0; non-zero gradient for z<0 (avoids dying units issue); smooth function everywhere. Uses exponential function, so harder to compute. paper\n\"exponential-relu\"
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In [9]:
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# TF doesn't have leaky ReLU predefined, but easy to build.\n\ndef leaky_relu(z, name=None):\n    return tf.maximum(0.01 * z, z, name=name)\n\nhidden1 = fully_connected(X, n_hidden1, activation_fn=leaky_relu)\n
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Batch Normalization

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  • proposed to solve vanishing/exploding gradients.
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  • Idea: pror to activation function,\n 1) zero-center & normalize inputs\n 2) scale & shift result with 2 new params per layer
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  • Net effect: model learns optimal scale & mean of inputs for each layer
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  • Algorithm:\n\"batch

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  • Does add computational complexity. Consider plain ELU + He initializaton as well.

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Batch Normalization with TF

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  • batch_normalization() - centers & normalizes inputs
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In [10]:
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# use MNIST dataset again\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets("/tmp/data/")\n
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Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\nExtracting /tmp/data/train-images-idx3-ubyte.gz\nSuccessfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\nExtracting /tmp/data/train-labels-idx1-ubyte.gz\nSuccessfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\nExtracting /tmp/data/t10k-images-idx3-ubyte.gz\nSuccessfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\nExtracting /tmp/data/t10k-labels-idx1-ubyte.gz\n
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In [11]:
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#setup\n\nimport tensorflow as tf\nfrom tensorflow.contrib.layers import batch_norm, fully_connected\n\ntf.reset_default_graph() \n\nn_inputs = 28 * 28\nn_hidden1 = 300\nn_hidden2 = 100\nn_outputs = 10\nlearning_rate = 0.01\n\nX = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")\ny = tf.placeholder(tf.int64, shape=(None), name="y")\n\ndef leaky_relu(z, name=None):\n  return tf.maximum(0.01 * z, z, name=name)\n\n# is_training: tells batch_norm() whether to use current minibatch's mean & stdev \n# (found during training) or use running avgs (during testing)\n\nwith tf.name_scope("dnn"):\n    hidden1 = fully_connected(X, n_hidden1, activation_fn=leaky_relu, scope="hidden1")\n    hidden2 = fully_connected(hidden1, n_hidden2, activation_fn=leaky_relu, scope="hidden2")\n    logits = fully_connected(hidden2, n_outputs, activation_fn=None, scope="outputs")\n\nwith tf.name_scope("loss"):\n    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n    loss = tf.reduce_mean(xentropy, name="loss")\n\nwith tf.name_scope("train"):\n    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n    training_op = optimizer.minimize(loss)\n\nwith tf.name_scope("eval"):\n    correct = tf.nn.in_top_k(logits, y, 1)\n    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n\ninit = tf.global_variables_initializer()\nsaver = tf.train.Saver()\n
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In [12]:
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n_epochs = 20\nbatch_size = 100\n\nwith tf.Session() as sess:\n    \n    init.run()\n    \n    for epoch in range(n_epochs):\n        \n        for iteration in range(len(mnist.test.labels)//batch_size):\n            \n            X_batch, y_batch = mnist.train.next_batch(batch_size)\n            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n            \n        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n        acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels})\n        \n        print(epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)\n\n    save_path = saver.save(sess, "my_model_final.ckpt")\n
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0 Train accuracy: 0.6 Test accuracy: 0.642\n1 Train accuracy: 0.73 Test accuracy: 0.7824\n2 Train accuracy: 0.81 Test accuracy: 0.827\n3 Train accuracy: 0.84 Test accuracy: 0.8539\n4 Train accuracy: 0.8 Test accuracy: 0.8686\n5 Train accuracy: 0.87 Test accuracy: 0.8759\n6 Train accuracy: 0.85 Test accuracy: 0.8843\n7 Train accuracy: 0.91 Test accuracy: 0.8903\n8 Train accuracy: 0.86 Test accuracy: 0.8969\n9 Train accuracy: 0.91 Test accuracy: 0.9018\n10 Train accuracy: 0.91 Test accuracy: 0.9014\n11 Train accuracy: 0.86 Test accuracy: 0.9065\n12 Train accuracy: 0.88 Test accuracy: 0.9078\n13 Train accuracy: 0.87 Test accuracy: 0.91\n14 Train accuracy: 0.93 Test accuracy: 0.911\n15 Train accuracy: 0.9 Test accuracy: 0.9123\n16 Train accuracy: 0.91 Test accuracy: 0.9141\n17 Train accuracy: 0.9 Test accuracy: 0.9149\n18 Train accuracy: 0.92 Test accuracy: 0.9159\n19 Train accuracy: 0.93 Test accuracy: 0.9174\n
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Gradient Clipping

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  • to limit exploding gradients problem. Clip during backprop.
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In [13]:
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threshold = 1.0\n\noptimizer = tf.train.GradientDescentOptimizer(\n    learning_rate)\n\ngrads_and_vars = optimizer.compute_gradients(\n    loss)\n\ncapped_gvs = [\n    (tf.clip_by_value(\n        grad, -threshold, threshold), var)\n    for grad, var in grads_and_vars]\n\ntraining_op = optimizer.apply_gradients(capped_gvs)\n
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Pretrained Layers & Reuse

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  • best practice: look for existing NN that tackles similar task, then reuse lower layers (aka transfer learning).
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In [14]:
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# Reuse with TF\n
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Reusing Models from Other Frameworks

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In [16]:
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'''\noriginal_w = [] # Load the weights from the other framework\noriginal_b = [] # Load the biases from the other framework\n\nX = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")\nhidden1 = fully_connected(X, n_hidden1, scope="hidden1")\n\n[...] # # Build the rest of the model\n\n# Get a handle on the variables created by fully_connected()\n\nwith tf.variable_scope("", default_name="", reuse=True): # root scope\n    hidden1_weights = tf.get_variable("hidden1/weights")\n    hidden1_biases = tf.get_variable("hidden1/biases")\n\n# Create nodes to assign arbitrary values to the weights and biases\noriginal_weights = tf.placeholder(tf.float32, shape=(n_inputs, n_hidden1))\noriginal_biases = tf.placeholder(tf.float32, shape=(n_hidden1))\n\nassign_hidden1_weights = tf.assign(hidden1_weights, original_weights)\nassign_hidden1_biases = tf.assign(hidden1_biases, original_biases)\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n    sess.run(init)\n    sess.run(\n        assign_hidden1_weights, \n        feed_dict={original_weights: original_w})\n        \n    sess.run(\n        assign_hidden1_biases, \n        feed_dict={original_biases: original_b})\n        \n    [...] # Train the model on your new task\n'''\n
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'\\noriginal_w = [] # Load the weights from the other framework\\noriginal_b = [] # Load the biases from the other framework\\n\\nX = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")\\nhidden1 = fully_connected(X, n_hidden1, scope="hidden1")\\n\\n[...] # # Build the rest of the model\\n\\n# Get a handle on the variables created by fully_connected()\\n\\nwith tf.variable_scope("", default_name="", reuse=True): # root scope\\n    hidden1_weights = tf.get_variable("hidden1/weights")\\n    hidden1_biases = tf.get_variable("hidden1/biases")\\n\\n# Create nodes to assign arbitrary values to the weights and biases\\noriginal_weights = tf.placeholder(tf.float32, shape=(n_inputs, n_hidden1))\\noriginal_biases = tf.placeholder(tf.float32, shape=(n_hidden1))\\n\\nassign_hidden1_weights = tf.assign(hidden1_weights, original_weights)\\nassign_hidden1_biases = tf.assign(hidden1_biases, original_biases)\\n\\ninit = tf.global_variables_initializer()\\n\\nwith tf.Session() as sess:\\n    sess.run(init)\\n    sess.run(\\n        assign_hidden1_weights, \\n        feed_dict={original_weights: original_w})\\n        \\n    sess.run(\\n        assign_hidden1_biases, \\n        feed_dict={original_biases: original_b})\\n        \\n    [...] # Train the model on your new task\\n'
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Freezing Lower Layers

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In [17]:
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# provide all trainable var in hidden layers 3,4 & outputs to optimizer function\n# (this omits vars in hidden layers 1,2)\n'''\ntrain_vars = tf.get_collection(\n    tf.GraphKeys.TRAINABLE_VARIABLES,\n    scope="hidden[34]|outputs")\n\n# minimizer can't touch layers 1,2 - they're "frozen"\n\ntraining_op = optimizer.minimize(\n    loss, \n    var_list=train_vars)\n'''\n
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'\\ntrain_vars = tf.get_collection(\\n    tf.GraphKeys.TRAINABLE_VARIABLES,\\n    scope="hidden[34]|outputs")\\n\\n# minimizer can\\'t touch layers 1,2 - they\\'re "frozen"\\n\\ntraining_op = optimizer.minimize(\\n    loss, \\n    var_list=train_vars)\\n'
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Caching Lower Layers

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In [18]:
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'''import numpy as np\n\nn_epochs = 100\nn_batches = 500\n\nfor epoch in range(n_epochs):\n    shuffled_idx = rnd.permutation(\n        len(hidden2_outputs))\n    \n    hidden2_batches = np.array_split(\n        hidden2_outputs[shuffled_idx], \n        n_batches)\n    \ny_batches = np.array_split(\n    y_train[shuffled_idx], \n    n_batches)\n\nfor hidden2_batch, y_batch in zip(hidden2_batches, y_batches):\n    sess.run(\n        training_op, \n        feed_dict={hidden2: hidden2_batch, y: y_batch})\n'''\n
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Out[18]:
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'import numpy as np\\n\\nn_epochs = 100\\nn_batches = 500\\n\\nfor epoch in range(n_epochs):\\n    shuffled_idx = rnd.permutation(\\n        len(hidden2_outputs))\\n    \\n    hidden2_batches = np.array_split(\\n        hidden2_outputs[shuffled_idx], \\n        n_batches)\\n    \\ny_batches = np.array_split(\\n    y_train[shuffled_idx], \\n    n_batches)\\n\\nfor hidden2_batch, y_batch in zip(hidden2_batches, y_batches):\\n    sess.run(\\n        training_op, \\n        feed_dict={hidden2: hidden2_batch, y: y_batch})\\n'
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Tweaking/Dropping/Replacing Upper Layers

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  • iterative freeze/train/compare process to see how many upper layers needed
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Model Zoos

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Unsupervised pre-training

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  • Tough problem, but doable.
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Pre-training on easily labeled data - reuse lower layers for \"real\" task

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Faster Optimizers

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  • Training speedup strategies thus far: 1) smart weight initializations, 2) smart activation functions, 3) batch normalization, 4) reuse of pretraining.
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  • Better optimizer choices:

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    • Nesterov Accelerated Gradients
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    • AdaGrad
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    • RMSProp
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    • Adam (should almost always use this one)
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  • Worth noting: below techniques rely on 1st-order partial derivatives (Jacobians); more techniques in literature use 2nd-order derivs (Hessians). Not viable for most deep learning due to memory & computational requirements.

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Momentum optimization

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  • local gradient added to a momentum vector (m) multiplied by learning rate (n)
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  • ie, gradient used as an accelerant - not as a speed.
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  • beta hyperparameter serves as friction mechanism. 0 = high friction, 1 = no friction.
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  • Momentum optimization escapes plateaus much faster than GD.
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In [24]:
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# in TF\n\noptimizer = tf.train.MomentumOptimizer(\n    learning_rate=learning_rate,\n    momentum=0.9)\n
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Nesterov Accelerated Gradient

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  • idea: measure cost function gradient slightly ahead in direction of momentum.\n\"nesterov
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In [25]:
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# in TF\n\noptimizer = tf.train.MomentumOptimizer(\n    learning_rate=learning_rate,\n    momentum=0.9, \n    use_nesterov=True)\n
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AdaGrad

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  • Scales gradient vector along steepest dimensions, ie it decays the learning rate faster for steep dimensions. (ie adaptive learning rate)
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RMSProp

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  • Fixes AdaGrad problem by accumulating most recent gradients (instead of all).
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  • Better than AdaGrad on all but very simple problems. Also better than MO and Nesterov.
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In [26]:
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# in TF\n\noptimizer = tf.train.RMSPropOptimizer(\n    learning_rate=learning_rate,\n    momentum=0.9, \n    decay=0.9, \n    epsilon=1e-10)\n
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Adam Optimization (paper:)

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  • Keeps track of decaying past gradients (like Momentum Optimization)
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  • Keeps track of decaying past squared gradients (like RMSProp)

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  • Default params in TF:

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  • Momentum decay param (beta1) usually set to 0.9
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  • Scaling decay param (beta2) usually set to 0.999
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  • Smoothing term (epsilon) usually set to 10e-8
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In [27]:
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# in TF\n\noptimizer = tf.train.AdamOptimizer(\n    learning_rate=learning_rate)\n
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Learning Rate Scheduling

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In [28]:
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# in TF\n\ninitial_learning_rate = 0.1\ndecay_steps = 10000\ndecay_rate = 1/10\n\nglobal_step = tf.Variable(\n    0, trainable=False)\n\nlearning_rate = tf.train.exponential_decay(\n    initial_learning_rate, \n    global_step,\n    decay_steps, \n    decay_rate)\n\noptimizer = tf.train.MomentumOptimizer(\n    learning_rate, \n    momentum=0.9)\n\ntraining_op = optimizer.minimize(\n    loss, \n    global_step=global_step)\n
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Regularization Techniques

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Early Stopping

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  • Simply interrupt training when validation performance starts dropping.
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L1 & L2 Regularlization

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Dropout

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  • Popular technique - typically adds 1-2% accuracy boost
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  • At every training step, every neuron has probability (p) of being temporarily ignored
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  • Typical p = 50%

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  • In TF: apply dropout() to input layer & output of every hidden layer.

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In [29]:
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# in TF\n\nfrom tensorflow.contrib.layers import dropout\n\n[...]\n\nis_training = tf.placeholder(\n    tf.bool, \n    shape=(), \n    name='is_training')\n\nkeep_prob = 0.5\n\nX_drop = dropout(\n    X, \n    keep_prob, \n    is_training=is_training)\n    \nhidden1      = fully_connected(\n    X_drop, n_hidden1, scope="hidden1")\n    \nhidden1_drop = dropout(\n    hidden1, keep_prob, is_training=is_training)\n    \nhidden2      = fully_connected(\n    hidden1_drop, n_hidden2, scope="hidden2")\n    \nhidden2_drop = dropout(\n    hidden2, keep_prob, is_training=is_training)\n    \nlogits       = fully_connected(\n    hidden2_drop, n_outputs, \n    activation_fn=None,\n    scope="outputs")\n
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Max-Norm Regularization

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  • Each neuron's incoming weights are constrained such that ||w||2 <= r
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  • r = max-norm hyperparameter
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  • ||.|| = l2 norm
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  • Reducing r increases regularization
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  • Not implemented in TF, but doable.
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Data Augmentation

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  • Generating new training instances from existing ones with learnable differences
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  • ex: pics with shifts/rotates/resizes/flips/contrasts
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  • TF has image manipulation ops built-in
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Practical Guidelines

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  • Suggested default DNN configurations:
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    • Initialization: He
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    • Activation: ELU
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    • Normalization: Batch
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    • Regularization: Dropout
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    • Optimizer: Adam
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    • Learning Rate schedule: none
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\n\n\n" }, { "path": "ch11-DNN-training.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Vanishing & Exploding Gradients\\n\",\n \"* gradients get smaller as algorithm progresses to lower layers. Eventually GD leaves lower weights virtually unchanged. so training never converges.\\n\",\n \"* gradients can also grow out of control (often seen in RNNs).\\n\",\n \"* [Significant paper](http://goo.gl/1rhAef) - using combo of logistic sigmoid activiation with random weight initialization (normal, mean=0, stdev=1) -- output variance was >> input variance.\\n\",\n \"* logistic activation: function saturates at 0 or 1 with derivative very close to 0 ==> so backpropagation has no gradient to use.\\n\",\n \"![sigmoid](pics/sigmoid.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Xavier & He Initialization\\n\",\n \"* For signals to flow properly in both directions, each layer's output variance should equal its input variance.\\n\",\n \"* Recommends initializing connection weights with random settings using #ins, #outs\\n\",\n \"![init parameters](pics/init-params-for-activation-funcs.png)\\n\",\n \"* Default: *fully_connected()* function uses Xavier initialization w/ uniform distribution. Change to He initialization by using *variance_scaling_initializer()* function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"from tensorflow.contrib.layers import fully_connected\\n\",\n \"\\n\",\n \"n_inputs = 28*28\\n\",\n \"n_hidden1 = 300\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n \"\\n\",\n \"he_init = tf.contrib.layers.variance_scaling_initializer()\\n\",\n \"hidden1 = fully_connected(X, n_hidden1, weights_initializer=he_init, scope=\\\"h1\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Non-Saturating Activation Functions\\n\",\n \"* ReLU activations suffer from *dying ReLU* problem (they stop emitting anything other than zero).\\n\",\n \"* Workaround: the **leaku ReLU**. Alpha defines leakage; typical set to 0.01.\\n\",\n \"![leaku ReLU](pics/leaky-relu.png)\\n\",\n \"* Also: **randomized leaky ReLU (RReLU)** (randomized alpha)\\n\",\n \"* Also: **parametric leaky RuLE (PReLU)** (alpha can be modified during backprop)\\n\",\n \"* Also: **exponential linear unit (ELU)**. Allows negative values when z<0; non-zero gradient for z<0 (avoids dying units issue); smooth function everywhere. Uses exponential function, so harder to compute. [paper](http://goo.gl/Sdl2P7)\\n\",\n \"![exponential-relu](pics/exponential-relu.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# TF doesn't have leaky ReLU predefined, but easy to build.\\n\",\n \"\\n\",\n \"def leaky_relu(z, name=None):\\n\",\n \" return tf.maximum(0.01 * z, z, name=name)\\n\",\n \"\\n\",\n \"hidden1 = fully_connected(X, n_hidden1, activation_fn=leaky_relu)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Batch Normalization\\n\",\n \"* [proposed](https://goo.gl/gA4GSP) to solve vanishing/exploding gradients. \\n\",\n \"* Idea: pror to activation function,\\n\",\n \" 1) zero-center & normalize inputs\\n\",\n \" 2) scale & shift result with 2 new params per layer\\n\",\n \"* Net effect: model learns optimal scale & mean of inputs for each layer\\n\",\n \"* Algorithm:\\n\",\n \"![batch normalization](pics/batch-normalization.png)\\n\",\n \"\\n\",\n \"* Does add computational complexity. Consider plain ELU + He initializaton as well.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Batch Normalization with TF\\n\",\n \"* *batch_normalization()* - centers & normalizes inputs\\n\",\n \"* *batch_norm()* - above, plus finds mean, stdev, scaling, offset params\\n\",\n \"* call directly or include it in *fully_connected()* arguments\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\\n\",\n \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n \"Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\\n\",\n \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n \"Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\\n\",\n \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n \"Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\\n\",\n \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n ]\n }\n ],\n \"source\": [\n \"# use MNIST dataset again\\n\",\n \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"#setup\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"from tensorflow.contrib.layers import batch_norm, fully_connected\\n\",\n \"\\n\",\n \"tf.reset_default_graph() \\n\",\n \"\\n\",\n \"n_inputs = 28 * 28\\n\",\n \"n_hidden1 = 300\\n\",\n \"n_hidden2 = 100\\n\",\n \"n_outputs = 10\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n \"y = tf.placeholder(tf.int64, shape=(None), name=\\\"y\\\")\\n\",\n \"\\n\",\n \"def leaky_relu(z, name=None):\\n\",\n \" return tf.maximum(0.01 * z, z, name=name)\\n\",\n \"\\n\",\n \"# is_training: tells batch_norm() whether to use current minibatch's mean & stdev \\n\",\n \"# (found during training) or use running avgs (during testing)\\n\",\n \"\\n\",\n \"with tf.name_scope(\\\"dnn\\\"):\\n\",\n \" hidden1 = fully_connected(X, n_hidden1, activation_fn=leaky_relu, scope=\\\"hidden1\\\")\\n\",\n \" hidden2 = fully_connected(hidden1, n_hidden2, activation_fn=leaky_relu, scope=\\\"hidden2\\\")\\n\",\n \" logits = fully_connected(hidden2, n_outputs, activation_fn=None, scope=\\\"outputs\\\")\\n\",\n \"\\n\",\n \"with tf.name_scope(\\\"loss\\\"):\\n\",\n \" xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\\n\",\n \" loss = tf.reduce_mean(xentropy, name=\\\"loss\\\")\\n\",\n \"\\n\",\n \"with tf.name_scope(\\\"train\\\"):\\n\",\n \" optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\",\n \" training_op = optimizer.minimize(loss)\\n\",\n \"\\n\",\n \"with tf.name_scope(\\\"eval\\\"):\\n\",\n \" correct = tf.nn.in_top_k(logits, y, 1)\\n\",\n \" accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"saver = tf.train.Saver()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 Train accuracy: 0.6 Test accuracy: 0.642\\n\",\n \"1 Train accuracy: 0.73 Test accuracy: 0.7824\\n\",\n \"2 Train accuracy: 0.81 Test accuracy: 0.827\\n\",\n \"3 Train accuracy: 0.84 Test accuracy: 0.8539\\n\",\n \"4 Train accuracy: 0.8 Test accuracy: 0.8686\\n\",\n \"5 Train accuracy: 0.87 Test accuracy: 0.8759\\n\",\n \"6 Train accuracy: 0.85 Test accuracy: 0.8843\\n\",\n \"7 Train accuracy: 0.91 Test accuracy: 0.8903\\n\",\n \"8 Train accuracy: 0.86 Test accuracy: 0.8969\\n\",\n \"9 Train accuracy: 0.91 Test accuracy: 0.9018\\n\",\n \"10 Train accuracy: 0.91 Test accuracy: 0.9014\\n\",\n \"11 Train accuracy: 0.86 Test accuracy: 0.9065\\n\",\n \"12 Train accuracy: 0.88 Test accuracy: 0.9078\\n\",\n \"13 Train accuracy: 0.87 Test accuracy: 0.91\\n\",\n \"14 Train accuracy: 0.93 Test accuracy: 0.911\\n\",\n \"15 Train accuracy: 0.9 Test accuracy: 0.9123\\n\",\n \"16 Train accuracy: 0.91 Test accuracy: 0.9141\\n\",\n \"17 Train accuracy: 0.9 Test accuracy: 0.9149\\n\",\n \"18 Train accuracy: 0.92 Test accuracy: 0.9159\\n\",\n \"19 Train accuracy: 0.93 Test accuracy: 0.9174\\n\"\n ]\n }\n ],\n \"source\": [\n \"n_epochs = 20\\n\",\n \"batch_size = 100\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" \\n\",\n \" init.run()\\n\",\n \" \\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" \\n\",\n \" for iteration in range(len(mnist.test.labels)//batch_size):\\n\",\n \" \\n\",\n \" X_batch, y_batch = mnist.train.next_batch(batch_size)\\n\",\n \" sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n \" \\n\",\n \" acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n \" acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels})\\n\",\n \" \\n\",\n \" print(epoch, \\\"Train accuracy:\\\", acc_train, \\\"Test accuracy:\\\", acc_test)\\n\",\n \"\\n\",\n \" save_path = saver.save(sess, \\\"my_model_final.ckpt\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Gradient Clipping\\n\",\n \"* to limit exploding gradients problem. Clip during backprop.\\n\",\n \"* Typical use case: recurrent NNs. \\n\",\n \"* [source](http://goo.gl/dRDAaf)\\n\",\n \"* Uses TF *minimize()* function in optimizer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"threshold = 1.0\\n\",\n \"\\n\",\n \"optimizer = tf.train.GradientDescentOptimizer(\\n\",\n \" learning_rate)\\n\",\n \"\\n\",\n \"grads_and_vars = optimizer.compute_gradients(\\n\",\n \" loss)\\n\",\n \"\\n\",\n \"capped_gvs = [\\n\",\n \" (tf.clip_by_value(\\n\",\n \" grad, -threshold, threshold), var)\\n\",\n \" for grad, var in grads_and_vars]\\n\",\n \"\\n\",\n \"training_op = optimizer.apply_gradients(capped_gvs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Pretrained Layers & Reuse\\n\",\n \"* best practice: look for existing NN that tackles similar task, then reuse lower layers (aka *transfer learning*).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Reuse with TF\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Reusing Models from Other Frameworks\\n\",\n \"* Requires manual loading of weights (ex: Theano)\\n\",\n \"* Very tedious\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'\\\\noriginal_w = [] # Load the weights from the other framework\\\\noriginal_b = [] # Load the biases from the other framework\\\\n\\\\nX = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\\\nhidden1 = fully_connected(X, n_hidden1, scope=\\\"hidden1\\\")\\\\n\\\\n[...] # # Build the rest of the model\\\\n\\\\n# Get a handle on the variables created by fully_connected()\\\\n\\\\nwith tf.variable_scope(\\\"\\\", default_name=\\\"\\\", reuse=True): # root scope\\\\n hidden1_weights = tf.get_variable(\\\"hidden1/weights\\\")\\\\n hidden1_biases = tf.get_variable(\\\"hidden1/biases\\\")\\\\n\\\\n# Create nodes to assign arbitrary values to the weights and biases\\\\noriginal_weights = tf.placeholder(tf.float32, shape=(n_inputs, n_hidden1))\\\\noriginal_biases = tf.placeholder(tf.float32, shape=(n_hidden1))\\\\n\\\\nassign_hidden1_weights = tf.assign(hidden1_weights, original_weights)\\\\nassign_hidden1_biases = tf.assign(hidden1_biases, original_biases)\\\\n\\\\ninit = tf.global_variables_initializer()\\\\n\\\\nwith tf.Session() as sess:\\\\n sess.run(init)\\\\n sess.run(\\\\n assign_hidden1_weights, \\\\n feed_dict={original_weights: original_w})\\\\n \\\\n sess.run(\\\\n assign_hidden1_biases, \\\\n feed_dict={original_biases: original_b})\\\\n \\\\n [...] # Train the model on your new task\\\\n'\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'''\\n\",\n \"original_w = [] # Load the weights from the other framework\\n\",\n \"original_b = [] # Load the biases from the other framework\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\\\"X\\\")\\n\",\n \"hidden1 = fully_connected(X, n_hidden1, scope=\\\"hidden1\\\")\\n\",\n \"\\n\",\n \"[...] # # Build the rest of the model\\n\",\n \"\\n\",\n \"# Get a handle on the variables created by fully_connected()\\n\",\n \"\\n\",\n \"with tf.variable_scope(\\\"\\\", default_name=\\\"\\\", reuse=True): # root scope\\n\",\n \" hidden1_weights = tf.get_variable(\\\"hidden1/weights\\\")\\n\",\n \" hidden1_biases = tf.get_variable(\\\"hidden1/biases\\\")\\n\",\n \"\\n\",\n \"# Create nodes to assign arbitrary values to the weights and biases\\n\",\n \"original_weights = tf.placeholder(tf.float32, shape=(n_inputs, n_hidden1))\\n\",\n \"original_biases = tf.placeholder(tf.float32, shape=(n_hidden1))\\n\",\n \"\\n\",\n \"assign_hidden1_weights = tf.assign(hidden1_weights, original_weights)\\n\",\n \"assign_hidden1_biases = tf.assign(hidden1_biases, original_biases)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(init)\\n\",\n \" sess.run(\\n\",\n \" assign_hidden1_weights, \\n\",\n \" feed_dict={original_weights: original_w})\\n\",\n \" \\n\",\n \" sess.run(\\n\",\n \" assign_hidden1_biases, \\n\",\n \" feed_dict={original_biases: original_b})\\n\",\n \" \\n\",\n \" [...] # Train the model on your new task\\n\",\n \"'''\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Freezing Lower Layers\\n\",\n \"* If 1st DNN already learned low-level features, try to reuse them by freezing the weights.\\n\",\n \"* simplest way:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'\\\\ntrain_vars = tf.get_collection(\\\\n tf.GraphKeys.TRAINABLE_VARIABLES,\\\\n scope=\\\"hidden[34]|outputs\\\")\\\\n\\\\n# minimizer can\\\\'t touch layers 1,2 - they\\\\'re \\\"frozen\\\"\\\\n\\\\ntraining_op = optimizer.minimize(\\\\n loss, \\\\n var_list=train_vars)\\\\n'\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# provide all trainable var in hidden layers 3,4 & outputs to optimizer function\\n\",\n \"# (this omits vars in hidden layers 1,2)\\n\",\n \"'''\\n\",\n \"train_vars = tf.get_collection(\\n\",\n \" tf.GraphKeys.TRAINABLE_VARIABLES,\\n\",\n \" scope=\\\"hidden[34]|outputs\\\")\\n\",\n \"\\n\",\n \"# minimizer can't touch layers 1,2 - they're \\\"frozen\\\"\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(\\n\",\n \" loss, \\n\",\n \" var_list=train_vars)\\n\",\n \"'''\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Caching Lower Layers\\n\",\n \"* Huge speed boost!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'import numpy as np\\\\n\\\\nn_epochs = 100\\\\nn_batches = 500\\\\n\\\\nfor epoch in range(n_epochs):\\\\n shuffled_idx = rnd.permutation(\\\\n len(hidden2_outputs))\\\\n \\\\n hidden2_batches = np.array_split(\\\\n hidden2_outputs[shuffled_idx], \\\\n n_batches)\\\\n \\\\ny_batches = np.array_split(\\\\n y_train[shuffled_idx], \\\\n n_batches)\\\\n\\\\nfor hidden2_batch, y_batch in zip(hidden2_batches, y_batches):\\\\n sess.run(\\\\n training_op, \\\\n feed_dict={hidden2: hidden2_batch, y: y_batch})\\\\n'\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'''import numpy as np\\n\",\n \"\\n\",\n \"n_epochs = 100\\n\",\n \"n_batches = 500\\n\",\n \"\\n\",\n \"for epoch in range(n_epochs):\\n\",\n \" shuffled_idx = rnd.permutation(\\n\",\n \" len(hidden2_outputs))\\n\",\n \" \\n\",\n \" hidden2_batches = np.array_split(\\n\",\n \" hidden2_outputs[shuffled_idx], \\n\",\n \" n_batches)\\n\",\n \" \\n\",\n \"y_batches = np.array_split(\\n\",\n \" y_train[shuffled_idx], \\n\",\n \" n_batches)\\n\",\n \"\\n\",\n \"for hidden2_batch, y_batch in zip(hidden2_batches, y_batches):\\n\",\n \" sess.run(\\n\",\n \" training_op, \\n\",\n \" feed_dict={hidden2: hidden2_batch, y: y_batch})\\n\",\n \"'''\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tweaking/Dropping/Replacing Upper Layers\\n\",\n \"* original output layer: should be replaced (little chance of reuse)\\n\",\n \"* iterative freeze/train/compare process to see how many upper layers needed\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Model Zoos\\n\",\n \"* When you want to find a net already trained on a similar task\\n\",\n \"* [TensorFlow Model Zoo](https://github.com/tensorflow/models)\\n\",\n \"* [Caffe Model Zoo](https://goo.gl/XI02X3) - converter on [github](https://github.com/ethereon/caffe-tensorflow)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Unsupervised pre-training\\n\",\n \"* Tough problem, but doable.\\n\",\n \"* Train layers one-by-one, starting with lowest layer\\n\",\n \"* Freeze completed layers & train next layer on previous results\\n\",\n \"![unsupervised pretraining](pics/unsupervised-pretraining.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Pre-training on easily labeled data - reuse lower layers for \\\"real\\\" task\\n\",\n \"* Often required due to cost/availability of large labeled datasets\\n\",\n \"* Common tactic: label all training data as \\\"good\\\", generate & corrupt additional instances, label new ones as \\\"bad.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Faster Optimizers\\n\",\n \"* Training speedup strategies thus far: 1) smart weight initializations, 2) smart activation functions, 3) batch normalization, 4) reuse of pretraining.\\n\",\n \"* Better optimizer choices:\\n\",\n \" * Momentum optimization\\n\",\n \" * Nesterov Accelerated Gradients\\n\",\n \" * AdaGrad\\n\",\n \" * RMSProp\\n\",\n \" * Adam (should almost always use this one)\\n\",\n \" \\n\",\n \"* Worth noting: below techniques rely on 1st-order partial derivatives (Jacobians); more techniques in literature use 2nd-order derivs (Hessians). Not viable for most deep learning due to memory & computational requirements.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Momentum optimization\\n\",\n \"* local gradient added to a **momentum vector** (m) multiplied by learning rate (n)\\n\",\n \"* ie, **gradient used as an accelerant - not as a speed.**\\n\",\n \"* *beta* hyperparameter serves as friction mechanism. 0 = high friction, 1 = no friction.\\n\",\n \"* Momentum optimization escapes plateaus much faster than GD.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# in TF\\n\",\n \"\\n\",\n \"optimizer = tf.train.MomentumOptimizer(\\n\",\n \" learning_rate=learning_rate,\\n\",\n \" momentum=0.9)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Nesterov Accelerated Gradient\\n\",\n \"* idea: measure cost function gradient *slightly ahead in direction of momentum*.\\n\",\n \"![nesterov vs regular momentum optimization](pics/nesterov.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# in TF\\n\",\n \"\\n\",\n \"optimizer = tf.train.MomentumOptimizer(\\n\",\n \" learning_rate=learning_rate,\\n\",\n \" momentum=0.9, \\n\",\n \" use_nesterov=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### AdaGrad\\n\",\n \"* Scales gradient vector along steepest dimensions, ie it decays the learning rate faster for steep dimensions. (ie *adaptive learning rate*)\\n\",\n \"* Works on simple quadratic problems but often stops too early.\\n\",\n \"![adagrad vs gradient descent](pics/adagrad.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### RMSProp\\n\",\n \"* Fixes AdaGrad problem by accumulating most recent gradients (instead of all). \\n\",\n \"* Better than AdaGrad on all but very simple problems. Also better than MO and Nesterov.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# in TF\\n\",\n \"\\n\",\n \"optimizer = tf.train.RMSPropOptimizer(\\n\",\n \" learning_rate=learning_rate,\\n\",\n \" momentum=0.9, \\n\",\n \" decay=0.9, \\n\",\n \" epsilon=1e-10)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Adam Optimization ([paper:](https://goo.gl/Un8Axa))\\n\",\n \"* Keeps track of decaying past gradients (like Momentum Optimization)\\n\",\n \"* Keeps track of decaying past squared gradients (like RMSProp)\\n\",\n \"\\n\",\n \"* Default params in TF:\\n\",\n \"* Momentum decay param (beta1) usually set to 0.9\\n\",\n \"* Scaling decay param (beta2) usually set to 0.999\\n\",\n \"* Smoothing term (epsilon) usually set to 10e-8\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# in TF\\n\",\n \"\\n\",\n \"optimizer = tf.train.AdamOptimizer(\\n\",\n \" learning_rate=learning_rate)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Learning Rate Scheduling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# in TF\\n\",\n \"\\n\",\n \"initial_learning_rate = 0.1\\n\",\n \"decay_steps = 10000\\n\",\n \"decay_rate = 1/10\\n\",\n \"\\n\",\n \"global_step = tf.Variable(\\n\",\n \" 0, trainable=False)\\n\",\n \"\\n\",\n \"learning_rate = tf.train.exponential_decay(\\n\",\n \" initial_learning_rate, \\n\",\n \" global_step,\\n\",\n \" decay_steps, \\n\",\n \" decay_rate)\\n\",\n \"\\n\",\n \"optimizer = tf.train.MomentumOptimizer(\\n\",\n \" learning_rate, \\n\",\n \" momentum=0.9)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(\\n\",\n \" loss, \\n\",\n \" global_step=global_step)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Regularization Techniques\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Early Stopping\\n\",\n \"* Simply interrupt training when validation performance starts dropping.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### L1 & L2 Regularlization\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Dropout\\n\",\n \"* Popular technique - typically adds 1-2% accuracy boost\\n\",\n \"* At every training step, every neuron has probability (p) of being temporarily ignored\\n\",\n \"* Typical p = 50%\\n\",\n \"\\n\",\n \"* In TF: apply *dropout()* to input layer & output of every hidden layer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# in TF\\n\",\n \"\\n\",\n \"from tensorflow.contrib.layers import dropout\\n\",\n \"\\n\",\n \"[...]\\n\",\n \"\\n\",\n \"is_training = tf.placeholder(\\n\",\n \" tf.bool, \\n\",\n \" shape=(), \\n\",\n \" name='is_training')\\n\",\n \"\\n\",\n \"keep_prob = 0.5\\n\",\n \"\\n\",\n \"X_drop = dropout(\\n\",\n \" X, \\n\",\n \" keep_prob, \\n\",\n \" is_training=is_training)\\n\",\n \" \\n\",\n \"hidden1 = fully_connected(\\n\",\n \" X_drop, n_hidden1, scope=\\\"hidden1\\\")\\n\",\n \" \\n\",\n \"hidden1_drop = dropout(\\n\",\n \" hidden1, keep_prob, is_training=is_training)\\n\",\n \" \\n\",\n \"hidden2 = fully_connected(\\n\",\n \" hidden1_drop, n_hidden2, scope=\\\"hidden2\\\")\\n\",\n \" \\n\",\n \"hidden2_drop = dropout(\\n\",\n \" hidden2, keep_prob, is_training=is_training)\\n\",\n \" \\n\",\n \"logits = fully_connected(\\n\",\n \" hidden2_drop, n_outputs, \\n\",\n \" activation_fn=None,\\n\",\n \" scope=\\\"outputs\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Max-Norm Regularization\\n\",\n \"* Each neuron's incoming weights are constrained such that ||w||2 <= r\\n\",\n \"* r = *max-norm hyperparameter*\\n\",\n \"* ||.|| = l2 norm\\n\",\n \"* Reducing r increases regularization\\n\",\n \"* Not implemented in TF, but doable.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Data Augmentation\\n\",\n \"* Generating new training instances from existing ones with learnable differences\\n\",\n \"* ex: pics with shifts/rotates/resizes/flips/contrasts\\n\",\n \"* TF has image manipulation ops built-in\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Practical Guidelines\\n\",\n \"* Suggested default DNN configurations:\\n\",\n \" * Initialization: He\\n\",\n \" * Activation: ELU\\n\",\n \" * Normalization: Batch\\n\",\n \" * Regularization: Dropout\\n\",\n \" * Optimizer: Adam\\n\",\n \" * Learning Rate schedule: none\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch12-distributed-TF.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Multi Devices, Single Machine\\n\",\n \"* Check if GPU cards have nVidia Compute Capability >3.0\\n\",\n \"* Alternative using AWS: [helpful blog post](http://goo.gl/kbge5b)\\n\",\n \"* Google Cloud service: [uses TPU hardware](https://cloud.google.com/ml)\\n\",\n \"* [Which to use? (Tim Dettmers)](https://goo.gl/pCtSAn)\\n\",\n \"* Download CUDA & CuDNN, set their environment vars\\n\",\n \"* use *nvidie-smi* cmnd to check installation\\n\",\n \"* install TF with GPU support\\n\",\n \"* open Python shell, verify TF detects CUDA & cuDNN\\n\",\n \"\\n\",\n \" >import tensorflow as tf\\n\",\n \" \\n\",\n \" >sess = tf.Session()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"config = tf.ConfigProto()\\n\",\n \"#config.gpu_options.per_process_gpu_memory_fraction=0.4\\n\",\n \"config\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Managing GPU RAM\\n\",\n \"* TF grabs all GPU RAM on first graph invocation. To run 2nd TF program while the 1st is still running, run each process on different GPU cards. (Below: program #1 sees GPUs 0,1; program #2 sees GPUs 2,3.)\\n\",\n \"\\n\",\n \"> $ CUDA_VISIBLE_DEVICES=0,1 python3 program_1.py\\n\",\n \"\\n\",\n \"> $ CUDA_VISIBLE_DEVICES=3,2 python3 program_2.py\\n\",\n \"\\n\",\n \"* Option 2: tell TF to grab a % of memory. (Below: 40% allocation.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"session = tf.Session(config=config)\\n\",\n \"config,session\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Placing Ops on Devices\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Parallel Execution\\n\",\n \"* [TF Whitepaper](http://goo.gl/vSjA14) - dynamic algorithm, distributes ops across all available devices. **But not available (yet) in open-source TF.**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Simple Placement\\n\",\n \"* Mostly up to you. To pin devices to specific device, use a device() function. Below: a,b pinned to cpu#0; c can go anywhere.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"with tf.device(\\\"/cpu:0\\\"):\\n\",\n \" a,b = tf.Variable(3.0), tf.Variable(4.0)\\n\",\n \"c = a*b\\n\",\n \"c\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Logging Placements\\n\",\n \"* Use *log_device_placement=True*. This tells placer to log msg whenever a node is \\\"placed\\\".\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"config = tf.ConfigProto()\\n\",\n \"config.log_device_placement = True\\n\",\n \"sess = tf.Session(config=config)\\n\",\n \"print(config,\\\"\\\\n\\\",sess)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Dynamic Placement\\n\",\n \"* You can specify a function instead of a device when creating a device block.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"def variables_on_cpu(op):\\n\",\n \" if op.type == \\\"Variable\\\":\\n\",\n \" return \\\"/cpu:0\\\"\\n\",\n \" else:\\n\",\n \" return \\\"/cpu:0\\\"\\n\",\n \"with tf.device(variables_on_cpu):\\n\",\n \" a = tf.Variable(3.0)\\n\",\n \" b = tf.constant(4.0)\\n\",\n \" c = a * b\\n\",\n \"c\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Ops & Kernels\\n\",\n \"* TF operations need to define a **kernel** to run n a device. **Not all ops have kernels for both GPUs and CPUs**. Example: TF doesn't have integer kernel for GPUs. Changin i (below) from 3 to 3.0 should allow op to run.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" i = tf.Variable(3)\\n\",\n \"test = sess.run(i.initializer)\\n\",\n \"test\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* To allow TF to \\\"fall back\\\" to a CPU instead, use *allow_soft_placement=True*.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" i = tf.Variable(3)\\n\",\n \"config = tf.ConfigProto()\\n\",\n \"config.allow_soft_placement = True\\n\",\n \"sess = tf.Session(config=config)\\n\",\n \"test = sess.run(i.initializer) # the placer runs and falls back to /cpu:0\\n\",\n \"print(test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Parallel Execution\\n\",\n \"* TF executes any nodes with zero dependencies first. If those nodes are on **separate** devices, they are run in parallel. If on the same device, they are run in different threads & **may** be run in parallel.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Control Dependencies\\n\",\n \"* Use *control dependencies* to control/postpone node evaluations (ex: premature memory hogging).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"a = tf.constant(1.0)\\n\",\n \"b = a + 2.0\\n\",\n \"\\n\",\n \"with tf.control_dependencies([a,b]):\\n\",\n \" x = tf.constant(3.0)\\n\",\n \" y = tf.constant(4.0)\\n\",\n \" \\n\",\n \"print(x+y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Multiple Devices - Multiple Servers\\n\",\n \"* cluster: >=1 TF servers (\\\"tasks\\\") across machines. Tasks belong to **jobs** (collections of related tasks)\\n\",\n \"* \\\"ps\\\" = parameter server\\n\",\n \"* \\\"worker\\\" = computing engine\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"cluster_spec = tf.train.ClusterSpec({\\n\",\n \"\\\"ps\\\": [\\n\",\n \"\\\"machine-a.example.com:2221\\\", # /job:ps/task:0\\n\",\n \"],\\n\",\n \"\\\"worker\\\": [\\n\",\n \"\\\"machine-a.example.com:2222\\\", # /job:worker/task:0\\n\",\n \"\\\"machine-b.example.com:2222\\\", # /job:worker/task:1\\n\",\n \"]})\\n\",\n \"\\n\",\n \"cluster_spec\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"server.join()\\n\",\n \"# blocks main thread until server stops (i.e., never)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Opening a Session\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# NOT YET WORKING\\n\",\n \"# open session\\n\",\n \"#a = tf.constant(1.0)\\n\",\n \"#b = a + 2\\n\",\n \"#c = a * 3\\n\",\n \"#with tf.Session(\\\"grpc://machine-b.example.com:2222\\\") as sess:\\n\",\n \"# print(c.eval()) # 9.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Master & Worker Services\\n\",\n \"* gRPC protocol to talk to servers. HTTP2 basis, bidirectional\\n\",\n \"* based on *protocol buffers*\\n\",\n \"* all servers can provide *master* & *worker* services.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Pinning Ops Across Tasks\\n\",\n \"* you can pin ops to any device\\n\",\n \"* ex: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# NOT WORKING YET\\n\",\n \"#with tf.device(\\\"/job:ps/task:0/cpu:0\\\")\\n\",\n \"#a = tf.constant(1.0)\\n\",\n \"#with tf.device(\\\"/job:worker/task:0/cpu:0\\\")\\n\",\n \"#with tf.device(\\\"/job:worker/task:0/gpu:1\\\")\\n\",\n \"#b = a + 2\\n\",\n \"#c = a + b\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Sharding Variables across Multiple Param Servers\\n\",\n \"* sharding across servers mitigates risk of network card saturation\\n\",\n \"* TF distribs variables across all \\\"ps\\\" tasks - round robin setup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'NOT WORKING YET\\\\nimport tensorflow as tf\\\\nwith tf.device(tf.train.replica_device_setter(ps_tasks=2):\\\\n v1 = tf.Variable(1.0) # pinned to /job:ps/task:0\\\\n v2 = tf.Variable(2.0) # pinned to /job:ps/task:1\\\\n v3 = tf.Variable(3.0) # pinned to /job:ps/task:0\\\\n v4 = tf.Variable(4.0) # pinned to /job:ps/task:1\\\\n v5 = tf.Variable(5.0) # pinned to /job:ps/task:0\\\\n'\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'''NOT WORKING YET\\n\",\n \"import tensorflow as tf\\n\",\n \"with tf.device(tf.train.replica_device_setter(ps_tasks=2):\\n\",\n \" v1 = tf.Variable(1.0) # pinned to /job:ps/task:0\\n\",\n \" v2 = tf.Variable(2.0) # pinned to /job:ps/task:1\\n\",\n \" v3 = tf.Variable(3.0) # pinned to /job:ps/task:0\\n\",\n \" v4 = tf.Variable(4.0) # pinned to /job:ps/task:1\\n\",\n \" v5 = tf.Variable(5.0) # pinned to /job:ps/task:0\\n\",\n \"'''\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Sharing State across Sessions (Resource Containers)\\n\",\n \"* local session: all vars managed by session itself & vanish on end.\\n\",\n \"* distributed session: vars managed by *resource containers* on cluster\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'# simple_client.py\\\\n#import tensorflow as tf\\\\n#import sys\\\\n#x = tf.Variable(0.0, name=\\\"x\\\")\\\\n#increment_x = tf.assign(x, x + 1)\\\\n#with tf.Session(sys.argv[1]) as sess:\\\\n# if sys.argv[2:]==[\\\"init\\\"]:\\\\n#sess.run(x.initializer)\\\\n#sess.run(increment_x)\\\\n#print(x.eval())\\\\n'\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'''# simple_client.py\\n\",\n \"#import tensorflow as tf\\n\",\n \"#import sys\\n\",\n \"#x = tf.Variable(0.0, name=\\\"x\\\")\\n\",\n \"#increment_x = tf.assign(x, x + 1)\\n\",\n \"#with tf.Session(sys.argv[1]) as sess:\\n\",\n \"# if sys.argv[2:]==[\\\"init\\\"]:\\n\",\n \"#sess.run(x.initializer)\\n\",\n \"#sess.run(increment_x)\\n\",\n \"#print(x.eval())\\n\",\n \"'''\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# launches client which connects to B, reuses variable x\\n\",\n \"# python3 simple_client.py grpc://machine-b.example.com:2222\\n\",\n \"#2.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Async Communications (TF Queues)\\n\",\n \"\\n\",\n \"* Queueing data\\n\",\n \"* DeQueueing data\\n\",\n \"* Queues of tuples\\n\",\n \"* Closing a queue\\n\",\n \"* RandomShuffleQueue\\n\",\n \"* PaddingFifoQueue\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Loading Data Directly from Graph\\n\",\n \"\\n\",\n \"* Needed to avoid file server (bandwidth) saturation\\n\",\n \"* Preloading data to variables\\n\",\n \"* Reading data from graph with **reader operations**\\n\",\n \"\\n\",\n \" * CSV, binary, TFRecords\\n\",\n \" * **TextLineReader** reads file lines one-by-one\\n\",\n \" * record identifier (string): filename:linenumber\\n\",\n \" * tf.decode_csv(val, record_defaults=[...])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'TO LOAD A GRAPH\\\\ninstance_queue = tf.RandomShuffleQueue(\\\\n capacity=10, \\\\n min_after_dequeue=2,\\\\n dtypes=[tf.float32, tf.int32], \\\\n shapes=[[2],[]],\\\\n name=\\\"instance_q\\\", \\\\n shared_name=\\\"shared_instance_q\\\")\\\\n\\\\nenqueue_instance = instance_queue.enqueue([features, target])\\\\nclose_instance_queue = instance_queue.close()\\\\n'\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'''TO LOAD A GRAPH\\n\",\n \"instance_queue = tf.RandomShuffleQueue(\\n\",\n \" capacity=10, \\n\",\n \" min_after_dequeue=2,\\n\",\n \" dtypes=[tf.float32, tf.int32], \\n\",\n \" shapes=[[2],[]],\\n\",\n \" name=\\\"instance_q\\\", \\n\",\n \" shared_name=\\\"shared_instance_q\\\")\\n\",\n \"\\n\",\n \"enqueue_instance = instance_queue.enqueue([features, target])\\n\",\n \"close_instance_queue = instance_queue.close()\\n\",\n \"'''\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'TO RUN THE GRAPH\\\\nwith tf.Session([...]) as sess:\\\\n sess.run(enqueue_filename, feed_dict={filename: \\\"my_test.csv\\\"})\\\\n sess.run(close_filename_queue)\\\\n try:\\\\n while True:\\\\n sess.run(enqueue_instance)\\\\n except tf.errors.OutOfRangeError as ex:\\\\n pass # no more records in the current file and no more files to read\\\\n sess.run(close_instance_queue)\\\\n'\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'''TO RUN THE GRAPH\\n\",\n \"with tf.Session([...]) as sess:\\n\",\n \" sess.run(enqueue_filename, feed_dict={filename: \\\"my_test.csv\\\"})\\n\",\n \" sess.run(close_filename_queue)\\n\",\n \" try:\\n\",\n \" while True:\\n\",\n \" sess.run(enqueue_instance)\\n\",\n \" except tf.errors.OutOfRangeError as ex:\\n\",\n \" pass # no more records in the current file and no more files to read\\n\",\n \" sess.run(close_instance_queue)\\n\",\n \"'''\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Multithreaded readers using a Coordinator & QueueRunner\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Other convenience functions\\n\",\n \"* *string_input_producer()*\\n\",\n \"* *tf.train.start_queue_runners()*\\n\",\n \"##### producer functions = create queues\\n\",\n \"* input_producer()\\n\",\n \"* range_input_producer()\\n\",\n \"* slice_input_procucer()\\n\",\n \"* shuffle_batch(list_of_tensors)\\n\",\n \" * returns RandomShuffleQueue\\n\",\n \" * returns QueueRunner (added to GraphKeys.QUEUE_RUNNERS)\\n\",\n \" * dequeue_many() = returns minibatch from queue\\n\",\n \" \\n\",\n \" * batch() --?\\n\",\n \" * batch_join() --?\\n\",\n \" * shuffle_batch_join() --?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### One NN per Device\\n\",\n \"\\n\",\n \"* near-linear speedup: training 100 nets across 50 servers x 2 gpus/server roughly equiv to 1 net on 1 gpu. (**perfect for hyperparamer tuning**)\\n\",\n \"\\n\",\n \"* potential option: [tf serving, released 2/2016](https://tensorflow.github.io/serving/)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### In-Graph vs Between-Graph Replication (for Ensembles)\\n\",\n \"* Two approaches to building ensembles:\\n\",\n \"\\n\",\n \"1) one big graph, one session, any server in cluster (\\\"in graph replication\\\")\\n\",\n \"\\n\",\n \"2) one graph/network, handle synchronization yourself (\\\"between graph replication\\\") using queues -- considered more flexible\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'NOT YET\\\\nwith tf.Session([...]) as sess:\\\\n [...]\\\\n run_options = tf.RunOptions()\\\\n run_options.timeout_in_ms = 1000 # 1s timeout\\\\n try:\\\\n pred = sess.run(dequeue_prediction, options=run_options)\\\\n except tf.errors.DeadlineExceededError as ex:\\\\n [...] # the dequeue operation timed out after 1s\\\\n'\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#RunOptions ... timeout_in_ms()\\n\",\n \"'''NOT YET\\n\",\n \"with tf.Session([...]) as sess:\\n\",\n \" [...]\\n\",\n \" run_options = tf.RunOptions()\\n\",\n \" run_options.timeout_in_ms = 1000 # 1s timeout\\n\",\n \" try:\\n\",\n \" pred = sess.run(dequeue_prediction, options=run_options)\\n\",\n \" except tf.errors.DeadlineExceededError as ex:\\n\",\n \" [...] # the dequeue operation timed out after 1s\\n\",\n \"'''\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'NOT YET\\\\nconfig = tf.ConfigProto()\\\\nconfig.operation_timeout_in_ms = 1000\\\\n# 1s timeout for every operation\\\\nwith tf.Session([...], config=config) as sess:\\\\n [...]\\\\n try:\\\\n pred = sess.run(dequeue_prediction)\\\\n except tf.errors.DeadlineExceededError as ex:\\\\n [...] # the dequeue operation timed out after 1s\\\\n'\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#\\n\",\n \"'''NOT YET\\n\",\n \"config = tf.ConfigProto()\\n\",\n \"config.operation_timeout_in_ms = 1000\\n\",\n \"# 1s timeout for every operation\\n\",\n \"with tf.Session([...], config=config) as sess:\\n\",\n \" [...]\\n\",\n \" try:\\n\",\n \" pred = sess.run(dequeue_prediction)\\n\",\n \" except tf.errors.DeadlineExceededError as ex:\\n\",\n \" [...] # the dequeue operation timed out after 1s\\n\",\n \"'''\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Model Parallelism\\n\",\n \"* Chopping models, running chunks on different devices\\n\",\n \"* Fully Connect Nets (FCNs): not much value in doing this\\n\",\n \"* Vertical & Horiz slicing don't work well either\\n\",\n \"\\n\",\n \"* Nets w/ partially connected layers (CNNs): easier to distribute\\n\",\n \"* Some RNNs use mem cells (input from own output at t+1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Data Parallelism\\n\",\n \"* **Sync updates** (aggregator waits for all gradients to be available, finds avg, applies result) - could be delayed by slow devices; params could also saturate server bandwidth\\n\",\n \"* **Async updates** - more training steps/minute. issue: \\\"stale gradients\\\" (when computing gradients falls behind rate of parameter change) - slows convergence, introduces noise/wobble. To avoid this:\\n\",\n \" * reduce learning rate\\n\",\n \" * drop/scaleback stale gradients\\n\",\n \" * adjust minibatch size\\n\",\n \" * Start first few epochs with just one replica (\\\"warmup phase\\\")\\n\",\n \"* **Bandwidth** - At some point, more GPUs doesn't help because network saturation won't allow more data traffic. [google report](http://goo.gl/E4ypxo). Steps you can take:\\n\",\n \" * group gpus on single server (avoids network hops)\\n\",\n \" * shard params acrosss servers\\n\",\n \" * drop precision from float32 to bfloat16\\n\",\n \" * 8b precision (\\\"quantization\\\"): see [mobile phone apps](http://goo.gl/09Cb6v)\\n\",\n \"* **How TF does it** - \\n\",\n \" * you choose 1) replication type (in-graph, between-graph) and 2) update type (async or sync)\\n\",\n \" 1) in-graph + sync: one big graph\\n\",\n \" 2) in-graph + async: 1 optimizer/replica, 1 thread/replica\\n\",\n \" 3) bw-graph + sync: wrap optimizer in **SyncReplicasOptimizer**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch13-convolutional-NNs.html", "content": "\n\n\nch13-convolutional-NNs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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Intro - Visual Cortex

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# utilities\nimport matplotlib.pyplot as plt\n\ndef plot_image(image):\n    plt.imshow(image, cmap="gray", interpolation="nearest")\n    plt.axis("off")\n\ndef plot_color_image(image):\n    plt.imshow(image.astype(np.uint8),interpolation="nearest")\n    plt.axis("off")\n
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Convolutional Layers

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Filters

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import numpy as np\n\nfmap = np.zeros(shape=(7, 7, 1, 2), dtype=np.float32)\nfmap[:, 3, 0, 0] = 1\nfmap[3, :, 0, 1] = 1\nprint(fmap[:, :, 0, 0])\nprint(fmap[:, :, 0, 1])\n\nplt.figure(figsize=(6,6))\n\nplt.subplot(121)\nplot_image(fmap[:, :, 0, 0])\nplt.subplot(122)\nplot_image(fmap[:, :, 0, 1])\nplt.show()\n
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[[ 0.  0.  0.  1.  0.  0.  0.]\n [ 0.  0.  0.  1.  0.  0.  0.]\n [ 0.  0.  0.  1.  0.  0.  0.]\n [ 0.  0.  0.  1.  0.  0.  0.]\n [ 0.  0.  0.  1.  0.  0.  0.]\n [ 0.  0.  0.  1.  0.  0.  0.]\n [ 0.  0.  0.  1.  0.  0.  0.]]\n[[ 0.  0.  0.  0.  0.  0.  0.]\n [ 0.  0.  0.  0.  0.  0.  0.]\n [ 0.  0.  0.  0.  0.  0.  0.]\n [ 1.  1.  1.  1.  1.  1.  1.]\n [ 0.  0.  0.  0.  0.  0.  0.]\n [ 0.  0.  0.  0.  0.  0.  0.]\n [ 0.  0.  0.  0.  0.  0.  0.]]\n
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from sklearn.datasets import load_sample_image\n\nchina = load_sample_image("china.jpg")\nflower = load_sample_image("flower.jpg")\n\nimage = china[150:220, 130:250]\nheight, width, channels = image.shape\n\nimage_grayscale = image.mean(axis=2).astype(np.float32)\nimages = image_grayscale.reshape(1, height, width, 1)\n
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In [18]:
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import tensorflow as tf\n\ntf.reset_default_graph()\n\n# Define the model\n\nX = tf.placeholder(\n    tf.float32, \n    shape=(None, height, width, 1))\n\nfeature_maps = tf.constant(fmap)\n\nconvolution = tf.nn.conv2d(\n    X, \n    feature_maps, \n    strides=[1,1,1,1], \n    padding="SAME", \n    use_cudnn_on_gpu=False)\n
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# Run the model\n\nwith tf.Session() as sess:\n    output = convolution.eval(feed_dict={X: images})\n
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plt.figure(figsize=(6,6))\n\n#plt.subplot(121)\nplot_image(images[0, :, :, 0])\n#plt.subplot(122)\nplot_image(output[0, :, :, 0])\n#plt.subplot(123)\nplot_image(output[0, :, :, 1])\nplt.show()\n
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In [21]:
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%%html\n<style>\nimg[alt=stacking] { width: 400px; }\n</style>\n
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Stacking Feature Maps

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In [22]:
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import numpy as np\nfrom sklearn.datasets import load_sample_images\n\n# Load sample images\ndataset = np.array(load_sample_images().images, dtype=np.float32)\nbatch_size, height, width, channels = dataset.shape\n\n# Create 2 filters\nfilters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)\nfilters[:, 3, :, 0] = 1 # vertical line\nfilters[3, :, :, 1] = 1 # horizontal line\n\n# Create a graph with input X plus a convolutional layer applying the 2 filters\nX = tf.placeholder(tf.float32, \n                   shape=(None, height, width, channels))\n\nconvolution = tf.nn.conv2d(\n    X, filters, strides=[1,2,2,1], padding="SAME")\n\nwith tf.Session() as sess:\n    output = sess.run(convolution, feed_dict={X: dataset})\n\nplt.imshow(output[0, :, :, 1])\nplt.show()\n
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In [23]:
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%%html\n<style>\nimg[alt=padding] { width: 400px; }\n</style>\n
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\"Valid\" v. \"Same\" Padding

\"padding\"

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import tensorflow as tf\nimport numpy as np\n\ntf.reset_default_graph()\n\nfilter_primes = np.array(\n    [2., 3., 5., 7., 11., 13.], \n    dtype=np.float32)\n\nx = tf.constant(\n    np.arange(1, 13+1, dtype=np.float32).reshape([1, 1, 13, 1]))\n\nprint ("x:\\n",x)\n\nfilters = tf.constant(\n    filter_primes.reshape(1, 6, 1, 1))\n\n# conv2d arguments:\n# x = input minibatch = 4D tensor\n# filters = 4D tensor\n# strides = 1D array (1, vstride, hstride, 1)\n# padding = VALID = no zero padding, may ignore edge rows/cols\n# padding = SAME  = zero padding used if needed\n\nvalid_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='VALID')\nsame_conv =  tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='SAME')\n\nwith tf.Session() as sess:\n    print("VALID:\\n", valid_conv.eval())\n    print("SAME:\\n", same_conv.eval())\n
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x:\n Tensor("Const:0", shape=(1, 1, 13, 1), dtype=float32)\nVALID:\n [[[[ 184.]\n   [ 389.]]]]\nSAME:\n [[[[ 143.]\n   [ 348.]\n   [ 204.]]]]\n
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Pooling Layers

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  • Goal: subsample (shrink) input image to reduce loading.
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  • Result: aggregation function (max, mean)
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  • Below: max pool, 2x2, stride = 2, no padding.\n\"pooling
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In [25]:
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dataset = np.array([china, flower], dtype=np.float32)\n\nbatch_size, height, width, channels = dataset.shape\n\nfilters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)\nfilters[:, 3, :, 0] = 1  # vertical line\nfilters[3, :, :, 1] = 1  # horizontal line\n\nX = tf.placeholder(tf.float32, \n                   shape=(None, height, width, channels))\n\n# alternative: avg_pool()\n\nmax_pool = tf.nn.max_pool(\n    X, \n    ksize=[1, 2, 2, 1], \n    strides=[1,2,2,1], \n    padding="VALID")\n\nwith tf.Session() as sess:\n    output = sess.run(max_pool, feed_dict={X: dataset})\n\nplt.figure(figsize=(12,12))\nplt.subplot(121)\nplot_color_image(dataset[0])\nplt.subplot(122)\nplot_color_image(output[0])\nplt.show()\n
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Memory Requirements

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  • Main memory killer: reverse pass of backprop - needs all intermediate vals computed during forward pass
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  • Example CNN:
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    • stride = 1, \"SAME\" padding
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    • If image = 150x100x3 (RGB), then
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    • params_count = (5x5x3+1) * 200 = 15,200
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    • 200 feature maps contain 150 x 100 neurons => each needs to compute weighted sum of 5x5x3 = 75 inputs => 225M floating-point multiplies.
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    • If using 32b float => output requires 200x150x100x32 = 96M bits = 11.4MB for one instance
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During inference: one layer's memory can be dropped when next layer is computed. (You only need enough memory for two layers).

During training: all computed values have to preserved for reverse pass (You need enough memory for all layers.)

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CNN Architectures

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LeNet-5 (c. 1998, used to solve MNIST digits dataset)

\"layers\"

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  • MNIST images zero-padded to 32x32 & normalized
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  • pooling layers: mean x learned coefficient + learnable bias
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  • output layer: output = Euclidian distance (input vect, weight vect)
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AlexNet won ILSVRC 2012

\"layers\"

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  • Uses 50% dropout on layers F8, F9 for regularization
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  • Uses random image shifts/flips/rotates/lighting to augment dataset
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  • Uses local response normalization on layers C1, C3.
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  • Hyperparameter settings: r=2, alpha=0.00002, beta=0.75, k=1
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  • ZFNet (tweaked AlexNet) won ILSVRC 2013.
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GoogLeNet won ILSVRC 2014

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  • Much deeper than previous nets
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  • Uses inception modules to use params much more efficiently. They use 1x1 kernels as \"bottleneck layers\" (reduces dimensionality). Also: pairs of convo layers act as single more powerful convo layer.
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  • All convo layers use ReLU activation.\n\"inception\"\n\"googlenet\"
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ResNet

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  • 152 layers deep
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  • Uses skip connections to connect non-adjacent layers in stack
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  • skip connections force learning model f(x) = h(x) - x (residual learning). When initialized, weights near zero => network outputs values near-copy of inputs (identity function).\n\"residual\"
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  • architecture: stack starts & ends like GoogLeNet, stack of residual units in between.\n\"resnet\"
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TF Convolution Ops

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  • conv1d() - 1D layer - good for NLP
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  • conv3d() - 3D layer - good for PET scans
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  • atrous_conv2D() - 2D layer with \"holes\"
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  • conv2d_transpose() - 2D \"deconvolutional layer\" - upsamples image by inserting zeroes * between inputs
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  • depthwise_conv2d() - applies every filter to each input channel independently
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  • separable_conv2d() - depthwise convo, then apply 1x1 CNN layer to result
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\n\n\n" }, { "path": "ch13-convolutional-NNs.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro - Visual Cortex\\n\",\n \"* [LeNet-5 paper](http://goo.gl/A347S4) - intro'd convo & pooling layers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%html\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# utilities\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"def plot_image(image):\\n\",\n \" plt.imshow(image, cmap=\\\"gray\\\", interpolation=\\\"nearest\\\")\\n\",\n \" plt.axis(\\\"off\\\")\\n\",\n \"\\n\",\n \"def plot_color_image(image):\\n\",\n \" plt.imshow(image.astype(np.uint8),interpolation=\\\"nearest\\\")\\n\",\n \" plt.axis(\\\"off\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Convolutional Layers\\n\",\n \"* [math detail](http://goo.gl/HAfxXd)\\n\",\n \"* neurons connected to receptor field in next layer. uses *zero padding* to force layers to have same height & width.\\n\",\n \"* also can connect large input layer to much smaller layer by spacing out receptor fields (distance between receptor fields = *stride*)\\n\",\n \"\\n\",\n \"Layers | Padding | Strides\\n\",\n \"- | - | -\\n\",\n \"![alt](pics/cnn-layers.png) | ![alt](pics/zero-padding.png) | ![alt](pics/strides.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Filters\\n\",\n \"* neuron weights can look like small image (w/ size = receptor field)\\n\",\n \"* examples given: \\n\",\n \" 1) vertical filter (single vertical bar, mid-image, all other cells zero)\\n\",\n \" 2) horizontal filter (single horizontal bar, mid-image, all other cells zero)\\n\",\n \"* both return **feature maps** (highlights areas of image most similar to filter)\\n\",\n \"![filters to feature maps](pics/filters.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0. 0. 0. 1. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 1. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 1. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 1. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 1. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 1. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 1. 0. 0. 0.]]\\n\",\n \"[[ 0. 0. 0. 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0. 0. 0. 0.]\\n\",\n \" [ 1. 1. 1. 1. 1. 1. 1.]\\n\",\n \" [ 0. 0. 0. 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0. 0. 0. 0.]]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAW4AAAC7CAYAAABFJnSnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nA8QIN0CMcAPECDdAjHADxLwBhJQYclPU1XUAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"fmap = np.zeros(shape=(7, 7, 1, 2), dtype=np.float32)\\n\",\n \"fmap[:, 3, 0, 0] = 1\\n\",\n \"fmap[3, :, 0, 1] = 1\\n\",\n \"print(fmap[:, :, 0, 0])\\n\",\n \"print(fmap[:, :, 0, 1])\\n\",\n \"\\n\",\n \"plt.figure(figsize=(6,6))\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_image(fmap[:, :, 0, 0])\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_image(fmap[:, :, 0, 1])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.datasets import load_sample_image\\n\",\n \"\\n\",\n \"china = load_sample_image(\\\"china.jpg\\\")\\n\",\n \"flower = load_sample_image(\\\"flower.jpg\\\")\\n\",\n \"\\n\",\n \"image = china[150:220, 130:250]\\n\",\n \"height, width, channels = image.shape\\n\",\n \"\\n\",\n \"image_grayscale = image.mean(axis=2).astype(np.float32)\\n\",\n \"images = image_grayscale.reshape(1, height, width, 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"# Define the model\\n\",\n \"\\n\",\n \"X = tf.placeholder(\\n\",\n \" tf.float32, \\n\",\n \" shape=(None, height, width, 1))\\n\",\n \"\\n\",\n \"feature_maps = tf.constant(fmap)\\n\",\n \"\\n\",\n \"convolution = tf.nn.conv2d(\\n\",\n \" X, \\n\",\n \" feature_maps, \\n\",\n \" strides=[1,1,1,1], \\n\",\n \" padding=\\\"SAME\\\", \\n\",\n \" use_cudnn_on_gpu=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Run the model\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" output = convolution.eval(feed_dict={X: images})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Vg5JlIOpJfYN7Wd5oqWYC4yaJrDRGvo/hSzhM5XqoP7mP0knCUQx5He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\\nQS+KojgI/g+yFcR9VFzVxAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(6,6))\\n\",\n \"\\n\",\n \"#plt.subplot(121)\\n\",\n \"plot_image(images[0, :, :, 0])\\n\",\n \"#plt.subplot(122)\\n\",\n \"plot_image(output[0, :, :, 0])\\n\",\n \"#plt.subplot(123)\\n\",\n \"plot_image(output[0, :, :, 1])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%html\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Stacking Feature Maps\\n\",\n \"* images made of *sublayers* (one per color channel, typical red/green/blue, grayscale = one chan, others = many chans)\\n\",\n \"![stacking](pics/stacking-feature-maps.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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wk8GsB1c44/KlmVqn1sQByaAdt7fIPW1bREbbLeN7Xv68e/v9IKG5aJ\\nLS9h3d/a20YT0K3hdrYdndJPc8YZqEyQHmmMBFkIhAYdg84MWVIwjKcMa7z40hhqwT9uPpVVXA/n\\n1+R3QdQBg+t8HOvrk17jKosFJuqyqrGz60TR1QUR/KAYyk7YdE4loZD5R+UHHgJqgMTjWWL7Dhl9\\n/QbHunQ1IjaBfSLkwv6u1X6aZBXFER4XEK9db/WwNr3oXXKiNO2rtLeQUSsUxQkNN7m6DjPXdOt5\\nqtt48K4UihvnFRJf9kI7Tn8fO9EJw3TKvYFmuiFJjyAZaRAClUHRE5jYgvhWfMpAToL3yBZkWNa8\\n3etrTBfsuTaf98psX8BIOd+/vD0YYVqKdCYMt4963+53ZebGzq5A3oUTD3mpuOJfCTUcX+7r8sR1\\nAWtfRGKTLHqyBAA50J4vh3i1rQ7eHwfFMSsaEcSIdjD3XbNNqOVPMR2SxwPES6m0jHpSqy61LdcJ\\nuljHw2S1ZFl64e+6sgReZb6W9gjI7m5oVS7tzXSCyTSqFyFzg4lAaIOcCuIxiGIepNTkM77Odbmy\\nSvbDRyVdXE/bZFX6pevxjyIcP+SF0qQFQ0cD5ho+3l004bqxsgJFv4fHI7IzOO6I62jWLq0TCjLy\\nGVXXzer42IB4UzbCpherKSCjMeihNVin+wOxWLLr4wefuetlC+B4woSbwHxsEj69cYfvbFwhH845\\nbyMFJoaiB2zmXNk44on04KEAHFbTxLu2HfpdQ4U1Hmfx5woK/36LQUMWNH30yap5wZuyD7YBT1tw\\nTiiviQ+QQx4oTQAbOr4xe6FYPNYnbe6Mdd9119AKdXvA4l1KhPrBZDEUQlwD/i5wGevA9GVjzH8r\\nhPivgL8A3C0P/WtllZ9OEgoAWYc+SURTrZPmc1eRejraVWWVlLCrFj1eLhqwbNQEmBrrS/7i4Ba9\\n3kvkQ8PoUkx2qDASojGYGNJ+zuXeEc+k95Z4+y48dz3ytKus4mrZ5u+/qkviurJuMqyFNmpAHkqd\\n0EgfOoCgzBzYmwyakkXgDhkw6yDd1aMlFBIPFvC8VEMAfFetzrNK3nDfea6m7ct3Ih1Y9iW8mn+u\\n6B+zsO0HWWOzAP6KMeabQohN4BtCiH9W7vtbxpi/uW7Dq1bn8b2U2shOeVRCPOe6KU19EnqBm6r7\\nuNIGesoIr1buT9fqf8UiYchNxG/cf5EsyTm5knP/+YIXr97m951/l8vJAZHQjHVCT+aMTUKvDGro\\n6udcB3zb78ODaFvfde+UdaM9fVJ/Zrw5Xzw546ttTWNZhUJpyljYJZjHF2npBupEzAHJdcurKthX\\n5/vC8Ot91HObhGpk1qXNT/thq/e08dch//Im/3N/8YdqIq6Ut9o4VlQI1wZxY8xHwEfl5yMhxBvA\\nk+u2F5JVKY9Voi5hkXJZJf+GO7427wdlwkDvFn8IFux18qVAM42ySEF4eMNazc1KNJJvHj/Duwe7\\n3L+9RXI3QT2lOMlTRjpFIdmUp1yJDwCruWtsKP48vcHqfHijl4dDqTR5tfiKOSxX7QkbsKtxLPTt\\nPEe+mqyza24Y/7ytxcm6q1Zery9bl1U9kLoG9bjrV1++k+VxmEAWmpqIRc1z7h/uoTxm/fs58FVT\\ny4JfA3cnkhD/Xu9foWZauK96/WyMor6e8Us9O+KqsR6PhBMXQjwL/CjwVeCngL8khPgF4OtYbX3f\\nc84vAb8EEO3uAGE+fNUl78NU6lm1nYehUdyX2efH3Fb1PTimJh9xT35xZQRjnXBrvMX+0QBxEiOn\\nAn0n47rZJZWKw+0eLw9vEGHoyel8HKJqw9U0tPdB7OI2GZIuFX26Sv1er5M8a9XJPwTcK634PKkT\\n1gm1r0sTBVIH53W5cZ8kK5yzjpbdhWbxJcYK9VcPHvJV9XEBOZS6tp6XPFTOras8dECWEGID+D+A\\n/9QYcwj8D8DzwBewmvp/7TvPGPNlY8wXjTFfjLaGDzuMMzmTMzmT35XyUJq4ECLBAvj/Zoz5FQBj\\nzG1n//8E/Fp7S/MshfXlcltx4o9TmoKJqn2asGdKKEqwaTntGjnrxRW6LLPq1e3dvpton1dPrvGN\\n7z9N/7U+V781ReYGnQhMHDHKnuTryVN8Nf0iR88Ipj804g+98CY/ufkeu/HxyobKrsd+XG6GIWol\\nZPBcpWRbN62+Oyfu+od3pU9Cnilt5dXaXA1n7TifH1WwT0gq75VV84GH2vKdX4Xpz74HNG6ftl1J\\n7lT1CdXdbJKux/nkYbxTBPA/A28YY/4bZ/sTJV8O8O8D3+7aphTzwsYVSOY6ti99DcybEh919R1f\\nN8Q+wp8Le3mZvnxMHcCb6JgQ2Pk8eHzh9hXvnZMs7K/nF79dbPMb118ge7PPhVdzkuMCIwARoaRA\\nKEM60eTDiN4eyFcG/NOjl/nmk9f4d594h9+/9ZZ3nHUQDvHZ7t/Zb/KDcNUMgWdLbh6XD1+nT3+a\\n4GUwb4rQDAN/7TgnSrMNwJue/ijw2ba7HqhWd7QrfHV1RawfW8+r4u4PGSV9ecS7UB7189ahSVal\\nHB9GE/8p4D8AXhNCfKvc9teAPyuE+ALW7fD7wF98iD5IpI1eaisC4HKWXZL5P4qKLMt9NBs529LP\\nuu1A+49ZN3jac5wyXTOjqQpOFsoILsZH9LMphxuG6XaEzO2x0+0YlQmKTJCMDKPLEp2UQY5aMCki\\nNuJJUBNf19fbd24X6bIimo2F9pzkXXLOL7bZ1JZozFjo57aX7Ret/czOdY5vCbevH2/7WBYfP94V\\nwFfJF+6bUJq07zbNvJ4Uy1d+rW5ctfnCFzXqZQOkx7FihYk91Peq8jDeKf8ffpf0zj7hcxFLgOUL\\nh38Yt7C6Z4HbVsh4Gs5wF3DTC2jf836Wb5d2XpeFHNM+Tb9W2Sckq3gtpEIhRc7F4Qn3t3cYXYyJ\\nTyNUKphuSKbbgmIAKhPkmxq9XSAzxbWLD3h6c58n0yWbtReAm/LFdxXf8fXfEWgNlAgVhphJG432\\nEBRfJyNoXeGo/Z5NQWx18PZ9rosyLAa4sAzYrjR5rPiAORSOH5oirU+Hf8BdojKbtO26e6DfM0Ys\\n7Q8VQX4YCYH3qrl+HpuIzbbshL7cGA8bIt1FmvoIFd5t29bEia+ThrZOoSxrbov3tpq0qmsb6wRl\\nJHIsiceG5Eihz8VEuSE+BRMJogkII8l1jDoHSaQYxhPy0kVvFV68DYxheZLqck51fY190x2AveX6\\nxLxEXfO5hlWDffxpl5vsJ2Hf8Dpoh4pBtBWBeBScd9dshXXxcdhNWrfLbfsKP/h8tdukquJTl1By\\nrVUqBIVkVY+3xwbEV5EQsK7jjtjW5qrthYAc5j/OuiW9QpV9VimIXL/OqYn4ysGL3D7aID6yYJ1v\\nRYwuSibnBZPzGjVUxAcR0RRkLuBBzPv982RRwcX0mIvxIalwDTtzF8m6cXY+xtWMoW3+4aHr61LG\\nzyer5qdfHsec63cTYtX7db+vkqWwaexL4/UAuO/vbOzMwTaYvbBW3MEXCOQ7dmF7BxokJD53wCaN\\ne3aeR8vuIl1zjfuiN1cF/E+oJu43UPpkFc3YlZDW1lREuIv4s+MtGjjdgJ7GttzVSO3YUFFk33ia\\ncmdX+6bavppfP36Or1x/HvH6Jle+WZDtTZCTgvH2FuoU9LFk47pk592ceKTQiWTvcynHssfh+R5v\\nHV8C4KX+TXoin/URVRo/i8BdXWe9atFCwE5HHn1VAPbfi2WDObBEqcy45pbn001DLIXxa9ct3izN\\nueBX9InvcHjbU+8rseZW5elS7LgevDPb/pC+4qF8JKEixvUixyFxK9CHJNR3fX8IyB9FEQp4bED8\\n4cV1RQwZnB4mMVId7JtcBmXNkNjUx8LEYeag5Wra1qPEvkqu1t2kcYf6nn0Wkm+dPM1bh5eY3h6w\\ndd/iVjGIUbsZpxcF+abNH64TgUolQoGcajY+VKQHkoMbV7jx2fO8fu4KyQuKl/sfluNZXDHYe6KX\\n9kWiPdCmS3h+3buo64rMR608CldWn+1jlXbrK6tV88LP23HbWP38VQodh0DaJ13Tyza20chz66XP\\nyvjBu827ZVV6JLwCWA7FDx33iadTQmHz/pwUPlD2A7ibO3ppKe15duoa/4Im7ITaN4Fzl1S0aS13\\nsM9gmZtoSfsO+X77VhN1zXxsEt46vcJrD67y9s1LJAeSaGLIh5LkWHG6GzHdMZgY8i1FchBT9AU6\\ntjnG+3dzjEwtf36ccPDZmF8uvsjPf1bwo4Prs+voUiN0mdddXTtpy5Hjrk7c1AZOC+Vx7fUufV5Q\\nruIwm1hZ5MSrZyzEp0csa+2Lvv5+t9kFwy4ODeLxEbdt2r8haGqjTlzxtVH35/aFy4cyC3YxWtZz\\nhtvC4M1Gx1CmRLe9kFRZ++vbqnarNrSxAKxQjVp5/bP9/nCeco8NiK+TpbDSuJpejLbPoX4XjVrL\\nroOrzpa+oghtOVGqHCfNIeftq4vquxSa3MS8dXqFVw+e5Hu3LsKdjGgimO6AUDC6nDA+L9CJodhW\\nkCmm2xHHT0qEhsEtwfCWJj1UjHcjoglsvSU5ng75+/w4o0+n/NTm2/ZazOJ1u/lSVr1vPglx4k0G\\nZ/fvOulpuz6nlWGzvkprA3A/RbbaSsEF81U18Dp10tWF0O1z3pZZ2hcKuNELSbXCft3V/lW9V0Kp\\nbduOixBBzdlXfNmVJp/zRymPDYiHlsBtGojr8tW2jF7X6LlufcaFNhq4/FAkZn1ZXW2LhAlolM5x\\ntTFXFe1/++hTvP7gCt9//yLJnQQZwfiyIjqW6FjAHVAZ6NSQnhtTfDRA5jA5b0iOBCYSnF5IyA4U\\n/bsF490YoWHzXTiZDPlV9SNMPhPzpa230CZhUJZwWzBq+iIkO3ijrCsfZ01NX/yBa8j0HecD8pBR\\ns2tQjwuU7r42SqWNMplW3Ldn3wzwZ4mt/OOsxuPurxsVXTpmRmVU7RqzdHxVxb7av4r4CktELE8W\\n87EFXIobcoK7PuDV2OtgHtq+qjw2IN4E4O5nXzSdu5x2gbyuqfmMoo/C+yTkbRLKRriu1EPpF9Kr\\nOvx4XQufmojcxPyb42f4xr2nuHV9l2Q/Ij4RTC5oomNJPBIkxzDdFuRbBt3TTI9TkpFAKkGRGjBw\\n/JQgPhUIbejtFWQPFMdPxhgpSA9h+l6fXzcvc/SpHj+z8wbSJDODpx23/4VbJ7inq4SCfVYRr7uh\\nRxaftwqMFr1SfNTJbKzOxB01BAetIj4KRdf+rmPcbNPSlTGzzIUumC+MrR7h6E52ZhlkXfFV3WkD\\n9JBmX18h+IyWbdz7Iu/tKighOipA8ayIE48NiPuka4BPiEdfx32sq3SvnzjnzoMFL1q9VvwTxGw/\\nOrhszk3EAzXgtaOn+Nbdq9y/uU10ItGJ4fTpAqRBp5JoGgGC6RYUG5pkZ0J+kGFiYArxicBImOxq\\n0geS0eWI6YZkeLtg8/2ckycSwE4Ek+M+v3H0Ejc/vc3PPvEKF+NDhmJSjnsx6Cp0/Y8y3/ijkCbl\\nwJXFtLi+363U1OteGgE6pS2gZxXRLHPjleSB7XMN2QL5AvvseKj4JHIAPCShFLP17U0Uigvm9Two\\nbSJr0ByiRXT5r/oOELeuY/ya+MPkSfHJYwPiLhA3eZP4pI1CWTJSemiXLkmiXO0tFMjRNReKm661\\n0kK7eCF0cWmsZGoi3ptc5PXDJ3jt5lWmez3i44hiQyEGit5wSi/NOfhwG1EI8qH1RjGJQWtJ8iAi\\nOhWYyBBNBcXAEJ8I4lNs+H0EGBDK0N9T6CRCJYJs3xCfxrx7cI2//Zkt/tTzr/CTw+95xl0B2qIH\\nS/1ezY6v+YoHtXoj/ZOFWFyhLN5XP23n7gsZNX3HLufIqdMHzjPjFLPuEm3r054bgdIB6NBkn7Tg\\nnquJq9o2WrTfeuEIV9p46VCq2LY2ukhdsw5p3KFtldRD9Ktt83Obwbsezv+DzJ3yyOVhAnW6auGh\\nfnwcchdpjL6sGTNdEPL5fS+MJ/Aye/OOL7lNSXIT8ZUHn+HVe1e5d2Ob6Cgi1gKVGUgM6WDKRn/C\\n0SgDbQFZRaBjQ7SZo04jTM9gAKlAp5YrTx8IpjsGHcHACI6uxSQnho2bUzY+NIzPR5jI5lrBCCbj\\nHf5h8WPwGfh8/wOAWT5yO9bmoJv5/ZALf+u+5YvXL7yeJqsWDGnyhmobr0uxNQE4LNs+3N++Sev3\\nGRNn46ux31taAAAgAElEQVThWhd/8TaJan/nfa0Hoq73isuDr8oRh4yZLrXSlvjK3baq+Ma7bgIt\\nO6ZPMJ3iVlCxxqFw9J3rkdL2cnV/aT3+1AFpo1Pq/tDeNhq19uXqO+6+uoyNLWycCMXXjp/jnaOL\\nvPHBFdjLiKcCnRpUvyDZmrKzNSKNFCeTlHwag4F8U1s3uAsTkqRA76W2YQEyt+Afn9rvOoF03+ZU\\nKfpllOcwJppqhrdyplsx003rtjj4CLi1wd+78yWe/vRt/sQTr/NCdptEFLMoz1ARifC9afZyCWUZ\\n9NlT5u/aqraR5cmniUZZVVat2tMmbV4q9atfBc5CuVHqbfk05iVuOqDV+4KEuvDfbcUdXAkFCPmk\\nIlf8WnvovX20NEoljw2IN/mFt9XH9L6czufQ+W1V6ru+jKsYL0ORmPU84L6XeEljQzLWCZHQvDe5\\nCMDbx5d49fZVRh9sInOs++CFAtFTbG2fMsimFCpiqiJLCRlAgh5o0LC5Oebo7gZkGnEaIQzoyCCn\\ngmgM0x1DPBLoBPJNQ/pAcHJVcHoxZvMDTf9eTjTWDEaKo2sJOgFZwPZ3Im4ePsHfe3HIH37mTf7A\\n1nc50TFDOZnRSYupBGSniXDxfnTT6itJxHKGTLUAKu3PoE9cg6bXNTZo6FrkxavfW+Fkplzy0qjO\\nbZcmTxXXg0SyrOXr2jZfBsNg+bby2CaPFPdvSFzgbiql5murqQ7mOlKv7GPbdh0NfKvr8DPk3ptP\\nNJ3ikzavlbr4PFB8Gn09um/BAFl6lfi8TnzFjV0vlDr4LP0gzu8uZzN/nRNbDBxxpXqZqwCge8Um\\nb51c5uu3rgFw9MGWTWSV2+r0xdCAEsSpYrM3odCSSGpyJVFaokcxxAYiQ//CKdNpjJhK0CByARp0\\nBqIwmAhkDnIC03MWwE0E0/OadF9yckUy3UrZfD8nHiu23zOMLicUPTtZDD8U8ME2/+izP863PvsU\\nP3v1VX6odwOFKL0X2oHSR5PMnhHPhB2aqBXSoab8sQZNnk2PwjgOFrhDoN5VE/cG3XgMlU1Jr3Tg\\ncyWJt49FDTyUP2XdBFhLY/R4o8z6CERYhtPddv/96v7e89D9RS5cGb2wbdW0tLPaoyvepccGxLtU\\npq+kbrRa5wVr0sCrbT5NvIsRcyGwpZYEKsSL22PrWpYkcRJLRWimJiIShgdqwCtH1/ju/iVuvX8e\\nkZcW82OJ6hnycwW97QmXt48AyLXV87QRnE4T8jxiOkoh1ST9nCwr+PTuXb575zLJhVOKm9Y/XKdW\\nCxfG8ubxSDA9Z4iPLYCrgSHdt3nGR08aBjcFR08lpMcxwxtjBrdB9SSjCzFF304C574tubV/lb/9\\nmW1+5lNv8TPb36UncwalB8vs3ixojnPPEPevvafzYsf1lVcIwN1tFtCdvpj31RQYFvIRb9vmigvg\\ns88zAG6w6wT3LEdudgn46RrBGUqItXBOALQrbX5ZWqhJJ+in+m7bbge70OpgqY8OPt/1767niSvr\\n5Ad3z/nEht2H+G6fPEy2wlWkLdtg57wrHs28jQ8HSLH+1VLokjaBI93n9ZMn+ebdp7j9/fOIQpCM\\nrMsgWKA1sSEaFGz0J5xMU4QwSGGIpeZo1GN8nIIRiNOI5PyYfi/n2s4DXvvwSYpRTPQgJj4VJRUi\\nEMry4EJDPjSzd266o0kPLYCrviE5EIx3DflQIG/A+GJGfKpJ96dEY814N2GyJdCJoH8bxK0B//KD\\nL/Dtzz/BH33iu3xx+O7s/tQ9VnrkZe7z5ZztisVYgSo9ris5kfXP9niouGC+SqrakISLf3hSR4R4\\n/ZY23DtQh7JFl7l6u95mg0bSeim2h4nkDE8UHt66Qdu2ba2u00dCLPqet1zDLLTfs69rLpRVZF2K\\n52FrbH4fOML+1oUx5otCiPPAPwCexVb2+dO+avddxQVsH9fdVl2lDfDXCb22/XRzJYRF7bviYX3e\\nKQtVeUpaZ6ztQvZ2vs3bo0u8vneFu9fPIZRA5gKTGnhmNAOO3e0TskhRaMlURSgtMFoyyWO0luR5\\nhDiOZ0AcJ4pIar578zLq0Bozo7HVspEgx9YrRZbxOkKDnAqKoSF9UAJuz0ZzFkObbyUewdGzgvFR\\nxPCmoOj3SI4KBrcnZA8iRhdjRpcl0USw+Z5h8vZl/u7nLvFbn3+O42nG05v77I2HPLu5x89sf9eO\\nKdKkKLSR5MxzmPvud8i4HUqQtRA0tcAOh+0udQkF+dg2w1y9nmmVYfvHOgbOLvnEO7fltlN+71IM\\nAjpy9TUfcF9ATrOLX7f3t8pvEpJlDbw5uKerll+XNornd4JO+RljzD3n+18F/oUx5m8IIf5q+f2/\\n6NJQKEdE9dI1ZSp0ZdViEasCeduxdR5cIcnL1K+5iZFCz2iSoZygjKCeHzzHFmp4f7rLG0dX+M6d\\nK4xubCDHknRs29UpFH3L3Q/61m1Pacmk/FuJENbve3yUIR/E9O9KdAr5lmY6SSjyyAJ4pkhupRgJ\\nqmdpFJ2UAG4sx46BYmBmBtF8Q5McWQrHxJAcCCbnDdEYioHg4AVJ/45hqCP0VJKMCoYfGfr3BOPd\\nmNNdiY5h47rgxo1nEBq+1b/C+KLmnd2LbLxkKZaf2HiPKCoBtQLYFnuJO/m7AL7Sb1nzlFoE/7lm\\nXaf43JS09rz2/D5L+zqCd924qaFMDLW4fd5uuJ3mwJywJq48gT9NfuFtmrbtz7U7qeBxbdKU38QV\\nF5i7GG3X4dW7GFYfBzrl54A/UH7+O8Bv0AHEV/Hr9mnfjf60Lf7B6+ZDWRzT4tK8LlIYRjolNxFv\\nHV/h2/evkEhNFhf89MW3uRAfoZD0RM5IZ3xvfJE3HlzhrfeuEN9LiEeCDCg2DNNdRXJuwuZwzCDN\\n/alLpQ0ekrEhkpqjkx7yMCbbk2T7hsk563YYRwqVR8iNHHmzZ71ZtrTVxqVN/CgKC+CiAJNY26yc\\nWrfE+ERYABc2qnN6XiOnAgTkGzY4aHTFAvbgliE7kMjCEJ9qsn1FPLbnTjck+aYtypzdh0vfVDz4\\nVJ9fOf4iAN976SJ//upXSjtBQU/kC/aCKsDH9RF3uXL7G3g8Bup2CcJa/ap+4tXvHmpv4fiARh6S\\nyoNksY35vuq7j1rxGT1drxNYpFF8UjdqJgu5vJf7b0p4NT/Pt4Ly+FsHXUubNdxqYmvSsGdJuwLu\\nve75qwQLNYnbv1wwuHeThwVxA/xzIYQC/kdjzJeBy061+1vA5S4N1TWZBaDuwIHX3cS6ZCp0PVNC\\n4OvPRLeYfCr0wtYDc35z/1O8c/8C+7e3kMcR0RMjXrh8j0QopDC8PbrItx9c5a3rV0huJ0QTQZoY\\nir5hmlpA1IlBbuYMBxO2Sm8TtxSY0hKkLUQxVRG9uODG3ja8NyQy1lvl+IdPuLRzTDpJeXB7E7Qg\\nfhCRnNhKPtFpee8lRCWdIkzJixtLt+jEEB9LTGQQGuKxpVOikbAa+qalW1S/1NqF4PA5GNyKGN5S\\nqFQSnypkrik2ItJjTX/PIJQhOcophrHV/Evteys95Z3JFZ7P7rApx+Qm4kj3yE08A/WmqEv3965+\\nj7qslPPbExHq7zOUV2eRYlmloo89t8lY2nCux9DpQoYPvH3bln2s5201+YV3lTp4a2O8gO5KU2j+\\nQltNVGjQ/dN5lzvmMF8aXwf6RaHmrr8d5WFB/EvGmBtCiEvAPxNCfNfdaYwxQvjXhEKIXwJ+CSDa\\n3V7Y5+MQV4nK7JSus8HFEFgAxvqsLMssgpUkQs28UKyRTcyMbWMT2/0Y3j88x/7eBvI4wkQGoyXv\\n3LrIO7cukj/I6N2OiY+h34PJBU1+ocw1HmsGW2Mubp4ghCFX9gqL0uPEpU7SyL5yWVyQRgqlJVfO\\nHfHBSYo8iolGgmKvz81xgsklclAgb/aQyuZFiY+FBV5A5oJ801ju3ZTekcIaMUUBRhqMpNTeLYVi\\nYtAxZPcl0x3bHiU9E58ITq4ZJucisvsQTSM2PirI9nPyQTzzgFG9CJ1Ktq4rkiP7iH7j3Zf5yjOf\\nJTt/yrMX7mOM4OrwgKf793kuu8vVeH+WzTIkTUZNu609l/iq1aPq0lab03vOGvy4T1N/mKjNLomv\\n1nUf9AF22zFNEkp36+27AYybZB1jZtci0qsGiz0UiBtjbpR/7wghfhX4SeC2EOIJY8xHQogngDuB\\nc78MfBkge/5JA12WqIu0SNfgjvp5XTjz0HLKzRNdgfXEJCgEPZHTkzmJVOQm4kRnaC0Z6ZhIaH7+\\nma/zzXNP8/q9K+zvb6DuZUSnkvhUYPqG6bZmulP2P1SIVLFzzgJ3Lymo5sOkBOe81MJ7cTEbbyQ1\\nxgjGRUyuIu4fDJHX+2zdEWXlHig2gIOEqACxF6MGGtUv3ROz0pXwSFBsGKKxpUaMtCkyjLTauEkA\\nA1JZL5V4ZMFf5jYoKN/SJIeWKxcGKD1bpLKrAZXZ6xxdTkhOEjZuauKRJn0wBSGIcs10MyE7KDXn\\niWTjRkTR2+Cj/iZCw9svKi49v8efeNJwLdkjwjA2CYkoZsZkVaYqXs5xUi9j1/7irEKnuM/LKse7\\n4qvcNAP0AD7N8rys3FuoPd+4FnnjJc79ITRwWA2w67LIo7eDuQ+MK2Cfh5/pRU27xUDqSgi4fd9n\\nKX0bqByfrA3iQoghII0xR+XnPwr8deAfAb8I/I3y7/+1bh8hCYE5LPOfXfJlLIC6qGtnclEDM1W7\\nYpaj5F8/eJ7jPOPBuE8SKS4PjhgXCffHA3Z6pxxOegySKZf7R7xy+0lOT1MLouM5yOnEqromNoid\\nKRK4eN76eMcld66NIJXKgnn5dhkj0AiKmiEzkZqbH56n/17KhW8XyMIgCsPkXMzpfoQoDA9eMrYt\\nI0gOrBEzORaAIN+wHicqA1lAlU3WxHasohB2UR8xOy6aWK8WndpAoel5ZQOGhC04EU3my0STVBOC\\noTCC46uS3p5gdLHP4K4iH0qGtybEp+XvV2hUItGJRKeCaGJITmLuiF1+M3ue3zt8mzE25e7fuv5H\\nZvfiD19+gx/ufVgS+vNnwa30Uz4FS89FiEtfFcBtP80Kh+sv3sVTxaUc55TMMi9eBfhUn919XSSU\\ngrbJaLmKPAxg1/tdV6uuSxdqxA3R93Hhblm40EQR4tClMMFJ2icPo4lfBn5V2IHEwC8bY/4fIcTX\\ngH8ohPiPgOvAn+7SWBcOO3S8T3yZC11p4kbdpXYilEWgpfbtXR6pjFho9sd99g6HqCLihtwGI1BK\\n8qE6B8K68r0X76K1ID9OEVqgetpSFpenRKlmMByz2auKKFiD5DCZkquIwkhSad0BZ6sALZmoGG0E\\nJ9OkvC44Gaec7vUZXE8495YiGmuS44LxxRShDOmh5Z6f+FcwHQpOniwfplJLlhPI7tushdbAWWrT\\nRhAfgU4q7dwglEDHBjm19IuJLOir1BDtWy8YjHVLNJHV1JlCResKTcmZw+lFQXpgePCpmP49zb0f\\n7jO4be/9ZEsyvK0o+sJq/5uCfCjYejPi3eNr/MXrv8jnnr/Bn77yNTbTMd94/XlELvhgf4c/+fzr\\n/Hs7/2ZJ+/b9/m35Vuzz0Y0Pb2p31v6KGvq8KMjyW659n53mZ3SKByCagn182qTPT73ipEPpZT9u\\neVT+2qtKpUlXHjRubvFICJJavGt9nLlRs2PXEWHW9HV8lJI9/6T567/2w0DlnrXsStgU0VkP4vBp\\n33U+c9EwuWjwqruHzftdvlcfTXf4tQ9e5t77O/RvxKhBeW5kASoeCZITyPYNp5cE4/OG4lLO5vkT\\niiLi0tYxubYxYP3EUjEAE2XpkHER2xwcUlvQzmOEMFYDN4IsKZjkMVli+fMHR334/pCtd+DCvzlE\\nFBp5OLJciNJQFKgru3aMvZj9lwYIBfmGQKcgJ5Bvzo2ZRrIAtiYBHZuSShFUt8jExhpEy2PkxGrk\\nMgcjrMZe3b6qGJPbtizsXGkkNif5Dgw/sl40AINbpZ/9qSE9KFCpZLITMd0q+4zseePdUvMvID41\\nqFRw8KLmmR/6iL/87L9gR46WJvAmo/ji798cCOT3T29+v7qCuzejZY1u8Wnn8/Y8fXu8UxbHVh33\\naDlw23bY1bDpeHA9SObGztDkEUpr+zDS6B7YknrAzRIUkv/18Bp/+Y9/j8l7H3Ya8GMTsemKL5Cn\\nbUnrAnEV1edqXXUAX/g7o2VK955Aopq6p8FEJ2gEu4MT7vU3GV+SmJ4iOoxnNrbxRU3x8gidWU67\\nDyglraadFByOLTncT3MmRcxB0ZsZJw3WUKlKrXs7GzPcmHCSZ2ymY47zjImKube3yegj287227D1\\nfk7v3T0QAnE6AaWg38P0M9RmD6E0clwgcsXF3xphoojxU5uMz0UUfTvpGAmDuwqdCGRhmG5K6wq4\\nZfOJ5xsWwFXPGjJ1Imb5xdF2u1DlZFAuZExk9wmFDeeflBNHYSc9YspScJDt23O3rs9XQfGJJj5V\\nqEySHubEp4r0OEYngulQgoDhRxphbI7zkysRDz6f8+M/9B7/2ZP/lBOdcWJSch2XLopFaXR2fZCb\\nAdw+J8ua+MNo5F20cV9CNF8h5VARiXWDe+rtgN+F8OMWn+dJPUthW5bEj0NCmQ8fJmfL4+An/kik\\nPdKy2S+8MbVp7f7WQb1de9LkJuJ+MeTWZIu3b15i4/WM7IFB6JjD560/NxcmSGmQ0lAUEdvDU65u\\nHHAuPWVvMmSsYkZ5ShIpEqk4mmb04oJ+nHOSp+QqIi+iGd9953u7RCcSYWDnTZC5NRS+cP2U+GCv\\nvDHaat1CYIY9iCMolP2exug0gihGjgvkgyNbPqufkd07JbtrQIPJInQSMdlNyAcClUpMPNegix5E\\nE3ts777BSEFyqlElzVJkgtnCSTKL9owmlpYR2o5dx6W2LyyQR2NNNNUIbdCRzddS9C2kFH1BvmH5\\ncACdpuQDiUoFoydsuH9yKMgOBL37Gp1JJucg3ZkwiKd8a/wMNybnOFYZPzS4iUJyLdljJxrxQA9m\\nwDwUU3oib4gp8D8bIYol5H66Ko0S0sTdvOOh8WoIuqxV4O4Ccj0/SuVB0RVa6sc1AX0X18FQtR93\\n2zpgHfI3b2vP1aRdbrsuD5spsas8diDuLgfDPLbf4OQe9zABPE2eKe7n7XjEr33wMvJGj8Ftw9b3\\nx8hcsf1uzNG1jP2XevDcCK0kaVZwdJrx6oMnybIyJ4q07R3fHYIWiJ4iezejd99qpr19w86dAqEN\\n2Z0Rcv/mDJCJI0wSzz4zreLiBUJpyAs4PLbbisJ+N4Z0OIA4hsi6m5iTEWbvPkynICQiTYh2tjEb\\nA+LDhN4wBSGQkwITS0ShEVNL3ZgsQeQKIwREAiNt7U2dRJhUoiNJMYjKbaLUzC3Qa+fJM1KAMOSb\\nERyDLAzRqSLfnB+UHmmKgURODelhjsgVfaWZXOij44TxrnXLHL845cEkov/9hN49Q/JrQ14ffI5v\\n7L7M+IJG7xS8+/wFXty8zTcPn2EnGQE238rv2XyXzWRsn8EAfdcUdblKPvFQ5kKfSIzfuNlUkMQx\\nds77dNv0f3aDfepAvtC+Uxy5blBtg65VAbcLUHf1EW8aRyiys6JAQhIC63XOgd+ZsPtHJl2Nm7MX\\nqclVUFRt+jnuVSrY+zIXRmgGcsovPPdV/kH849xRl0lOU7ZevUeUxOzePmL3G4Jip0++laKyjNPz\\nEVkG/T1LfUS5oX9rTHznrqU8CgVSQiQxcTQrMksSY2KJ3h7aS1MG8gIxmWJGY5hMICl/SuncRSkQ\\nUsL2lj0+khb4pbT7jkaIQR/SxGpDcdmGEIiDI8RJjLwvQGvMNIfJBDOdQhSBMYgoQghh/2apPT+J\\nkWliJxZjSFM79tnqQAq7Gij70alEKIMotJ0klCEaF8jRlPTDMWh7v02WMrm2w/h8zHi3R3qs2Xj7\\nAICNmzkmSjh6TjDtx8hhTv65nHEuMeOI9E7M8KZh40OBEQnXv/Mc34+ew0jL2yNg8vyY7z+zy89d\\n+hafzW6Wt+/hAoKazgvl/Kmnpg3V3uw6Jp9WHIkw0LrgXfdMWdXw5ps0HjW9sa4m7mrQXYJwmoC6\\nHnHZdI6bETGU7fATS6f4ALxLKH5bzUmFXE7+w1xTbyyvFoq8Yp5vHOALuzf4v3d3GW/HbI/GmPEY\\n0e9jeinxg1Piu0cgJRuqfC2knIGuEQI96AHW3Y68QBQKMZ5i8hymuQVO51wTRRaQo/L1iiI3pthy\\n+lWC+iiymm7fThximlvKJc8hSSCSFoCNmddKzAvIUsx4gkgSUAoRR5BtIiJpJ5dCWQ2/An9d0jiF\\nstcxFZhIIk9O7TFCzPowaYLJUpAgJsW830g61yHQ20Pyc/beYOz/7df3ESenmDQBIejdOGZydYPN\\nDyyQF3diTi9H5LsFoq9IdybkPcX+pQh5GhGf2AyK6YEhPdYUPcF0UzDZTbh/OrC5bJBEZbItO5Rl\\nTTyU5wdCwWFhusMFbo3AV3+zfo4rbUFKPvhxNeeukDE1Zokvr9Ms7ue2ij+wmothXduu+4Q3Afkq\\nxsglUG9QKOta+irRmj6Nfx15bEAcljWfLtXum3KWdJnRmtLNNoXjK2ygza/c+AKneUJ2OyY9NugL\\n24iPcguOJWCaoxPQCmRkgU8pjFIzYBa9zAKhkKAVJi8gLd2S0gQRxxaYjYE4pnTrxChtt8eR9S2c\\nDw6MRsQZpAlm2MNIiZwWGK0t3SKshk1s+zFJjEkTRNm3mOaI0hhKbsFajMblhFH2WT2EkZxvK5Sd\\noIyxE04Sz89xgFxMc7tN2nPNsGc19STCRAKRK4QyxIfW5VL3EowUqK0eMrYTic5idBYTH+VMd1L6\\n9ywogyS7n5BvxRTDFLOpkIMCBgXTYUy+KcEI5FQSTbETxFbOVjbmg/w815K94PPihvb7Prviy5vi\\ndw1c1rxnP6XHAyU0pnkbYVnVV7wtd3gTUHsDXWqas68OZlfx1eicteUB13rovHsNq0gb2Pr8xX33\\n4lFx5o8ViK9SyLbJ8LnKcmSdeoiV58v7k132TgacvrnDpddsaTKRK0SawniCURoRR4g0gXRgAU4n\\nmMLy3IDVqI2Za6BCQJZZUNZmBvqVmKmdIIAZ8M/asDemdCdU9thphlQKM+hZDT5NrFt2tRJIYpAW\\nPBEClABl5ny7lJhehlAWnI0UdjJKE8vDl8dQavMiUvbckgaxwBwjKk09LyyoRxIjE4Qx6DTGJBKd\\nxah+RHxSoJMImStU346zquEplEb3EuQoR45yogcj9LBHtqdJRjHTLZuhS6WC9BCKvqQYSIqNGJWC\\nyDRmqEALdCQRSiJzGL7W4839p3n70iX2XxryJ7e/NQPHxWAw/ypxOV+PWTJq+rR4n4GziTpZPRNi\\n7fzq0DVYjarSfZcQfFe6UCkKMzO+NoF5Vx90H2i7YN118mkDeJ/mvY6/+ir1PevyWIF410IPXbLR\\nueIzkNaB3mfMdHOkLAeFaJ7O9nj23D5vPptwsL9BMtLE/QQ5SSztkMQWXNPEarEAcfnYaY0plKVM\\ndKlhRxJEhCkBUMRR6Tid2HaUQkT2L9pYEE1iC8olsBsJIkogTWfcNZMpHB4jpLATDNgJwBg7IcgI\\nMSkwSUxxfmgNlEoTHU8wWYJOI3QigU3kRKGzCGEMogRVE0uMFETjYn4/k4joNJ9/j2NEriCN7YQB\\nFvRLV0diCRLS/clsu70e+7uIXoROBJPdjOTIToKi0BiZIE8sdx4ByQ0YAmbYY3JxwMnVFHUCxbFA\\n9UBlEcVQzlbIyZEgHlmf8skFwXA45nODGzNKpf7cVFIH5LpWXl/htQanLSR88x/TVihi3pbTrsOy\\nVfski3lU6kAfegvdohCzvn25PwLnh3y2u1a4DxkifeJy1Qqz8H5X57UBtEuvuNfYZrAMceShccry\\n36xf5A80AdYjlVU0cZgDaygKr9q+7GPuBgG5D5MJeqbUDaG5iUiELb5QTCOyaenyd3iKyAvMyajk\\njKWlS9LEenMoDdJgimLuj64UGI3Rc4MfgNFmpmWLOJ4bHqVzvVLMOWeYUy1SWO1Ya8TpxG6PIoqr\\n5xlf6s8iH4Uys5D8eKwQU000VWAMepCCNsT3TxCnk7mx1aFRdC9FZ7G9LmNQ/YRiI8EIEFsJMrdJ\\nskws0bEg25+CNjPvFTnQJPdHiMNT5L71vNHbA/QgYXouQ/Wd+36qSQ+nRCcTS7vEEpEb9PbAAn+u\\nEIW2vHyh6X1wQO+mpV4mlzc4vZgwrYKaphBN4cFnDPmPnvDnf+i3+BObr9noV5MsUxQd86as4qEC\\nfi8Vlxd3P3tTDtfG0xZNuuCJ0oCbPo+VkCyD3HIbdSOny2GHqtbXNfJll8Bw0MyigTEcSu87vwuX\\nHzo2FI1ZSb0MnAv6q/TrymMF4r58J25+6JnUSnjV2/CBeuilqi95u758Aznh73z4+/j+q1e5+A04\\n/60962pX0gsiSSxnDTPDnajcAytQ7mVWS06SEtSNpSooNeuSSxZZutj5NLcafGnYFEnpDQLWaNhP\\nUcMUE1eAvonQhnwQc3rR0grjC4LkGERhZoE60dggC0hObFFklZQGn2zDAn5hCz3YPCnY1LTKHq/L\\nXCgyt1RGlX+88taLxwahYLrZI5raiUPmBtWTZfsGldrcKDLXxCcFvVsnM61d5AqTxlBoTBJZDx1T\\nfs4VJhIYKTH9CBNn5bZ0RlvFx1O290agDGorQ/Vibv7+jOilQ/7Mp7/JTw+/y5FOA9z2sq2mkhAv\\nXlcGNGHbS1ct3fVUcd8Jt+9wbhbneqrrMIueKiHVqakYhCtNOqcf0Jc1c19VHzdCc9aesHy4Qnkp\\nlabyaV2MnNXndav31NvztZMbtRDJWZm1IvQPLHfKI5W6b+7SvlK65LZwZRa5GYjCrCREp/jHI1Em\\n5Ref+k30U5Lf/v3P849ffZnzX024/JV7MC4L/lYUR2XIiyKrdVcadQnypihpCBnNDJjGlK9uaVzM\\nz1DomTUAACAASURBVA9Qg5h8I5qBq1S2uIKc6FlIu44Fqi8RBUy2rfZrJOSbApWWGQwBlRpUT9hU\\nscL6bdvQe0M0ttV2ZGEp4PGuISqLQBDZnCk2tF0QHdsya7JgVjxC9Syo69gG90Qj20d6INCxpS+q\\nkH4bZm8BXvXtGPMNEColmg7o7dkLG94uiE8U8fEU1U+ITvNSE1cWyKfFDNB1GmF6yWwSq2ifYjND\\nGDh6psedL8ILP/I+//mz/5ihmJbPir+4SMj+0uShUj0/7nMVzjvv6dMBbm+gT+0td9uYcdDlIaEU\\ntPVAnyYQnumTAS8LZUyNfKprnSx99kVgesdZHScWtzUF67SFtjcZOn3i05JdusXXX31CqEtdg//E\\ne6doI4mcRFMhcO6aftaVVfzAV2kjNzEKyRc23uff/dKbTH8q4pW/+DS/8rUvcvkrkvNfvQ1HJwCY\\nQlm3PmMQaYLe3GB6eWg9MQozS/Gab0YYIZC5IT4tE+qUwJwPJEVWLkETSr9raY8v31QjLJWSjMxM\\nM4YSMCNI9y04y1k+cMF0C1QPJtemRJlichKT3o3n4KxtfyY1dpCxgUJg+ppC2kAUE5X7tLD5UIxA\\njiQmNhSbxuZPl3I2bh2D6tuozeRYkN2n5Kxhel6jexqhBCfP2vHfzyOSg4TeXo+t9wuSQ0k8ylGD\\n1FIoSVpGqSaIvDSiIij6MeqcQBRwfDXi/k8U/OHPv8ZfuPQbS9WA7O/sVxJCuXvCYfrdts9y1i9w\\nxGaRIqmSXnlC733paqvR6cBjXdfE69JU3cdX7X4VGqArcM/7CylSPqrIH5QTcgN0jYmhAsquNu4e\\n47Z3XxdlO7Apo/k1muVJop521kfHfDI5cS24M93kSnYw37aUsCpEh7TMXu4SxSOVH7m731+Jpbmf\\nE52hkLw4uMVPff4tfrN4EZ1c4eL/e9MaIJXCKCASqEs7THZ7TLajmQZdZIJ8aAGzSgil0giZWwpD\\nlkBf9K1mrfpW85XKRndKVWpuU+sDLRQ28nFiyPYLdl85RT44nvHp06s76CwqNWOFUNpGYxoQ0zEm\\nlkwuDjAR5B9EDG+MZ8muorGN3kQby3WnkfUIySJUZicdG35vKHqWg1aZLFcEVuvXqbEa+2aB2hIU\\nw5jeXUE0gcGHktGToDON3LKzUK8/RV2VHJ4mHLwcIU4TkqOU4Q0Y3NX0b0+RkwI5BQrNdLeHia1n\\nysFzESdfOOWPffZVfmH3X3FiUkY6WzBwN632mp6xYJEHz+NqV5v2s4++W3YVNDPwrsC6Hrm5kGu8\\nQcHxXYEL4G7YfY7ff1zSzo+v4ncOkJvK22dOp+hZBRI/rVLfXkkdwH1l15rOC4XQ+zTwoxK4H2jJ\\nV8fPz0oF/pHB+2RljMZdNeVfjl7ge+NLZLLghd5t/mD/OuejbKltZUyjP3qTPB4gLmA3sRrrcvL+\\nxYe0HhTU9IKFKprP29YLfLlbycd9uUJUSz3vePX9Sztvc+H3HvOvn3+Wdz51leENuPT1I8S4QO49\\nsG3GZcKoUt0R2lhfbGP5Y4wNBHJzlsSnsHGzID0oiE9y5MHIJriCOSfez9BZgtpKZ54dGDBJNI/4\\nLDTJ/ilqM0P1Y3QqiU41cqrKPiUmiYhPC1QW0duzQBofjhGjCUQRepChezEmkpaOiez1GCkwkdW0\\njSw1eQPxyJAdafJ++dCmgqIvGd6OkMqQHE2JxtbrZrKbMbgd8eAzEeqkhI3np6RpQRxrxFaZxVEL\\n9i/1eTCRCJXRu9Mn2zf072lUJrj/OUH22QN+7rnX+PzgfZ6M98uiEcs6pq+0m5tQzZVZVSj8Wnlo\\nQmjLfugaMytxNW1XE1/FmNlFfHRH9bkCeB+Ah8q01WW2OvCkA2jTzn1ZC93tPmCu71s3UjSkfT/Q\\nkoNSEZBolBG8cvI0/+rw0zzT22MjGjPSKe+dXuTNg0s2gd2TI+7riJ7IGcjF9LTrpqGFxwbEDT2Z\\nk5tocTYycumhmFm+hUaZqPGhdQ2kdU3cBXUfSLdV19BGzKMinTarHOQ/sfEeG9GEbw1OeP3Npzj8\\n1Ca9e4LB7XOkx5rsfoHKBEVf0NtTZHtjooNT1LnBzEdanlq/cyMlajOzxrukBJBYInr2QRCnk7lf\\n9jSHzFIKlaeKUJpimDC92re5wA307ufIiQWz8fmY0wspkx0LusOPNINb+WyVMLqUMLqcMd7tM91V\\nkBji/XiWRlb1DXo7hynEBxIjDbIQFJsaIwzpfkT/tiA9huGtHJ1IVE+gTmSZrtcmvTJSUGzYrISq\\nB5vvQ1R6ZvLqlo2sfNKQ72h2rj1gkOaM0pzxNKHII/IrhtEoZV8Jzl0+5Oeeeos/tPUdemVFCxug\\nVWl+ep61kjp1UHkLND9foZViF4Bd4MpLo2eTJl3XxBfa7aCNhwyb9vxm8J0dzzKQT10aYjYeP3dc\\n7aurUwvUSO3S66HpFZD7Kvi4sg5oa/RS1R6fJ8s9PeUfHf0o/+eHn+fWh+dBGq49tcf53ogbR9v8\\n41svI1JNkhU8d3GPy/0jrg32OR8doxBIEaagZn1/Ug2bXbwAuroftsk6AUG+ogL1tqr9Y53wdLbH\\nS0/c5J1z13nj+Apffe0FkqMYoSXROEIWhuFHBdGosMa4CxvIqSIeT2a5RvQgtZRFFjG6ktpiCANB\\nMYDp9oDenlh88Ev7cP+uobev6d2dIAtt/bq15cwFVdIpgSm1fyipHDWndCrPDlud3gKzyCUUtnCE\\nVGXh5onAnMSYRNtCEYXdbjIFhTWSFgO7+pCFtpORkIwu2UyDGFtzc/NmQTyylvkZl18ig8og37LG\\n1PRehHpnl9HYkA8Fk6sac2FK1s/JxxI5lRweDXj94Al+ePABTyf3l36zJnrMjQ14FFqu63bYlKN+\\n3fZ94jN01p/4ej7xrkAODmB3GEuojNvScR2Qy9XEK28VH6/eZPRs66tOw0QIbirFb4yeB+DG9Bz/\\n5MZLTPKY81cOSCJNEikOpj1GkwQiA4cJuUl4a3KZLzz7ARMd89tHn+K10TUuJMd8afgmP5JOlyic\\ndQKFHqY824vAP3A2PQ/8l8AO8BeAu+X2v2aM+fXGxrTgoBhwPj4JHtJm6HTFq8kEHqG2/Ciu5CZq\\n5cvdHOWVP/kz2T1e6N1Gvyz4Wvwc26+mDG9pZK6R41Ibvphxet6mmc0ONP17U6KTnOn5HuPzMSeX\\nJcXAUioqsyCXHghbUadglhVQpxbEi541jspCW5WrBGVhbDbB6hYJY1CJsPTGwGq+KhGoTBKPbVX6\\nybZkuiXItzQm1WDKfkzpAx5Z4DalJ6RUoGJAC4QSZTFlZrU2hTJ2UlFlmbfqllb2gZ71UqloGoDR\\nEwadGHTPkN0ref1tm9N88z1J/HpGepIyuig5+HzOlfOH/OzlV/hs9hFHuoebFbOrItCUdsEVHwXo\\n93JZ380w2HeH9kKrWRzDZtNV1t8cn8bd5IoXAu86kHbRqOs+5V1C9dfNqaLRJEQc6CnfnV7in++9\\nBMArN56k+GhAticpBob8ypTNcyOSSJHnEWIUk+zb4DU1znjl/qf4VjmE+EQgXzjmracv82cufJUv\\n9U7W9g+vZG0QN8a8CXwBQAgRATeAXwX+Q+BvGWP+Zte25Fjwy6/8BJcvHfCp7T1+bOv92b55BNx6\\nlcbXDc33Jcjq4sXS9OLfGW0ixhHJsSEaFZhEzjRTWRjisbGfaz5h2q2KE0FyBLosaKxL4DTOLymV\\ndT9EWF5dRMJ6cGhmQGlk6ckirNufiUFl1qOjmhCEtgFAMo9nCajEoMAUEnMSWRfCUrvXqQYliCbC\\nAnNsjzdxqccZSj/4eb+WP8eqd4LZEnL2kxm7CrDbBAhrCD3NJKcAsSH7KEYY67Y4PmcnqK1vJ9w5\\nucjf4d9hdC3jR/rXF+9nIMVxJe3eTP4VYz2ewWv0NP7nKDfSWfEtc+NBcZoKAbrLa7taNSy6H3Yt\\nHOFzN2wCIR+VA5AvpB9olxBgh4A9ZBR1RbFIpLn1OiuvFgV85ehF3rh72e77zgYbB9abqhgYzu0e\\nkyUFUhju3++xcT0iPobxJZhezO1keRhb77Pdgmd2jvjM8DZbcmwnCrG4ntGmtrpukUdFp/wh4HvG\\nmOtijRlFaDCFZFpEPNE7mEVDAiRink2u6eVbd9lbX+K61ezr4la5X05uVHuxa2Md64Q/d+23+e9G\\nP83R/R3691N6tyeW21Z6nktFWaOmyK23iI4syM6AtbDAZuJ54YWqjiUwC7oRGpu7uzJuCiyARgIw\\ns+o3whiiqa2lmRzbSvXxxBBN59cTjy2VEk0E+iiBnrIFnYvSEDxUxDtT1NSGtJvIugeSGIQ0FnwT\\nURaAKMck7OrBRHYC0nGpdWtD3hcUA/sgR9P5fTYSZKrQU2k1/ERbf/TEauXR1AYvTc8JuDDhs+fu\\n8GP97zu/rd9A2VXqKZBXTVU7z8Wy+LwBJB1WB22FJFzPFYXrjlhNCiz8tW265ztjXeE1Vjiuc3QD\\n9LpfurvdtrMMwAvcOmIG1nWKpQL1Op9erwQUvh67ZFTOffvhwQe8ce4KAN+5sImcRrYCVqZ5avuA\\nP3X5m3w4Pc+v5p/nYHSeNJV21TmRiNy64Eangngz58d33+dnN1/haixQgOw4GYbkUYH4zwP/u/P9\\nLwkhfgH4OvBXjDH7TSebCLLNCVLA944vcOn/Z+/NYmxLsvO8LyL2dKY8Od68Y92qW9VVLXY3ye52\\nSxRNUZINQRJMyLBgyNKDLcIC6AcDfvGDpCc/CAIEAfaTYAsUYNiCYcs0bM2yRdOySHHsFtnd7O4i\\nb81Vd76Z92bmyTPtISL8sGLvs/PkOZlZzYZQBhhAIjP3EHvvc2KvWPGvf/1re4RFyUvmTQhMrsbE\\nz9bHPM8OaAenVrFVzgxcFvKypTdnti+up878XogbnYdU2teKleXbkzvMZwnJRKELL8bbi2cq3Orw\\ntwosj0g33qk3sl2HepgoqR5kpipoYgdjMAr3VUI8dk06PEoRTayk108qWQFo4ainxxXxROMei2cc\\nzdwCbnGeeOLY+MTTe6ICfVCEsFxUwzgGrzuNB618iJSpuPHUTSm0x6orsQAXKeKxl6xRJ7Uzo7nD\\nJZp05EBJOTgXgvhee5k4jAfj5ZEi37oHAmQUfisxem0G0yqjXe+/utRDOzi51N8lJQRXtWWe+LI3\\nfqZISisF/8J7VP68Qb8gk/OyqWyd0W+ut/T/VWRXl/u8KNi5Cnax+NbEsTDmDUWx3X/r2uWKpL+L\\nIJncO/7F9B5/+4Of5OB3dwHIXmi8gZPPV/SuTdhLxxzbLm+kz3h96wW/dbPLvJOiCoXPHD5WkDiq\\nPUscWe6f7vM3yz/JjwwecCd+ydzLIL8VHXEnGq29l3Xt92zElVIJ8GeAvxo2/XfAX0PMwF8D/mvg\\nP11x3s8APwMQD7awleHOxhGv9V40y9KrvFiXaT232zpM9KoytldNyV826AaHUY6v9D/mt3du8fBa\\nh9lOROexUPoUiFHOVDB4Wgy49Zjc0T2sSCYh2JZL1RtTOlThQpRSGCggv1VuRXUQQEtf5nSOAeL6\\nrVUK2wvKg9ajCd5zveZWgQFjPbp0qIBv1xmgkkrPolBy+y1svRPtIKmUXhPOuMkdmVVEc8k41ZXc\\nh+0Yyq5mtied1HOpLiGaQjGOQXswYBKLTTy+QGqBlkCdnFQpJjZZmaRzGZxy8YR8uTzyumNXnbtK\\n7bA+dnEPbXy7Xu4vQW5LBv5cUtAlhlz6bB3fwsuXj10X5Fxu7f0X4b7LBv0c5MNZrfBlL73ev06D\\npd1WBUaXqYvtbNCJ87wzu86L4z5ZiMUkI1nFVj3DfDPmpMzo6oIH5TbjMsVOI6KxJpopbC7vdLXj\\ncJVmY3vO996+Ax3Lr/Ve5UdvPeJWdoxF8y+rz/NjG+/zS0dvgn/v0mep2w/CE//TwG95758B1L8B\\nlFJ/B/gnq07y3v8s8LMA3Wt3fDVKeHi6yVYy41Z6vPZi56vW+7UDHlYP3mVjfpEm+dnzVlCCWsyV\\nxTXP+yvWawl2ajlOKr0LnOHSiORlQXxSSiDSeQlCWktyOBW1PqVEzhVotLqVkjetPXidBCvRWtL3\\n2y+O1lQbCeUgphiKRyyev2Dy+OCFh+aNalYIqnDoqlXdvgXT+ACPeL34v8bcoU6xl1qduvQN1m1T\\nyUK1WyZsg7KnmO0p8m0vxZTLxcoCD3pqwIGPPdUoQVuatP56cvERi4uvaKu88+Wcg/bYuCqT6Soe\\n/Tmc/PuLZ55rbQO+KrOzvvYqTfMFBl/3VfcT9i/d8nkj2+5r9d9XbetWBpdVsq/bKiP9aVubY75t\\nDD/a+5hf23uNh5sdAJKRohwovPK4wnD/8BqHsz4b6ZwvDJ/wlX/rAR9Mdvn6x3dxRynR9pyv3n7E\\nH9+5z634iF+7+QaHeZ9H0yHvvNjjYbLJ0aTDW3vP+aTYkZu4hOLcbj8II/4XaEEpSqkb3vsn4d//\\nAPjuZR3o0pMcGsa7KeWSl1QPunWexGVY+EUl3676cq7ywNdVFGp73tbrswbdt2AY6xuYQ88Xkq1n\\nRrED5Z2o8rWLPtQiWt7j0WK06wHb1ib3HqUczG3DwzaRxnajUKhYEeUOZ6Doaco+2MzgImGp6FI0\\nU3QpE4MuA8OlfvT6loKhr41zjeu3bYXXkj1qcmG8KOvRqQ5cc4Fsyr5ovUi2KSiriGYBbgiB1+Sl\\nptxwmIlurqG8BH9dXyYeCdDSFJhuPto1K7F2Us8q4748ya8trbZSIvnsuL04r+G8R76qD4Cyuadw\\n7eX97QlpBW/9oiITlzXrW15220dgNZccFrj5VYS0Lodv1gcubYOHt871qyGT5W3LRt8FL/96dMKb\\nw+d8tLsHwHya4LUwpVCeyeMB9skmzzR8d/8ur3/+MT99+1f5Uzvf4ReOfojnswHDeM5eNOInsmcc\\n2y7/1/SL5DaitIZXN57xld0JPz54jx9Kn/CL0Vv8fX317+X3ZMSVUj3gTwD/WWvz31RK/Sjyin20\\ntG9lc5GiuF5yZzDhlc5RoPKdfWHWYZFnBvgl7JXll7j00VrP6YxX5c+rIK6jLFo0ziumLqX0pnmW\\ngZ6TaId1epGNaQxoFhKqzcWXb14Lp9uoxgNX3jc63q4T4aJgfGLdeMQ21U3A1CYaZxCFwfpN8gKJ\\nKAfxVDztGlvXpW88dFk1LAKmqMAn9wtj7YzCR5K8VPfdPE608NqrTtTou9QsnPm2otwQDDw+VUQz\\nSF8oZtc8+bZ8GNmBxsyVCHINLIPrpzivKMqI6UkKTqG7FbO5Ie6V7G5McV4xchk9nTdjYGUwvPVd\\nf1o20w86f2G96NpZZyXGnWGvrGOyXFajc+X7xQLWgIVP0AQd19jf5cDpIkh51rBbWMlsWYWlW84b\\n/HMBy6VzDGrpev5CCmT7vOU2955Tl3Ew70uQHrBhhahKcJOIdG/KzHQwGwX+JOH5aZ//88WX+Kmd\\nb/Nnd38L6zVfyx4z94oPqoRX40O+OHjMqLjHbJbw6x+9iisM39h/hc9vPefXPnkVVd2/9H7r9nsy\\n4t77CbCztO0//rT9uATuvnLIj+99QKbLK2mmXHX/Re2iF29dDc/L1A6dV5Q+4rDq83i+yS9864dk\\nh1e88bknlLV3qAjVboKn2YnxsaEYJlKAQYkh1YWkwysPPlK4SIdkGRoIRPoP3VZO3jKliMfiL+ki\\nJPtUEkzNt1OKDRnmZU9jclFDjGZyLEA0lcILNouEHZNqXFxj4gqRPg/BzaCq6LV40DZGmDBajH0t\\nvtVAO0o86/muohx47KCCxKGmBm80plT0Hzq2f9fSeXgaPlhQRcnoS7u4JGbU6/DGjQOeng4o0gg3\\nj/CVfG5pVjIrYl7Oe9zPb3AvOWCgZ8S6bCZnSb+vmvFTrgmA120dTt7IwYaZcdXK71LaYes869dj\\n12fGWVidrupnVfBzWYu8OX5N1aGLAp6rVBHX4eft37T6PFOQ+QrFGZYThtp9rsLQv99WnxsrzePK\\n83Mnf5D/+8nnefRgBzMK78x+iR5F6FLReRIRvT/Ab3oKI4k+p8ddvj5/hUeTIX/q+tv8mcG3+Vez\\nu5zajA/zPe6P9nnnYI/ZYZfk0FBcL4n7Bd4rvvH4FexHfYLfcaX26ThWv99+v/1++/32++0z1T4b\\nafcKUlMxtilb8SJr89PxvS9e7rbbcgDqLE3xbLBzcYw6540ti2DVyniHVZ+/97tfJfp2n9f+dcHx\\nvZijr1R8/Hybchqz/a5i44OZyKhah+uk2FiDVijrMVaKHKAEovBeo3KLmVREpQXrm5qV3hh8Kh4s\\n0AhSqco1yoQ+0py8NWByQ5Nv15xwMDNP2VcBhTLoioVHb1O09cLd1kL1c4HmaPKAl+dgE0JBBoFa\\nTCEZoHVSj7Lihbcxc2cUVQ9sJrRBPdOoiXC/zTzg8RUkJ+WZohDl/obANV7yCh4eD7m7fcSrtz4k\\n0RUGx9imnJQdvvHBXcrS8M/UF/nJa+/xZvaU//a9P0oWVeRVxJ+9/S3uJocM9AyAJOQlXAle+xTj\\nbLnPT7uyXJ0hvNoDb67b2r+OkrgKXrlIb2XR3+p2Ve98ca3VAdF1WaC1N1573csc81UMm0XfFwcJ\\nGzy9WZV57pcp/+D4q/zzTz7P6UEfKgUurFaOI3CK9IVAf9HMo5yi2FHsv/qSe8MX9KKCR9Mh3xzd\\n4eP5Dh1d8Duj6xxM+hyNuvS6OdfuPWf7hyYkxnI07/J4tMHs4wGDhwpT/hvCxH9QTZfw/tM9drLJ\\nWWbKmqK0l7XlF25VIGqZZlaf0w5Otptg3ZoT22HuYpxXxNoyNDNSXRIrS1fnGOV4M3vKK7tHfLjd\\nZb4dsf/LR+z/KnhjcF2F1xaTW1RRoSZzjDHEgBQPFgVBnxpcXfndSlDSZhFkkRRDqJkngWLYfJaF\\n4OvKCUPFdQwuNZjC03kuWiPzPUvZ08RjxeBjTzJxco3WtUzhpOJPA50Elkr4WGysAuQjeigoSQry\\nSoK2ulxg61VHN5mXNpG/qwPVcM29Foy/DoompyJJUOu7AFLtPlJ0n1coF5GMUspeyofxkPeyu7hE\\nqhG5jqO/P+ba7oi8jPj48Q5/98EeG9sTJtMU/zwjOdL8rdf+OD/x1rv8R3tfx+Ap/MKQrxpDq+C1\\nq4zLT4OVr8pzkKDc6rKBy5TEM1DOklFfZ8zPwCwXGt3LYZartHWfRg2vtPW2l7Hy5jffX1LMVZpB\\nceAUvzx5i19+do/TpwPMRKNzRTWsY2Sgc5hf8/QeSqFtryEKcEtqRKb26emA3315g85gzqCT8+K4\\nTzWLMMcRo2HEqBrwsQMzLLHTCBVLXMqmLfbXFdpnwogrC3Yc8TuH+9zITriWCA66LPl5ETtlfVLP\\nRRjj6iFVhhz29otbByljZUmjBZvkSTFEK8/90T5zG3GSZ+x3xzw+3sBuVZzeSUiPB2LcSocpHVgv\\nAcgsg0FKLSOnXGCW1MdWtQqUGDcfjLo+zSWoabTIx0a6MXYg/G4fS7FjbzSq8nSe5hSvd9ClGF7b\\nd6RHhmKgMIXCeDCBXqitZIpGAVN3sQ7euAo1MxXR1EmAtPVW1163LkOGZvC6vZaA5+SaaaQClFtw\\nyyWhRwo3JCNPvqHpvrCkL1zzXHpeoucl3hii04jhfankU24klD3pd3TXML0J4xddqqFhfpTR/TCm\\n89zTOdpg6Dw29kz3YX5HMSo69FRBrKrme5+7mKlPm4zbTBdkqmyCovacD3i2rasK1B7Lq8agwVOg\\nm/PbBr2Nk6+SSNbqvPFfDoQuBz7PFg5e4PGwmrly1hE6/z6tpAWGw5YZLOta2zM/m51Z97PaS28r\\nIy5/vtb7Jr1/GVdvs1xqrvncO059ymHZZ5onmEGJs4msNDekApSbRzg0ZixZmVVHpJaTE8Xh7+zy\\nS2qX7EAKoOiho3iWcJB4Ok81esMLu6pbYUeJEBBGMdGw4Nr2iIOszzTuLhLYrtA+E0bcGYg3Cm4N\\nTxpd8VVa4avohBctYxvdlTXX1WsM/Nnlb+2de3756DXefnad2csOKnF0BnO09nSSksPDAYxikpeG\\n6Fu77DjPnUdT8LJc97F4xPXN6NKhRjk+retF+sYwoyWAiRYqYTMrG1EddKkRhkpt2BekEjlWB/ZL\\nasQzdx6XRaGupUKXiuRI1AXzbXCJFsNaGXwkSTU2lbJpVUdh5q20+ZbBrgsOVz2ITz3FhiI5kbJv\\nnQPXeNguUsy31YLVEmpvqgrQAp/UjJl8SwpDnPQjuBsRT+oHg42PC0zuiJ+eCEsnjkgqRzSRDNLO\\nM5heTxjfSpjeiGFgcTHYjiKvghFNYPS6w3QsO+mEv/30j3Gcd7jTO2YzFkbLHxq8T0/nlD5qDHhd\\nPnBV1u+qQOeZtgSrtMf1sqOyrm7mwllZxdY6a8yXjW87aLnKI1+VQGS9OpO6v8qgrxPZOpd96ReG\\n/LJ1SZvRsrhe3Y8/s73tqbe98/b1dYBg2v0vAq7nKYUg38m/PXiX8o7h/33wOU6PEpJjjR8JT7wc\\nONxWie3DzCdEk0VlKl2KM6JLyHecpNpPFdkLGDwsmW8aZnsG+7CLCcyw0VuWL9x6wp+//nW+Ob3L\\nz42+JtDmFdtnwojrCuzTDu9wjdIa/p1r9yldhFFu5cBvF1Bebut446smBddw5pb6aDENHLq53mmR\\nMTvJyB7GdJ96dJnQPbTo0tN3Hhc58A6TO7QVLFrPK1RpYVZiAJ9EQT8EfGrEGOFAm4YmqKyDoBt+\\nblmlwCYGnJfBqkMiTcvdUQ7MPIwCJRrks71Y0vU1qJA442MwMyg2xXB7I/9XmeDeNV5eDpR4yx5c\\nCtEkZGFWYhTjU0++JfhgviWZk7NrGjP3mFx0UHQpgxwfvPWKpiRdnajT9s7Rgtk3afdKcfy5BOUg\\nuZOSvbSYuSU+nOJ7MXpWcfpaj5N7mvk1R++1E6aTjGKjpFSy0kufSfWgzd+B8kGHX333h6m6xQmQ\\ngAAAIABJREFUwvD54O4O9669IDMVx2WXd0/2uNk/oWNKfmr722yaydqV20UJQRem6XO1WM4qga11\\nKomwhIkvGeGyZeLOY+ACu5xJ2W/pl181C3QlG2XpsKvQFM9TB8+2ZVZLG25ZXOes977O2INg4Ra4\\nG814I57zavSCYTTj/t4+v3H/HlEm79Te5piiMhwdDij3SlwUM7/mSF8aoomi7HvGb5XEocRh55nk\\nW0gZRam8VXUVVUfeie4Dw7eTV3gwGjKZpaD9unLDK9tnwojbFMz1GYP+DKMdL6seQzM7c8z3w8O9\\nLEX6qtl1tVEfFSn6NKL7zDP8aAGpRKel4IlKNV6xGecCBTiH68QCoXSihlet86qBCsSbDhVx0jBU\\nw3HK+UWmY6vV3rNNBR838xqDdhSbEbMdkTdMRp7e05zssCQ7hKM3UzrPId+kqa1pZsGj7gAd8SKU\\nhWgiHjixBCFtIsa36oq37LpybJWJAVcVQYRKDLVNpH6nmYfrzGtvZUE/rP+O5x6bCKzjYrmOi4Ns\\nLmByj+0ooplnvqklgJoq5rtDoplj+lomQl0BLjp9MqB7bUJZGrzTRIOSql9xeg9cbogOYwYfSWk3\\nM/cU3+ryfLOHTRXvhyLRj+/s0339hEE05z/c/sYZnPy8bkq4Tz4d5LLOsK8t+9Zq65OOLg+Grk/B\\nPw+zLJeIW1WEYlWfF0InrUMvMujrMkMX17y4rcLY2/3XrfCeD6o+L20f6zVzH/OsHBIryzCe8dNf\\n/VUezrcA2E4mnFYZv6FeYTJLqY4jOk8MVc+TnYiQXHUcoyuY3nTMXy+Jniao0tB5LmPYRTDbD3pC\\nmxZiRxJZ3rr1kG+q2/jLHqzVPhNGHAUmckznKU+9Am7yla0HDMy88ciX2zpv/PditFedX1+j9Abr\\nNLoIDIrcogqHmeSStBP424RgXM08QUco63HdKGRoiqFFa8HFjabqhQSYKGiAly5of3uckUBgjUdD\\nzeXW6NIRn1ZUXcPpbXFZJ7cUVd+TPVdsvm/pPplhRnPc9QHlwNA9sGgL0UzRf+SZb2nKvmq0ypUV\\nVonN5H8XB+McEm1qsa76tzMs2CsZxGOpo1l73roIxRyswC41lxzk72JDDP98T1YDdX9qCra7WChV\\nXUX3ucemMPyoZLoX0Xta4rVorfSelJjC0XvkOX0l5eSNiHk/odMrUKrCOYX3iqo0qAyqPTgaao6U\\nx4zFg+o+QWCgwMM/fd2TRpa3uk8vNuDtbUsGaZWXflmW8bqizD/IdhUd/lWeOXBpWv/i/KsHQdcZ\\n9cuyN88VqfiUAc+a9VJ6eHt+i7//5Mu8+93bpC90M4arvmPwxjGvbb0AIHeGVFv2egL9vhxm2Ilm\\n4wMwhRMHxEiSm4tlQrCJx/c8ujTkO4r5nYLOcM52f8qsjMjiiq/uPuBPbn4Hx4/zcXzRXZ9tnwkj\\nHs2A7wwoh4749YrSGUZVdqZIxKehaX2adhXq2KnN+O3TWxw82mTzI0X3oJQK9ZGm2O1JZDrogzuj\\nxUuOlAQqPQKvFMJI0dNCcO40xncjbKwlocc6VGBw1Of7yFD2xIiboPCnC0e+HWMTxekrmtm+w3Yc\\npGFlUGqyxxGdQ5kwzIsxAOknL0kBtMZu98B5qkFC1UkaTXLB8oLnUogBrf8vnUKPoOxBPIFigLBd\\nBor0pWDnydhT9BXJseiXmDmhZqj0U/ZlAnQJDc5uckn86T0CqLVcxIs31QLrT489VQbpiWe+Zeg9\\nq3CJJppJPc2yb1CnoLQnnnriUyhepMyNRyuPtRoTWbxXKOVRJmD2M9FtsQnM9hXFhkxWNgO1VXB3\\n+JLXk+fEqmrU5toB9HVytm2q6mWCWst91uOyPQaXj181Ttu4+UWB0OXt7bZKWKu9/TJq4vn7u8Cp\\naveh1u+7KGmopiq2cfN6u/S7OhjabtZ7MgV/onef8nrEPwHe+WQfdRrhY0+8NWeQ5TyfDgDY2hSU\\n4I3BIRvJnOOTHsrGYvTjAJPEimjuGXzs2LovCp1VpsmHntk1uac7W8co5cmriCwSz6anCn5s8wP+\\nYXR1G/eZMOJeBS5xWAbneYxRjp14QqwsZkku9KpLw6u05RdxFXtgYOZ8PNrCnEShVJkVOMR7Iuul\\n2rtWeOvxqSKayBdi5nJMU2otMdis26TQ68IS1brdOlDuYpkEgFCUwaEs5FsR45ua+Z6n3KlQuShV\\nRGPNxnuGOBQUTk8svfdf4jsxeiJl3nyWirecxaG6jpdJZVax+U6Jiw0u0eRbEVWqRAoWxOMOFMDk\\nVGFTRXwq8Ff/EdgYuk9FTjeZiEphNJMAaNkVfRTRZ/E4Q1PAWFdiNOtJTrbJsbWmSzxeWmGVns4L\\nUX1MTkrKvgzdqivp+r0HU8w4p9rq4nbkOVUF9iTBdSu81bjcCNfXIVrnqeii20JYBj7WVJmMrWiu\\nyL7X4TfH9/id5/v8J29+nS93PhJNnFa6fj1GQAzvKo98Xe6B7FsygCviNqvG5Krg5SpjumzQl6WU\\n6+3tflbdW23MnT9PV1xX77M9sVyEnS+n9Uufi1Yb+HWGfTkYusqot/evrm0JQwV/sv828/2IUZHy\\n7HRXHIrK8Ox4wNZgCsA3n98iLyN+5PpjvjB4wuCNnH/dv8PzV3t0PkwwucSNTO6ZXdMUG3L1queJ\\nR4r5GzlfvvcJXxo+5qAYYL1mEM9JdUXhzafWuVf++1T6+kG2wfC2f+2n/0umtzzZ54/52vUHfL7/\\nhKlNASTAiSduhWwvkxNtt6tALOs+uNrIz13MP3v8BZ799j673/JsffsInJMRqBU4j+8kjZ4JCNat\\nSitsk7jWVAVnFkZaWSnwoErX8L9VzZEG2Z9oqkzOr7oGr6H7NG/OM6N8QfMrLT6JGm9fzUqBemIj\\n+HwSYaYFappLgWWlIDLYnQEuMU2qveiZqybZp+GkE4xuoBFG0+DRWo/t6ODVBs0VEyAgJasRVQm1\\nUjlPvincdZuoM4UfXEgcsokkPNQrgXjiArvGUWWmqTpkY000rai6EclxLp+zVoxezZjclA+lHEgd\\nUBDPvup6qWSUeVzqRJ83rAAoNBiP6Va8fv2AP7L3HjfiY+7ELxpY7ypKmavG2EVt2UO/qmNyWWGK\\ny1gli34uvt46DnrdVjFc1um1LPdZM0aW38CLoJSrFq24qjn8hek9fv7FF/j1916Dkxizm+OeZfjI\\nkz01MlZaEIdLJGnOfG7M/vCUf+/md3hveo1feXiP4p0NOk8U3QORXs6HEtuZXVusPKc3Hb1XRvzR\\n2+/zRvcZuYt5Z7LPm71n/Ivnb/FLP/2/kX/88EpP+dnwxLXUUFSvTfjR/Ue83j3gebHBs3zAdw9u\\nEBnHIM35yvYDduPxyiSMi9pVNKAv6seimLoErTw+iOCoyQz/8hg13ACj8WmCmhUiChUMozycB+vE\\nCwga37UolY+MHGtts60RBqq9Be/xRhMH5cKmb+dphLHaPHHlUJO5qBbOCkhiqk7M+JWO1MnsK8ys\\nSzSX5BzRTHFE4xI9q8IkYrHdBCzEucWlwpzRlUOVDpeKETWTEtuJg7F2+KmiygzZQQVG/pbMTkvZ\\nl/JUyXGBSw3pMZi5w+RW4KMAK4EEa4WeCDZ4xsXAUHXkZYhyj43BRbFU9MkTspdi5KPTHNtPJIHp\\nIVQdRXYoL50zUkDDK0U59FJSLnboNPDhC4PKLPv7x3x19yE/tfUtGRstVcoznvgV20WQ3foar9//\\navOy+1stKneWpvhp+2z6aSUWtbH05UIVQGu1EmDEC/td+t9fzUALG2X9/rqPr2Uf83iwxW91bpPn\\nBnuQkd6eUOQRc5WQvNR0DqSjyS3R3i+vlbiP+jwwPf72966jC0Vyoui/lHjWfFujC98wr5KRYOVR\\nCcN3NOXjTf7p6z/MvXvP+MO7H+JQfHN0h09ebqHKq4+xz4QRR4n3lSYVcxsCdDblk9NtJrOUYpQy\\nGs7RyvPjux/Q1UVz6lWrsqwT6L/sfOeFI/6NF3d59L19dr6r2Pr6U9mZxPjxBLRC5QlkKT4J03Ve\\nNMk2mGDQY/m4pb5k7Y07MBqntfDAI1naU9fFDEaVohTDbBQqL8Wgz3PQGqW1GHTA9bv4YVcYL1rJ\\n9b1n8OGEYjtjtmuoUgncRTPXBE1dSN038yokJlk5XynMtIJM7t1mkQRmgaovYLo3CqtlhRCPZbXk\\nHURzqSQk6fdBwlYpdG5Fgtb7BhZxsabsG6KJFW/de5xaBHNVLKnNVVcMu00C9hhoWrNdw/iWIT5N\\nBc+v+fVeoJh4Kp91XT2pyjQ2ZMUqF6MqCQqXn5/yxe2n/Lmd32Decr1qAw6Xr+yuskpc8MOX9fEX\\n0Msy//wqEhLL7bJCKd9P+zSFMZpzPoU+9lXapwEc1hW4gMXk0FWWL3YecG/vde7n+5iNnDiuuL19\\nzPN+n+l0k8lGSIYrFP7WHEYx0Qzikab7zIdygwup5pplVUXCAjAzDx1590RaGeLDiIcHt/g/5rcB\\nWTW6pCX3fIX2mTDiXoPtSPBpXKa8O73Gt57d4vTdTbpPNGrocX3NMJkxNDMsijKkNDkUOrwQbW8J\\nzr88y97Ecrbnqpehxh//zI1vs3P7V/gHf+jLfP1rn2P/VxTbv/IIP8/x+9tUvbRhnuhpAZGh3OwI\\n42RWSdJNaaEUnLy+E2UdPo6EyeI0ah4MdF0AIoklFX/YbVLjAVwSLTyZoLECMkHUgVGX1N48IdAa\\nJpAIrFHkQ/GUtYXpbiQB2nwhPWsKL2XVKij7mnwoy0FdedH7doJt6xBTVR7GXTHszoApZbDKsRJw\\ntImk8XstcEmVKYrAQ9cVxKeL17PWWwEx1CJ3K/ETF0Gx4ZtjagfSpqLLIgFUwb9tRlNRSJfhHmLp\\np9yQEnez1wtu3XzJT+6/x4/13+PUZSE5xjVjqz1u1lL0Lqi/uTyu6rauoMhCAmI9pe+q7JVVnv1q\\nOYrV9WUv6m8Vq6VuF2m31PdwLpFpCVdfdf+0oBi55uqmWW/A2xmlsYJX45f86WvfA+DhyZBuUnK7\\nd0wnKnn7VoYxC6T+Szef0I1KRmXGXjrmF9//HPpBxv7XHbrw5Ju6CW/EU6ljW3YWFGQ8bHwkbCsb\\nq8aZSo8UeMWL6f/fApsGfGbR2nP/0T7vf3RXsqCA+Y6nGjjSyHFSdHhSDBlGs2ZwxMo23s+6Qrjr\\nWATL6fl2DTmzBPAwddt8eeMBH7y6w8vjXUZ379B96qWwcO7pfzDGdWPynQ2U8w08UGxnmLmVFHmt\\nQoV73VTVkc9AIAlVWtkXadBgU9NUylFh1CkPZc8IdEMIItaxSAXaitF0kZKBEwnVT/nAyZ9DtQXT\\n64p8M2RL+nCeVQ1DpL637FChC2GUSIKCJt/yzXcHdSbmAneuJwLhuofJw4BNRfQKJZNFNIbspfQx\\n34HpdbBdv3grw9fmQ0k2AD1XRBN5LmdA350QpZJcffyyJwcVGiLpROUhU9Yp4lMlOitaCj27VLBO\\nnVj6SU7fLDRAxXDoM2NpsW8NPFIH31sJYxexWEC88XZbzgpd5ke3i1Msj9l1GcgXZXHWv5eN6ar+\\nVhn4MwUognFePNtq3ZbSa0rOsl6SdsbqiiSjVbGIBcWwjdnX1w4/a+xh28DLcYo/0n2HH7n7MVOX\\nMnEp35ze5buHN9DG4oOM9E/ce58/vf3bXI9OuBeN+Y35TR5ONvnwkzvMdrTg3kbeAZMjQnKRwmbB\\ngYrE8Sm74mgUA5i9XqJji5vEbH43Wn/TK9pnwojrAra+FVH2h3Q9ocivvFwuUdiuwlrFB092eTnt\\ncHd4xDCZY73im09v45wiiSxf2X/Im71n5/pf9wKt8ppWLYXbx2nl+Iuv/TrZvbLRUvnfn36F33nv\\nFv13hmz/bsVsxzQaI8qJ12r3okZXJMp9IwjlIsV8GPoPHG0QGp6LVJPhWFenl0nAowvxmvFS0qxu\\nVabCuYuMSFWJlyvlz+TzLTYlecf2nBhVLTiD6lSiy+2BUqOcMCZ0pdCFCFZVPU+1ESCRUoS04rEi\\nHnniidyXPPeiPJyuPNNdQ76tmF2DamhBe1weMb3uQzaoGNb+R5rOoaP3pKTqipHSpWeyHzG9rpje\\ndNgUoqkSJcT3e5wOHN6El32zoLud00sL7m4csZtM6JiCl0WPg7zP0/EA7xUb2Zw3Ng54NttgK53S\\nMwLTJcoGA6kDpnoeRnFozvNGzsIucLFzsK61dYBWtWUI5sy5LIx8I1q1guGy6EtayerVw3L6v/Pr\\nGSxyrUUAvN3HclsIai1YL3X63Dn2S8ugw+qAafs+r4pE1Aa+PnegJZ+1cJoDt4FFkbuITlxy5Lq4\\nQr7HSZXwPz35w3x18xP+sU35cLLD83EfryRjufPMk0w88amV1aCSylnxBNLHjrKrmqA+eDqHsPO2\\noexGeKPIXlbwKQgnnwkjDr7JzqvTsOvf2SGoSuOP+9ihY/Qo41udLdgsSbKSzf6M0TQjMo6TMmsM\\n62XY5LJ+xUWVXZa35S6mVPKFnrpMBm5rOW9TcLEiHyrhQhcLz9TkimjqieaCe7lYqHvJ2JOMHfGo\\nkoIMlUPnlQRLJzN8J8X3MqrNjNPbKTat61gqkpFvBHP6Dyuy51PMs2N8XuDu7lNspsz2YrQVY5hv\\naEyhKPtQ5YaNDzzxTHBzfTTGZymTNzaYbRkmNxXpCZiZp9gMEra5IjsU+EWX0HviyI4qoVYGGQBd\\nOPS0RE/mqMriI0N8OuQoTpldA90rcbkJE7ZMPHkaJhMU6bEjeTEjeSafvUjSdsm3YhFM6ziUl2pF\\nJofkSBNNZAwVW4ZJL4NPNO8fXePgk4LsnWdMf+g6p7djyg3FbM9zuGn5eLgjLNCThPTalGFvRvdu\\nzr3kOXAxjFJ70G0TuSox7artjOjVJVj2RWPbLoEMq6CTi/pebhcFNS9K/1/c29n9l133MjhmOWB6\\n2X1edr24tT9WMPcx16NjSh9xIzmhsCKVpQ8EKvxGeQ8VO343u0YcW+5uHy1KATpxwMabmvyLCuUU\\n2YHI1W58VIQciJiiX2dcy3tc9iD/AzM2BjOeH/apfv3qE/+lRlwp9d8DPwU8995/MWzbBv5X4FWk\\nBNuf894fhX1/FfhLiFPwX3jv//mVbiTgpdmRk0QXI8t+m3qiucKcQHKsJYiVKNw0wiUVJ5MOzims\\nUzwaD9lKZtzMjtF4jHLES/Kiy61Wq6t/r8PTz5yzxOftRgWmX4KP8QoGDyvyoWHwoCI9mGIeHeKu\\n7zDf7zLbi0IlHaHLFRsGG2uyI0syKkk+fgG5eITeOQlaAiov8d2Ush/JRJEg3nEleFuUy0BMjgvM\\n4xf4UrB1/f4jst0tdLWJzQxVgF5cDNmBJzt2dJ/mAvUYhe+m6PGcwTdGDID5529weieh2FCB2y1Z\\nmdmRo/usxBtF1dUUG0Yw9lBfUxKFEqJZhplVmFFO8vCI6+8V7JclOM/0a6+SDz3jOyIaNPhA4yPo\\nP3a4RFENU+KDIIh2MifVsKW7oGJwIt6llSc9UqRHns13p4xvZ4yMJnuu6Bw6OocVynry168RTSqy\\nYwng2jueVz/3jP3uKeMyJb8hr8IXNp9wL3l+1ngvedfLre2V2wtgk7qt66s2iMvG/CrGdl2R5/oO\\n110Pznrs9fmr4MnL9IouNc4Bb1/lyV+WxbqurTPoV7nvxXU9c2/4zfkdfmX0Ob778gbjecprWy85\\nzjv82P5HzHZjjl7pAvDdpzcoPxjQeZJSdeCDbIN8v8JEooniTRCCS8HFjqqj2LwPk5sxXoV310I8\\n8SQjiUl5DflBxny3Q9L5wWun/A/A3wL+bmvbXwH+H+/931BK/ZXw/19WSv0Q8OeBLwA3gV9QSr3p\\nvV8le7BoSoWlBYxviZHxRrL0tIXuMyfBMwXzTYWyGtDEpz2SU9HIHt3rMXt9jNtSpGrBJ69fquXZ\\nvVkiLw/UJaPePqY9uOtBd1T2+PYnt+l9s8PeN3NQcPAjKePXLdEoIjsccv3XIqJ3H9N5oshu7GI7\\nMeO7HXSpiKeO3sM50XuP8fO5SMdmGRjBvP1shi9KCXS+PKLzoaZ74xp2s4vrRMz2EjrP5g1jxDw8\\nwBeBvVNWeO/h8TPS0wl+0MPu9Em6MZ0XmjgkJanSQuUwR1OhOpYVpAnM5mRvPyJ90MNudpndEBU3\\nXXqiqSU6zXFphC6MaLhYyWKtmzeC9bvEoPoJdGOBWU6mqHlB951DumnC4JNOqA0qcQGpDRqCtGlg\\niISAaTSz7HxHWDXj2wlVpjBhAptdS4lyR++xIp458IEt430Ti0BJCr/fySmt4U/tfJe/89FPMM0T\\nnFd8biOi9BFGFWfGQDMulsZEs58WBn7Bgt6hzxrccxmci3b+Ghd7mp9WoKvefi7gX8M/q+QFWI2x\\n17+XOerLUrpn+vJqwS9fwtLrtkqw6zJt9DPe+RIUI8+w4LQ/qIbcz29yf3qdn3//LcrjjPil1L79\\n9k6fvZvH/NbhHd7afM5WIsk++8NTHt6OqI66bHzoSE8CbOId+YbUjh3d1aQvg8LnSPIo4qmn82SG\\n7UTYTDPbjSgGAeosoPfMkp5oTOl5Nrn6iu5SI+69/yWl1KtLm/994I+Fv/9H4F8Cfzls/3ve+xz4\\nUCn1HvAHgV+75CKi0eHkJz2SB47momNR9DTKOfKhDp5gCGpOCIUFFP2HUJwM+MVPvsgv357y5dsP\\nudc9JNa28cbrL9+hcGtwyrPJCme9sVVtGM348t0H/Ob4HvE4oXvgyLc8w9snxJFl9Ju7FFsJUZ28\\n8+ApcafD1mMNRdl4zCiFSmIJJhoDSQyVhn63oRD6NAbn8A70vMSczomfIzTDWQjIGQNRJOckify2\\nFrI0sF680Ah1JMyNVFMMOijviSapZEuOCnxsMJMCNZ5BHIXEJIlRzPsaPTDEg6gJilaZbgof13U1\\nXRRWCrmm7C9iAplS6JHGZzEujSgHscgLWI9XMiRNIUZYF60h6sEmGptpzNxRpSp4PgJNFUoSicoe\\neC0iWfPdpEmcik+rJs5AEHTKVElRBaZTYwTcQlN+FTvF63Mrt7MepT5jNNrHLhv5NiSz7hpXKY6y\\nLsi57JWfL5hyNnArBnq1dn+9v8bY9Yr7ugy2aQdBlzNJlzXPlw17e7I495xrriEO23k4x3pFiSbT\\nJe/P9/in3/kSg+8l7DxxeC1xrOlezPHBLvbOnNJpfBgfSnmq3BBHEoOKZ6K9X2wKY6zsKDqHnuTU\\nNyJwNoWXNw3uh3sUb8wwpqI61Gy+rek9tZjc4ZXUvPUVi3q0V2jfLya+771/Ev5+CuyHv28Bv946\\n7mHYdq4ppX4G+BmANB3Se+qosoAnJ+BKKHUt/gQnNyVtVZeivxGfeghcZ1Moyp4imkD2UlE+7vON\\nR2/y9r19fvLWB+yFIhOrvJzlCih1NL029Gc1llfDMeMyRRVCvxPhKtmXGItNxMCpJJHMzlwtEoGy\\nVAy3dWK8qwq0FlphZM4kDvlEgh7EMVRO+OXQZD3U5dmU0SiVCG/cuiYhqA6UKB/g+6A7LtV6fMMk\\nAfCRcKh1YVBx1HDO6/2m8GHCXfBZ67916ZuiyMpLQFP5+qI1PTL0m0QNS8drcEEFUhJ9hKPu4wXW\\nWHvULlYopwPrxovmeH3/4beLkKCsknuyqUga2DhQLQtNZQ0WTWQs1slSvwxljDROvGbEmLqW4W0n\\n/8g+3QrKuZVqe814aTFe2q3toZ/zzlesAq4q/LZOrGvZaz9fnnA1vHGRFvry8csUwnX3uapqUb29\\nRK88Jw5Z3M2xF+DobchleaIoveGTyRZqZiR3Yu6ZDzXj24rZNcerP/yYYTLjOw9vwQNZiXaeK4aF\\nQJLdQ4s38OJLKbN9T7lXoiaGwYemqVo1ve5RVlFsW3zXQqlx04jswBCPJeBfDuocEug+X77Li9vv\\nObDpvfdKXYFYev68nwV+FqC3c8ePb2l6T528vBpmu+J1V10ohh57e8a13REATz/cofswQltP58BR\\n9qU+o80CI6OCZKQZH3d5d7DHxvaMdEllvcY5XWAh1K3eVreLSlvVrQ7c1Mp+eLXg29bweZ2dGUVi\\noJ2nzuBssjC1eOFSGKL1U1lUUQrrr7JQ2YVxDwlEBOzcp0kTSMT7MCn4ZuJwQRag/rGppuqIAqMP\\nPGxRV1RixHMT9gVDkqiG6+1qbroR70N5gsymsHLE2Mo1dOWbQKiP1EL1cfmztOC9cNOV8gs413vh\\ng2nhixPYO/M9sCcS0IxPPempI1caF8tkahON8U4470WtDinGXYUswvr708qTO4P1mgJhiBjlzsEa\\nbYO77JEvs1ZqY98+R6h1q4+XbTGZkmmhnkyKlpedYBuK42WB1FWOh1GO5QIXdSt9tAILP5uEdK4/\\n3JlVQNlsb+uf6zPHL/rWZ7a1oZ/aeJfNsYs6t86dvY/2PSetafRMQYzWysGiSJCY2U46xWeWeKKx\\niaL3vKIcxEQTxdOTAdGmo5rExOESyYnogwON3k//kQhceR2jc5F1LgaiE4SCzXcd2UtHPLGc3k5D\\nrVhxcEwJ034dlPeUG7qBl6/Svl8j/kwpdcN7/0QpdQN4HrY/Au60jrsdtl3YdOlJjzzTPSlpZDPw\\nEczvFOjEMhxOGU9Tnj4SPV8z0xQbYiTm2zG2A+kLWXVGU/HMohmUvYRPelu80j9iM542hrwOeNYe\\nN7Rm6FWwSctrd62g5gIr9wvtDUWTedIYd70wgioYc69B1cRnQyijFq6XxMLGqPniALr1d+1VWw+V\\nE42WsPxS3gsOXnvfWkHI/ERJpZ061ag25PUjNVBoJN6uD5ordfHler+NabzyWryqDqyiaIKbztBg\\n1jYReqKOFPFY+nWRlpVBPYFAkwyhozpNPninlaMYxEyvmaBn7kU+dyzfOV5WCFWqcIELb3JJJso3\\nxCtKTzTFQD57rEjTnroO1umGXTAuUyZB1tE0y5azzXpF6aNzWLNo/LiQyBIKmoTz1wXHiCICAAAg\\nAElEQVQNa9im6SOcVxvtVSyUuY/PYs/huu1WwzRCk1Q4f15NsRaXW7nCbBm/WFVrj6uvv3J7Czpq\\nc+Ev88xjVZ3bVj+LWeHxw9nPqVBhYlkxcbY/+2MX84+OvsK/+vge3fcS+k8qsoMCPS/ZGyccvZEx\\nijZ4d6NPdKrpfyL9dw8sJ/eiBt7rPbOUHUVyIsJpou0vkEydCNd7UvLiCyn5ZkzV9WQvFd2nAYJL\\nRI+/ysAmHlWuNkPr2vdrxP8R8BeBvxF+/8PW9v9ZKfXfIIHNzwFfv6wzXVqGH+VUmWG6L0qBZU+h\\n80SglO0UPVfETjjKbrfATyPANAkmo7cs8YkmGksVmXIAdjdnuztHK8fEpsxsyCZkIXBfuAjrVfPl\\nxsrRi3I6piQLBZBLb1o1F8tmIE1dgvWa3MoXWq8CdK6YzWOMFgGcep2ilgj8y1V7Gu+5rGSY+5Yn\\nigMrnugZ71RivIsVem4X+6DlsUsZOB+0VgT+8CE9XS8q00dhcogW8IlyYbJQobBxRJM6DOKN1INO\\nV/7sM1sagSwfa4FElNzfInlpYSy1I+iiBPGt1uObwpGMgzEMuQTFRkhjThcFJGzw0qOZYJE1Du5i\\nmaBUSGrKK8NJ1aWoDNYK5jkpU05sr7lsbVDaBgEW2LHDnGVAoXEeJj5ttq0KKtZjqD53EYAzmBVe\\n66pgYm2YrasdkSWDHyaUM9dt3UtdN3bZ824zZGRlen7l0W7rCrNfJabUnoCa433UrECWDX77Oyhb\\n/Zw5zq8OLpsQeH1p+9yf3+B5MeA3D2+TH2UMx6Bzj0s0PkqwqWHr/gzb6TIrxbmcXg8B0coQTRaw\\n6ehuhC7FecweWaKJiN6N7sQcfs3hO5bj5wnpC8hewLgL0y/NKL5qKZ93MDPFxvueeAy61MRj/4Mt\\nz6aU+l+QIOauUuoh8F8hxvvnlFJ/CfgY+HMA3vvvKaV+DngbqID//FJmCuKFTvcTyq7oOetCLZJh\\n4iA6pSVrMH2h0U8zAPJtz/y6hY0SxjLDOSMeo92soNRM5gnfe3mD7c6UjXgOQC/KiZUj0pa9ZEzu\\nojNeeukMY5syqdJmXxQCpDMbU3pD3+SMbYpWnmkZY2aSqVWPJRe8O10GI9h43wEiqTHtyAh2Dai8\\nkONSg+smIXtTiReu5HPyZqFTDuKhulijayqi9ahp2WRbujTALEkkUrkBxlDWi365E6NnE8GZq0yR\\nnViprZkbotOg4RIMP37BTz+j6haFZ/eKqKp1JGikaAWGaXndkUaXYWi4RYapV4sAt2Zh6HVeyfHO\\no8LsUfY05UBKwHkTJiBHsyISfRiIp3KdaOJwUSRys36Rk2K0D0WYFOMyYR68Y4Nv8g6WjZ31htzH\\n54JsbWOX6TI4BovgumuM9eK4RZZn8BpbRnEdfHFuW8tTXgQrz6bQr8KedVg1LF9rlSCWWTa2S+0c\\nTbc1iVivzvHq2yudVVTBdvGJeoVyGf2w/j4SZc8Y8qTljN2f3+A3T17h0XjIs4Mh0bHQftFQbETM\\ntww2haqbEJ96dr9dO2Ny7SrVDc13cksxf6VATQ2D9w3pqdiuqier1/hYE98YM1EeM0/Jdzy27/jc\\nzQNe3zjk+Lbg7Pf/wB7dpOTlk23iB0kz7q/SrsJO+Qtrdv27a47/68Bfv/IdhGYK8dDSkehno8DF\\nhuwlVC9NE8gshaqJSyA5UVSVpowiSEQSNTmRR0qOY6q+ZxqLwb/eGzVGPLcRaVRRV7FPtdRVBJjZ\\nmF6UC/9YF2xEM0ZVB+cVE5eyGU3BiRcj+tEy4L3xjcepHNB+YXzAsmtt8ZDJ6bUSfFs+OAlSGrU4\\nrtWUR4y9Nq1tHpyTAJ47/2LVhYoBkaQFfC64iUvE69elI5rJZCNyr4LxmUJkA1RVe0e+SaGXYg+B\\n6x4GW63DbfKazidJOPFUGEZRvij47LUSWqNSYH3zWXizWJ340gdZ2vB/pKm6EcUwIt/QxFOB0+Lx\\ngq1kcpk4lKWp4VlmIeKvFd0DJfep5TuxTnNiO1RWSxk3D6U1jK2MGY0XA67EgJdOJvBVTbc81zqz\\nt2aM5O3ZbtW5wTitZF2sMJjLqfy1V17/DTKZtCeIOGShLgc1TXjGWFWNQa+NeW3Ak3o1Unv+tbFu\\nMWrqfc6r5jPSymO8OzOpnH3usxPMMj5en18rRxo086aOqD7n3bcnj1JFDWRUesPPj97kN57dxTpF\\n5TSDLOd0nmKepEQzyaAsBoZo5siOLGVX44xiclNx8qbEWDrPAo00Fwjv5A15D7rvJXQOPZvvCtV3\\nvpeQD4zAJBOYPumjkJwXM1X0PjG8E93kHXMD3amIYpEcyeKKtFeQb0ZSFvGK7bORsakgH2ip8Rg8\\nyGQUjEQF6YnwL00OOgVVydJDOcHPyxeRMFoS0YoGqAYWEkfSLdnozjktMnZSSRyJ9FnPKPfRuah1\\n5QwzH+O8ZlIldIwYwdIIoyG3htJrgWOcpKe3BZvEDqvF9jabhBaU0paYDbK1OIeaV8JA0RrlK/Ge\\ntcaXrjHeIJ63r1rGtqwWN6BNU78TIwbTR1r+VovPumZz2GwR7LSJarzmtldgA63PJeLBmzx45gFi\\nUVa8b1MKq0gyVcXj0pXw/kVb3DUYv7J1mr58Bt7IZKAi0GGV4hKDC/fl6mt5CSBVHYXtEPRbJBiu\\nHOAU82s+YPkem2lsAskp6EIcgNNKMm59AOTLyjB38criCZF2wSP0zb5YyziKlcV6TenNORjh03AN\\n2pNB20i1A+wXBTMbudzGqxfDMydu+mkfu/yMNc4vTopqnjtWtoEXV8Evq7ZbDyXmjKfvAkZvOB9v\\naJRFa1kBL3BV6c36gGqrz3qCak9+H8z2+NXnr/H0wTbRy4hquyIeFLy5c4B1m0xuzcmPEvJtwIDK\\nNf0HmuzQ03nh6D/xjaxFlQbb0lXkW4r0SEoK9p460mOLskJpBYjmTjI0dSATKBn3+XbM+Kah+0lE\\nMfSYPBLGXQ7T8YAkge7Uc3hxvPpM+0wYcRdpwTaNzHAuFsK8mnnSkWh06FJT9DXJSJbY+VAzvuup\\nhhZVKdLnhngE0USR74hsaTLIuTYc88XtJ5TONJDJcdnBBDpZjYPnNiI1st81y0IJ6szU4gVwXlEF\\nGlrlDNarhj/abnFSMezMedoT9bLQ8eoPoGaRKCWUQK2FveEQKMUH41vDKoDyUuQBHfRJTI0vm4VS\\nYmWhCPi6EsqeSk1D5QNALwSvVKDx1Qa1Nt5tzqpygWIYMs6aKLqDakMx2xOZARdJoKfKJHmh6gh9\\nFAfxWEvxaGggIxUWH7XWS5u+CGLoi75hek1TDKHsy0RicrmftmxDrVRcF3yueh6XOXAmeOkyESSJ\\nlALc6s4kqOoVWVIyqVKiYJxrQ2FwzIJM8hkP0i684NpA1jDAci3K2giLp3715XLbM223q/ZRTzxt\\nr3nZcC9DL6muznnO85bcRLvVE8fyJLPM+mpPMKsmtjOrmQv6gvNUwfb57WPun+7z/HADPTEoD9mj\\nGJtGvP/LbzLb9/htR7Q7Z2dzzMHRAHeUCi24A6YQx6P7tAr02RCM3oiwiW5WuelRiSoc6BDED6vS\\n2X6CM4rZjoxZ5WHjI0fvmWPzfcv4ZhzGeC3D4ckHOjiDP0A45d9UM4UwCeplxGxHSwWMffHQtRUm\\ngoulZmMxhO5jRfq2IZ55dGXJBzrALDDbN8w6KdduPCFSslypA5uVMzydDXg+HVA5jXWaTlzST3I2\\nknljzNsvXfOCtjwCvYK9ILxrcFZjvVoE+ZRCop9+URQijoQ9UrfamEdC68OYRfCvctju4uvSM0l5\\nxzlI4+ZLr+mMqmqFImov3yjKfkzVM7TfAW3Bl8K5tqnMEoLBtw/STf9l8MRBnlWYLYoqQzyPWB7V\\ndsDNwnc3c4BQDZUNFY0UTZBWikcITp+cFAEj15gQqLVdKSphO7La0pVMEpJIIfKzdSsH8r0I1zxs\\nDFh5cipL3M5TzcQP+cX56zhr0MbS6xRsJDk7ybj5/qcuIaVibFOc1+QuIg+TuFGeKEAnIJN/5bWw\\nmJR4g7mNWhTGmuKmVrJeVhkhjadiYTjPlIILAfo2RFJPGu2x2zGleMXekLuIwkVrabNnC66cD4pe\\nNHHU+87XEV2P6y8zZrTyzSqg6e9TMmPqyeLhbIsnpwM4TOl/oolHQUEzrAo7zxVmbiimXY7e7TJ4\\nqth4UKFsgB7rnIRE4xJFNF5cI55Y4lFBuZFIfKgThcLfhrKrmO3KmCwHjmgCxaZjeF/YUbpQjG8Y\\nTj5v8bFHFYqN9wzTmx4zkxq1+tJI4qJ9Noy4YlGmy8sD1NKpXgsG6yPhMJugdx2PBf+sOpCeyrb0\\n1FH0tRgTgFzzwdEOp0VGm8puvebR0ZDZSUZ0EBNNFS92HL5fsX/jmFEna7LI6lYPqGezwbntp9NU\\nKG2FR1WC6ZfTmNM0w8wllbw9szZQirXglGiJ1zh4+Km1w1VpF3BIfV6DhSPGybkFvFLDKc4JpXA0\\nEYhiPkdtb2LKDkUUyecdZv/kpBJ9k4kUcValxQ476Hkl148NVRZhY/E00hN5CWpaX9kVT8PkkD32\\n4fuE+a6i2FTkoThDORAYQ5eG9LlDGcHsZWVlsIloL8/2uvQfFVRdQ3Ic+NK5bQK6LvVShbxSVH1P\\nvg3uWk6UhELIOhi61x2vhmK09Xc1LRNeTjv04orraU5qKrpRwScjoa+Oy4RPZtthnMiqKzVVQy+d\\n2ZjKabpRQaRcE/Se2ZiNaMZGNG+guqlLGFUZp2FAWregDa4LMrZbbci0ciuM89ljm9Xh0j7nFadV\\ndua8tmzE4rizwdBlsbjV99DCrnE4r4j02UnAeXUGZ69b7f23n0Xw9sV90/671Wf7+Wq46HywV6OV\\nJ4ksLnXkW6oJypsCsmNH56W8W+NbRpLGpo7kqMDMK2wnZr4rsg7jGzHeQDyWayUTKRtYdTKmeyYo\\ne8o9xmMZo+mRBxS61CQncP3rlt7bT7A7Aw6+MmD0OcfWq0e8fDIkfSETQDxSTG9XFJuLWq9XaZ8J\\nIy415zzJiQgZxROPjRXzrcCAUJC98E2mYJVJIV1t5cOsk2y8hnRkiWYKFxnKnuFIDTlKBlApVCJf\\ntC80VIr42BDN5IvtPdLMrkUcZgM2M6lmbdsJBXrhhWjlm33LQyd7cML1fID9bUPV7RGNc5KTAopA\\niIoi8YyVgjhkYQaWhprNAcngFP3tkIQTix52zZkG0KWIbem8avYDqMgLzTB44r6TomwQ0ior6rdE\\nij04zFywPF05VFEtmDKVC5oqViridGNcpKg6AnvFU+GBlz3dCGMpKxXj28L3xQZS8CPzoWIQPN/U\\nvPjikOREvtdoLuXW/j/23uRHtiy/7/uc6d4bQw6R+aYaXlV3dVU3W5RMybYkg/AgmABhGPZOG+9s\\nLwT9BTYEGzDgneGNF14JlmEYMAwvBGgtbiTahklJNEl1k+yuZs1Vb345xHjvPZMXv3NvROZ7xS7b\\nDaIa4AUeXmZk5o2IGxG/8zvf33do7yiWJ7I4L98Tip7uivzdy/xjyDa0G1Humkrh5+V1zYrQWkwT\\niFsLLxyfekko10Fsa4UimVlreD6NmGnAukDOirr25Ky47huqgwCAla/H4jEUlLWvWZbiNtz+NB/v\\nX59y++s618PbbqgObxVQofgbQBNGGuFeSPbz3AL/3x4aebxucGQqhx2HqEP3PDQTemSYaDK19kx1\\nz4kVj5EjLUSC2zOCNju65FjFhm2q6JIdF6Gvg5qGIp0OhXTsF4qEGb/WKvOyn1DrwKLZsXqwYaNm\\nhJlGe4XyAuGaVrBtodUq6o8z/WlFdQVhJrRB7SQ4fLCABqlXySqS3dNdp08TycgsCESsmK3sGrcP\\nI9d/PVHPjok/m8vfdIrOOx6++4Iv4l1QlvolTL+w2BZe9N/8tfxWFHEdoL4QzHT7hqI9kzfJ5EUe\\nxRntuUJFRXORR4Os4BTUhRXhs/j0FrWUjpnpE0W4tuPgLtsCTcRiqdrC9FmkP9Js7yvSJHMy343y\\nax/NjTfNK9P08rN+W9F0hfpXCm8c0nWclnzK06PCPikfjnGoqMjGgNVwdkLWgrXpTSv0wxBROwF5\\n6+tNEQXFQlkUyMW0/gYsk6e1CIYqK5RCq8lKoX0kVQLVbB5o+l8xY/6fpO8clesj18utCwvoKo9e\\n5LEuUEqG7lQxeZ4xnRRY22ZywcMFXy8Dzyt5XQBJL7kW9WRWUK0Tm/sGfyQU0tmXMnSdPY60Z5r5\\nowJt+czyoaTYb95QuBWkWoq6ilLAUyxQUFJMFzv8vGc66em8pWsdaWdRO/kgA6iNIe00PtWkKkkO\\n6PGOja9ZebUf7JUiFLK+IQyyOqHK+8Iq+doOi31hLg2H1fHGQjCcI+T9e8zqNH49nL86IAyHZMYi\\n/nWZmPJY4o0i99rPnMrj7sKOi8aAP+8ZLgBRvR6yASneVkesjizclhOz48QIgUB82fUBpi+LwYwO\\nDOD2zJyvw75/Htf8dXDKx909frq8z4++eBP7WSNkhyYRF4HsIuarhthAe1cozCoJcyQbzfxzhZ83\\nzD/bCQ02WGKjWD60I9YdK6lN9XWmuUiEqSY6xmDvMNFiLXueSA7ctcF9bgnzSmrSFCbPMu3zE9ab\\nY+5uGf1a7E52Cb9QiuGfxzEM1uwOwVJrgVS6UxFs1JdC+8lGMKr2VEtCTaGUmQ5CrcZikm1R/5Uw\\nBRLYANWV3J/gshKmsLujUXHY/hhW2wVXizlu3nMyb7EmEqKhD6awGBRay9bS2UhjhRYUpxWxAnIm\\nTAz9kRbs+I5mOtPo+7UM1fok2ZM+jWn37d2G5tmO9sEJbiWGWN15TX3R4+d2DBvePqipVhE/N5hd\\nQvuEW/agJSMTIEws7rqnX1RMHm1o701onu0Ix9KRx0aGo7GRLthtpJDabWL1lmXxoYQsr99umH3V\\nEhuDaSP+xNEfOVINdiOOa/PHmeqyJ1WG7szh1pFYaTGzCjLjiNXAZCn/1zKUNJ3C7jKgR1aJP5ZZ\\nh4rQncrz2d0pA9DS6Wcj22FKV2830vnH5FBJSbaFtbRKOvnraiKhs1mYhSoq5JcKmwUlrJikSdqy\\ncxWXbkJjAzErYtIYnahMpDZhZKPErPHR0EdZ7LXKKCVmSwOkENK+z1bsoQCj041d3tAkqINGYWgk\\nOizmFkRxeAzF/2ZHLwuIFM1XYZNDGESrzDa68euhuO9/Lu/1Wvsb5xkIAQMPvtZhVIcOKtSRm42i\\nzRWVCjc44tIEuZu88XIMhf91lM4bOHp+tYj/6e4eP/riTdLakSw076z41ftPeLI55qvnp/jTSJgr\\npm+uaVtHzoocFeY7Lc/fnTL/aUV0U6bP/GjwJhkA5bmX91+1jGzvyqzGdJn6KoxD+vpSy85Z3uLo\\nAIs/zjSXgebxFjRjhm6c1ehefJPCzEmU4y9bEY81rD/w2AtbPDCEPSCsFMFYUyXyaZCdnu7lg+22\\nmX6uJL/RlY4vy4Q4mz227pZ5HObpIJzoqrgghkagG7uV+0m1ITaGbec4noo5UmWLYMRIl1OZiI+G\\nNli6TcVkozCdvKGaJ1vsrhbus1VUz3dCF4x5nGhnpeS2nJnsPCpGmnbfdU23xQ9860d64PxTDwnq\\np2mERVQSqp7elR3JWixhm6KwrC86iBl33UKSRSQbgV92dxXrhxmVNWTxqtk8FPGBinD1/lRcJa+k\\n2zY+o6+kG9+dG9w2E5pGmC0R/NyUeDh5bG67l+NHp9BRFtsBBjM+Y3aJbmElNHYtHG7dMQYdD82X\\nSmVgGhlFRoMwCC04OQNtLe9VmWqnil8LhHmEJhXlawav0a0muEyuEscPVjw8veIHR0+58DMeb4+5\\naif0QWikPsr7L2ZFiDeLSyoPKAJdsKQsuwOjy+tUirdWmZD0WOiVyjh9kz2Ss9p33ChSgVL2A9Ob\\nu8KQ9HhbSK9izcN9jbeXganVwg0fdg8DnPK6nE2tEnPTMbcdU92PAqap7m783qECM6JplCeimamO\\nVWyKXYG4Rx6Zloqbi8PhcbuLHx/LbYuBvPeWSWiObMs79y/4dHWf+kLRf3jMP3855cHDC6azjnVn\\n0MeBzbMZ7rTlaNZy3HScNxua+4Gv3j3hs588YPplxb3f79Fd4niX2J1LudwVe5BkLaaD+aOeMBE7\\nDRUzk6c9dmuZPdOFCKCZvAzESmOL8Ey3ge13jsdz1svI/E8uhP2b+eVjp+gA009cGZRJQTCtbFd0\\nlO1LdySmUclIBxemCt1KAe9PFKt35VyT5wrdyzanOy7QRBARUX1ZtuahdMsnhu5EsbsnnVqYyoDU\\nbDWhcgQXcTrxvTtPueimrPsaZyKLesuDZkVCcdVPuLgSnMv0ecSQ3YstuXTHetuhtm1hiAhVUBU4\\nRHVeirlSIxVwGErKSTWjy6FWe5qi0SLTV4LxDS+6TnnPO4cx7X5gveggHaotrBF1pcauVMdiCRzy\\nmAqvvQxyVIJ+JlF5WcvCa1vGRHmGOLjCVhmcA8kyw9BBYC6VQPWMbJ04EWfBUWWpy/+FwTHgkADa\\nC+SmIoV9IrcLkyZjTjyuCvjeEluDXllUANNrchBJtTlr+VcffsnfWvyUf2PyMfeNp83wRZzzk+5N\\nfrx5i5+t7/FsM6fz8vGQAgx9NOQsASS3aaVaJ4wuxVVlSHp8gJUNHFUdlYlYFdn4mvAajnYXrQxm\\nbxXQP6trH2Ccoeg2xt/o5ocC30bHLrhx91CZiDORI9eNBbKNwo+/N1lxt1pzr1qhVWIb65EhsgwN\\nj8Ip11749VPb80azZKp7vls/GztueWESKD0ONk/NFq3E5jcWUdAoTrqlYB0LtRKWF9ncMAu7bRx2\\nqAbdRemuVZDF3K4VsTE8/fAu+ShgLi0ROH6w4sHRitoGvloe8+nnd9FLS3WtOb6QIWWY6LGRdNuy\\nyL4EUz5zbpMIE3EcTVaatqwVdhdxm0DWCrcuO/OZJtYV1cTQvGhRoVALY2a3MLz42/doH0SqC0P8\\n029emr8VRTwbaO8lmqea5mWmuYxiMbuLpErTntniTy1FwHZS3Pu5KgwIcb1zK0WYgEvSWbtNLkpQ\\nRWg0LOTpTp576ssOqPFT+aCLaZKiWgr/eXvPslM1kzsX3KnX3K+X7FLFVT/hqp/wu0/fpfWW3bZG\\nf94wfZJpnktXojay79KvS6wOki2J0eKmpxWg9yvvIAoav8/77wuPeyjQKkrgskp5/3cx7fFxrUdK\\noxRHtXce1IxeIyDYdzTFprWWpB27E4e1/dZOOg2t9qn3QzEda9EA9Rf+66DeHDI/s4G+bDlVNLKg\\nTkWwE6aCcetpWRgy43NNDtxKFnm3zkwuorymGUyr2WWDz4rT95b8m/c/5jeO/5i/VL3kRBu2KfJF\\nrPmD9l1+9/o9fnp1j//uq38X3/8mh3iHUkApiEpnjCk4t0n0AaxJVDYcOt6+dk4yFOLB06QPlpfB\\nYk3E3BqQUs5lym2Zm4V96jyzSjpgqyPbINzOQbiWsmYXHVf9ZOyktcpUBfa5V6/xWXPtJ/hoWIV6\\n3BHEpNkFKe4pq3EH8XwzQytuLA6ViRidxq5+3VcjdNRGx6La4lTkjlvdKK4+Gza55jpOeNYfk7Ji\\nbjvuueVIKXwF3y9Q0OuSesTRce//7m7h7hHNw+aCz+szPptGVDIcf5bY7iTc2ytDdampP67Q0fHp\\nO6f4t3qmxy2Tk5Z2N8PPMtNHspOsrzzETHdejZoIccQssxhTdotJlM74jN0EWcedJjSGVO+p03Yn\\ncKq+2tB0HhVnwjsvVjtuKVmcKrymdnzN8a0o4gBpHuh7wTXtTmN34gkSK41bl7iuiSaRizmSFIXq\\nKmO3qnSQhYJoioz2RI0ffBgKC2wfVOjgiJUUmPoyEyfCc04WdncU3Z2EOfIcVS1T3fNGdc1Tf8zd\\naoVPhp+Y+3yxXLCJDZOXCrtLYvBUrGGz3g8UVYyIP8hBh12O/c8PivX4w1sv5Ou2WFqT1cHA1O5l\\n7ComKfSTWgp4Zcd5QLZlyl443QMhoTQ9UmxPpZOpro3g17lcwyghyBLYXAyvQhKYpIvic7LtycaQ\\nG4s/qfFHhu7Y0J1KvqU/TejzjpOjLW8dL/lg/ozvNC95WL3kTXtJoyJHKuDLquDIHLKuPLBJmlV2\\nfNzf4/e377IKDetQ8bQ74n94/G/x1fqEy9WUGMRxw9qIMYkQjHTXJhGDJhffcXRGoaQcRIXWGa3z\\niEnnrOhLoQPxXKlMpLZlh1eYHY3xzJ0s6Gtfsw0VbSjYfimwhzg3wNx1Qlk0IkDaBkfIhpA0XbBj\\noQUpXpfdFKvSWFgb46l0ZGZ7ah2YGPn/q/aU5+2cl7spIZqyuGg2BQ7aUIlRm8rUNjCvOuau46g8\\nnsMCu4uOpW9Ydo0MeLNil2HTn/MJ5/yhfqtclwIVAbUNN6CiYTjpdOROs+bt5op7lXTysJfyJ7R0\\n8jBK7A9piddxOg5Fp7qj0WHsxk/Mjqnt0XVk9X6gOzdknfEnEd1p+oVYTdArpl9B80eOdlELNfYN\\neXHbO1J4q2tLfdnRvOjFQlmehEQdzg3RSYM4eVm8l7oERmGuO/K8QnuBZ+vLiFsHzLpHr7ZQIE23\\n9qRas/gw4I8M1TKgUubL/psTxb8VRVwFmH5UjdNZP1V0J0JpU1mKhNsUa1In8vDCvJKh5S5TXwpm\\nGytFdwrdUVn5trKNj5XwQQHCPNO80GW7jvz+eSJNEioqss6YY8/J0ZaX7Ywfpzf5UN9j1TckFI+u\\njuk6R1w7VK9p72R29xUvf61BFd+NXPw5tJdCaHqBJgYV4eCxIgpIbphaDerJVwbvWSCPgfkxqBqH\\n1B0A7WWyPcjns1WyjXPFgMqWIaMb2CaZVGdylcnTQDX1nMx3vHN8yffmL3infskDe82p2TBTPSe6\\no1GJRoFRijZnNiPdUtGoyCo5nsRjvvILvuzPuAxTvtye8nhzzHozIewqsteky0YO1fsAACAASURB\\nVJr1RxM+vbrDo+V3+T938jxsJ4uDDoNknhEfD025r+Gdq4QNE5rCS18k0rlH2YTWAjukpFA6E4Mh\\nBENVBVJSN4d+Q+dfIBFjE8YknIlYk27AKAOkkbN029teCnTOClu6dzjgSeuE0/vhXRctjr3tQ8qK\\ndV/fYKXI30TmrnuFf91HS2M9V92ESkdWvmanHF2w4zAWBIZJGULci2duY91S1M34mDddxaN8TEp6\\nvE2uoXw/DG/1weMZFjX1Gix9GPrukhvZPFaLCKqNjmWYcGJ3HOmWiB6dFYdj4KBLEIYMUq/jbM+k\\noYR5ZEub9x41781e8Pj+MV/ZE3ojBRqTMddFtevBbgRqHRhwu3tCc86a0czu+nsOsmPyIuFKZJpb\\nh/1uVwmtUCWwa48OsjsO82oU59ltwq59+ZxG8nxSYgst7b2a/kjDQhol3Sfss+XeU+kbHN+KIg6y\\nnW8uMm4t0WzJQnesR/8NP1XYltE9LDYytGyuBR/vHfTH0lGJKZJsScJUYTeZMBP5NYA/lgtt14r6\\nCqqlYMfBi5GV2Wr0Y8u6ari4FzCzACqTo0JpuLNYcTTpeLo9Zfap4e4fdNht2O+xB/pfgT5ysVUd\\nwhZAOl1hWJSdhRUmhyTQlK64YMyjJWvBmnXPaFU5dNOH7Csd91DJ0FmrBLt7ivZuQt/f4apA6C2x\\ntdBrVFCYC0d+UrFq5/yku8vP2u9jt3mETlSWxzJ4oAx4tUpSdIdFJtaa5EpwhFNEJx+MVEFlFW74\\njA6dvdq/xirL/6M3+/CrB89vqL25GImpJM9Ze1BekZN00UpLIQf2GHaBO6xNUsjRKA3aRFwxIhqK\\nljMRXaCHSdWPhVQjv5NLgR4KZKXFP6Qx4Qbfu09WCrWXPXNj/MhDtyoRssaqNDJfumile4+Wy246\\nMmQOu+KVr/dsqdLpzqtu/HljPG0UDLyLlsvtZPz74W06/D0Ipq+AykaOnGdivSxghYFidWJme563\\nc3w0vNhOb4xoAGKSFB8fBwOt/c+1glB2DTFp7kzW3KvXvFlf8Ya7BBALXrUfXA62F4M5WJscba7Y\\nDnJh4I5d4ZR04TPVoUlUKvJpusPVdkJ4OWH6pUjuxatEFLuDZfL0mcetfIEksxTfWtOeGZbf1ftZ\\nTlRCAACysePcLllRGu/ODZybkU/eXEa0Fx1Gtop+UYmdiK+xm1iCySWQRaX9ezpVmnDvGD77Babd\\n/3kctk0cfyzp0P2xCEq6s9I5ajEscqtMaKC+SoSpwkdhHWzvq2J+JdFtdlNOmiBORBQ04L/VtVyp\\n+sLg51JAugWESSbME2rRw4saHRTNC+jOC+84y9YZm3GVeEqstg3umWP6NFNdtujr7Tg8zFaXQq73\\nMWoDjFIUkWM6D8iQUmvZWWiBYCgY9g1zLCW2sHkIdziwqVUHIp0bPuSFypS1JpxPaM8qdmcT/Fzh\\nynXJSmAosW8tBXSA5G2BquLBwuEG1kjB5gv973DQiKKk+RQfnB7oIOu8H4aq/Ycpq32hzqbstNRB\\nwTYHRbsU/5zyuEANj1eV1z7HIUe1CKl0En2ViyMm3GdLXDnslWX2hbzfdg8y+t0Nv/HehwCjrB0E\\npx242ofWqcNtA2d6kOMPx2CeduJanJYwksmA8Q0vk8qsQoNVkUpbzqotx3Y3FvbBAiAkfUM+75MZ\\npf4AtQnjfXep59nuSPIhy/0MXfrIaS8D2aGYG51og6UvtNra7aGiw1lA4wJ9sGOhzmVBOLwuwNi1\\nWyMkAaMTR1XHeb3lQX3NfXfNJ909eV+hWNgNjfJjV+1UpFH+hvT+jl3duI9tqkdcXLryxInd4Wwk\\nm8z23QA2oWwmXjnsRnDn5jJSP9uBLVqOlMoMTorx0eeZ+nqwMY7jzlj3kTAXsV21jJhdRCVHfyQw\\nsDDsPGFmCTOL6SLTT5ekiRMv/8GTKGVmX7bEqcWuPbGxhInBaPXa1KuvO74VRVzEMYzbGD9X6I7R\\nq8BtBwwWGWR1kFWmWyjprhWYrZKCus5Uq8juzOKeJrb39GhzOghOwqRAGwHsFbhKEdaGfid0ufZN\\nT/tORlcRgiZtpJPCZLx2bLVAJnUZ2MVphW5LqroryT3Di5CVJOswZE2Wf4NMPmWydvvEnSBy9LEI\\n3y7eKR2wWAqN8hArLzTGUempZHeBhmT03vdbCZUPXaiDvXTKiv2iN1zvrCmLCiPssId0bhbvcTed\\nuRF6MQRHjD9XZWA6nA/G1PqsKe+HUsxhZL3Euixw7N8vUR8MXxOQFLaKTJserRO7rqLbOfKyInjx\\nrki2mGK5THqr5XpWobwoQf2TKb8Vf4Xz0zWbriovg+Kvv/k5E+PLAHEIDC4QyOiKud8yTEyh4unI\\nk+6Yy1664cpEZkaK1JFrZdhnOu5XyxtpOwnFNlYjL7rRHvSrxlKHxyFf/DJMx0Lv3Z7Lfgh5jFL6\\n8v+wq3AmYpp0Q3A0PN9dwffnruDYWjz4h05/Vm6f2668JIo+GhbVjlm5bbCIvY5T3q4uxud8btcH\\nvujCOR9phjoxBGEMkItTkRndyHoZPMOvw0TYRXXEPqsAg1sPIj9RCPupppo5+kXF7syMcZDJqjG3\\n1e5Eianj3ko5NobYDII+cFsx52suoqRcrb3UqW0gzCyrt2t4WOM2GbcK1M82Mi+qHGEmPHN/XGG6\\niO73deGbHt+KIq5CptpIPFt/LNQ1HSTHLlZSULoTTbUSBeeAAU+eZSbPoL0rF7c7K5xgZQhT2Lxp\\n8Eey5VFIoZc7FLgFyoCvEkhi9qV4g5je0p5LwCknYV+8ooKgRsfAQe+fjYIS6DB2xAM1EMahY7Za\\nps4hjr830ASlsBf/70OvFV0GPDlLBNvQmack57plVa1yOX+R1UvIsR5/pkorNcAtpttj8aZlpPgN\\nXbcOEnmmPbcwe+mgB7bLwOsfa3jpkEWirIvMnzG0Q+YEpUsvf5fM4LMuO4LoGI22YiXy6NDsH39R\\ndxfbgMJe6RST5w7dOZKDfgrdvYS631Lf39BUnpg0vbd0OwdXDvOiIVVyv8lBdoKBVyZy0Vn8toJe\\n8+PqDf7m/c+k2yaNF3HojEECfIfivY41yyQL/q4IavpkhQZaFqd56SwGH+6EQuf9IHBaOvbX2dBu\\nU8Wln45eLQMlcLBinRrhdE+M57md3xiOfp2Sc9jE+2gISuNTplN7NsgAwfjCmx/mBAN+nrNiWZoc\\nayJ1EUmpQnMU2CnQmMC9ZsXctDgVaLQnZc2n/Z1xIXQqcmR2N1KSXnFWPPgADPj58PW7i0t+0jr8\\niUG3GnVVZifn8jqHkwTKMf/I0t6R8Bm31lSXMHsSxHRuYkhW4eea6RN5LVKlWT8wLD/IpCpTP7cs\\nPkxU116SgZxG9UJ0cEuP3YRiPmfZ3nfs7p1SLUWsV133oqbuA6lx+LNKtBzuFwinKKX+R+A/AJ7l\\nnP9yue2/Bf5DoAc+Av6TnPOVUuo7wJ8APy1//js557/78+4j1prldzRuJQPMaimYayoeHaEuKyPy\\ngU2VwvZ5HGTarbww9SXCSFkzSsdREKdJYIUBq+uFbzx5Ji2g3QnmFWbS2UkIcPmMJjVWJt2J30Lz\\nvPitPEpj8IJK+aD7zvsiPMApWu2hjqEY54zaBfE00XpvQ5sSqt+n82RryjQ7iuR+OKcx47kH8FEV\\nG9pszfhYVNx7YQzQhy4eDwNtU/sCWQzrXNrvfGyXxxT6ARsny+9LWg/jz0d4Y8D4ygA6TGUhcFvJ\\nP7U7GRS5TcBetWItMFyTITDjkMlzEDM3QE/9oiHMhIPbzxXtPegWCU57cmdksdUZW0eUyuxeTPFX\\nBgWEeUKfdZx974Kp8zRWGCXbUPH55YLNxYTn/9cbTK7geC0L1tX1Of943fDvf/DH+Gy48pMxIWpi\\n/D4dqhSS4TatMuduM2LXPu39sQ8HjoMFqwh9DJr9bUaJx/fhcKBWgXvVHlqI+Wbgg1ORuem4U1wZ\\nN6Gij8WL5mAnMdgADCIjkBnB8PiGw6pEUmoc1h76yxwuCgOcc4ip73nsgYnxHNsd527DkW5LJqls\\n6+7a1ZgC1GdLzPqG0tNjR5bKbVMtXaAzoxI/nDzih5NH/NP6B/yTf/krzD7XnHwa2dzT9AuIs4Se\\nC8y1eUdTvTTUl+LdZNtMe2aIlWX6NOC6hOkiZi1F3KbEvauWxYcNqdIkm6muPboLmHUcP/v2OpEm\\njjit6BaO7V1TWGrAsaZ5mejOatwmEJqa9sxx9YFYYYTf+8XCKf8T8N8D//PBbb8F/L2cc1BK/TfA\\n3wP+8/Kzj3LOf/UbPwLA9Ik7P/L4qcbu5AJ2c+m+3CaP9MHurhSJ2Ah2rqK42JEz/ijTn0J1KdFJ\\nKMTwyEF2Gd3pMTBCNRndKexWcfy5SFyPvsisHlr6Y0V3mglHiazFJlJULxm3VMw/g/kjj59rQqPx\\nc9jdrTDLGcpH1PML6D2568gxfu1zHoKNB4tXjJHu3VpQAgEppeT2w0n1sAgYKfYcFGiAXLs9jOIM\\nY5ZlziL1L7DD4AMhEy5GkY8uQhqQLtxupGOQQi9WAWbTi0ip86N3OUaTK7dfjAZ+98QRZw4/t/iZ\\nIZTXpjvRtAuNbS2804wT/n4ui2Z3psaEpxvXLcruwW7laYeJUCH9POOPJM9QZQUuoa9FQBaOFM1J\\nx8nbV1TfCfhomFU9M9cTkub5Zsbnj89gte/qqkuNXYuxUWjEjTG5ROMiv/vsXUI0vHV0DQg9cGI8\\nXZKtf1LCXxZWxc3Hf5iKc/uQUImyZS8FanAg9Flz6nZj1z3ALmOhV69Gl6UinJmanjO3YWL8DQ99\\n4BVY6HXHbS+V8W9K0T+043UHX48+MdmM5xD6oxhlTY0kaMWs2OTqlfsdRELtQTLSIR/8ts96ygqP\\nIxXcf5sqPlmeozcG05bdnFaokDEbTXAW1WtyE+neSKjoqC9h9lWL6YrPfRdlhgUjjKdaIb7WO088\\nbqDk1epNBymTnSVNnaRnAdkq3Coy95l2YahWqbyPJeAl1ma0vTVF2m9+kQZYOeffLh324W3/+ODb\\n3wH+9je+x9ccKmbq5y3TdUe2mskTS5zIB393xwr75EzYJaZVJJfRSQk9rnR+9UX5cBT/FBVARwU+\\n467MyIAA8dtINksBPpOJeLLi1dKdFc/hRAlG2Eev6V7oRNVVz/T/foxqatLiiHBU0z6YYrqEnRY+\\ncEwyYCuqzSFYwey8wCrl/yGsAasHxbgM7nyUj2TJt7xxhNKpAyrEva84QEzozpcCX4Q/xhAXM8zO\\nM30UmX3iUSGRZjVh7vAzK9TDIRKqBCL7iaY90diuOEvWSuYN35sQ6+FDwb7Q5gJPle574PKr0rUP\\nNeYQGzc72X3lnUBZ/bHcd3dHMJYB9hEu+gCXKdw64bYZtxN/ne0DGXDrtaW/E9DTwFs/fEpfCvZg\\nOftiN+fJixNeXFSQFfnYc3ZnxXffesHVruHi+TFqY4jTjE+K9UMt8wInYrDd4zn+jqFpPJ9fnzKt\\nPH/4pw9RNvHG/Su+d/KCN5rl177XjZLCXhdVozvAm7epotF7iEaTmVb9jSDmQzhl70+eb3y9/2Al\\nKBmhw44g6debuR0m+QAlqSff+D4i5l9dcnTlw2QLfFTrcONxyuO7CQENcXdDQpAwT+Rn1cHfxoEj\\nrl41vTrsvodCPnTlQ8LPcB1WseHBbMnTt4/YLY9o72n8USKeBnQdsVqICrsXU5onlrOfJCbPe8zG\\ny2cuZWLxC7frXlw+QYzpfIAQsc+W9A8X+JmFPMH0ie09R3umae+CW8LkeWb6PNA82eKuHaYNZKuJ\\njSVZTZhqVJAhaX0VUSHxmX91kf+64xeBif+nwP928P13lVJ/AFwD/2XO+X//eSfIWrF7Y0KsZ9Ip\\nlq15aCQpxs+FTqi9KArtRro128rgK8wK1aeVjiwXdeAwMIM9TADCZEkGss1c/UC+FwVhFoOkMhwb\\n4AS31FSrYq6loF9U5A/exOw8ettjnEGFhL3YSGdsDOmoob/bEGvxBh5UjdWqEgvYRgYaqdLF6bDQ\\nEgv+fpgAfzhQHL5261CofZFY7/GzwQhr4J2rwlTpzh3JCGfcdBnbJkIjjy1M99z7XLDtAc8GCltH\\nuvSsRBXrZwp/jOxWkhpNrGxbxFUZqi4XDrx0zzrmkaIIctsgV5bHLGZcySr4+MYGo9As5XXSXgZE\\n8kEYLEGlwNcXCrdyxNrxxdphT3qetha1suQ64U5bzhdrzHli3dasryZcfHHKhc3SvbuIOg+k3tAd\\na/pe46401bXi6FNAacJkLteggs7DLAp97VF7Du/C283VK6rMsTAh3fFLP9vfjnSYA4sFBmgk7jMy\\n/4z48yHLc8jVfF1XXeuAI96MfBuxMz0uKofHYae7jvXYGc9sx4zuRmLQgNkLc6c8p8OdAhlXCv3h\\nojNEr7X55u5kkN7Pbnmz7K9P2R0UIZA+WPEHiOrMbgjpPn1nUbPE9LEmK008y6SdhSYQfnbE6VcK\\ns8vszhR2Z/Ezi2kjsTHSHXcJd5XQy+3+AYQIRpOOZlK/zi1X3y+hI1th06VK0R8V509nMW0tvvih\\nxC/6WHbBU7RP9CeWvlg7p3/+50QxVEr9F4gP/f9SbnoMvJNzfqmU+teAf6SU+tWc8yutiVLq7wB/\\nB6Bxx9QXns1b9SjI0V7ohM0LSnEWYUpswOykKCYF7R3Bu3UnftEqIhP8SUKFfXHIinE7FGtE+p4G\\n+bf8zOwU9aWieSkFZv2WZvJCnMfqKy9+KEqRJ07UpBOBD9TOY2NE+SBeJUaj2kD9RLDIAc4Y8zOH\\ngechq6T4h4/H4WBzgE9K156dWNfGqcU31Ss5mGT2VMEExgf6WX1jWFldB1S0ZKPxWvj42oPeCm59\\nKC0eMWqEXjh7GmWnEwp+rcpiVMRZZleGu2XhUjFD2S4Oj2tkpjAwYbJ0JDHTz2Xq353JggHg55IS\\nzpFnMu/QOrN+NqN6bmieK04/ipBh8kxM/du7DZc/cCx/AO++94x51bHxFRebKattQ/CGlBSmSmRX\\nHq8WLUDqDbqKJJXJKtOfZ/xcs3lXvndLw/wzaC6zJBYpeP5XLWESeWO2xGcjBRPNWeG8StCyLQlB\\n+6jA20XwMNh4yK1MWbMtuuzDrvbrEm9uOBqWojr+fzDYHPNBeRX2G353eHz33Go8n8+mwDL5xmMB\\nGYwOHflQuIfdwe0MzmEQ6VS4IbG/TVOU86YSGqHGwg2vpgYdQliDi+MHbz5je7finV+/5J3JBQu3\\nIWXNHy7f5p/p7xBeToqeBFZvGaYvEquHFX4mTePZTxOERK4F8unfPKZbOK7eM2Qrrp5+Duc/Tuzu\\nKKpVZvtA4cuMTcdi02yU7L6HnbSROVnzRIJbqueZ7TvHtGffvIDD/48irpT6j5GB52/kLBUn59wB\\nXfn695RSHwHfB/7F7b/POf994O8DHM/fyn5uMX1m8aFne8eSjUSzdQuBN2KVqa61CHlWQi9MlRSK\\nWCey0thtGUKWTnqg1uVSzPKQQxlU8RbPJddRWCfVUjF7lDj6bId9sWbxL/KYupNmE/xiQqrFj1sF\\nEbX0pxV2W+FWPcxqUm0LxrV/rmMkWREU6BK4MGZXFuXp+DWMONyN4lzohapg7XrNXk0xLAA5i0uh\\nUSOLJc5rqnWiOzGECWStsTuxmG2CBFLHSorrAE+JOnIY6qpRlj8wUrSXa6+DZIiKUEnwv1gh0VSl\\n8A5FEp3RtQQw5M5glgYVFdWlRK3V1wnbweQiYLcR80cyLAJItfgx9yeOWFXipTKXD1m1SlRXAbsJ\\nQvUE2oUkBSmv+PzROdlrVB3JUaGdqDGVZtyi5aDRVUS7CC6Sk0bbjK48MYpNLVGhW01sMtsHmt19\\nRfNcFsAhmEIMpTJdkli2Z/3RuO2fHJhT3T6G7tkX/DdlxVWYYlXkRTfnXrMaz9FoPw4/vy578rWF\\n/KA4CtPkZpDCjQKvCp/+NeeRAIgyxFVpX7RVIuXbRfXVAq6L+nJvN2tJQ9d/a1YwFHSZMLwm4f4A\\nI/fZ0GU3Bi7/s6vv8qcXd1DAyaTlb558wlnhLces+KI6I+4M2cDskQgMh0YnVortWwl9v+Wz9yvq\\nF4u9zuQyM3scmD7VXPxl2LzvMdcW9RHc/90l/qSmWlmW3xFXT9PD9KlHd1Hg1IkjTxz6ci2MthDJ\\ntSVNK/pjXT6jfOPj/1MRV0r9e8B/Bvw7Oeftwe13gYucc1RKvQd8AHz8c0+YJYm8O9J0Dw3tXenS\\nkivp6DuFu1ZjbNshrU0Fhep14XgWmpNLexxlgCIMY2eOAZVEXo8GIuQ60Z0q3EpRX1W4x4FsDWne\\nSLiCVpg2YFdi7Zprg0OKrfJxTJk3MeFGLngenx8xkgdu+AHrIg/UQaX2Xbf80ohn56aSLrx2pIKj\\nZatH+EWUoJRrcgDJJNC9eIi3C8PmDUV/ItLiMBEbzf5E0Z7lMUFpKNRxMvBVFbrbw1huI341g6Ob\\n2SXcWrixgxgn68HOV9OdGPzUFl4tdIssYQ4uo7wiHEfCHLbvZRFkGJG856xAGXJhU+SkyEFBn3HX\\nGnctgRT1dWLywmOvOnTbQ0z0b51g+kx1LUOjTgE2k9cWNY2krSWZjCr3p3RGVZHQyXBZqSx+KkDK\\nVhguc0/oDEmBWZmRJx9riZ2bf6bZtTX/ZPtD3n3vGb9+9+P9+499kRmOQ+ggHdx+KFo5cxuMSizc\\nfhsf861B32FxJN8ourAv6nCTQXLY/XsMX7Wn+ELKP3U7zqv1yJA5/Js/i5d+u4APMXCGfMPH/Ktu\\nwePuZDTs+v7sCVPd3wyeuFXMD+X3mvRKN16pMLonxqxxKvC8nbN6OcO+dKzqzD8yv8Zv3v8Ttqni\\n/3j+PT756RvMPzXUl3m0PvYz2D4wbH9tx3ze8sO7T9m+VfHjT95CfSy7ofW7CT9z2F3m6BNwf+QK\\n9Jh58uvHTJ4n/Ewxe5yYfdXhjyx24zEXG2GPhcJgKaEvWIPqelTrOTJilWHaX6DsXin1vwJ/C7ij\\nlPoS+K8QNkoN/FYpSAOV8N8G/mullEd6y7+bc774uY9CiQNYvUr4mSHWkO72TI5atlcT1MZQvzSk\\nDP2xKDNjIxzN5DIYgRuzG4omI5SAyaj61QuiTKZppMsK3uBcpE8N/sri5+XNWDlQithYYmNGTBeK\\n0Y0WQn82Bl2ogYN9QzaFDuf0yPnMVqF8giwDTrXt9gPIch1GeuLwNYxQjOo9ZgNmsJYdbGtz3kM0\\ntxWiIRLfOMNPZ+KxbjOSWCaOjXaTabIoWwfJv0rQn5gi6S9slqJq3bks17TXMAtol6hqwXKDL940\\nvSZ7hWrF51zlMsBcw+mHMH0ecCsv5vedl+vXB9K0gpDwiwaVM9v79SgA2tzXEtE2UWPIctYSqK2y\\nwzaGyWNRw7qXW9yFwr055/hzWL9puX5fuqzQafIkoScBbUSpmJMSBmN5n2QQ7xSbxpciBo2po2zn\\np4oY5PH0p0BWbB5m8ts77p+u+VfOvnpthuRrGSkHmPjh/0PBP8yQfJ2R1CGswQHUAqXolvu5jVmL\\nodS+ML8zefVjegiLfJPjEAuHfVFPt352x625Vy1vDWTFdmAYUg6+469bGAbmz+3rGrPmOk4BaJPj\\nyLXiGd/D5Knm0eXb/IPmLUyr6E8T1VJ2j26bqS8jq4cyp9q8ncQsTSd+77N30B9NmC0Viw9lkZh9\\ntiZOHe1d8T3RAU4+aulPRLW5fD+TbWLxI0N9aXBLz/K7U/xfmVFfy+d7+kRgP38kEE2qNN2pkbB4\\nA+kPfoGYeM75P3rNzf/ga373HwL/8BvfezlUiNSXPe3dimotwzBconaBfNKy81P8vAy1Ohlkxqlg\\n3natRwaDXyRynfYDQaRLp9XkWWR+Jh3Nd88uOK837KLjZTujNoGfPLrP5LFl9ihx/OES/+BUuN3F\\ny1vHTGiMhB4DcWLKym3RvSTsKJ8Y5PJZK3QrYQ9sPartxFGwcqi2GzM3c0rkMOjZpWMHhANurXSG\\nu1aohwN1TylxLsxZcjqLWyKwl/pH4YeHU3lTT5952kXN9i1Ffx7xDxLboFA7I34sQaF8xpYQhclT\\nYaRUm4Rpi71mTJh1R64sqg/k2qF8JMwrVMq09yQIo59ryIyYoD8SWCZVsHlLsbvnyNqNwx/TiseN\\nW2e6U1X+Ri7DsLsynRrZKjqWWUdhqwhLxqCjDIjMxqN8YvrxJWleU107Tj4x7O44/FTR3rHsHmj0\\n2weDKiAVHUFOCl1F2UxF4U+HtROxVwbVC9U1ZfBnkendDe+fX/D+0fPRHjUkveeCpz+7mB8W8kPm\\nyIAD7/HgV5NuDs+170ylWN7o8G/BKcN9DH8zFM/hMTgVR2/voZv/2ni08nuvdOkHjBl3g32iRtho\\nKMo3vMJHGmbGqHiDcTJAMaNF7gGHPKILi0Z+/8FkhTnusZ9PqZbimzIEEOtOs30nYNaGq+9rTj+E\\n2bOIXUfqa8flr8y5ft+QvEYtEscfa/EHKoe9bpltPZPGjmHiZDj6PHP0uaZeReYfvkRdXIO1LH7a\\nQ06oI8keyJNaGrOZExZOLU6ople4ZcB0v2QGWNkZNm819CUxunmhUE8mEBomEdxMprz9qUjPTQtu\\nZTA7YVP4eR6hFZIIQbIpLBebGVJc1s+EEfCj5zMZanr51zzXTDvhBDeXEb+QaZpwm8Wv2+4y1cpj\\nr0v3XAaMceakmB9XpFogjljJC0JuyEZRX/bk02akDqppDVaj+jKhHoaeSpFrK4KAGElTYbIAQkv0\\nwmlPjUV1EYySgjqcA1ApoVcl6HkxQ8XE8v0Zmwea3T1Z/NyFwe4kmDXMZDDTn4riMdZQX2f8TGCZ\\n7QMrHNvGoXvIdiay5YKho4qKEmHvSDpScY9sRKzVnUJ1LcIgu5XXbOj8BQaTohimpTgPMH/JSwVh\\nvgzZnqGSn+Wyaxgi9rojR5wIHGZ6OP5pQnURu+7Q8xq78WSjWfqGZDXbVlW4ZgAAIABJREFUoxo9\\nDWVdzGTEWyUGje8tqTMSsB2EE6lbLe8nJYym/KBncbrh/bMXPGiWo4dKOHThK4XnEMp43TDuNuUP\\nIBTf7MCrQ7zRi5siCrq1MNyGaA7PK9zsmwvAoDhNSsmgs9T7YdG4PXj9Jt35YScu95vGgm5U2AdC\\n5FKGs7mhOD0s5sPfD8/HJ6ETbqlu3NewKNxxKy7DlBSkE3frgZEmC/H8y0x9ZchKcfXXPE/eTUyP\\nW3arhtwmMBH7qMHtFPMvSy7BgZgvK4XyETNAqdZQFc+TMFG8/KHl4lfuEOs7qADnfxyprzz1h0/2\\nMErxQIqVIdaS01kvkyg8fxldDEU6LRxh7YWX6xs10txUgvqlDDZV3svF+9OSNHOUyHUUykpSe5EO\\njDmbw2cg1QlMZvb2mpQU26Mp9WPL/JFEjIWJHiPEZo867PUOtDgQdvemxFpjdnE8d5gMcImwQqSA\\nAwrcJoxZe6qP0jUXEU6u6/EcWQk9MCvEp0Ep7NaTKkuqjVCTnCHVBhUyeVqNb0gVDrDWjSeezRGu\\nt2b3RkO70OzuZ8Jdj20Cvnb4pIi1QFeSJJ8ha8JMKIPdWaa+VLR3M+5a4U8y9QvB1JuXim4hhXlY\\nBPwxuI0UXdMyZqCGZv+9FGqBZmKtRt5/rDKml9fRraE/ltmHFP09w0j33LBiiJVcY7lmEGaqMHN0\\ncUM8pV4mmmcduo/obU84bsQxcpLBZqyLpKhJUeTjbWv3VSfLyYeBeK4zBJkRhEVgftTy3uIli2o7\\nFsHRi+SgyHXJvhIufHgkNQyQ99gvvFq0b4tbYBhQKsniPCjKt4OSD73Ix0UCve/Ih4ViOHW6yfM+\\nZLm87nm8Lgtz+FvgRic+FO29KOhmBx4PRR2H1+ngNq3EPXx8zgeEluFxd8midCbMYfOGZvN2Gj2B\\nJs/k/9U7menZFmsSH5w/Z72o2fqKR//yAYs/EnM+lQRTG/xMxMRKGifV9tLUbXb4h8esHhqW72d4\\nsCN5DRvL8U8Mph9sMuy4g07TSuT5jaFaim+KLhkAmFef/9cd34oinpXCT3XJZlRjOMEQWADSJY4u\\nepUIc/xcvLDDfMBTRLijvGC/qnh5A+iyBQbQSQaB6xcz9NowudDMv8hMnntMG9Fbj+48aVaTjWL7\\n7jGDzFyHjAqZ5EqXUQ/bM3ns9aoXJowtQ75ihqVSFnK/U6Ol60ANHIaBOsow1M/sGO2EUtiN5G1m\\nqzHbIINMq8VrJFPCMApHduogZ66+P8XPFe0dCE0mnAjzIqwEFjA7TZzk8brYnSLVcp3CVP5PTobK\\nSYSPxIlw9VFSoAcHRLljea2SzWitxm48VVLs+2Ox/PVzydKMDtyyeDenIVFICrM/ylRXijDNVKtS\\ncKZyvnExGAaxsvYQC7/dbgWKsRsJ9wBNP5tw+rMN3YM5phM3OtMq1NrgNxM5T5HoSwMgnX2u87gY\\n61ZhdoKhhrues7tL/vX7XxSb2X0XyHg59lVlENoMpk99uvWxOyiOOmcC5hU63h7dluf5usBkodvI\\ncbvTvs1CuV20b0M54zkBf+DT7bMZf3eAbQ4phrcHocN1EYWnxhfoRZ6HeeW68TXd++F5hscBMpQd\\n72/A2HNim2r+dHWXfFFhdtCfZtI08d77T3h8dUz4S4Gz2Za/cfKM02LC8/H2Do+XxywfHXH6iaK5\\nCky/2kqGbRv2Yp8Q9wNKrQQiRWrD5EXCbjX692vmX/Zo32GfrxhiFv2bC7KRhlB3EbPtiVOLSmK0\\nlXOhIX6zMUS55n9x/MXxF8dfHH9x/NIe34pOHC146u5Y1pSh66quKXFr4pXij4q6skjuc8G+dadH\\nWt3ADVdJkV1CJemqhF8td5ezIpoMpVPXRVUYG8G/U1WjUoXeBTGoWQf6E4vdJoFahmzJBKaVNI4w\\nlbxIP7WlU5V4ORGwyPAt2f2WLBZTL62AWIgF5WduHdA+jW5osTalC/fEqSVMClY6KfmaR4bteUli\\nnyiW7yd0J4ZgIoXPmONe8MGNKT4oSoQ4QTpgsxPGj1uLwKa+gv5EWCX9IqO9mEvZrXT3dq3oT+Wa\\ndouMjkqYQ1HcBlXgRriF9vJaJqdwq0x/quTcx4xh2KO8fafEOiGpA7w97wOaN2K94NZqf+6eEYrT\\nHqqlCMNsm9mdaVbfmRb+ux0NznItVFTVanSvyS6P0IndKsyleLUPO8D+NBPPPHfuLbk/Xwkl7lbL\\n9DqhyjpMCIW+tw419kDSbnXirNoPWNOIE5vX4s6HHfsrP7t1UzjoUm/ytAWyGUIuBohFDLb2f3/Y\\nnR/i9oeQzAATj0PPA9743LTjbmCbalYl9cpnc6NzP2Su3ObvaBRp9Gu4Cdkc4ueHj7cqroi/ee+P\\neeNvLNlFR0Lxg/lTFnbDF4szdqkiZsWp3fLbT9/n0ZMFbCy6Vbhe3sPaZ+LMIXGEae8GqtINKrDq\\nPXk+RUWBX8PEUD/fygxr246/R+9xu06GmnbvLuquuxH+NKuuxDX+kmHi0SnWD+WDKtCAFFe3yWJw\\ndKJoz4VSaFqBIOxGCcUwCW6ejUjmJby0DDQR2qG46+3fneL3sS/2ppMk6+bJlnBUi1QdEZhUlz1h\\nZpk8afHHFW4dBQPXEu6rMsRTg20T0agxtzNMJN1GxUyu917akrojKdliMbAflqRarGqTFttKHTKp\\nxMxpn+jOpiUFaM/n3t419AtoHxSe+lpTXWhOPkq4XWL9QKTD3hfmRZXR24OFrTA+VBLoY7w2CE5r\\ndyLJr66gS8IT7xbFhtZlqq1ADHZdcPGVsFKq9R4P7xZSWAXeKGyS4nw4wEFaSxEPU3lt/UmmfikL\\nA+wLt90JVKaDGoOXTUfB0+V36ytZ+OvnmX6mOftJR6q0MIiKPe/Jp5rlOzXL9xTd/aKe7BQ6yLVx\\na1nkMLB9GLAnPdok1IsJL7485fJ4ir9veHN2Lc6DB+KZ20ddXA6NypxWu5vv/VtBCka9yvUejgEH\\nlq9v0esKpfH2UHU4DjnY8nuOLnFj6Hrb+2Qo1imrG7j9cP+GohhVe/dFnxwftXcB+GR5jk8aozIf\\nnDwfaYyHHPXxvm45N37dMdAOb2PsEYH52mTwyoyK179y9CVORb7qFvzo+k2ebY/Y9o63T655b/6C\\n3376Pl89XmCfVTQvpHnIBjZvZWLtqK8s868C7mI7DhuVL86jRkPbkRfHdA/mdKciWKwue/RyS16t\\nyWenY8FWzpIbJ3oPq4Vm3HmSFX8W7QtMEw8CY77B8a0o4ioJPlpfygc/VdJxJQeTF4lqJdFm7f1E\\nfNCTe4MfPE4A5YWrmV0mTSOqSpgqSrbiQLc+eM9YF1EK/JMp08eK8x93NJ9fgdFU235kfKRZTWyk\\nmPYnlXDDy7AyVlp2BJWkeYBI0nWxeQ1TvU+tyZlB2RxqhUoKSoepYx47dR33IdBDQrwOssC057bQ\\n64rL4ym0b0Sa+0vaZYMqMWTukcXu5LH0Rt7YzQuF3TTESR4xbrTg1yoJ2ydVQBI8miyMEtSepRIn\\n8n3WxVscSh5p+ToLO8h0B4PHpnTIdca2in4uQqPuNFMt1bjj6s6kq/ZH8lhBPCiygkk78PKFjqhC\\nloFukg9bqvaLkGnL0NtnzFp2SJOnXRl8ihtjVmCuW/JpU5ziFNVLQ5hm6gs9niNMZLGK557JccvD\\nxRW1DXxSnZGz4nS6Y+a6sYAPWPPraHwjq+KGYvI1cnmV5HdufX5jVhiVb3Xngg8P3fZQjKsDD5Tb\\nA9JhNzCkEO0fi9xhN6bI7wv+PoFevrc6iTCnnKNLll2sb2Du92qR6N+7u3qF3z7e560nGQ+w8uHr\\nwxzN29fphZ9zFaZc9FM0mXvNijtuzYmRRdIXL5lBaPRO/ZJ36pdsU81Pt/dZ+YYfXb7Js6s5amM5\\n/SnU12Im154qtg/kPtszhVsbmmm1V1H3fhw85pgYVNjtQgtFtrecTS12fSqUXGNkxnY8Qa+kM7fL\\nIAU7JYwPxcpak+YDHeuXrIijyla1MCXsVo0pPKt3JOChPyk2oyuH9sIQyGbPVkhNgmPPYrHhZNJi\\ndWLdV2y6ivVyQm4NelMgh3KfClk0Vg8r0AuqS/nAm4s1aC3WksVFUAWBNJIrNrFBOmm7lanyMOhU\\nIREbg91Ktp5AKKKczJrC6RboBkTa3t7XxBrW70XyzKOvHfnUk3cGs9WkOwH9vCLOE+6kI3jDZNYx\\nN4l2V2Ga/Qe3fRCYfmHxM8XJJ56sLf2ROP3ZUhDzwPAIhdo3yaSmMEXq0gkfJ6pLTX8qLBU/kyGj\\nn4FbSQF2K4G+qqVAMHYL/ZF0xskNUmYxjko2Y7eyM5EYvYw6FrOgZPexesKUAbvM+LmieVnELzPF\\n7EmURbCEZ4cyVB6oh9nA0ac9yWnqS3kDDUItsvD8VczkRU1ywmA5/Zl8+MJM4Y8yu7cKyykq7KVF\\nLy27OMGcX/DF1Smb6wk5KbbLhrc/uBpZKYdwhTmAHeSC61GdOPzuCAF8jQwf9p201XHv/c3+nIdH\\nyBLQMJxTk4v39+CbcnMB0SqNC8NwDIuFLoVPfrecvwxbH29P+Hy1EK9wnXhjes3dan3DF31/P2XY\\nWoy/OOjAb3fi+pZT48gfP2SsAD5Z/unzD1j7itZbTict7x8/50U3ZxcdbeVotGcbK176GU4lTt2W\\nE7vls90dnnVzvlidcrGc4Z9P0J3i7CeKapUwnegVYiNioFQXR81zxebNY+oLecx3fyeIDXPOqGlD\\nmlSs36yF+BDh6MuI2UqoRFhMIIobonuxhUIBzk1Frh3hbCaLgI8kZ0o3vqcMf5PjW1PEh/ek9kUY\\nYmHzhrrppndtxQ62K94dCXb3E/pOx+J4Q86Kq6sZl1+eoHIRxFBEIcVWVm4QaCY3ifZXO+JfC3RV\\noOscfDjj9Gdzzn/vAnW9xjhLbipUb4nzQgkcfFByRnvBrLWXFRktrmcASWmxRbGK7syWHMpcuvjM\\n9XcN7Xkm3Oup5x3sHERN/faadl2j5x69iMRVjXt3g82C55+ebui8pd1VzKYdm12F3wmDwK4M7v+h\\n7k1iZdvS/K7fanYb3Wlu++7rMl9lVWVVVmGXZQzCgAdIDBESAxASAxBmgGDCCCYgWZ7RDEBCKmQL\\nMQCE5AEWQkICAR5UlcFV4HI6qzLfq7zv3face/rodrvWYvCtvSPOuedlviolVuaWru6JiB07dsRe\\n+1vf+n//7/9fw+EPG5JlS7KytAcJ9YGJWYLQ+4Y6QDuHfCNMGTMoRfYBV2hMJdh1fiEZuN0iRh2r\\n+LiWIK57xmDfZMK3H5yYmrng032hyG4cfaFIXzl5fR3oSsXsZcS6K6T5pyFO6DuFw0EZ0TYiOuUT\\nRbqU8zCtKB/arSjPKR/oywSXR1roXNPOZcUxjiclNoDVo4DPPO6wJ5815Cpg4uToek36cEuW9jwu\\nVpysZuOQDV6Netqw42VLRhrHawyUgwnyfXj5vofn8J5hGxgYP8mNZ/cZjvhTjYG7i8wZH9Q4Ebz3\\nfsLOqWg4p3t45SCZ/dP8hqN0O55noiSY79Mf92mF96kb7neL3u3yHES/hgx6gE+G7Pzz7SM+noqx\\nsrB+PD9aPmLdZmyalDJrKZOORVpR2pZeeRo/58humNuK//PVZ6xPp0yeW8qVQH1Xv+bxM5m4sxMt\\ndbYo0TH/MUxOHZMv1+ibmGl4L3IYpSyn+2kqAbyD8sQzeb7GnF0T5hP0tsPNMnTVCWQSW+59mXLz\\nyzNcJvdkftHTzo2svDcO/ugXjGLY51D82jXLtzNMY3AFhETR52Fs/FC9OM27iUd9UnN4sKbtDc2q\\nxJ9n3JxkQr0i8rVtQM168rIdW+vdMmp9h1j0zB3GOozx4m6eOPRvXNP+ZuDkX4JtfYT/YsrRDwIH\\nP9yQvL4UvvjgHRmNkfVWxRb7mOlnBp+aWDgMKAX5RYc3ipvPUrF++7hHTyvCtTQrNKsMWo2edVSX\\nBeiASQNdlaAST7NNIChM6ri6mEJQpJOWzTajqy3ZSznO4gs4/rvvIATcQUn9QAZYUnmmr3uqRwn1\\noRSKdZSOdalwvH0s5igHVOJyX5wGcePZ7gp+ohYYO2hbGbx4GYxDV9wgMWsjBDzIziZbgTnSlZjP\\nmgMbdcwj3TCqJ/aZIl1LIRkgv3R4q0hvelyuyc7FDk90dwx260clRZ9qXKRy9rlg9u1CEQ4EKqmP\\ng0BvuYPMQWVIypYk6cms4/JHR+RnGlvD0hQ8+PRcxo0SlUORBRY3HG1uB1Yf1Jh177JaReXu+Ojt\\nbbehjR2uPnK7x4liB4Ps4BtDDzT33Mo3Xc62T8fjPCtvxmNF4ERWwd6+Z/xw6/ziRAF7kNCQqStN\\n42W1sB+097/bToq24+52F4cfzmGfRy6Y9gGNt6S657SaM08rfFBcNBPWbcbZzZR2nbIueoqygRm8\\nWB7ywXSJ1Y7fab/N998+xf7BjM/+bk365pLqW4esPrS0h/DgyZJtk9DOEtxlRn4qydDktCc/bdAn\\nFzuIwxi599sOvyjRrWP2ssFse/SyQt2spNt6Uwn18OJafjLvCT6gpiW67pk/30oydFPjc4srJtht\\nTAa9f+93+brt5yKImwZWL+dSwD12qDbiyU7J/aKjRngiPnjdKuVkeyh+l16hvRpZEJho6pB62Fqq\\nOlbcTUCVURsjMCrZGRPGjj15TdEH8Qw0xqO+s+ZSTXHplMM/1tjzNfvONTgPzqHX/dh1aQD6ntE+\\nbc+NJ3+RgjV0RyWbD2Umd2nC+kMJeH1uqZ54aQC6sajjjtBpzKTjcL6lahOyvCMxjuWPD5g918y/\\n6pk8l+xEX60JRYbqHeZ6S6nVKJhlth2zdUs5STBVz/qjAp/EBhkNppOgZ9oQ2TSiK+EyRXHe41NF\\n9lWLy82otzzIEUxOoZto0qWiK0WOs5tIttxNZLlaLwzZ0tMcWPKrHpcbytc1PjfYVSukZQ2uSDBV\\nhytT+nKQJh6MpiFZdgKLpHL9TedxiaYvtKgppmr07Wxn0phUP3KYBw1l2VAC221G8qLAJ5r0SlPp\\nHA6gnHeENIzMqOTacLUp6OZmdIQHdm2lcMvZxqgwZteN++m31z42fNc/UsdJwOpdoNs3b9iXk90P\\nwmMzj4+SsYhBsnC05bz7eMPcK/u617BktRszfXlOj/j7YBxxt1v0ru7LeNx75AaG55I7k4BH4WJr\\n/tplrPqci2bCq5sFZdrRB2lUeruac/56QXZqUQceNWkp047rqhAGEYpvTS6YmoaTzZy3jybcfJpx\\n2EwICqpHivCk5qP5FV9eH+G+v+Dp/9Mz+8M30HZUv/pEGGG/+RHpmWQk5ux6tD/UqxqMxlyuBdde\\nrnaidtFuMRzMRoG8eJFQVUP74Uw6vHNLer5h9ofvRI7a3v7dftr2cxHEfQLp4y19Z3GNZLDeKoLy\\nY2s3Jsg/JapzBAnwePDajfupxKN0QFuPLr0ka3HpGwbNDYUEcOvR2o8BXF6LEAyyT9+bXUNLiG+O\\nF0Q5P7bcArvArTVkKSGxkCbSojtUnKORcXKx4eB0KdZrvePBgIENriHD/saIPkqayPG0iMoHrXna\\nSYaIc9IJBoRpQUgtbGpU02JfR0oTgPeoTYVpWzg+ZLGKJr2xkNs8mWALQzfRJFuBKLJrh8sjRBRk\\n8tJ9kOYiozDbHlP1uMKSXTbSyKAVaNBVT3eQCQ7e+lEATLfSJJEgNQTdyXcN2S4LbA9FsqCbxpul\\nD6CgzzR9EZkjSlYPLh8YSvJcn0daZRW7eQuBzvSPC/IfFSRbj3lkqB8JMya7Ei2czhWoxUbULWH0\\nEgUJMtb4XYF80EPZY6aAiOvft32dBO1wvLtqg0M2fxc7N/saJEHRODsWGfe7NLXyTJMmZqz61nv2\\nj3ffNli4oWIxVDu41WykJVlU6l49mP2VxN2u04HWuP+eYf/9Fv99iduFrXi+Oeb1cs7V+YyrXvHa\\nBtTGxCQOmoeS/PmXE96lJaF0rA8yvFdcVCWrWu4BP3Esf8kSbEl2E8RnNxT84elnmK0iXcVrEpVH\\niy/OCFkqNMKhizJ6BwASzAcBuqreYdkmWiQaLc1BEX4FcA/mVI+LCMEE7DbqKG22qCKX+/wXrrAZ\\nN6095AFyOJxvKRL5cpebkmqbimRo3Fc6m5AsMqhdZ52RrENpj9YhSpoGGUp2L1jrcCt472+K3XI1\\nSRx1ImyT+lGGWaT0pXRU6U4wcdN6dOPQVY9yUpgIxogQVdOKu7lzkqmPA8HvLrhzkMTltlKCtaXi\\nlTnqNSBYPNFsedAUD5kFlYzdZKpuUVVDsAZ3NKc7ynGp6MmY2qOjFouu+lFFUNctPk+lW7UPKC+a\\nEtl2CEnyfQfuu+7kHHQl/pqq8SRVh88syjtMHQdlajGNoy8sfSlYtbdW2t7TKG/b+KiLIqwcWwk9\\nUDnYPhJZAJAO3UGeIFjEmi8VmmNIRAxNdYJ7aydWerYW2dxupkiWSYSPpDPOJzs6Y7oM1Md3cGcl\\nY0s5cE6y6hAk85d/ULuE1NyGEL6uLb3fE5q6Nebv4V4Pn2+VuzfYDoF426ekUfmvsB2DFydIkB4C\\nvlHuFvYuBhV7blBB3ZpkbrFpItyyn5Xf4rKHONHc6Ra9uw0Bu/f6VgH0vsltoCoO59YFQ2562t6S\\nFB39RY5eKSYvtBiqJ1A/ULSHDlcGVK0x15baFdh5y3EhPPyz6ylmZcRkZgI3n4GbemYfLnlYVqya\\nlKsfH5HdGNJPjklPV7iFeAj0+e73Sq8azKre+cyGQGha8ciNiVfoOqhrOD7ELQq6AxGHy9+u0Vdr\\npu+uCWUucSImgspaug8OcbklPP8Fw8RBvrvSAqG4TnPx5eFoJpDMW2ziRlRi2ELEJuUAO1hE63Br\\nn33z+WHb32dQmZNEWaAUH6QFtnoxY/G54vDzhvSiwhUJ9VHB5okelfbKt0EU0LaJLJVismGqHt0m\\n+FR+ZldYtPOiMOjDqIhI71Hej7rkqutlSdaAMkYy6KphsH4btcaVQjWtZAMRuhm1x61GhYBdtejM\\n0s0t7dyOcEkwkiGbRoS9dOPQrcMD6VWkUvUenxp8qulmJkJc0E8M4TjBpSLaY9pd7ULkEtQYKIOR\\nPgCfRkpgwphFBwN40YI3rWTF6Sre3BHaiaw3bBXF+E868tdr1OtTQtuiphOUUkLjXMyovnXI8pOE\\nzTMJtLG/hOJM5Gtnzzcsvz2RlUInmH6y9ZF1JDg2AzQXA3mIQS6zvawGdQAnz/X+tuTsLXbGHpY9\\nvL7/WC69vvWe0rbj30nULLk7SRgV2PQpN23BTS1fcJK2HGZbchNNNO5pn98b/bcfvdfCv2Ov7GPw\\nwPuc9pgx77fz73Ra9rjp7HHow+65QUJg30Vo2K67kmWfcV5PaZylaSx9awgmYLaa5jhQvgUSkUSY\\nLEVKNr2Wgvwmle/5D18+5fhwzcODNfWk5vqLI3QrtGQ373gyW3FZlVxfT6QWsvXiqVm32OWGUObY\\nLGXz2RyA9Sclui+ZvNqKscNqA8ETYoJF34M2qCSBusE2LfY00D8+YPPpjGZ+gLcwf9FK74JzYq7e\\ndiQvzjHH87Ep8JtsPzdBfNiUCtjUEY48Svv38Oq7Utu79EwRYjoeRQZvQSUhBuW72/CcUmHv2CpO\\nBp75t65ZdoegUuYvpBln+1izfeZxRx3pq5RsKXZnq48yMQmYKBZfCszQzzJ0F4tuiXDEfa6jvZse\\naXLKM4pu6RgUVe+j43wH8wJX7qzYBiqSGgZO7P4azJd9asRlJ1LpCBJc+1zRTVV0HFHYJNDOMnlO\\n7zX+xGA7QCM+crJtJVhxdu3RXaArNH2pxGkpBmmfhfH9Kq5YTC00wvwsMDl1FKfVKGNbP8qojiSY\\n2zpEXRlQXtgrEBuCXBgdwnlwiK5boWolVm6CPKH48prihWL1q0f4RLH6UI8erbYGb6Xekl5L4VW3\\njKsd1Ss6f79uhVYRCvBqB4EEhdX+niC5e/2+x3ef772hjxj6yWZOYhyF7chNR2m793RU+qghPktq\\nJjHoWy2BdMj4v057Qw/FAtjLhv17Gf8+PDOccx8sNp7LwAzxQbN0Kf0evr5/rFT3tyaF3bF3uPpw\\nLvtdnKWp8UHxpppzVRdcrUqOFxtWVU5zmomN4mU0SnchyhIH+omiXYhQm/LQ36SQes7eLlCJJ2wt\\nkxPN9LVH93BBxvPkmL41mNOM+XNPcS70wGC0TMNpQn9YsH0Y2UJWrPnovWTieSaBo3eMUtJaE8qc\\n/tEcn2rMpqN6KhzwdONjv4hoMGmAy2uwFn80ozssbls1/pTt5yaIS9CEoEJs0hmC7y6w7mfPoN7L\\nxIfjjNKid26W++CTr4NUALzX1K0RsScvll/bx5rVd3rmT1aiHvc2lVMIgtte/4qiLzy6taQ3EsjH\\n5ZhCHD7eXeNevyX4IFnJ3jLBzOeoowPc4Yz2OBezVisUQJQSKAfJbofiI4pRUsB08XeL2a5QMUPU\\n3JYA7mIji+rBtHrMOl0WzRZsGFvnTSPdkrqLAd4Ja6UrRUSsXSgxSN6KBMH0TU/xYkX4/LmsShIL\\nxkihR+9dDx8IzpE+OMIuJ/hfWVAda+pjjd0IZJIuA8WfyBebfblFf/4CNZsJ7l/G1uVU8EMF+DxB\\nK5HnLd9WYl5dlTQLTTuLIltGGC7BJKOvKCGQLjWgubiZRAG13bAakiKlAmM3q1c4v/OhvLdFXqn3\\nAvb9TUGOVPWUtuU4v5sV7/B2uMNUUQEbxbX2C4J3M//914b/x+fi8YfMPdHuPTxe9jO3GpsGfnzl\\nEs7rCZ2XgqvzmnlWj+87yjYcJNV7DUQDPp5EM+jhfAaRsN+/+ZiTzZzWyXFnZcO78zm+11gvY9En\\n0jWdrMToxFaQnwvMpjtFO1cEq2gXmpB4dOHwEXKztXxedqVZbRIwIco+CGW1fjLBNDnKB8yyJf3y\\njEc/kgkzrDfjuA779oiPjhkMXlTTionLTY2O5uXTP7qQGlgI+Hn82VVcAAAgAElEQVTJ9a8t2DzV\\nmHZCeXrE/I9uQCmS63pn8vINtp+LIK4cVOelMJ46DQctwWmUEfssjB9vnl0gjzfTnaLkrotdcbdg\\neTvrVuN+t7cwThqJcRwdbnn5IMedWvJLT7IKmI0sgcejD8XXmPHaSmHrgNl0wgpZtiRnG7i4xl/f\\n0A+V6jC0nO4xFJoGozUhennqVpZ2QOSgW/mMVYN5d0P/6jUojYoBUqUp6oPHdM8O2D5KJdAnmm4i\\nmfbQmZmfSWAuzzzTlzXJ60v82QWh7XbnZQzmwTHNd55w8+2M6qGSm6CVFYIUoDXNkWS06Q1k5zV6\\nU40yu2hF6HrRieh2WcqQsYSrG9TNksWbMxaHC0KR0h8WqD5gLzeopXBz/XIlTkvRQ1T01ZW0M3dS\\nHJYhImwcnxlM3UenFMmelRcYy3SedOkIykTlTOnQVF7RnRVMvzIc/rCjLzVrbyKdMLJThl6DEIOh\\nej94DttdeGX/+Vv73IFLhiC/XwT8SZuP2PV9x7/1ufdMNPvMl/1z2G/4GbLm/aDeejsG7oNM6H43\\nTQHasemE7lrY6PgUDDa2we+f432sllf1Ia+3C15cHbK5KEEHiQFa7PXKl5byNDD/qsHlwkjqc0V2\\nuYPrto8U7aHHZwFz3EBrMKnDNYZkq+nmUG+lj6HPwV4muInoAikfSK5qsVh0AdX10qHp/WirqGZT\\ngvMoo2PHZoQfr5Zi3hLx2zApou2aHCcs11L4f/YIN83oyujj2ShpRAsLps/X+GgH+U23b2LP9jcR\\nQ+R3IYTvxef+I+DfBM7ibv9BCOF/jq/9+8C/gTRG/rshhP/lp35GANVodCtt674VZ/Zhtu0WDjtv\\nQXucu/3lbmPkEkn3IZjbr+2221n9MAG8/9xNlWPWGluJMYSexlm+NxLsdcR8E4FLfDqiO1I4/PyV\\nfH5VEQbjhniBgtfj43FGd47+y5fwJZQfPMEfzHDzjOpJTn7WYtcd5u0l7vQMR8TMYUdnBFTXoxtH\\nuvK7JqroJl8dGrqpop/ELNPIefp35yM3VaXpeEx/fYP93Qse/J5CHx+x/Y1ntDPN5rGhm0J+EZi+\\n8hz88Rr95dtddhKdidBKzlGKHihrxGu0aQhdH7F9PZ43SqE6KQL5PJUaAsiNpBUYcS4K3kQYCbAa\\nnManBmXE39NbhbJ6rE/4VMS0+tLiCkN1bERSACmmFmcB3cPGG+wm0E2lEJssod7sHKAwQSSPA3Qx\\nS7yvSDcE4rvZ8Dj+vqagt//6fnDe1zG59Z74eAj4d497F9O+z8xYjrPr0Bxoi4J3e95Uc86r6ZgV\\nHxVbStuy6nKqLsEFRd0m1G1CW1tMIu8/XmwobcvcNlx3xYixD41Hmz5l26dY7UijvsxZM+Xzt4/o\\nN9FJySlYa7IrTXEaRKp4qlh+kqKcWP2l1/Ld6wfidZlfQnajY32mxNRQP4Sw8OLt2u8av5ItqKDI\\nv9BM3/RMfnguScZg/JInhCIV2GRIvi6uJTkZVtHOycoyROJF7B9RvaN/IIUz5QM6SwlpIqbfhSG/\\n8cx+bMZmRgLoqkNX3c88E/+vgf8C+G/uPP+fhRD+4/0nlFK/BvzLwK8DHwD/q1Lql0MIP1WSS/eQ\\n3kh3YBbxys0Hiu2HTjjfSOC9G6AHnFw+fxeM93Slxv1uv2//vOV17xXe6/G1dvjMqHetQogCVvKe\\nIu3YTjx9qTGN4/j3Vxz8KAMf0E2P+uoN6kj0g3U9gb7H3yzlthqws+AJjjGQhb2Chjs5RS9X2DRh\\n9iM56bDZ4oYLrNWoyRKGAeYc7s0p6s0p5fEhYTHDzwv6aYJuJCgUF57yBx35D0/w5xcScOM/pXfi\\nO8powl4G4t6dk/1v5xQHCw5KoUIp5wnrrVTms4gNOrfj0luBO4LzKBst54JAKcpHrELHtuwQ5OaJ\\nGTVWj2weNWCEsWiLhsFn1OcC1/hEy6mrWGNonZiHTGOBNVPCca8ck7eefiITXzPTsSPVM/tSGjzS\\n64bqSS6do0vLj378FLM0pJWifdijCkduu6+FUoYs/Cd1W+5v+3h57+/HQ4dOTHh/Umid3suYw/sZ\\nfojdpUN2PcIpZsyy94PsflEzNY7M9txUOU1rWW2lmNo2luAUodMkE/Gr9ZuEECUlzvyMedqQm551\\nl41F20dJTaIcme4pTMeLzSHXVcHF5RSWCXatSTz0U499WOOcRj3ruPqgwCyNUEdjHcPlIivhCqgf\\nRGmOVmOXmu6RrARU4kleZix+JAJus5c1yeUWnyeUb0xctUkgFqpgM45f1XaCdfc9odkVnYUCLCvK\\n0MbnQ5BAnybQgMoy7JtLoQ03rTC5jEGfO2yWUjjPIgRCXcNAZljMZez/LJt9Qgh/Ryn16Tc83r8A\\n/PchhAZ4rpT6AvjHgd/9yR8CZivu5coLzuViMpheaVqv6D3omb83W75LqRwy8rv7fd02sFOOplu+\\nvTgn047KJTxfHnG1LkdTXuUgWzoWnxu61wuoAh+f9KTXNf0sYfO9QzZPhPZ08HlLeZqPxg7xxOK5\\nKHGf0X7MxmGXoe89EbNPCVLBedD6lhxQMGL2qIal9z7V6/wCva0wmxl6PkEvCrKLGl136MsVoRLH\\nIjUEca3A6d3naoMyRiAW51Bao9JE9CIWE0JiUI3byaLqQVfGjYM2fmFUmgiGnaUymEMQb9H4Wcpa\\nKVAmlmAEyxzeO/4fgvDlU4vLLWiNt1qCtpYirA6xjnI3820kkAcDm6cp3UT45SCsl3YB3VS0YvrC\\nMv9KehUmb8SMo5r36GdbAjCLzVarNmOaNPcGTbingBk0M9swsc0tBsdlW+46KZXHmtuyrzDALP5W\\nsAduBe272138fP+8PEo+607AHs4JPF0w3HQ5b9YLNk3KdpvRby0q9WjrWcy3bOuUpsvoz/OdJaIf\\nxrvibDOhD1pWMX3Kpk95tT7gpsqlB08FrPFc3YhlYkg8ymn6MmBqjX9dgIFGZSSVwtSKfFj/a7ES\\nnH8lhcjmKKE+sGyfiJrp5A9SfCpGJPUTx81UkV5rmoOc7DrD1lLcnLxYi+lD1cgYm5XR8EGLlKw1\\n0PeoLB1/oyEpCb0TdcK6kUQjSXdUQ+fknt30kqx1PcoYVFngHi6oHxfcfJqQrAP5tSM/b9Fvr6Wv\\n493P0Cj5J2z/jlLqXwP+HvDvhRCugGfA7+3t8yo+996mlPqrwF8FSCeH5JeCR+WXflSsSzaieZFd\\nKdpFSv1IYxYdNrm/pWJ3v79f1Nx/vJ+dD4+D17w9X/D2fBFVDgNF1pFYRxvx+Ox0i5umVIeG5S95\\n8k9WXH8x5+j7ltnLhtnzDcmmQPlA9k7gE1WJVnCo612VVsesM2bRY/AexYLe+63ufJewg1GciwVD\\neTw8H5wDryVQNi30OXYpBSdVNcJjhd2Mb7QEUm3GAqRSijDoGg+UoNjMFIzBp1ZkDly2q/5ZA74R\\nfeXeEawRqzxgaCce7K4kS99NVJKBRKcjBG9XUcpApcloLk0IIwVLjdilUCSVE7nWvjBoq/GpFHP7\\nUlgFy08sLoftE4+tiJLHUhBLtoFlqoHA5qkdi8PdLKBsEAZUUNLtmThS625BHPuZ9/D4bkb+5fqI\\nqktuQTBPJsvd+4a3B24d00a6Ieo2bj5sX6ekuH+Mr+Nm73ecDkwZgqZyCds+RatA2xv6yqK2BnuW\\n4IrAdacFr+41eIXdaLFJ3CMl1G3CSWexxnNYVmzalKY3dJ2lXmXo1GEj/BI6jWo0LhPIwxWekMRz\\n3mq64x6/NDSPAmalKd8qqiPN9qGmfgDtoSNMepQJhNrQ/bpkyH1tMVcW3SjxlS1lVaaCIbsMBDPF\\nbj269WID+eJyF0xsHJdlMcIpoW3jfWrg0RGEgK5bqGpC3RDCsKoUqCWEgLIWVZYyjrMUc7EiB7KL\\nRuCTtketK1kBtN1YIP0m2581iP+XwF9DhtpfA/4T4F//0xwghPDbwG8DlA8/Cn0BBDGl1b3MnsGo\\nUZI2RFjDO4VKf9qx3w/Y9+83/KHQRpb0AckkgoI2eleqgVamwWxbZi81SZXg/t85+ZWj/GpJSExs\\ndgnjvnjJNlXUWdjflFJC9MfdG7zDkF0PWNs+N3wIeDDyyMP+Fx0Cc/B7f8cbSynB67SOAVtLpm8t\\nZKksJ/fafpW1u8HoHFgr9CcNOio5jgNOKTl+9BEMeSrskb3j+VwkPbXfNYgopXaTm919t+GYt/73\\nXhQkTfxu0U9zqJH5xOAKI2YcgUjHFBs63QmDxm7AVBrdRSGvynP1XUUaTSDy60DxrqM9sFz9sqE/\\nbqO0scatEsy8lW5eZ8YgBxIch4B4Hyd8/+99vnXdJ+S2u7Vfv4dbf5Msf/+5/YnjPox8v0i5bAtq\\nZ1m32QjjPCg3dM6wbDPWdcb6bCLc+KAIaaA/aCRR2iTolWFyoUiWgAaXmhGm6qaBVue4o45iUXO+\\nnrBdZ4RN9COddxwdrOmd5vp6gl4Z0eaJNaX0xozMqW7uSc4t3UGEVzea6mmgOFFk14HynSQGXZlQ\\nHyn6KfgzI/aAvShv5meB/MZBgM1TQ3oTyJaO7LLDbFuC0bhJQv9oLsyms+VuBZjsxrGK3dH9ozl9\\nKU1JpnHY0xsJ0s5H6EU6XXVZyAozsfTHU5rDjO1jK0Y3E9Ehmr3qKP/+EkwmcrR/itb7P1MQDyGc\\nDn8rpf4r4H+KD18DH+3t+mF87idu2smSNl0G0o2nnWgR/1eCjRsnS1vdKFxl8Fl/L71Qzmf4f8DP\\n9897+F+N+4bAqMWtzQ6uGQqfIShpw9bSQms2rbRoF4p+oki2CjfJMKuG5M0FycDXrgealRhIkEgw\\nVMQsOTI0xmA9wCD7AX2AGuIXeQ8kGo4xzPoA6k5A3/thwoAz+z1sem9yYNB7GH4cP3DQI/autUw+\\n8XWXGUFdQrqbJFI7mmoEJcJgqh8irHC81bgiUbvzhnEFEJSIiBEYj6X1Lqj5IsFnRloDjQIXMIAr\\nJXvuJ4b6QKObgMt0pGgyGmm4QrH5SChlKNE6142ieuoJjxsufl1WYl3jZaz0Gj9knQFcZemMZ1bU\\nY8fmPn2u90YWHXe6N7XyLNKKWVK/x1C5r2HoPnPj25OCjoVK9d57hH7obxU2d3Kxfnz/qstY1jlG\\nC+VPq8CL6wOaJqFvDX5rRZLXAR4xt77K6Gee8rUh2bDTuV+InG8/leMHG9C1Ri8t/dlMiuhKRNK6\\nuSe8yzjbWFSrMRuNqaMEdRDqZzdj1LovTgQ2S5dW5IpTkVPoJtAcQbLWpDciJWtaYC1ibcGImXdf\\nwM2vwFUqCVl+hlBja4VykozoqkHXnYzzdTVytZWPhfUhWek6MAb7bikBtOsJdSPF+jRB5Tkq30ld\\nhIMZwWrqJxPWz4Ta6o1i9soJDbmOjXaPj+Re2evS/ibbnymIK6WehhDexof/IvD9+PffBv5bpdR/\\nihQ2vwP8Xz/teLoPZDcBlyjqhaFdxKJFKp18g60XPs7SX5Nd34VTvm6//c7P20lfuBP0d0vcoACt\\n8GWKqRz5jUKfB8rnS9TJmcy+zkGaoLIMDhfy1qHl3otQFsFLQFSCA6s0Ymg+ENqW4KLSmTHy/IhX\\nyxJN5dlYDQfBxNU+M2UoLDpHUCLAs/uhJbDem9UbsytUxkyDECDxsoowQTrRhmAff6hgtdBBGycy\\nAzrqySQW5RzBxZVCYlAIg0SHIPCN2plKAPIbmN2x1d4FDBE3J0vxiaGdJTHL9igjmZDLhCFUHQsv\\nPNmayA2XayhGGJKNJzdRYVGxY7kARdnS95rmvMBUWsySU0/ozCjvYIqeTx9c8r2DNzzLrvmyPubF\\n5oiqT8hsPwZsuxeQhyxXjJXvQC57k8D43F42Lf92hcthH/YoenchHR8UdZ/QesNVXaBVYNukPJhu\\n4lAIGO2ZJC2Z6Vk2Oas6Y3VdEhqN3hqRLojmJD4Ok2QtE97klRndlFxG1KAJZNegnHzX+liCuhSZ\\nFcWS0Zjb1EYogVs5b1PJb9vORXfeFeLsVL4TDRcfV+V9IfLB/STgpw57aZm+VLRzgUiqx4F+5jFr\\nLUwUJ2qdPgm40mNXhvQ61t8cNAea5ScT7BbmL3ry8xr7biljeEhuhntoSHD6mOBsq5EVpowmPHss\\nK/LE4PNEWvN7L/dIKtITpg30hSbZBNJrUeS8+TTFdNI5vPiDU7k/f5Ydm0qp/w74K8ADpdQr4D8E\\n/opS6s8hieGXwL8FEEL4h0qp/wH4AaIF9G9/E2ZKQFqshy4/Uwvdy0Q5kW4ipgPDzn1rsWm/y8CD\\nGrPpn1TA3H2nu4+HoC+57sBWcU7YKsNSXPUe8/aS4g0UNgbWrheIIUofqSwbtU8IQYoZkeERlL5V\\neBzoSSFCHsF5CF4w8iBZ4Nh+O2gHxGKJFE+0BMqB1QGjb18QoD9m0343GK0meC0Qz1DMVDsWyMhP\\n3RPuCfA+3KOIrtxB6H5acr5+kmCtHnFqYlAW3ReHboQzrtIE8gzVtCPLRLBuyf6lXV+PQhzGGAjt\\nyEqQwKygQ8w3Ej1avoGwnZRnqHIKR95IFt7kUD+S3gPdKuqHiv5Jw+Hxis8OL/BBcXE44fXFArdJ\\nodGQyQQ1WdQ8XSz5C0cv+Cx/x8xUdJHhcVLPqfud5Oz7GbUWNUDFnWAs2xDoBwee/ex+3zRhOOZ+\\nBn9fhm61IzU9Vnv6aJO2ajImacuqzbDa0zrDusqozkvMWmOccP4Hi0Qx3JAO3H7uSS81+UVgcuop\\n31asPy7ZFhqfiolHdrXToVFejtMXkp3XH3jpsWgVyUom8OxSmsV0J1h1slZ0c6BX1A8D1WPF5LWI\\nrnUTRTeF7sATUo+5sZRv5bOKdzJm5l9IA1ufQ7sQKmg/Fecwv9Z0M8/2Y8f2E1BFT/CK/KuM/CJQ\\nvlqjr9dSx7FG/u+dJBBBQd3sxv8AU/bxvp+UhNQKfAiYdbN3jwf09YZJCGw/LLF1EAzeeRyabBVQ\\nLpBd9YSrGzi7gHaPCfNTtm/CTvlX7nn6b/yE/f868Ne/8RkAYnkWxg66ZqHkgs3EHGAYv5NXCp8m\\ntAeWfpKiH9UMXXT7nZ1wO5jf126/ey2ewJ3nvFd0Nxn2xjJ9IbQzvW0J20oCWpYJn9poiShNG3nR\\nu4CE88J97odA63bBMAbesF/AiK+Nmfg+vCFfSgLvfUWPfTGtYYDtZ+L7IjJ3Z7Hh2FFxEauHyyKt\\nx5Uh9MJYGI7x3lw5MG9ckJZ/rcCId6BVQgkLRkvB0QXsNZiquX1++6fTS1POrc8xGuUC3czSTeR9\\nQ3lEd/Ez46pJ5AOkU5UgGirNoSccdZTzmm/NV3x1eiwa82tDcIqrqym/fzPBWEe3TlFbM4qmZbOG\\nw9mWP//gNd8qzvgkPeeL+gl/6+S3ON9O2NQpD2ebsVux95Z2ZILGgL3XQAPvX8NdYHd3Hn99YmL3\\nMvhdw5EfX2u94TKq+NVNQt8Z3l2msiqLBtpF0aKnHT7XhK24SZlG2tr7XGBDb6F4ZzC1TIg33zac\\n/qVJFJMSu73sCjYfKFwZx0KPuDhtFb2WGhdekSyj01Ql+9noE51soHocJ5EW0muihZ6YivQl+DSQ\\nnRuSlUEFgVK6mUc5RXGmyC7El7ebyP59IeemHTgdpLh5bunLQLKxKC9F7dnLFlcmBCv6KObkSlhW\\nWmBMWY1G0kDXCTSaZzsY8WaJ2mwF9rMWv5gSEoObZIRU4+0E5UWew1436ChUl1gTqbaeUGR0v/kp\\nLtGE38m/9pq/Nwa+8Z7/P25BK6qHmmQdaKeMMTVZIUWoRnwVu4lkUG7uSGYNgzjH1+Hed7ni3A3m\\n9/DGR8aK13dj+24nFTNZayTI+RAz157QKsmkezMGcnlLLCTCqEE+Zsv7eDgIRq5j5uxiNm3M1wdg\\nuLWsGwuRsdNsd3y1m2CGxwNsAzKQ8pSQiY0ZgNruso+xYy3KagrWrNFGo53HRNVD3Xn0uhZmTt1I\\ncTfPCWWOKTNxONk2It05NgLt8K3hsnRTuysC+Almm6A6R7Lp6SaiHd7MpRBuKtHOcJngqmImLfZ4\\nPpGAYmqF+XFGl6R8OZvhc0/+oMI+cXSdIUkc201Gd5XH76kI1pPMWn758Rn/1NGf8L3iJStX8H/c\\nfJf/+/Rjzl8vULkjLTo6r2l6i7pD+Ruz5lst8ZGv/bU8810Wfhcq2Z8UrHZcNhNO1zN8gNQ6nk6W\\npMZxUU+4rEpu1jnGBPpWpB9CGrA3UnMI65z1dMcUyE8s+SXjb9keQDABl0M3D/gsoDqF3QjdT3WQ\\nXanRki9dgr6M59tFbRMf4J0UnYNBjESifIPuAvWx2BMqH1dMiUy69THYbby+tXQE51+KaYhLIsTm\\nFOmNSEdk14HiyscmHwMrwcXTZfy9OkWyjiblwyqNCOeeV6htI4G77eIqr7t1v4SYdQ8khdC1Y90K\\npeT+V2KIoq9XkFh0YqVZaNsIVKnVTpZWKdSm2v0NmHWK7Zycxzfcfi6C+CD23xfiONNNBUcTLE2W\\nwN5KtduVHkzAe40xMXMN3BugR77q/R86/hm8ugXNAPjYLab6uDR3QQoYvZibDp2IKpi9GUDYJPRu\\nF5i0FN4Y4Y1dAFXGxL6WOHHsFyhh1/WYJrsgHSUrh3bf0AtzZb+YOf49TAT7UEgIo0jWwAUfoJaQ\\nWkIiLetBKXSrMd6jKhsvhBx7wKrlGgWSZSP+o0MTjvfyG9QNZCnMJtKpViS4IhFBoNxijR7b6off\\nhBBo5wmuEPaI6uONVgseP1jkmTZEqzVZag9MFJAMrJtD00W3oEa0YrrHHWbecDTd0vaG9Tanay31\\nRYFqFU00jsYEyDzmoOGj4xv+4oOv+E5xyhN7zd9Z/Sp/7+Jjvnp3hLtJxac03f2+ffTTFH+L97uA\\n31cM3Fm8vad1sgeV3KUvjh2h3mCVJ7U9VZuwbVJe+QN6p6nbBGM8rjd0lSZ9k5JeqbFIqbysfLUz\\nuESMugfYsp1F/Rgl96WbOFTpUMbjK0t7ENDXFj9x6EmP31pUoylfG3yMdSqNNQ+t0A1xbIi+TzeN\\n9/RMGvxUr7BVIFt5mpnG5bD+SAJ6cSakB9OJAFpXarRTtLlAr8U6kF056iPD1S8bukmgm3uKU830\\npYzXrlQ0B7D+JDD9SjN95Zg93+AmCetnGZe/uSBde7KrjuxHJxKQ+2j0opXch8OWZQIHpomM9aYl\\n1LVAp4ncmyr6Cai2Q602hKMFIQR8kWAuVuO9SCrwW6gjLbdxhPxPF5Z/LoJ40ErsuxwsLjztVLJu\\nFUTg3xXQHnjcVOhFysSij9P3c8Lh/aB+Z1PhTjvIAMPEh7416FqWlboVKhppgkoTyXC7nmC7yFN2\\ntyGOISgbkYMlCsfT93s49Z3sKzbxBITfjQ9S8YaRoRKcJ2y3hLaTppuhKDpALyDyl72Drt1NOPGc\\nglIxE+C9z6bt0CvQyzhZzUqZUMyO8zq2Fe9l5P3E0M1LkaVNiR6BCck8w14X6HUllf1tjblR6DyN\\nhhlRNH9SoOoGmhbVaGkG0iJloJxAKgB6U0cu+44xMGLdicKl0mgkkgeKzcc9Lrd4K25QwcDksOLh\\nbD2OF9cbXGVQnWR0g3OUmXdMJjX/9LMf873JK/5c/oLfqz7jt1//s3zx7gHNeSGBPiCBqTE0TcFp\\nb8jzjjJrOYoa1vcF7f3n7XsXY9jvfthlv71+26f0sYj4dLKkLwwnmxlnlzP8OkE1GrTg/knk6rsM\\nVC3n7U1sR9dy3exGRePriGdvpFgpssIW3VuaA+gWcq/qDtR1gvIJ/SSQn0s7e3YTOee5ojmQe9kd\\nyTXILhWuEOy8nSsRYvOSLCVbqBea8sJhKs/8hVzXzRPR/tlONKbV5FeB/LKnPBVu9+V3C8CIA9W7\\nwGIduP5M4xM4/wsBu5Jiqk89+TvByV2m6GcpLjMU7zpcbkhvOjEoyVLCciXKmIPRS9vt+jXSZC9J\\nE/hk2EfqVQi8uq1lX2tR6y3900NU4+ifHEhRfl3TPZqiOk9INPZKnIP0tv3ZFjb/UWzKy6AwXRDq\\nXhEdWRJGHEw5hVkb7MZKlXriYdqjrN9RBX9y3N77wDDywUcdLRWfH4J/p8kuNAdfeOY/XGGu1/Ih\\ni/mIFQ9ttqHfZegqtpmjtXBDh490EdYYMO2BrUIsaN6Hc2uFMrsipLJErWJ9azeZVGSJ5++ZHEZd\\nFR0XHPv76Fhpt3bE5cK8lKw5t5hth3ExV1yvI6NE0c0zkdbtA/l1h1nV0j2qpQCsemG1qLaTwT1M\\nJl0n3oP7uuixZqCQczO1xx1bgtZjITM5msjgdiIslnUenxnahaU6Mqw+SmiOAv3cE3RPcmPIz0Qy\\ntJ0pVp9CXaVc6pLeabbXhRhvI/CCLzzJQU2Rd/zWk1d8p3zHX5p8wYWb8jfO/hn+4N0zLl8fxPEa\\nnYU0In+sAqrTuMbQGY9LxHFnH9MesvK7Gid92FNCvIepItdUj5TFk02JC6IWGIJintdY5UeDiIO8\\nQh0HzvUM1xq4iYXWAOl1rA9ET1WzDkxeS5MTQDuTibg5HIKe6JTYGrpS7pPm2BMOYzu79XCWYTcS\\nhLdPghQmp7uJtpsFmkcyztMLQ18G0tVOunjyWgl08kjogbqH6pElXSJdtDMpTGfnimQjEE91DOsP\\nUtqFsE76aZxUWijfKPpSgnk3U5hmp9KpnGT/LoPNB5qL38jQnfwuk7ee8mWNfndF6N2Ygass262g\\nY9assgyig5WKom4qTXfQiw/4zRY9KSOUKM1w9vWlJEV75jDJW0nohvuEIo9x4RcsiIuMq2RQA1ZG\\nkAtqGsmak3UgXQfqA0VzpKisws8lGwowuv7s5EIF13axmyz0akyzlQnjslCyFfCJx047lJGmjsGk\\nUcflPL1YoIUsoX0yw2VSkLArEasx19vxIo7ca7/rLBToQqpuysQF86C1MODi+52SscEnxICvhsA9\\nBN09mERZOy73VBxwoW0l8OdSgHVlhs+T2Bbv0Vq0zWEo1JzbQ0MAACAASURBVMSBuqnQaYLOUkxs\\n1gmZhZDLzzdMPFZa45VjbOMfPAXHwTzAQ8bsygvWyg0Qm3pGoZ+2I1Q1qndi5aakOFUfy/e+/mzK\\n9qlHPW5EvfcmJbnRJEs1MlFmz6F6KAa39YMYJHOFy+IaZ2tZuYJQG1St0b30AJiV1D/CXPNsccM/\\nufgTniWX/I9Xv8U/uPqAr94eE9ZWGApxBaBaDUYULX0mBTOnLC71pHZXmByC911e+PhzKLEIBFH7\\nA3i3nY37DYXSAT6ZprI667zhdDnjelnSbxKSacvBfMthXmFUIEl7kZMIMdPuJIDZGsp3woZo51Jw\\nbuYK2wjWPDnp6S4NpvE0C0OfK+oHamyoshuFb1P6mUMvPMmHG7rOwJt89HsdzE5UgGZhaGfyvZJt\\noJ0K/NUcKJSV69QeetJLCfCTV4ps6eWmDlrigJPzNg1MThy28ujOk9w01A8L0FAfGHwC5alMMNe/\\nlFA9kesSbCA/kxVoN5HEMDZhC/OsZ5zIRPyqjffZcE8a8P0OC49FTlUW3OqtiMJuGAOfPKOPtQZX\\nSBOgaj1m06AvV/hpKUyxxAhhglg7GDjnv2iZeDCK1cdSHLG1ZHc+g3YmSzYAvKKdGZQT5CM/01Qq\\nxZdeHMu9Qq/syDftJ4KZZjea9Eoq4T6NGGP0Y4wOUGLkMFP0To1sBHttSdaQXvcy20Y2idrWpK9i\\n4UFFDz2l8JMczG6JZtdi5mtWDXpbE/IMBjsm7wUrW2/xm60EXOcIXRBFQ2JgRgL62NG418orwXJg\\nduyw9oFuGEIY3UK0c6ijGd0skRu69djzGFb9XrAd2DHbCjabKIgldKtRPtbI5BUUbB/ayMMuKbet\\nTEhKCSOnjq39kW+rJhP8gwXVB1PWH4i+Rf3EESaCs4baoDeG8kRj11LM7mbCVJAxAuUbjf6qoH4U\\nSK8V9YMg2ffMoVtNdiZdmu0BhES6fu0WdBtIltKc5LtIFzPgco/dCDUx+faKv/LJF/yrx7/D/77+\\nNf7Wm9/ii7cP8esEEbBGmr5iRhcyj640rvToWpNdKlqv6V3KdiI3b2akaSiLbjv7nHEX6YT7glZV\\nL+34b94dCIU2KG6KnDJr2TbSGdp0lr4zdNeZSPFmHlU4lA6stjkXV1P8xoIGszToTpFdKLLLQHnh\\n6HNh+LQzjXJQH0qC1Fppkrn4bipa8pHm2M4k29WdYvI2oE4k4XKZYfOhxScBv/CkG8l4XaZo5zJ2\\nu1Kx/kjRHsWJP4nNP42wU7qpJ1lrps8Ns5ciJax70bkX/1RJFFwu8bydKepj4WG3i0DQKdmVfLeD\\nH3c0B5b1B5b1J6AbyN8ppm88zVyRX3uahSJZSd2tLyFMA81xQPea+kCTz3PSVSX0wj064XifDAlM\\n3+8E7OTC7iDG6VSCuvcEpejmCcFK0d1EsoA/mhGyBG819qYSN6/VJmojqXgf/oIFceUD2Y0E0/o4\\nLleNMFPSZZA22kkMvFboR0MgV0GjnI3cVlEZDIpIaYsuOC2gwERK00AS0UMjQ6KwlRTmvBVMMNkE\\nivNh8BlCmQun2XlYrkcsTCHav7puCcaQ9J4ExPXDid2aGBwr2icz7LLB5Tm66dFpQvjoEXpZERIr\\nreggF7V3osXQR3WzgU/u9qAHQFQQ/QinjANhfwWg9C4zvlMrEKXCSH0cMCmjwSQxw981GjE0EnlP\\nsu4xjdi9rT60rJ88oHqkaB6I45FSEGpDcmXILuWmnZw48vOO4lIzeRfwf6SoF4lkXkiQnLza0DzI\\n2Dw2mDpSBJGx4ApZYnsrTSHpjawE8jMbEwApYLWH0M1FL1wFNYpfye8hE52edkxmNX/52XM+yS/4\\nXvGSl+0x//nbf44fnD1m/WouWbcNIoeaDSsGjZq3qPMMP+9RW4PPPC43HP4AdK9YvTnk5Nsdzz6+\\nwGpP3ScY7bnLWBk2qzzLNuNqVdJ3lrC1dFuLKntWbcFa5/jaQqegcFAZlFfYtSZ9Y3BZQv20Jzmo\\n8a1BdZr81FCcBpm0XcC0sD02cZEqNahsKZIEolsvE5R2mmYh7jhBSRIVLDQPHPW3emgM2all8lqg\\nmPpo6H8QS7Q1hsmJ3Dc+EcKAfVjheoM6z8guNP0k0E1kYlCdBOg3/7wje52SXcH8K4dpPbYJuBpc\\nKs08divXniDY+vaZo53ryGRJ0U6cow7/KBCMjIXqWCaXroSDLxqWn2QEC/00kKw1upHOz/qhYrPN\\ncMUDij98Kd2XA+wZpWnVSC6wI+lgFLsCRlPzxAp98GqJrSrBycsiUoaN3ENlgZrmUhvSOopvdXsk\\nhW+KDf+cBHEpRsUbLcaZwSasm4h8ZDsjztSCfQ10pL4AExkktooHVEJdUj6Mha/B7xFi91cRP8sI\\nfYkAkzdB9ImvWsyywcflkI7O8cNyamypBcF9ndsVLqNm9ohbR60StCZ9sxS3D6V2x4raIm6eERIN\\nHuoHKflli0s06UWNbrooX+vh+EBEoQbIpXNiwrCNbf7b7Y4KBSPXPKSW5sDi0rh0ruekb64JIRZQ\\nB5pTnqHKEvdgQf2kZPPUsn2s6ObSsGEqTXEi2ZtpoTlSpNeB6dse3VsOPw94m+BSRX7pCNbFmy4u\\nqTTk5y3tQUIz0zSHiu0TQzcTN6H8kwnJJkRHItkfZAJOYxalO7mePoVkKb6cfSE46+YTR/l0jalS\\nwsagmxiIDAQbUGVPVnT8Ex99ya9OTvhW9g6Av3355/n75884fXWIWRnBypWMpRCd1cNEdO0PFxs+\\n+OgNP3z3iPBqxvx5YP5lTTCKm2+ldLOALnqutwWLUgwTMqD2mra3GO0xEUJJjePdZkrnpCPUrRJU\\nKwFaXZgooRtIKoED1ZURU+ggk55LkW7Jd4auKlFJILnSY+ISjKKZQmKijHIH2dKPFMI+16RrR3Vs\\nJQnqEVVAF9g+TmhnCpaK2XNNtrTipLNx9LmMo+wa2qkmv/bYyuNyRVfEpqVSsHLzx1P8zMPTmuTT\\nhr5KcauUyQ8Sko2I3s1eWUwrzWXpdc/2ccrmsaZdQPPIkV5qnFeUJ/GetuLK05dKsng/NAwq6kNN\\nsLJP/UC6O5O14vpXM0wd6xkeXCrdppNX0kTUzhXpUuE+eoR+/oawWe3uc63G+2okEUwnwF6CA0Kb\\n3QZCTMAwRkgIA7tl0CpvWnQItynK64YQHa9+plK0/yg2FYTjqc8DuhcqUTMTp3Nv5fVyG5XHvAy+\\nYIS5gpJCjTeSoetO6Ge6iyYOnScYRV/oiI3K4PfbXYeo8lLo8QbaqaGbFARVkN044Z8C5pJRbGoH\\nP+zhaFbw5zF4e78rYEYdYrUNO1gEIEnGDjF74WQGDwFT96i2x1g9QjnBGFQI9IclLheOb18asquW\\n9tMF+amwIXxmMZuoXezCOBh8Kk021VyzPFac/sUCn+aE3KEajam10M86mQyTdaA5UpQn4nhia0Xx\\nfUX1SHH4o47qyI5i9sGCyzXleY/LNF0MqOtnln4acFnA1DmmUiRryZjTpchvlu9CDEjxJ9n0dBNL\\nOzNUDzXVQ/mtuu/2lMdbHs42bKMKYNsbyrRjVWe0nRXDkMayOSvRG8PkRMkkoCUQhqljPq/46OCa\\niWn5x4oX/O7ml/i982/x+etH6LNUIFCvYA86UVuDn/UU85rHixUhKE42M7T2VA8c15mmWeQM5s8u\\n9/itZdMYtsuc4BUqQn393GEOWo4W0bEoiKFC11rMlzlZL6uLfTVD3Sj6iUf1Cp+HEWI01aBzL5j3\\n9IUaoULTSLJSnnmyy470ph0NuYeitdpzvJ++kjHaHFluPo1O84lgxUHB5pmiOTLoFtqpyGRURxpb\\nB/Jr0cSpD8Voo3okJ58u5f5NNpBea5IfFEBBOFAkKTRHcj8ka0V5Kkwv1Yk59+KHS8rTnOpRiv4H\\n0OeBZCv68M1CWv7XHwm//OYzg6mi5ZqPZIg4yetOMf9C7vXiPABS6DaDNLGWsT7/ymFqR7JsMSdX\\nUpAc5CdiNh4GOCVEFdG2kxpPkuzu6Tt9GMpoODrATQv6RYa9adCtNAByebOjARsDjx/iy0y6ln+w\\n6/z9advPRRDHD7zfMHrfpRtPq6ShoyvZ8ca7aG5rZaAOBgDJSjLDAa/1VtEsFNUDSzffw9aBZC3/\\n241g794KR3V60lO8WgkXWUWTgT660FsTUSoJ5EOXFSDBeiDn612Bkr7fmSokiWTEcWCorhe82Hv8\\n4UyyeaVQywqzqQhZSvXZIfWRYf2hiPvkl4H8oiO9kKw7vQzSRNCVwqMG+nmG8gnV4xzdSh0gXUoT\\ngmlkYktXislbqB5qpi8V28eKdBXIrwQ3PPi8QTeO1acF5WmHbSz1QrIe3clEp50sw00tGZfc0Ip0\\n7Zm865mcDisgFS3SBFsdbpzmQK6NCoJZQ6S1NRaUTNA+kQYdAPM6gecLzv1C6iJG9r820hQ22wTS\\nlafPFe1MUT1WrD/2hBh8v3V4zXcPTvhOccqvZ6/54+Ypf/PkL/P9k6fUbyeCcw9lgjTCbj240qPK\\nnsPDDU9mK5ZNTvP/sfdmobamaZrQ803/uKY9njEiMiOyqoyq7LSgukGhFZEGEUSFphURbxuvpKrw\\nUmjw3iu9EERtvOhCsaW1+8YLRRQqS7q1i2otM7OqIiIjzrDnvcZ/+iYvnu//14607YymRCKhFgQ7\\nztnrrOEf3u/9nvcZnMa+zWEHjVg5+N7AVaTNqQH0wy6Ti2JgcwCkoOYHje5U4faC18F3PryFMQ5D\\nrymW2bPomR0FNj6jDN3sSY1TazEdq6B4XZt9TLJ1AbsCQs5BnjlE1F+19Lp/XcHsGHYh9wNka6G6\\nZCQWRoYYi3H9poVdZLA1aXveCCw+DxjmaZHvIvK1R3kboPoAV2sOy21Ed2LgysR2WaQZVwTMlsdG\\n9xHZIaJ+00HvegznFXYfZLj7QU4K4mNE+eDRn8yRbRivt/5BugZazmCyHXfmAJDfE0dffOkQJdCd\\nKuARUJYQip0JdOeY8lWLO4Z8uyKx4BS55hCALxWyxwB/voTset6f4/zpZztjIWhqp9RkUTEFR4wE\\nBaUIpdzeQ1wFZHk20YFjzzmYWMyZ3jXPqc+QAmZ3fI1v8vh2FPE0tJBOwOecYI/FmdJcFqDuIjFM\\nYvJXsSwGUfPfq4EKLd1EZDsuCOp9QH9IGHuS94pIvJv+wex81baD2DWAc/D1M+w+KrB/JTEsI3Rb\\nQTpgzNCUDijvAur3Ftn1AXKzP1pHKpWoR/RUEYHdeCyypPBU/F0/IJ4uYU8r2IXG4Rkv0H51wgLl\\nOA9QQ8Tl/z4gu29pXL89HLdvMQJGkxmTFge95kCmuCPXNGoJaT1cZWAr8m1lP0aSYeIIt5cCu4/4\\n5+3HOXTDrtlWGfJtwOy9g24dZOsgQsRwWuDwzKRunwyQKIF+qTmHGAUlqSlJtjQTjxtIhUgDw5mY\\noC41AKpNn6sHzNih7znb8DnQnQu0rxyK8xZn8wM+nD/iRbHBr1RXAICNL2GEx1x2eG7WOIQct26B\\nG7vA/3l4hb/x5V/A7XoGu8khm8ScSbBJ1BSeIAr4hUe+7PDiZAsfJK73MzRdjqHTCFZBHBSk53Xb\\nvvRon3Mwnt8qFHeEJVzB2Ywrjl1y0AKqJ1T39volVCdQNbwPzI5mb2MBB4jzRsnr3FW8NoLh7tUX\\n9Eu3c+4wVS9QvwXKe58Cobk4ldcdZOsAJRBmGVxtyPMvmeXqM4Fs52ko5skNF0mVO4ZMS8ciXDx4\\nSB9h9jYVbzYw6xc1hhVJBQAQ84jiTqF+H2AaYtSb73Lu9PBphdmbEvV7j9N/sIXwEbtP5jS4OlUT\\nNFJdW0RhMCwEhiUL+OILh6h5HTWXEroFHn6F4Qr5LiLbe/QLhai5sFbXmAQ/di6QvQ/oTyTmbwj1\\nVe86iBjhS43HX1tADRHFRYni8wfg9v6IjaeH+FnVs5Twz+ak1foIuW8odDNmuv9xaBiwXJbAYjZB\\nsvHQsn5IQPhEYWz/8fzExT/UtvT/58d88Tp+/1/8TRrApIGknSl4w26OMtr0//I4wHI14AtWBuF5\\nkWVbQDfc4g0zhkoM8yNkArA4zt4O09ZptJbEE5XVZABfZHCXCzQvCxyeKfQnvEjj2FlKcHhzkMjv\\nBco7wgS6CTDbAXI/wJ5X8LmELyXMjls2uT5AtD26X36OzccZTYMEMHsTUDx6+EzCVryQi7VH9aaB\\n3HUMXJViKtoACO2Mi8hTue4T3xV7VqN9lqFbJX5qQ5WbGtJNm9EB0Fa0bnWlGF0NeBxS10e/7QBX\\nCtiSwo1guKuRaTYxSqdHvwtfAEERl1Qdd126ZQGXqSDZmUjCLqoG/dwjO+nwyeUdAODXV2/wSXGD\\nX8qvkMGjiwZDVGhijiEqrH2N98MK18MCf3D/Em+/OkP5U4PZVzwf2dZBuIhhZbB7rdCdCfSnIeHr\\nETGPiCJCDhKh9lC1xevzNS6rHd7ul7DJKMpZBWcVAwx0wHiQYqshW8l0G3DHiAiEMqJ8S/pcdUXh\\ni10QYgIAvOjhd4ZDVEn4hoHhKcXmXkC1FNCMJnE+F6nx4b/Re3DRjDymuuE9MntrJ549BKAbD3Pf\\nQD7uWVjyjM1EXSJWOaISDOnN5KSUtXMDs7M4vCpg9h7ZxkLte9iTEnam4Qv6sTfnGrqLaM/lRKsd\\nlgKuIm5v9gLV+4jqjoZS+dpC9rRglS23ye3rOXfAWsDWEuWdRXth4A0XkmGZ7GdnETHjca2uIt+n\\nTKZmAPReYP4FkO0jsp1HyCh0s7WATVa15b2H6snqUn1gnGKI1CJYB9ENiLvdZF0hz8++fl/1w9ci\\nB2Nu0gxOHBlagnTg2HQJauVgMxbZEWqNpOD6k4qKZyVg7hr88Cf/CTb99Tdqx78VRXx2+kH8+N/6\\nbUgXYQ7cHkUJDDOBkLELN2m7LF2ErYhv9ws5iRJ8ln4WEaolG0K6hO0uyTWViTUkArd2ponINh7l\\nVzuaPsUIP8uZEDM44tVNdxxUhkCGSEqEF3WNuJqjfz5Dd2bQz2kW5EpaBISMW1s50GtCDlxIaCfA\\n4148hkk+7koWMi5k+Now1ieML38AZu88ln+4ZnQUQMz8qYn8U7vY1LW7sxrt8wLtiYQvydoR4djV\\niYAJix3x6VGyPu6KpqKrU6FOsmpXAYiENcwhQnqKL7rTo7Og7mLyCRfYfxRQf7zBbzx/g3+ivsK5\\n3uGT7AYf6S1qKXDrJd66Bf5oeI63/QkA4Ee7Z/hifYrNpgJuc5TXEsVdRPkQoLojL9kXAi4nDAdx\\nlOdLxzlJv5ToTiX6U2BY0GArGKoGo4yIc4dq0eFs1kDJgG2XIwSJ3b4EBLnmUMnp0rGYChkRvZgC\\nlIUVkEktqVqB4pYFqLyN2H8w4uapW81IURSJVZbfS5o/bTisVX2cvGB0Rzw326fOXJLGFzJCF2rg\\ngikHzjVcQbFM/siONNtH6C5AtWmwWVBMNcwk6qsBqnUIRqF9lqGfy0TxTBh8UtGODLD6ysHsLEIm\\ncXiR4fBckkEmMBlg+ZwiKuEEsgcJ1QpkWyBfB5R3DuVXW5rEdT0puGlnGRXtFdzMoDvV0O1I8WSB\\n71YC5QNFVXwfwnW657ke2WmuTL9LdNP8kdfqeF0XdxHLLzqYmz0dO7uB5IQRIvGBNFnnJntlAJiU\\nzCOJAZhg04lU8MRxFADkrAaKnCZXFzWx+1rDbAZEzYVvWBhCT5sBf/fv/ofYDN+siH874BSMNxlx\\nLNVz2xOSdwPhFKC5UMntMJ2gZPQlLTvBKAV86lZUj2lLmm352vmOB1R1EfnjANnY5IHN14lSQg6c\\nDI9KLGbjPdnaTLatErHvIe49is0exdOTFjxwfgp3WsPNDYY5fYSHmZxuOoCFcZiz0I1OjVGKCXbI\\nthG6IV7n0jC3vA8or3uIfcPV/Il5Dg8GBQRUlFHIJKxnArxC6vbitHNh7mTq8EpMirYRPqLhP7u7\\n4j4kZguFIM0zMQ2W7SLg8MsO87MDXsx3eFlvcJnv8Gn5DnPZ4Uzt4SFwCDke/Ay3bo4/bi7xt978\\nAFdvTpG/16jes/iU9x668ZOHO8BdWFlJlGLMX+VN7EoxBUjoLhCGSucu6PT5TDLCUhz2AWRpQGBK\\nPw+1hyg8VssGl7M9tkMOG+g/0rUZ054GxQKeum2RRUSb1Ik6AIahIvExm4qXCPRx0R0XmPKG5707\\n5+myMwqN9F5Ow/UoCRnpBnC1QHvBGZDZCczeBkKDbUC/lMg3xKrnj2RXlNcRpqWtqXQRwUh0J7z+\\n1MBhd1QCXgqoLiQ2SER/ZtB/kHMu1UbM31LnsH+V0de/B4oHCm1sLRPko2DnPPYqMcba5/E4A9gL\\n6BsN3fB3+Toi2weUNz2E9YiZhnzcfb2QAxAuwJcaem9RJEgwGH5eW0uUD5EL3sZD+Ajdedhaoz3X\\nyYJWwOzSBKun+E63vK4RQdvZ24D68z3U7Zr3rfOIwTMrM7mTCpOGiyM18Gk82+jxP3r+hwjMarJQ\\nkoI7+gB5cYZQFfClwf33ZylDGBw4r1PBl4CtyO3Xo/T/Z1TZ/6jHt6OIR96kw2IcYEZU1wHmEFJo\\nspi2ksTGeIHLdCNGBdhFojMNQPHAC0kOLIpR4Wv8ed2mbVyTglEzg5grelLLo8AEACYlZYj8OaZc\\nO4pYorUIL84RKkMv9N5D3W4Shikn9ovPCI0AmCbj+YZduG4ChoWCzwSUHPm6vKH7lZy2aT4HhqXC\\n5rsV8k9LVDce5XUH9XA4up6N3uIxQkSHMCsRjYIrFfrFkaGDyJmBOQT4nBdMe0p4JIqjY12oPGTp\\ncHG2w198/if49fpL/Fr2DnNpsZQCNkasg8Q7P8cXwwXeDKe4HhZ406zw+9ev8F/d/3noW4PijkOl\\n8pFQk2o9B025xIdGwBcBPkuxeHMJW5Hfq+xxpyg9qI7tufBknp99XJyCSQ5//nisASQ/aEyDRoDf\\nL2ZkM4S5g6ktXp5tUGiL/ZDDB4nNnm5Q0UnEQZIvHiJiuu6ECRA6QJkA1yt25EECS4s4KKiDnmYB\\nPiO33ZeRzYl8sgOO3LH1hoVa9cTGs8Nx9+IKDi6bZ5KDuNnxO8pkWWH2hLDsXGCoDbJDhMupgJQu\\norjtIJsBvs4xrDLYBY3OzM7B5TQcC0agO6VyevdKT4v0GGJuDg66FRSztRZR1NOMAwCK25TOA9Bn\\nZRHRPYtQjUiFXyKqHLoJKK4bPP7Tr6AG2m0U67QrhoDZ9CkMm4w0s/fQuwHlVzZBQAWaD+foThX6\\nVQbVcvFyJWdiwvO40TSLjU53ETGceBy+C+zvFc71HOUyR/7Te8IfAAt3ScfNGCM7dOdJBUz3mEiU\\n3AlOkZICOZeS7qHZlTctwtUNIARkkePiM5DNMlh+ryxDXMzYNJ7PIV0AXIDs7S8exRDgBZJtuHUs\\n1gHF3cDBxAO7SJ8r2JmEK1isfZnc6wKL9bDk9nJY0PtB9YQKdEv8VQ1x8qD2uUGeSWRGQt/tWcA1\\n3fuEI3czZhqiUxB5hnC2wPZXlti/Iv+2uvETVrz7gHQnfeCEXL8biOsVGUImSe0SeeoKA6RJQ9aO\\nwyPdBeiO2LQcAtpnGdpTfk9fIEnKBcyWHZRIlp/dmUDzXMOVNdy8nAJlAeKqeiORr6lmG3clw4IX\\nsl15mGUPvWhwMdvh+4t3+DC/x0vziJVqoBCgENFFg7fuBD/pnmPjSqxthf/x8VP8t+7X8fn6DPd3\\nc+irDPVXYuosVO8RhYDPJealRFlIQkGafvHtqUK4VMi3mt1hT0aE6tNurI8p7X66NNKX4iArKt7U\\n0h6FQGS8JBM1B0gbEgSRBuGgSjFKdvG2TmwFEyCygJPTPc7qBpuuwLbL0VuNrsn4np50Q1F4xF4d\\nP5OKiE5A5gHeUh4ee1o8jM9xcw+9Uwh5hN4LZGuBuEtqwdTNj7sys2VjUl0H5Fs/MXB8JpHtaL/r\\nDWGX9tLA5cTipQOcxrSzqq95bUbB45k/8voSLkJtWna9VZZYXJLQpRKo33a8zzKJqAWyrYXs2VDc\\nf99g95GAbhWiyqCbAFtr2FnNYWHN89WvyPyy9fHOzh8SQ6kiJzvu0+e66+BLg2zH71peJRqu9QhV\\nBjE4mEMPc+05B8ozhHmB7tUch2cGugsIRsA0pBHvX0vsvsvjnq8FwuK4kxUBMFces7e8LqUDmksB\\n3XqoziFmBtjuuKM1NLcSTUf49HGD4BzDXsYvJQVN6LRGtMMktosDA05ol+GnoScAajESRVEUOYQp\\n4F9fwM4zWlhIAdVxXqD3Txhu3+DxTZJ9/jMA/xKAmxjj99Pf/ZcAfiU9ZQVgHWP8dSHEdwD8XwB+\\nnH73ezHGf/vnvkcEhIvTkEx6CRHp8zHUOWw9Sn3FNDSTjh336A2hehapKIjNyoGDDeHJQx7jnQBi\\ntd4IhFwglwKqc9OKT7N8CV8a9Jfn07AvCuDkJxa2VhhmEq4kzjnmNgYjsAsa/XIB3c2TTFiiO82n\\nsAsR2WGZQ4TZh4nu5eccKkEJVO86mJ3h1rAiBBBTMYoCwOjHbLlVpJMeB7gAMb/2hcPs+w/4Jy/f\\n4Vdn72CEx0o1GCKjwZqQY+cLPLoKB5fj/9i+xH93/+dweCih7wyKJNMu1iF5Lx8x2JHJYLTASx/h\\nDSk7thJwuYH0GjJ1z8pG6DYg2zKSKmiyWEacmpimfBJmzAErLTPENNR++jBN4r1rfg4/2p2CYo8o\\nAS0kzN7BbB3sIoMr6Kli52leMY9wLwaUsx4vVttJMTnK2vveIHQaopUQQSAUgZ1zKt5sTwWgA4KT\\nE7wCGflfEBBWQgz07RCewzQm4jj0K7IvgONuMug4idt8pmFaXru6IW4vhoBYKAxLNR3b2XsOufuV\\nIrTgAlzNSLp+KRNV0aI/y9GdKOz/2QoQpPGZPVCsOdyTQ4C53sI+X+LwwmD7kUS+0aiuPXQbcfqH\\nDtvvaLoenkjYV4rX2fOAWDrCSI1G9SXDh4vH4znqAS2E+gAAIABJREFUlyxQuiGM2D4TaJ9p4Ptz\\nmD1w9ocDd6/7ngttpmFXBXxB4ZvZWZj7AyAlQq7hKp7sfikxe++oHG4cZl+ShTWcFpwvVWlnkYak\\nvVHktD9wQczXOkGJyU7CEguPcWCqTogI+wPkYgaR5xB5Bn9BAzQ5OMAoBCGgru5JmEuKaVpdDFQF\\n5/nXcXQAOFshLCterzMD89ghFBqqtbSIBmCX+ddnXD/n8U068b8O4D8C8F+MfxFj/NfH/xdC/AcA\\nNk+e/ycxxl//xp8ALE6Tr0lGumDQiqusi8j2iV3SRHSntJiMGmjPGSQRJbgCPx4TQOIMcDOBbE08\\ncoyMArg9dBVwKBSasxLVvYe0EWZLdgw0EIzmEOVEoV9KDAtuZ6Mmd1kOfD/pUypRwlzphz4yanjz\\nZgmfG4eI3YlMn11DhBrVrZs6rTDT6Bfk55pDSlZR3CaPohiX1KuuZPpK/c6h+HvkT6r1njuJIsf7\\n8jt4U30P248Kqi7nbNHKG2Lr2dZPHe6qEJgbgaiSVNsl7mxBm1bhuNUml5vFGQBEweKMIRW5aQAF\\ndjTjxB6YfL9dIeBNnFS3gIB/Eg4rRuOwmBYwYCrsIojEbgkwadAdMkkr3LTIs5hrqCe7NzsTiTFD\\n5alQAS9WW/ROowPQW43tjs5IvtOAZ7L76FIIn7p6K1jIg0jw2oiXCYgsdeuBId+xChADRVTldUSx\\nCQhZMuxK6Ff5kIIE0vc9PE9J6gXPv9lZRCPhag3VU7gmNwHBSPRLBdMEFHcWuvXwhYI+ePhSobzz\\nLI7bHtW+R/lGYf5VDlcpyD5MnZ4cAlylED46Qcglins/nUf6fnOOwx0ur+P8AaiuIi7/voetJe5+\\nYFDcczda3/iJ2cKBuMKwAA4fAvbUQXT0HF9+5mEOAWbdI+QaoTCwiwzDUqNbSbiafiPCawRdob7x\\n6E4ks3gL3nM8TgqydfClxnDC3VNx00H0Fr4mI8SVCpACZjtgWGaM/YsRxVULue8heovgPfDxa7hl\\nTnaQC1B//8fsoKUE+p42ygBZbEpAdg7xdEnVtvOIuyRAkZIJ96N7aN/zdZQCru8gxQVf5vFA+HYH\\nwGiEooSrDYaFPsYkfoPHN4ln+59Th/3/eAjqT/81AP/8N37Hf8hDemD2lmosdq+ScMEz2mCOnbYr\\nyDYRnnCJGuIEo/h0fLMtt3TjcDMYAbkPmL09wg224ra6P6Uh/VBLlPc+hZoet5QcSLL4FPdA2IrJ\\n1nJkakwMgcTwmBSjjvjtRJHU3Hbq7rg7MC07cjkE+FxNyey6i9D9UXAiHRe2yYUxJKsAcVSnHgex\\nacHoBwjrIDcRJ/sB9VWJYaHQz+UxUCEN/Lg4kMHDgI2knB0YyjBCR6oLLMwuTIHGMr3/iGGPe87x\\n2CAlW0tH+MMbMm2EEtNzREwLQ8Otv+wIoQkfme4DKkLdKH5J3VjI0tAywzQEN/tjgECUHGKrnsym\\nw3MFOwNiHnB2csDdvkamPZo+DS+dBPaaxJ4hFXGZKhrS35lUwMfCriNiq/iUjYFKboExZwGv3igs\\nPw+orntEKbD5bk7RVGIn7V5p1NecD+Rri+UXhKPMYwcoAfgUwhFG+lwSi3UOZdox+lJPQ8Zs42Cu\\nW7pDBqD9YJ4wZYfs3RZ5PyAsKthTKn/bC4Ns67H7ICONVwuU9xyeNudH+Ei3AKJAd8F0Glfy2pn9\\n9IDqikVn/zrHMJPYv5bTNcBE+Yj5Z4C7MrBTAyKhbER3WRI6iwb5/YA8DR6HFZso4YF+IdGe0aKh\\nPec9CwBmY6dGwS7otTPOCZqPFti/UMnfPqK+6mEXtIt1M+7Eo6ogTwoU7yREmUNsG2SbA7vzxw1Q\\nll8bXIo+ddvdAGkLzp26gbDLbj/ZUccYpxDlye+/yElXBBDfXh2HpeO8zWjoXQk1r5DdG8j+aJ3x\\n8x5/Wkz8nwFwHWP8oyd/910hxO+D3fm/F2P8X37uq8RjMku+8TBNmPDOYUaDmyioFhs7dp9j4sCO\\n8u9sOxY1djqTWGEmCLWMO5skutDNEaLRnYSdke5iK5m8zFngVBsnZdewOrJf+hU70/F15QDIJFUu\\n7ixCzi1hm9R5Y9jrCDeIIOCNgvSEa44MAnbAtqK/c9Dg1jB7spsokaKvJII2CIoStvIr0EtFK4qL\\nUvK28Cykuh8XmISxP52vjYtE5GcZO+EYMVnyBi0QKj193qBZtERIr5teb6glZHpP6VIh3RxtSjmo\\nlXC1mjp3O1fozshAmo73Im0xSxZrmVR/wgOmBaQLDEBO0IrPR0hCobojy8VVCt0J2TT9Bz30bYb7\\n4QSx8pAmMVkOiaudwh5i+QQiCQnOKgOQ/OshyFKJLnHDvWDKj0rwYCuRp1DhfiHQLwrSWg8RJz9q\\npk5L73r4KoObG9iKlqX5Qw/hPcQQMSYxyc2BGgApEU7n8MsiQU8C2X0Lt8xR3CTV7jyD6jxCJlHc\\nNAiFgRw87PM5+hPDnVPkQL1+R9vTVcPj0F5mhB+jIL4eYlKfkiGSP9J69/Aa2H0ikT0usPg8oLz3\\nKG85sCtveezG3YEvJNozTa+XXKA/5TzIVgqzKw+z88juW4hDBzFYRKOhmhnsIkN3ZqBsRH3t4W4k\\n+oWkFfWFwPajKlEWc2R7DvODkdh/VCF/cDj5iSO0MlNkf6TPdHg2Ph8o3u2A+zWHjbOatShIIMsQ\\nnSe/2/HCjk1SC1rHTM3krQ8gFexkbpW8lJ4aXkUfJrUm8oTres80oIwdaOwHoC4nxtk3ffxpi/i/\\nAeB3nvz5PYAPY4z3QojfAPC3hBC/FmPc/uw/FEL8VQB/FQDyfAXVBZjGAQGQvUPI2ImZVQY7k2gu\\nFEJGes7I6zXJn6a4j9ONHwX9j4dF4sxaQA7J7S6t4MU6pmEbk0eCFth+xC6NH44cV9WzaKjsaLhE\\njjRpaeZAFeEoWFEDkO0D5BBhF8yHpMCAv9PdKFQ6/httWbiLewvZebhaw82Ia5b3FKjohinxvjYI\\nSqB5ZmBnhHSKNVPH9U06xMmtUIxBC1ZCuYAoCshcAmWCJsZBYIwYc/+iOtIbbc2OXTdj0QWCSdBJ\\nOka2Tni2A9QQJsqaKxXKLsCVo4OcZKgAjjgfqYEsvqP03hxIExzxf1ceF7ziSRzbuGgjxfqpIU7m\\naKbha0ZFj2l/qeBqDomrq4jiLkd1FxCUQHueYVgB3YVn0pPhe0OAxVpHxEFSwSnSLiWFMYg+FW+R\\nrq/AgXJU3ClW7wWWnznUX+wgrIdblaSvCgE3M9DbUbQgoJoBet3QLTMnyylWGWRr6YWz7+FenEy4\\nquwd1KYjVbkdgMxA9p7RXkYiuz5AWMeCWBWISk4FUYSIbiWheqA9owOo7kbbZqo87UKjWwmEc43q\\njrBbcW8xe6fQXNAeoD/hTMjOIg4vJXYfyklTMHq5128EyjtSIPuTtJuJQPmeDdn2E2D9qUR5raF6\\nzo5Uy4Uu20csfryD2VsEo5hPqTJ+noNE/uigOj/ViigF+vOM9rXpEtUbLoZmnyEYBdVa2GWO5Rcd\\n4CPMQwOx2SMmuCO2o4MeiGlXFWIbpt3tePxFVU6GVtFaxKaFyEZrVH2k+2bJU0VrCJByONnYjnL+\\nkZM+Qi/eQ677fyx2yjcS+yQ45e+Mg830dxrAWwC/EWN88//y7/4nAP9ujPHv/aNefzF/FX/wl34z\\nFZYAs+U2yZcKdqZweEYRhc85jfZGpKk6lX7Cs8AK94QfnvP/9QGo7gK38YleN3pAqBTkqgZ25iO/\\ndaKnJXqY8KQDyuRzPMxGg3kxuaWpntPwYBK1MTkyjp4vvkifbzja5bIz53crHzzyO5rj+NKgu8hT\\n7NgIqZBuFiVhmWwXUDw4djD7dlLJTT7Ho9BHKW6fVwWGlcYwkylXMU67lWloORIr1BiNxiIuBzJI\\nRnbIuAOyM2Kww1JPLpRjNxzU0Qd6vKpVN7KFIpTF5DQ5MjFGfF4NYcJtx8DmYOQTEQfd96gVSCrI\\nOVk3cm4RrIK6NyhvyeqRDsi3kcXo6gDRDoi5RvPRAuuPNZoXEe7U0XZ2kPTpNpE+4pOAR5LN4gTX\\nIs+dlPAgBp6i3fRBonrH4jUqGHUTUV1bmO3AgrrMpgHdKEBSfSS+fUVlbqxy+MpAr1sWdymJjc8y\\nSuNdgLnecOuvFUKdw9cmzS4kzLpD1Hw+GRkR0gXoTc+ipyX6s3yS21OQ5YEAHF7lkDbR9boIs/Mo\\nrhvsvjeHbgLac41sx+ZB7ge0H87x+EsG7TMmy6tUC3VHtpCrBGbvPCEYJSY/72EZ08AzwuyYxpNv\\nA/K1RXdqeJ7zBJclCnLIKF7K1nHauZpDQPX5GmK9Q1wlr+5MQe06wh2DBTLDnNdZDl9lGFYG7Sn5\\n87M3PbI/fo+wP3zdJxyAKJ+kzv9sUEOygRY/C42MwSxjoR59kZSahp5C62lxiN5PryHnM0Ap/O71\\n72Az3HyjdvxP04n/JQA/elrAhRAXAB5ijF4I8TGAXwLw2c99pcBBlc8kQiZweFXA5wLdCYulL4l5\\nqwFwIYl30vY+27GoR526gwULwig+6M6B7kIiZHFiMahuFAil4WHilqpkf+twdEocMdtDLafFIiQ/\\nYtWyeOePyR5TjayYADmmxTtwa2sovJF9QMgowPAZh6GzKzIpVGvh5jmECyivOsjBsfguNA7PUlSZ\\n4I0PQfN94QuoTE1FXFjP7iwp35CMf6QNUF1EFonTk5IWIYfUaStuDUMmIIYw5VYOiZY5duAhOUZm\\nu5iwbJFgHS5Q2Y7/Vlpi5SPlMBgxvQeFXdzOypQADqROf5T+L9Uk6weAfgn05wE475EVDkOnIa5z\\n1G8FVn8SYPaUVzdnBZrn9DXff9eTHjhIzP9YYR4iCoBCDh+Pu4CR9y3pQx11IKyShpkiMHyBu0T5\\ndT8YERGKwA7c8jpztYA7CJg2YPXHPXyu4GYK+9ezaX6z/CLxkl3A4i4loSsJ0Xs0n5xOWgYAEIOD\\nkBLDqkbUAt0qg9l7dJeXyNcW6mCBGKefURjCaEYie78lr1kIhEUFtyxJIbw5wPz0lnYNSgExYvjw\\nFPvXObozCTvjztWVAt1K4uFXV9CHiCoA2Y5w590PaoRsBt1EnP7Yovgfdmi+u8D2wxQKUfP+Wnzl\\noPce5bXH4VUB3UdsP5LpmgHmPwWKRzJN2stsmoNIy7FA+RBgdg7FTx/hz2YMhVgZ6IOnJ0qtsfv0\\nFN6cYZgLLL5MUYW5htoPU86r6AdI7+EW+TS8z/aU3CMzCfbwxx1PXQKSuHacVUemSaIgxs0OIlOk\\nGwJkuGQGUHwtaSpEa4/B5j2HpAI4duIAZFmwW9eaeHrX/X/rnSKE+B0A/xyAcwDXAP5ajPE/FUL8\\ndZBC+B8/ee5fBvDvA7AgwvjXYox/++d9iGX2LP75f+rfAZTAsDAcuCmyT7pTATdnzJLqSCtUPbvj\\nEduOkh2Z6iNUx4uHyd1JHTmxJvh+jH9j4Slvw9QZE1flc7JDWihGRpmhcGCUR3MAy27T7Ijfmiam\\n7p7PlUkh5wsJlQpj/mihtgNCyWR5V3JFzjakNQkfOKyq9ESVIttCfG24CdCEy5ZcwEa2AyIx4/LG\\nQvoIve2p4AwRIW35XK0RMknlnU5sEkVs39YJrpA8gzQVI1xFXjchhNHHBhgHrWEqzmNRJnWQ6ldf\\nEYu1cz15vHtD4Va/AoaTAF8nKp8XMGuF+g1xWQCo3vdwpUJ3bnB4IdE8j3AnDrJ0EBIIdznKK4nZ\\n20gYKnX3zbmmtfFC4PRHPQeIP32AP59jWOV4+BWD7pyMh1Adb5xYpANqKZ+HCYCTEJ1Mw00QA88D\\nRE/IRVqB6kqgvAnIDoEirjnphKaNyauDQ9oREtLdaAegoGxE/mChOgfZWrhVAVfqiTqoD34SK6nO\\nQTVHUYhojvAMALgLDjRlazkcHSygFbqXc3r3dBbydg1kBu7ZCs3rCkMtEyxIb6Hu1CSBGM/x/Cs3\\n4dyH5xqHlwLdZaDNgAPqr8QUkgwA/Qk9WIYld2ejR7w5kMniKnbkbEw4lA6KhAM7j/Qiuo0o7/3U\\ncJhtD7lt4c5m8JWGdEzQ0Zue16xkuMsknGkITQjnEcsc4tBijC8UdYVRHBc3W0IqPrCTVooUwbKA\\nMAZxXsHP08xslUMfHIVn93vg7hFfC2JRSQyY1Js8L5Kva1KBSe8Rd3vIs9PJIA8AcHOP373/r7EN\\n99+oE/9WeKcsFq/jp//yb03d4ejTMfqjQB478ZEtMUrvVcciA4AnMXVWzPHjBejzY2cO8AIak2CK\\newvduNQhsgDZRfZksCgw/3JAd8YJfHsmMawwWdtGSZHSsBTINoQmqruA8naAz47bWABQBwvR0UnO\\nVxn6c9pvDjMKELIdU75Vn4ZtmUQwEnZGVskI30RJWMKVAuVtoCtewo5dnvDHxAfWbUR1Y5lbqQVk\\n7+ELDbtQaUJPHPwpH1z4SL588qoYv6f0RzGNbnyy4x3FOTJxyCUXnpKLTr8SsMsIu/BARmMLMUiY\\nB4n6LVDfBBS3A1yl0J9oHJ5LtM8i7KkDUrEEALXWMDuJbAsWyX1Aed2hOy8gYsT6Y0bPjbbDto4T\\nN1m3hNXydaArnpbIrxvIwcEtC3QXOTbf1ehPADsP5IUnKA0AU30C6YTqkMQbySqB55+7tGzNa80u\\niItnG6C6YV5o8eDRnmvaFvTcOQCASvS4YCSGBXcfpgmEnNIuyVVUaUYF7qz0kbo5ngdz33BhSZ2k\\nP6nog3KZTfCYz9Ns6NHBrHtELaeFQDQ9TZ/mFUKdo3lVQboIV8hpaJztyVknu0tMjpLjIwpg9uYY\\nvDz6tEAItOcKqucgvDvjItY+i1PgtG74OmYLDCum/qgWqK8C8g0VvpDcUcqBjUE0CsPSQHUh3ROe\\ndrou0MhKCIRCIxoFfbNF1ArDyyV2H1B7km8jVn+whlzvEA/NVFhFWbLrTgPGmIzkxt1u3O44hEwP\\neX6KWBVH87mb+2NX/9RxdBxWSgl5doIpl2A8fslEC/2A333/N7Bxt784RXy+eh1/6a/8NgAk/JJd\\nHSlmCv08FS8xGkjFqSDTR4J8aumTvD1FOKmBFzqpU8dO3M7E5PS2+OkwSePHwiYii6Gt2WFwiEjb\\nz9HLPIrj4DTb8gbRDdkf+YYnUzeeW1xJF7VQKNhaTzhvtmVKfCgMQq7RPOdgZrrRt25icPicsVmj\\nDHtkb8TET9cpes4c0pDPclcw1JKTeAHInlQzus+xwx99ldXA7xAypBQd4pMiKfrcyNhJnGtXEYf2\\nJU2ORhaH2kuYvURxQ/VgtiNv2C4UmjOF/kygfZa6bkPMWR0kzFYi23B3UV0xxEBYj/3HZN1EATx8\\nqlDeROw+BuovBZoXjGjrz/jTziL0QcCln8OCWOtwwmALO+ffA0xFH025RGQxyh8imhdUF9oLC9Gq\\nqeuWnaTDoIz0GLeCi8qG+GxI0JirSJk1O+4KReDiF4VAnnj5so8T/z1bW+w/IFvBZyL5ZYfEYsJ0\\nLQkXEHI5Yd7DnIEMug3IHjromw3gPOJyBntOR7xgJIqrA6KWsIsc2UNLaObQsuDkGVlMqWC5VQkR\\nIvqzHK4gu8gVnEX0K4F8zV3YMOd1EzUmC1xpyYUvH8K08O8+lDh86JHfKkgLuhjeEhvfv1LoziPs\\nSUB+qxjFuDm6W3YXYvJLzzejjD7txAcucGbv0J1l5LHP2LxIz+FodRNQPDroHe8/V2vogyP2vx8g\\nBodQGqh7erfE3Q5TZi2QUuqLKX4tWnss3NZOxlYieYqLp1TEIj+Kf5wj3JIZwGQQdYkwq6boRuwO\\nFADVOcPXI6Bvd/jhZ//5NzbA+tYU8U/+zd+eOpvyIU5eyLw5RjyVFzoHk7yg2kuBYRkQzi2w05h9\\nrrD83E9RUcOMHFMRcWSfRGK72Za43+ztANmTm2zn5Inrhr4t/ULSx8QkUY8SFA4lmIVxcUcMvXiI\\nKO8cipt2CpSIRsGeFPC5mgZ9qmVnHHT6jprwwnjTS8f3ynYe3YmePNbJLDniuCbJivtlGjbWiRbZ\\nxYQ5Y1I1Pg0ftjNMKfEjxx2J5x4Mb05fRvg6HO1VW4nsUaK6ooFQtuFW0ecSvpDoTmQKZOCQEQXh\\nA9ErmEeJ4p5Qw/yn5EDrdYeHH6wQFbD7KM0/MnbQvoxTPBqQ5hytACQQVEyOhdyJuRLI1gK+orzd\\n1amIr1jE7TyiuBPT81VPMybGxwHdUsGVwLCi29/hJQtof5a6chOg1pqQiUuLOMBhpgDMTqK4B4qH\\nMHnc2BlZSdGQypqtI6q7AH3waSvOY9qf5SmlSqJ45N+rzsOVCnah0c9ZTLM9dywIZBRNMEkI9Pew\\nDmFRoXsxgyt5zS8/G2DnKs0mAsxDB3W3+RpVLnzyAfafzNCeSgxLgflXAeWtnYbcbsZwcrNz8IU6\\n7ugsYQxXKRxeKOy+Q5ZOcUdzJ4CNRHvKMBGf87pyNc+F6tgUjc1AlED1XqC6CTBNwOFSkcFUsvjP\\n3g7QDSGkqEmtFDag+ajG/rlCd87B6OqPB2T3DbrnNXwpEZRAeTdA7Qe4RT7tRLOHFqIdaLyVBpqT\\nICcRAsQY/JCGjlMsoxCkGTpHZkqekRGWZYRQQmDhVpLF3Hv+fYyAVEBIuLv3iM5Bnqy+xkaJw4Af\\nPv5NbNzdL04Rn518EH/5r/wW49TGgZuNaZB43NIDpLLZihepnQF2mdKxkQaOCw+5sIibDPmNwuKz\\niHzrE6acjJ4uko1nsqesbgKyLVf2kEnogyNurAUOzzWKR4of8gcHVyk0l4rvVbN7i4rWnKN8XHX0\\nJlYbjumjUoglJ+ays4mPLAAhIPcdIYnCwK0KqjZNMr3SAodLTYpcTthIdZhwfrJL2KWqMbkoHt0b\\nSX+jACJkKc4OmGLsQsLSx5Sk8bljILXwmLI0yTyhR01/EuGWgZQ8LyA7ieJWTp1Y9bahhFgIdBcZ\\nmjNmRTYvaEV6eBlRvxPoTumx3bxg4W6fBeT3EsNJgNlK2GWA3qXF6TSguFLoz9JzFvQn708DdEtP\\ncLOlIyBx5wg1CNhZQP4gmeYUea53HzIpSTr64KghILtvp+2usB6H78yxe6XRPo/oLz3Mg0pK4WQt\\n3NAfe1TS2hoIOZNmfH5U8eoupqxHCtSKWzoETsPcVBDtTEF3AZuPTGoOSF+dvSeVTm96hELDJ76z\\nqxRUSy54/tBDbVqIxy3C+Qn65zU1AfsB6m7LAVyewX78HHZuMCwUsl0KRGkc3MzAJl6/NwLmwMSe\\n/GpPRlKhEYyCm4/sF37nfqmgW/65eBgweqLrxyNVz55WHFaWZKWYfZyMucaEp6jpoy8ikG0cDi8M\\nynuqQWdfdRDWQz0eIPYNwmbLzMrZDMMnlzg8z+FzgfrKovjiEWK7RzxbwZ5W6E85Xyuveqjeo31e\\nMoC7p+VA+eYAdfOYCng3fWZR14iLmgEu3lPk03Ys8mCRFUU+FXe0HTHxp8PJEDnIBI40wrHTDynm\\nbbC0qM0zDjkH2t5Ga/HDx//mFw9O+eW//NsoH/1USEb5MMAhonRU+uknqixbcjA2wiW+JP3IzsgK\\nGbu4kIEpMUkVWzwSUxWeqjSRCqLuSacSgVJuOQRuR6UEtISbGQjLiw0hws0NuhONfsmtnAjA7C29\\nkvObBiFT8DXtLGWfXjdX07BKHxzk4CBcQH9Rwc1UuqmTt0gu0M/lhEMyKzJh4g2LelTsxsdOPAox\\n0QcBcq1jgpe4lUfCINn9jANAhs+mIjZ6pahxQSDHvbridyuuG7hlThOkWmL/mp4Zu+8A9Rvyf2df\\nCey+wwLavmTQrV1E5LcSdh5hDizA+b2EqyKKe4H2koW5Ow/IHyWGRZjgD7sMKK5TEX+UGJYB2Yb/\\nNr8XGJbs8IYlj81IX3NVRP2WzUBU5DDrPV9Td2lAbtKO7J1F9tBBPewRqgL7X15i/bFC85IduUwD\\nTLM/Ol1GyW47KGoXQh6hdyK5UxIKyHYBaiCVsHlGamSWaP1mFzH/akB/yuukX9AvfPYlFxXZWMhu\\nQCgyyG6AW1VwczNBA+Z2D0gBe1anWZEkbtxZyNZO1y5CYOc5MAAlzEuE0pARYz26yxI+Z6MTNPFi\\nlwuUDw7Zmp256hxCrtGfZNi/IkTiSoH6ypPfHwDTOOh7FnHRD8B6B6EkwrNTIEb4Okf7gv4mzSXt\\nLCgW4/EaMwDMIaK6HuBqBdUGLkibFtAK/bMZdh9m0wKpko/4MCN0qPqI4qZF1BLDKqOtsRTI32+Z\\nKp+6aAB0Ehz54U9roXVHKmBirky/f/q7RCdEZmiSVRVUywIpdyAi9j2L89jpjyZZyVgLMQBCfm1x\\n+OHD3/wFK+KL1/HP/Qu/CZ+JaSApkvov34bEj+YJdoly5nLiX9k+oJ9T1ekqbtPG3Edi6syPNHta\\nVQJAt5Qpz5AsDtXTP2JYUDQwzIhLk9MckN91kI2Fn+dUgRoF1Tn4Kg07zw3aM4odxkGarVlQzSGi\\nunHI7zsuFr09DjhiZKRTjAhFBmhJb2zwu/ucEVP9Sk+qxCgxyebH9J2fZd5Q+HJ0+RsHwDFRA7O9\\nn7xGvCHPWndPhsqKLB1XH20G6EkC1O8j9h8I1G+ITef3hE/MNmHOCQrRjZiyFgEWSzWI48KhQU/v\\n8c8SU1qSdAxoGMUzwPH7icQKGYfYISMeO4aBuJpFeuQT+5wFery2xnlIv+J8pTvj84IB6neRPjab\\nAXaZYfdKY/cxED9uIFXAcFuhfKOmndDoTjjmfQbNRoHWEJh0BbojrDCqiZWNWHyWvG4eG4h+QLi9\\nh7w8T+IcUkNFCMRNtUKoMgwnBexCJ21DRHHbQ/ZksmCwCPMqdY6ONMrHLWJdsmj0w3TdxbrksNOy\\nyxxerhAyhYdPc+ZitqTKEr8mOSDfJM2AixgWCt1JCjlpI7oTRvlVNxbF2/10zmXTIW53ZGiECHG6\\nQvu9CzTPDbNQk++3bgHTks0TMg7H8wcLZQMAReTLAAAgAElEQVT0ukOUEm6ZY1jRV4gwYcThuUxJ\\nR2zglj/hMT/73+6n4zGcFfCFoiHaYwu53icr2IR1d93EHBHGJCtZII6BK47HcrxfAdBzfOSFK0XK\\noU55myEgOs9uW6Z7+UnCVhxzOAEW8ifW0RBioh7+sPk735id8u2wohVU/7kSCIpbYoUIfYhTNFP5\\nQG6qOQROzAvAKUGZ9jZODnb9ijepachAoUgnHrtZEBJQHZVr/TJDdcPB3+InW4h2QFiUgBDwlYYY\\nAtTjAe5yAQCI4JCkO09SWcWbsnjkZ9AtiybAYWOx9rSaPQwUbRiF4Sw9IRD/VJ2Dm2foTg39Q1oO\\nRKMUGCozdSrZ4ZgCRCl+Ktb5kRrpqqOoJt94+FxCDTyGquPx6U70xI+ektE1lZVMrk+dfpuggFNi\\nwW4W0Txjd9te8vWGVYTq0jCxYTHVDYeK+SOhl+JOwC4idMNOONsIdCt26f2CXbirIrK1QH8OqB3Q\\nXURkaVcFMN5rWKYufBWQ36XO/oG4qtkIhJw/fcGsSVsD1XVMCwhnDC7Z0I4pL+Utn6cGnrd+qRBF\\nhvL9AVHWgNBo9/RWrS0Xn/pNxOP3I5Y/Ts6YSSNg59zuuFKguqF4RzrSL7MtjaXoujdMkWTCOsQy\\nh7xg/Jd42ACzCsgMQp0jmJIKzYc98p7qwzECbBQCwZMBIr+6Qmy7tH1XiJmB2DeIZyvCIkZDrncQ\\n2wPC5RLt6/kE66gh4OwPO2w/zFE+0N0wSoH5Z3s0r+vJ579fKuRrh+Iuwjx2UFf3LIhawV+uECoD\\ntU+wQ5EhnL6A6BwXkW5AftvAbDVCrrH+XgEIXl9xQyYXu+70/lrg4QcrKBtxeCGhUhMmHTD73MKb\\nDP2JQPOhh9pTjl/eRlz/xbOJieNL2vsO8wzLQ/IuSZmXIy1QzOnpDX10HBRaAWPakBBA10/DyqmA\\n6yfl80mSllASMZBNI7QmXJKKsxiOA08oBSEZdiDKEnGzpdx/eBpo8PMf34oiLhyHgcNcQYQwYddd\\nCkTI9kml6SN8Tqpd8UiF3Ggula+Zkj1iwcNMslsY/SzSjQywM8sOzCws7+ivUP2EWY7hZAbZDIhC\\nIHv/MB1s/aAwJO+J9oRmPL5MMEyLKX/SlwA9jTl4jYqiHHXIACmYWHJwyXHOQu24vcuaAdlVRFQK\\nUCLZbqqpyzOJYaIPnn4jCnB5WvCGOBnzx8BOPGg6D5aPHReDkww+F2gqBstObn/T63N4Rsm9SFtl\\n8u8hCU+ELBKuOKSiu6W4qrgDugowDhgKDhNDGRA3Cn7pMFgNu/KTeROSSGg0CLQ1vaZ9geQ7PsI6\\nyXgJSX3bsPtjh84u3Bfs+LOBIdtZJxg52gD9KReb/iSgvGFHWX/FRa+8D4hr8v+jIC4/LMnhzrYe\\nbpnDlWSA1O+443Ml+fquAE7/QYoqW3Ohay8kVMdjVdxGMioCkG0dzEOHUGoMy4yOgaaALNnx6b1G\\nyBTsqwW6MwNbMcQhGEH/HSNhdgK7v/ACtiLNT9qI6ktmtOK2IfaqFKA1h2RJlBK1Qv9qxWFeqRk7\\nuCwgLIVE2eOQUnOYaGXnBtmewhq96TGcFbCrAqojpVR1DtXnHcKsYEas8wjPTmndXBNqNG8fSLcD\\nd51hWUJIwY1TzV1Gf1GgWylk+0D5vA0QQzhqI2YKuw+zyVO/fh9x/gc98p8+oPvOKYPFnzM82c4B\\nYQV8FRIuSIfO0fI429LNUQwBcptYOX0/eX9DCJpVScHfxUBcux+YvSskB5d5dpTaO4fQdhBaE/7Q\\n+vhaT/1TQqTpVddNg0yxXJDCaC13Ac/paBiUgDQa4eoG0bqvQzs/5yF//lP+7PFnjz97/Nnjzx7f\\n1se3BhP/9F/5rYmf7XNi2jpBImqggCAYJmuPzmpRCph9wu0yMQ33fApsMFt2k08HWAChg2yXZPLJ\\nDtUXR953cR9RX3n0C5nsWgnPlHdMrpGtg68MhpVGc0bv72xHWmB5a9GfGnjDjlb1QH3tUFwdJsWk\\n2rSIZQZfGoak+oBoJGTrOIxZZrBzxWitnUdx00/WlKEwcKXCsNKTCGOMJwMw+avLKbPzqEYdg2/J\\nWjl6yYzd/FNl66iuC4YwlKuIOwonkK/J7NEtyNV+5PA0WzNZSUSgPyXDJGR83dEbZRTQMAQ6Qrfs\\nyMcBJj3MU+5lwrv5fLJCIJ6cS3G0UhjfQ/UCQRPSiRLI14keuh9NngJ0e8ReuxMx2S6Mx20c8vYn\\nhF1UzwAGs3XwpZri7MgEEhhqmXYRAsUDKXJyiDCNo9+1o94hf7BQLQ2dZJfomYsMQUuYTQ83yyB8\\nxOFlDtOwI46KAq3+PIMrGEUnIsVDIkQUb3cQhxZxs+OgLNHixHw2ybpjmcMtC/hCMzTCRQaiPFKy\\nLzcNzbLyDO3HpynMQU5+7+Wdgz446F2PUBoqIgcPYQMzKoUAlESsCoTcEMsHSIH0HqIljS/s9pSY\\nX5xh/2vnyS2UHXP9fqCKeKYmh8zscUD2J1ecGV2eAFLi8dcWdCRNAibheT3sP+ROrLgTuPj9HuWP\\nrhCLDLEuEEoDuR8I6dze0+9ESbJQ0jGLzh0pgCPMkpnpu43+K9PDB3briWkyiXaSYGgadmbcgY+h\\ny+HlBUKhmWa1aYH3N0cVZ2KzRO/xe4e/jc0vlGKzfhU//Vd/m2EKkgM7+kATb65uHLJHym2hJOlD\\n5xmTYQSjpdSmI1d2XqC7KCcxy+TnPXpOgzeeKzjYKu8SbtkHdCtFfm/iUEeFlFQS4TNg/kWH4SRD\\nd6IwzIiv1u8D1aCJ2x0Vi2p5a2EeOLyS+w7+dDa5sQkb6KPSOYRMQ4TAbW1OAnLUEojxa65sZh8Q\\ncjEZRAHEWn0u6UP+M4yd0dtbBCQYhqEEo7hlfF3pOMC1MxaI0fJgdGUcB5EiHimGUTEgYMLAD/w5\\nOvr5kgwNXyUxzsmTn2sqOM2W3tT5nUR3GVDcSPTnxMm7Zx7lO4XuPKC4YyHqTwjfdM89incK/UVA\\n+Z5Ml+Ker12/ZfhH/TaiP6UIZ6Q19ssjI0R3jO3yGYVLw5IDyfyRYq2gQebEKqbA6sRmaY65qNJh\\nGjDaWiLbkYbqDc3JfC4YV9fSZa87MzAHBkxnew99SCKeGAHPc92ck7Knu4j6qwb6eo243SN8/HIa\\natL73qG51JPAS/qYgkZI/Zt/SS6+2hxpc/J+zeJiNPxqxoFy7yk6kZJQQggQuwPsd58zDeiU8IRp\\nA6r3zJUMGRlU2WagKtJ5xDJD96xCd0pjrGxD3NfcMUUeD6nQ+8CCeHmGUOdwtYEv1KRQlp1H87LA\\n/qXCsOL9SoEQE6ZGq9lgqJx2FX2U+iXDUZpn9BoPhtcsJAey+YNF8W43Se7DkoOWUCWFpBCQm4a4\\n+M09mSfPzqdjJ/YN8fBTJvuMPjfx3TXFPuNjhFLGMPUYKdlPIerwgYV9HGi2HUKfhqSjDzkAeI/f\\ns/89tvHhF2ewGQ3l2T6lngDH4iM83fKGRYVsm6dEazG5BKoe2L8ukC1Nsmz1yB97dOcFwkxOng1P\\nKXRU1rHtDJqskmz3VCQjYBeJ0SHoWigdsPleOXUnVJWKZOPK7m5YKHQrsmb2LwvkmxzF2sNs6/Rd\\nIoSXsBc6dYES2Z7e4+PvXcVunaZVibebCjgDpclaGTtw6Xix6+64GM/fePqwG/LHySNPKUG5SEX8\\n6H8SZfJAfwiIw5FeyAg0slQmxWs6J9Imteqax6q8JkUwWwOHGc+fqxJjZPy58DBbDV9EmI2YLFxV\\nx/MiLVkZai9ZPHaS4RdgVqO0QH6jYA6AshJmRwYLQMbKsOA10V6OvHoWblsDvooYICbL2s33OE+w\\ns8BdgaT5Wv7ARb67ILY/+s6bfYQrgf0rmVStlISPu5hR9ZrtArL1QMfAmy3c+RxhZhiW8FKheCS1\\ndVjy1gtGYP9CTXa8VGwyZmz/0Uu0Z4xZcyUVsLO3A+Tgcf5H9wiLEr40OLwq0K9EEp4xws68uUe4\\nvmUnaDRi6gxRZBDeI0Jh+6srXnOlQL4OyO97uI9OELWEOThUX24RlYKfZWgv8+maER7JA6eajOgA\\nslNk7ycWTJgVJAdczKHXLdyqhLnapAxWBb3pIXuN/iyHbj123+HAf/beo3GSi+Y2oP5yD/X2Dvbj\\n56Tt5hIlANsKHF7KNNeJeP6/dpDWo31eHLNWh4D8i3sSFS5XsPMM/anmPMNGFFcHyM0Bcd+wq84M\\nhJAs3M6xg/a0dca7a35RpVjox9mDD0lmT+xbKEms3GTHwhwjvVqSAlR0PaJSkIsFB6ExAvsDFyr7\\nzQMhgG9JER8fuh2HQalDAZBvPbeptxb71zl9G5JQYPQQNw29rPWmnzix5VUD1eWEJ+Y0xZmk+hqU\\nS2+ZBxgMaYXdGbfrQbNLZ1gEb9L8kaIQ1TjETGKYG+RroLlU2HysEJRCtuEFWDwMGBZc5YeZQjgV\\nyB8ooY+ahWqYS1S3nso9AejdADfLoFs6HqqthXQB2Zpwi0wZfKEwcLXG9gPDFPQaU8oPQEtbREq3\\nh5kkW6XnQmQOzDFFpFUA2jhZzPqMsXEQx6QiAFNSkRBi8mshr5piHREE+rMA4Qkp+BLTQqwSVGL2\\nPK7mUcGXqaDPSLez84jilsV88UXEsABmb4D+lP7fzfNjF2wXR/pmcc/jtvycw+zyljs4V/K75I9c\\nvJZ/YmkZkDEpys5S1B1tXHg9lB79eYTfS7QXfC+AsE5xS4hkjMMbVhGu5j+2cxbY2VcB9fsB5raB\\n3DeI5f/d3pvEWpak52FfTGe8w5tfjjV0symKTdEUbRBeENrZEmkLlDcGoQ0FCeDGsM2mDKhlAoa8\\nkw2YgFcGaMgAbcimDUgGuRFsUpAhw6BIURSb3exisbrmysyXb7zvDmeOCC++iHNflquqq8g2MrPx\\nAihU5s03nHPPPf/54/u/IUX12g7a3UN4Tc3D/O0awyTQ14IvDQDUe3woyB7YPADqTuLiRw3KR6Tf\\n5Zc075IDQ6az05afv/s7aA5oKtaVAnoToK0VH/7eaMAYwgVRbGIt/JNTXteiwCzsALv9nEZl3sMs\\nO+jHl/BZQhtXIaBOBuh/uRgLkhCCYb9ZBuQZ3DRnyIhRwODgw9DWFhqwHmrTQTQd1DefALMp3D6T\\n3W1poBcNik0Ln2jMr1vUdwus7zIP1St6/7tEA/cPoJYtME1HtavwEpPf6/h5PtvAFQlsodHsKNSH\\nFBbJDpipA/RTzQdu60Z75eSCMW7oB4gigzea0FTfw+9SCg8w89JcNvCGu0K1aiBXFQeTXc/OOio8\\no6FWP/CWFJJwSj/A9wxAiJ4q3tqRiSKinzgAIQXwrCPuZ64XAk6Zzh74r/77X0MXJOPtDjsps2aH\\nmV5zyxSx2X5KLNUrOgimV3TXK5/0UPUAfbHmk69u4Wekhw3zHO0+L0o7l6juMLCgm5MKp1p24cUZ\\nlWKy91RmhlBmMYSQCIvRD1l2W6GBTfmhiSn2yYZPjPykGQMbfJmhOyjGbntzxBzRIRPjjZwtHIT1\\nyJ/UUAtWE58ZDLMM7X6CvpRodiRsDtSHHrrhDVyc8DoWZ8T2mn2NZk+imxIWMhtAhKiqGAKQrGxI\\njZejr7hNBfqc8nn6YmDMhIze6e2+g1lJDDkLsU3D9lXEDp3wSnrF9yk7oyVw+dij3RGYfORQHUvk\\nZw6b+xLJwqM+FjBLUhDLR7QVnr/j0IQwgckTi/Vdqu3aXWLY3Y7A9AP6jMzf7tDtaOLWGSXb3STa\\nLdCcLAZfbO4GjD8NQSIBm1cNM1ltxnPpAu9d9izskX455Px3OCC75Hs0/dAiO++gVy2tgIMAh06V\\nIeBk1UPWA2TbM00dgN/UEFkKtzvB6gfnaKeSFL/Gwax6mmMFrYBXDHKAAKo7ITbPk7WjWmD2nsX0\\nrWv4N99lcbhxb6sfeB1upxwhvWGasCEJ6U76fM0t/9X1KBP3bcc/K0UfkZBy45uWDwetIeZT+JS4\\nrytTyFUDVwQvmFnCnVUzQC5rKi53Z7DzDMvXMpRPCLtQsarR7iiqOjeesFV0xVQ0+jKnK6ZVKYFh\\nlkG1FjZVMKcrDAcTyN7BJQr1ccr3bCrQz9g0TZ6Q5ukF0M1UiKxzKN65YuGOvihSsBgLAXe8x2vp\\nHHB1TXn++IYG0Y6So6e4t/aZYuyjN7m1gOGDbZTyOw/s78CX2WifYN8IAWlC4nfs//m54ZQXo4jv\\nPPBf/uu/yCiwoCZkkC9buvrAjL7e+VmPoVAYcon1XTreDXMH0QokC4n5Ow75uUV6XgPBY0JUDdy8\\nhC05QBgKZhK6RISQBErzZU8Zctyy6Zp+xTH/sp1tU+UBPmRUgDz6EuhnQbxQYYQsmEW4NfHxkvzk\\nuP0dyuiw55EuHJKVQ7MXvLRT0iplR1l/P+GuwiZMsLmZ/B7T04tT3phjAEYpgzAoDnj9KBbiTsMT\\nnxXcktpUBYvaGCzMn2VTjI5zNlhIjCrQbqu0a/YYDt0cBH74FMif0rMlvSBGnV2Q+pedsTAWpw7V\\nkYSuPZp9geycxd5s/NgVq87DxHBmy4cqJNCXil7iIS+U5wGYtR2LQLQwEJ1Dt8NABq8E6n0BswGq\\nI/K6Zc+i3uwFJ8WZg6ol9EZg/h0XaKkSzQ7nDtEpMr32mLxfQ1UdREPfagyW4bfzcgxnMFfBgCoG\\nFQD03phP4bViV6gkICXsbonmqEA3V1i+Smgh0lf7qUd7PCBGws3/RGHv2y2yd88pDw+KQb9ajcpE\\nvokK6ugQbm+K9rgkMeC6C8NkjX6qUB0omNqjOOmRProGnpzCrjcQ0eQppNGMHWSSbHMk8xz2eAfN\\nUUhzD7mr+VkPvenRHGb0qx/ACDrng01xwusTQq8BQHYOqu6hLtfwRmPYK9HPEw5Ylw3EyQVEWcAr\\nieorgWPvgOSygews6nuEMIecs7HipEe3o9EX9JLXjcfkg4pY+PWK9MHokxLgDF8TQ4fWoxFWfB/9\\nek0qoJRbsU5cIQBijGb7mKBnxNENH37+7gEfFgBwfglfN19I7PNCFPHZ5L7/4b/6ixgyMRY3XXmk\\nK2LNsveoDzSHSCF30oaiJUKRkyHQAZ6iGC+Bdkq8LCbHR4igL1lc0guMqe7OsFsbwxwsoFuH4sMN\\nhlkanPg0stMGzVE24sb1LuO/2jkLkuyDwdIlXQCjD0a9q2AzFu9ovpUsOGxUjUN21owJ2sMkgTcS\\nqh5CeLNkGG6qRn/xIaM8W3UO6XmHfsaTixFpLuHAs5uwq46CniyIn6IBV18EJWFHxoJTlF5308gK\\n4VDYrLeqyZhkFDF6l2IMhohqRq8J84gByM/9yKqID7WtpS7QTYHJI2LOxek2RaibyvFB1RcS2ZXF\\nkIntzmdDEy5zVUNULaqv7KMvye32EkgvOmweZFi+JqHDELs86SAbi+YoRTtXVKvWCPFqHpv7Eusv\\n9zh8sMBeXuFsU+LybIbJt5Mx3s+mQD+l4dLkSc8u+7qlArKhb7cXgrCCowmaqFpgtdl6bkRef55y\\nOFikjCSMFrNnSw4gw5B7/Sp3lGc/JtEdD5i+aaAaer+0O7wwuvYoH/cwVw3k4zN+jrWGz1P4SU67\\n2aqBb5qx4MJ7eCWhPzgFipwwjJLwRkF89JQFLbI19nZ4vAB/zqaCvVwA3kFOJuRTH+6NrBiXG9ic\\nsOJQauRvncGVOaAERBcKYD9sGSDWwU0zWuSuWkIVwdjLZQYu3APpyXqrdC44VI0zJNnTB6e+V4a5\\nChk4m9cm1J14YP52g+SjSz7w+n5kpJAf3o6BDJFLPu5C4hqGbQpQZOLIG2ztCK2ENSb8AOFhQIhF\\nJAkQk4Ni2HJHa+Avwk55ITBx4RixpDp693qxLcpUuJGd0u2mLNJrsjDih7cNikIROlt7LseABnSU\\n1w/Z1u9CBqiGcAFVls4A8NEKlLS4IZNo7hR8ejvS9NZ3JkjWFBGk5x1skgOCxZlKybjlFpCWhUf2\\nHtnCBciF3Xuk9NHoy6O6l0N2Hl5nLNBLi77MIHsOTMdBZ1CimYpOj8l1h35iMORBILWnRrgkUgtV\\nG1gmQVLvA4tEb9it2lyGLauDXvVIc82uJRfjENlLsMiHsGKbhQdd7eGGYGUbBFmjTSkFj6juCEw+\\ndNCtx87bHZq9BNl5h+YgQXI9oNvhx9AmcrzRdBMsR8PANjtvoVctk48Co0N2FmrVPFsUJXHJdNEj\\nebyAaqeYfCDhUgXZ2jEeLcbdISh5y6cW2WWPZKORXGssrg5wqRm3NlnxodUcCsiW72P52CFdOgyF\\nhFlZNMc5ZBvUwYNjkbh2LOhaQYSQY38dKDLBDEm4CezRDk2oJpp01cUAYDbeHxc/NkWzL1AfOegN\\ncPDbGunKonjSotsxYx4qg6o94Zo84y7AevQHBYT36B5M0E35mUzPKsgPT+HXG6gZ7X5R1fAHu7R/\\nKAzU/SM+fBZLiNl07FDRD5TzGw2d54wcW63h1xtIY6hQBOCnKfRVDVcYJBc9XJnDFQb60QW73pZF\\n0hcZlatSQD06h59NMOyVgODnX50uIK/BDnhTAfMpodI8pGA9WpPRJWlb0R7kaHd4H6we5khWGeTg\\nsfNOA1UNaI4y4MEehHXQbz8htU9IDn8BFvJYhC2Vl77rxsLsm4ZCIGAr8rnpGw48Mz8AMHbiPsJT\\nWsPPJvB5wvCKqyV83YzJQl+kuf6uRVwI8RDA/wjgmB95/Ir3/r8VQuwB+F8BvAbgPQD/off+KnzP\\n3wXwt0B4/j/x3v8fn/U7vORNny56mJUApMCQEzrYHGdM7Wm2nt3RI8Ul/N6h5JENuYdLiZV6KZCd\\nbYu7rvw4cJt+NMAs7Rg4rBpS58oTCy+B8qMG+nwFN8vRzzPGTQV1YSyI7Y5CfVCwqxQBcw5pQ7pi\\nd9p7AJ7udKZy8FJic1fSU2VNJWraBve25YAhpzOdV8wWldbDKonilApSERSrLqH1a3Vk0M01VLu1\\nhdWVZGp8YPsIx+1+ftbT4rTktrnZVfBhd6M6D1sE35r9NIQBcEjYzYgJR5yYzAR22S6NYcYYHy4q\\nsFYQ3ncfIMLllwLj5EEO2QHVYcZZx66GM4zic5o/ozizSK8Gdu5hfrB6mGIoUpRPLWcXb57SrD9N\\neIOMRRzjkNedXUBZC1lkNNzvA41uKNDtJhwwBsaSHBjw3OcS1R0B85Vr1KsMbaHglUKy4OeE/O9A\\n0VQYOfxp3cMnjMSzuUF/fw+ys3SpHCyLnlbAzhRiHRSCAOzBDLYgW4m7MSooXarhtSDPvCPVdf62\\nx+ytJeT1Bv2dHbT7KRZf1uPnOr0a6F/fD7C7Uyp/S4WhUOOuTPWeIROzFOr1O1CXG379/hTdHneY\\nDCC24XwUMC3IBzeakI9zwJqDPWepcJR7u3D7M2weTEYevWoc5MQge++ShavtAD2BO9yBqFr4WcGd\\nC0A1Y90Tb1+uoD4MRTDPgDwPBlQd+q/cg0sVurmG7DzyR5swY2ghVhuoNAGEQPYePU3SBztwShCm\\nsR6i7VF+e8lhbNOTAtgGjP9mvuaNIuqvl8S0owdKtKWNOZoRLonBxyFTE9YCScJzCM6G7PSDNe3F\\nFemKwwDbdYRnAHLNv8D6PJ34AOBve+9/XwgxBfCvhBC/CeBvAPin3vu/L4T4OoCvA/g7QogfBvCz\\nAL4K4B6A3xJC/KD3/tPnrYLYbTdJoDpuzSk64Qc9P6Pk2WbsumMwQhykMe2EsIhZkpYmh9ARy0h3\\nE6OzX3Og4ZWGXoe0+Kc9zBstRQ9NF4Jnc4jewlw1OPzXPbySaPcSYmwpWQ79lB4hLFSkpAlHIQx5\\n1WE4KBS8VGM3rAObZOyYJW9WLwVsppA9baHqnlJ6LSkSEsxQ9CpFPyPbwhnu/LyQ47kJ62E2Fk6p\\nMRC5nQvYJIHquePRlcOQKTgFdPsxpJcSdr2m33Vy6QApsH6lQLPLrEtrwq7QRwENgm0wRlaPGIB8\\n6Uaubgx7pqeMCPaqTFXXVQ+baTQHBrqmfN4rgWZXojpOkV6xeweI/ycbwldyAIbiDtLLHslTTvw/\\nMR3cOcA6ytNv8JRdGnY24eHuTLhWJcUnZgV035pDFh52NsBmFJrFwA2bsBHQ6x56UY+QgAg+1Dry\\nryNmKgSPoevhzy+B6RT2gJ12c6eATSSvWXDK3NzR6EsRvHccdt6q0O0kqPc1Lv6NObycI1sQqsuu\\nwvuzIbVRXW3gdkpsHhTopry2xWmH8o3TcUdA8U9OxlNiMOxPIDvueFXdkz8eiAHoetg7uxju7cHl\\nGmoZMivbDqIsgUkBn2q41EBWHcpvn47Xwl9dw1sL1zGYQU5KQi5S8Gv+5D2OVZKE/uw3nAJFkdPh\\nT4itqEYzUMUZiXYqkS0sqldKZKe8X0TTsbtXihS+3RmSp2t+Bpo24NuKHjLXGz5c23Ysnj64C474\\nuPNbC9m+2wp63PBMCPI4d1AKUmugLMcBqfee1h3WEiqJxd0Gif98Rj8VX8AtruG6HjLPIarPhaQA\\n+BxF3Hv/BMCT8OeVEOINAPcB/AyYvQkAvwrg/wLwd8Lrv+a9bwG8K4T4DoCfAPDbn/Y7nJboJ4EH\\nrULuYBjAmWr7dd0sJGmLbe7jkJOd0e5RsWVTuuCJLlhb9mFbHzxFAIxDOrsHCC8gnKERlXXEu/a4\\nvWzuTuAFIQqqRclwmDzqUR0blE8d+kKMWHxfBiWiEMHvZctmiSZLumJ4gKnpi97ONGzCzi5uib1K\\nIVwaUuY55KGnhAkBAiFPtNv6wXSz0P10fpsQs6ZpWIR4urkcrU7lAGSXAweCZgtFdHONNhhkeSnQ\\n5/z+eO5OUfmJYIikAlylKwfZO9hMYnNM0ZRLyB6KikmlsGVYgJ2mqu0I/QgbdzUePuDUzTx0P0F5\\nOhSBLRKGtLBuLBp8vwLPPm5towud96GAmy1FzZEiyetD5stQcAjc7VqoWkKtFdILOSY85WccgNb7\\nCthX0Mcp0gW95M1VHVhRpNTBe269h4rhPxcAACAASURBVAHeJOywDvZg5wX6ecRCgfxpA3jA5swf\\n7Qvu1PqS7//wao4YsBznCsuZwlAgDA65Q0uuNdQqgXz3MabvMKTAzyZwRQp7MKOV8uAJwRQSqs+Q\\nnXWQfYyK6+EyA18kwbMlIavkzfchtYbSmipGa+E2FWAtpBCATeATje6wBESJ5SvEj9PVPfq8fLiG\\nfHqJ6JndPdijZ/7DXcbKPb1kkVu1LKLz2VhIfd2Erpedrr7coN2notUrgey0hTlZAM2NjNGwM3NZ\\nAlcY7i7qDOrpgpg/AF/mENdrOhcGfxSfEQYSYZcRB5cjjBKLtXMjJEI15w2XQmuBzYYPHzxLHURU\\nhjrPYA4RDLHiQ+8GFv89hVNuLiHEawD+IoDfAXAcCjwAnIBwC8AC/y9ufNtH4bXP+MHEaoXn1DhZ\\nWpiKMWLtnB2sU5xqd7tAu+sARQMsCEBfK+iagpCh9OgnnuyIMwklKLGXQZABAKbnDUHhDBNYmoME\\nXu7C5grLhwb1ETv9mNnJTjOo/3ZksARwkH3w3U7k+HV9waQaeDAyrGHxZqqMRzcTqI403e9MgB0k\\niEs3HN6mi2B0JYC+MOP7FIU7znDiLgcPvWpHGXdMEepmGs1ukPm3PAYv/ciJVy2gWol00WPIFXRt\\nUR8SK06C+yHgYQ0HqRB8CHiFYEJFKEJ1HtUhefg244NKNZ55oyF4GQjukiHQop5SZFWcA3ltMXl7\\nibxIUN+hq92QCQzhwRbtBMQAKLu1JjZrB31dc4u/rgApkZ5skJ4JiE1DOlswhQofXnZk1tIVsFWQ\\ng+Rgs4pzGcJlyTUgPBWjqhXjrqM8sfCKA9X0nEVDrVpK1hODYT/H+n4afLhJORTWQb9/GlLQFVyZ\\nodvNRv9wmwDrBwW7fAs0u7wPbAbMPhiQP9pgmKdjOHFsdlTrkV2x6cmvLJLFQAjEKPjX7hI66i1Z\\nMkpgKIM6UQLpZYfiA+5ORDegPywBLaE2PfTpkvTHIDnv7u3A353BSwHZWehlg2GW8d/mBnIgtxxC\\nUJafauy+QTta2TEge5inkMkhpfrvP4FZM89STCdw8xLYmwP9ALm/S2xcSe4arpbsfj2FSv293VF/\\nUT7uoNYt4ZgIQ8TOOODV8sMTKB0YJ1kKe7wDZxS8lvzsFBnPVTrmZl5yR+Wi13jomn1goIyF1XkI\\nKbdug0KwywZGmGws/AED58UOKl2jt/CLEFuGS+CHR2z+867PXcSFEBMA/wjAL3jvl89MX733Qogv\\n9JuFED8P4OcBwEx2sX6N2/J2R0L2Eum1HtNn4MFOLPhzZGcUsQC0Qh1yjyH4n+gNh0+6ZuFxCeB6\\nAIkYVY1OcyudXjMVqDin6MYHKfrumzWKc3KyARYiawiNNIeA6AXycwBCBdc3CxFsTG1CjD69ArMq\\nNyz27Txyr8PxNYEfrgBbxsLBbT0KoC800qVDX8rRa3tUnW5YzPVEwSwtpfuhm/JCkNVS6nHY2Bci\\nBEWzi5w+IgdXdbQbHQqJdkeNtqmqtiGlXEP17E7htnz8/MKy6044EGVohx8DO4TzGFL6lauGdgTp\\nkjsPp9lNO8Vz8MGxUViH/LTF5n4WHujcVcjgYij7wBzyFEqtH2isHu4iWXmUJyWSx0uIfgCchGj7\\n7Q2nJNzOBPW9SejmAxadijGVRzgffNYd2pmiNaoi/9osI0887F5O2hGnl80A0fUQqw1EmsB4j9xI\\nDBMVPgspipMO/ZfuMKZPS3S7Ww8UgDJ9MocE0o3F5Altmds5cygXPzAfg76julZvEHZSvA7trkb7\\nMIWpHbJzUnDrAw3dBLuID7dDYXruSDidYfJBA9kpJO9fsGhax442MUA/oP2B49BIhTzPSkKvO+hF\\nA7muYN7YQEwnsAcztPsZ3F7C2VQRd4WMlFNrWu+K6zV87ObTBG6aA4NDfzQhZdR56MsNC+sw0Dlw\\niNCDh/72+zA7M+5wjAYWK8ATU45cbaTpM5ayvu8JwzgH9XQBFZJzxhUS6ePn5ePFOBSrYBsb6VmB\\nLhgLtJTPFuaPF/ybdU9rfu0NvxRX30gVkuL/nyIuhDBgAf+H3vt/HF5+KoS4671/IoS4C+A0vP4I\\nwMMb3/4gvPbM8t7/CoBfAYBy/6HPnwbDq0APg8SYeB+FFeUTxqi1O7T97OYC6aVANrBwdjse3Q6x\\nQtELJNcC+Tn518LT2hYAVPA4qY6YxFLd1Sgfe6iWsVXV3XRrLpVwW686D33KLbWw225UDEB9aNDO\\nebMXZw6TJyyeza7A5Z+nCZTsOBgrH9NnOlltM0C9EKPtaxzYptf05IgQQnyYjeEDFR9ALpGA0Bim\\n7OyGIvy8bBtrJy1ZC8zzBDZ39WhHIPvgvR5CIbop+fNALPrEXSNVcfWqwKURMCva/zLIlvxpCBYN\\n2Qvo1kFanh8EUO8z0i7yypOA1YrOjXxhdBbZBX1GfM/CHRdpkC7YHPC1bUaohJtm5PzGIdPeHP7e\\nIaoj+ugAoHmadXBGBlvZ7YPRh7lMX9LCtttz8NrDLDWKpx7zd2qyWZRgIlPITxU9iw19RTR3AxIo\\nHcIgT6E7nkL1Ft0ui5xZW5jwMNH1gL7UMBuyM8xVA1smSPYSXL/Ogboc6H3jDAM4nKEwSTcSuqEt\\ng9aAqh3qQ0r8bUa+e3niYI2EqXsk6xbJqWRGpVLwKal13YO9rXDmqobLNOSyhl61MJcV8iC9p+89\\nGSdeK4jpBN5oqIsViosVB6QHc4p8AFgTobBtxxmFTZASLtNQVxWSd87g1xt2tkbDdT3x8Bu+2vbq\\nGjJLOVOIq2e4gyhLyNIQxw7dOL/Jwa8JmfhN9Yy3ySh1j7zwkE7vA1bNDx253mLken+suErBh02s\\nk7HjVgoySPNj3ma0uB3XMDzLgkHo2qOh1hdYn4edIgD8AwBveO9/+cY//QaAnwPw98P/f/3G6/+z\\nEOKXwcHmVwD87mf9Dmk98lM/craFI4WQakEfpst0OKy/pMftJCOdKBBhqK1Hcq7CwC3AGq8BSymR\\nXgmYpQ+/j9N+1SIUQo/6gIHFybUcTY68AqpjGjoJGwsGAwcAgexUoNnjDZNeeXTTaOAlA5PFY/Ih\\nf48zhHPaHYHqjoJqFYVBjYd0VF7KYUs9tME9sZuLUBj5O6TlECu75PZZrzvYMkE/4aVspwo2IUvE\\nbNz4ngL0H5cbz27Vb5OQ5BA9yRHSg2Toqvk+enHTK0bCBfGPzcTYTRdnA3Q9wGlJdaHgQzBS+ACe\\ng9d8EKrOoz5MeB2tHwVKwgLtlIVaZOIZbw4xCNIcdeiiLX/W5m4CmyXIzqdI1h7lk3Y0leIQVkBv\\nuMOA95CDQz9JRpiIQjKP4mmPbCGx8zbTa9JLC1W3gbfcwE4zXL+aoT5gp777JwMmfzyQMz3JIZsO\\ndp7DZprmZ5pudmbRYJinMNc9+rkJOz6+J81+wp3arkHxIb0z+qlBs0NdgU2B+t6A8phqwfZpidmb\\nGpPHZCTZTGIoJJLrAc0+d18QwfjpeoC57ql0FALVl3apajQS2UnFXM6mg5sVGGYZH27zDObJAhg4\\nI/J5CkhCdrYwhCEkYPcmhEtyZoKSIqmQXDMTFABgALVuCUc8OgF25vCJgS1TDlKva2CxZJHb34FQ\\nCj5POHS+WMFvag4UAeiH9wgNWQu3CorssqD0XwiKbyIubgz52Fpvlac+dM9aEwoJ3uE+UAhFYoAO\\nobsO2HhI7UFUYoob2LV3W2l8xMdvCH9uiqyE0VvV67RE5OaLxZIPgQjNWGyL+hdoxr+r2EcI8ZMA\\n/m8A3wQQKQD/OYiL/28AXgHwPkgxvAzf80sA/ibIbPkF7/0/+azfMZs98H/+r34twByBSth4dCVh\\nj2TjQuyaDR20wfph6J72wlCrDxS34NcRE2soDMEodgEwOhRmFze4zQXFPhz6bYeD3YSDLwj+HLPi\\nG+wj1BquVbYINELBAtPOBGxGRoMXQRwj6GmiWtK82jn9WpyhoZLZ8FoMxTZ2LZ4DU9CDuMbweJKl\\nDwW9H4t4hBsihg+EoeFoKObHZCDIuJ3n79VtlOM75ioqDjr7kqnhcuD3Utn5/7VwHUJIRrrkz8nO\\nqZJzmu+HV5xtOMNjd2ab5VicDhgCHz/mPMaBMRCk/Z4c/viwGXIElolnev3VEHJLGYobXQXjw0xV\\nA1yqsLlj0M4l2n2gObKQ+y2SZEDzpMT8TT5c06ULniz9yJlv52R7ZAsyfPLHa9LU1hWQJrwxrYOb\\nZHCZ4e7vMINw3InUhxLzdwfIziM7CfFsizXszgTtUT46YZrzCt4oLP/cFNWhRH3Ho586+MICnURy\\noTD9AJi/3cEs6S5oJ8loeyusp/VAIVEdSkarNS6899uIv/ykhTklldalmu9rppBcNYCUUBcruElG\\nYU3XjzuOYZ7DFpqxgWbLFlOtDxz/OGhmQwIBJFcd7TAurjAm4GQZH3RCUJBkFH+XJb+esImiJqBq\\n+PflijBJFOJEu1jvxyIujw7GAaYYLEMfAHqiNIHiF3cHH183aYY34ROltlBLLP5A6OrVs1j4DQzc\\ndT29UJSiujUyVuLvuum3MgyEUrzD77jferlk97PJff8X/vLX0M7kGAMlB4wKS137kP5NYQqAYPnJ\\nGKdm36DeE2gOOdiM3tO6ErAJfTHSyy1P3FTM57MpRmVhpCv2RZCQr3gc2cKjm4gRy6V5FnMBI5/a\\nVHwY5OcDhpI3ejehdD066gmH0Z8lcoxtimfcGOPOAiIMQbutuCcWyNFPOwnDvg4jkwegsnEI3tYi\\nbP/Saz9G1A0ZH3K63aYi9SUfODL8DMa+cYBKq1qHIZejvH8MlV5bGgldNej2c36NERTshM+4tIA1\\nPE/Zs3NmCtOAvmTR2RxJDCXph+nCj0HPfFCED4knTVE3DuWjBu0e+ez1vtoW12uH9HKAagb0U8NE\\n+NaNznu6toFnTw/w+kCiOfDo7vcQtcLONyWmH9HorDqkX3uydoQrap7Q5m6C6ogzmWQJzN/tkT2p\\noM6vKQoJQhcGE28ZE/Z4B7ZI0E812UZBXu4SiriK95bo9wp0c4N+IgOVjnTL+tijvTNAJA7J+yl0\\nRVguWTOdqjjt4TRhHq/p1cI3n4I13VD1rGoLc1mhOyw5ZBwc1KqhcKZugP0dFmshIKxD93AXqwcp\\nIIDswqL4f97kIDnPAwXPjYwTdzAHrIfPzWj0FWX0MkBmsh0gN0E1ut4QL04T4tmJIZdeCPjMsJgP\\nNgweBdy0HKPNREvbAne0i2EaFKQeMI+v6EY4DFtcfNhCJbFTFkZjTJ4Htik/QYHpvd/aCYgtPPOM\\nF0qEZqTk61HEc2NQ6a0blaAiUBb9MDzDB/fO8yEQwpIjzPMv2n/ycsnup/MH/of+2i/ChTR7Gi75\\nAJnQUD+aYw3RP2LGLM38zAXBCm+8ZM0Bmq5d4P7y++qD7fthgleJDeyUuGVnujvZKlGkoit+bbJi\\n1+s1YYl2Ti/qoWSnmwS6smooGU9WfvROaXeBbpdMB7OkXNsZUg+9DiyZxCO7DLuJIGyyOTt0dlf8\\noHYzEYZHwalPUU6erIKvSIBPvBLjOQ2ZDKKk4MUy4EZhJvbMgSTGrlwOPgzRLHdAWsJmDJOm6IjF\\nJz6QdO2RXtGp0awH0hENB5/rewn6EiMebir6oMSACmdoupVdhZvEBxWp2NIfhfXcYfggqGqD+2K6\\n7QKjkjbmkA4FKZUInaIXPNduLtAcOcj7FfbnG1RtgtWjGdIzBVWHAW5HRk195NEfDEjONPRGoHzM\\n96U6FiieepRPBiTLHubJAq7IyCwpE3S7KdpdHR6Y3KFVR3y4mQ1DEADAXFa4+gs7WL0SYLnLoII1\\nAs0BYHM+bIc535vkTCE/4+ckWXFQHeEyxtlx0B7ZTqolPVW1NJnKzmp6gkcFq5EwZxt4o2CLJJAB\\nFPqJJq9/AHRlkX+4hGg6+NSgeThHN1OwhpCZ6jzShR0fdP7GUFBXPdS7JyNlsL+3C5cqmMsaou7g\\nP3rCh0E0iIqCmhg8DLAQB/n/8NXX0RwmDPo2wO4fLcM8pQfOLrZdOvAsjh2NrW4U39iJi2gtGzvz\\nmEAfu/tYtOPwMxTtOIAUUmCU0sdjDt372M3HB0Qo7ONrwDM/xzsPIcXL5yce4Q+n2SnqxkMHP2Bn\\n2JVIK0ZYwCmaJPWlQH1AK0+9kdzGW2DyUQdd9eh2Unip0YWhYyzM9aGn5WhNjjlE2OobUr5Ux4Dd\\n7NJCVxZDoWA2A6qjhMZWJtyIT2kcVe+RBtlPgveLJZZtgulZfu4xex/Bj5n0sSHwy63BCG1sHjio\\nijuI7NIjWfKYGf5Mj5hkSYxdB6N+c7qCL1L0u+QdD4VCs6PGQqYbPuTMmrazQy7HDtwHPNgZ3qw2\\nYZffTSnjFpYPgGQpIUNn6DTQB7gmWQHZlYWuBwy5DtQ4Dlplxw9r5L93UxpcFeeWyeMXFVxm0Bzn\\ngKflsFl2UBvyq7uDEs2+RnUQOYYYWToivL9xbkH+NJk/1ZFEtDPog2eMDOdfnDGdZ3MvhZcS9b7B\\nMFFItAXKAThVMCuMuZ7dHAxcBtDtW3ihsPhBXivZ8jiSRQdVdejv7qCf6FFpC9D4KUJaejMgXUgO\\nLmMYAYBhliE/HzB/u4PNNJavsrNUXUy14vkV5+GYJh5DFgfS27CG+PO8FLRs1ZIWBYODGARUa4l1\\nG41+NiOWvmcw5AJmP4GqHbLTCnBAN0+weqjQ7nIYf+d3aBV78W/uoy/ZBBVnAyZnhIRcuoXybKFJ\\nOQQoJ398tvUdEQLmwwvmS7YdceNopmUdRJaOqTsMf2Zn6u4fsVBberRPziV8ZmAnKWyZQF9V5HYr\\nBW97PhCs5bPtBk2bx3ijab1B/7vJtosJO0Ao8BHrHhOLAlUwQiix649JStFvJoicosc4sfrIBQ/Q\\njJAQkh15hFI+Qxb5ieuFKOJycJi/2wTRT3hTHbvBfsqOj4b7PDtzTVMbVxg0hwmqfZrqt3NGh61e\\nSeB0wuFmGdzRDAeUAJCfijCopES/LZjYznAIUgNVy626mhIi2ByrUT1I6pbA5g4l0bKnsCZb8Hzq\\nPYluLtDuYDSFUk0oLjbwkgcEzm7cdYBsjimHjcITzuiDyKjZD5xpxweMlxLV3RRmRu+MCBV5xd+h\\nOg80nv7rANxUIru2MGs33mTCeYjWwuU67HhY1NKlQ59L2hkIoNnVyBY8Z2t43H3B44qmWLJz6EsN\\nXbuxC2QoMAeW6dJxML1Ppzzxakr/l6UNUItnd9gpyLpHerKCaguonvBEnxP+iIwSL2mW5WWgaWbB\\ncCt8biBCOMaMKswhD9BCxc5cWgAbjYvrPeQnEpMWyC5ZIJ3Zwly6AnZ/X2PzgH8fSo/5WyEkefCw\\nhWaIwpMF9JlElhh2tcGGVXaAOQ2pMoEKp28UCSUFdNfTYTDLcPiWwfDwAENhsLln0OySvnn+Ixq6\\n5kM8vWYHni56nn+iRm/0ZNFC9APEVQU9zNAfFKiOg1p3niC5amCuGhgJ2ElCqum6Hb1Huv0U1REV\\no/mZR37ugn92jdm7CfqpQbvD69rtZkguGpiT6+2w7mC6LZRawr56DNn0gcIrqZSEYXc94Blowtc1\\nIGRQcZoRFpHXG/gyZ6RhmYxQWXLNuERxeb1Nz0Ho5mNHn6ZjEfVBzDfG2EkB35NeOHqjxKIe4Y/I\\ndvrYegYTN4ZKVCHZwUcLX0G4ybtAYQyYvVARNokdObaD10/C6b/LemHglNf/xt9m3JSgopGKRG75\\nvQqUuSAISq8d9MbCrDpK0r0fcTibErvlsM0jPe9gztYY9ku4lBeo2TOh+/ToSjn6Q0+eWMADyfUw\\nptTLwTOObSrGbasJw0/VsZPuppSlu4Cvy8GPOwZTEYZp9jgcHEoECGMrjIHgw8YltBkVgxhxab3h\\nQyBZ+NE+YMgJp5g1t9Rx2AcgGG45yIFb+H7KwWRkiERFJ4tQ8BQfSN8bSoVuwl1FNxVjahAplmG4\\nOADNQTDhWodUd49xCB1zRl0qUR3qkATP3YkMfuxOs2AK55FdWCTLHuq6gawaOtYlCvW9nDucCBUE\\neqVwAVJyGD1z4vHpyo/xc0MIv1C9B0LHnl+60QqgnQusXuFMJL3kzq48GZCdUXW5eWWCds4C2hcC\\nu2+1WD1Mg28O8ftuh914fsomo92VHI5vHNJLdlrNvsGQiZFbv/hygn5KmAYAbO74cLYC+YnE4Td6\\nJNcU3UBKpvekhD+cZkNjrhvCS/thYOCB5JI2t9HZD1KO70W3m44D7exkA3mxpPw8TbD+oT0qdaec\\nEfWFQHWPnzVV09OdHHEXLFxtEHbRJVNYj+KjDdTpNY9FK/iQRekzA5dqyKrn7uVyNRYpd3nFbjlJ\\ntvBESNGRWcruNjFBFRnaaSGoqoyQiFIQyzUVjhEvj4PDm/awIf5MJMn2oRG7fSW3yfbAM8PMqKAU\\n0aI2rGgzK4p8O5gM8nuR51vF6NXiWQgmdtoRBw/wS3wg+K4bsfqXz098/sD/yE99DaolxxseaHbU\\nKDEXzmPyxKKdKWQhYUevepjHl/CTgmKX8EbIyxV8mWPYL9EcpVi+okd58jgorVgAKLphQXWKXQ7j\\nu3jjRkxQtX5kg9hEwFRuDI5QPV9LF/z93VSh3pdoDrjtVDWLx03ssg9YdDengCgO7XwQwMQg5+Kp\\ngw6YZr1HawLhwzY+0CVHMVT42cUZXRN1xcGVsB7dDrvZbqbG87OJoDIzlzBLDsSiBH8MHwZpnXEI\\nKgeQ1y4RoAoeA9Wf7MpjvqRuHPqS3PB2HuCaEJwBgJ7vFy30ogqueNm4LR8mCWzGAWQfhCMc7t7A\\nWoOegF40ZF5E/viQbbNa212Bds9jKLbBD/lT4unFmYXZOJhlj243GbUByZrXq93hQ6TZJe00u+Rn\\nT3bMNo2OhaoZUN0v6Dk/DU6PA7D8ElB+JDB/r6dFsPVo9g2WrymsXwkHqz18ZqHPTTg2YPp4QPa0\\nZfpNQfvVMRtVxaca/6ca7nyyJ2v0e0EBJ8H4s82AodTQaxYotW7JLwv2trAekEBzb4r1PT36xTsF\\nFOcc6KZnFWyRoD5O0MwlbC5QnDq0IXmnmxNyya4cJh91SB4tMBzStqKbJ6O1bnbeQJ0v4S+u4Kpq\\nG6AQh3mhgI/FLVD2bnKrRRFMxGIR73pSBeOK3Wz0QgkiofFnROjmRnEfMfhgZjV24MHrxFv7DOZ9\\nc8lJCUgFMSn4M9sO3jkObYOPziip73r4oX/m+z+x8w6vfRF2ygsBp0SD/nSxDSOIQyrVWNRHCUOM\\nS9q7plce/cxAVZNRqRj/DyUh+gH6YoPJ2QrTb/BCulkxema4IkFzlKEv6OzmFeBKdtrRC8UmhC9c\\nws5Tb9id5ufhA+CD8GeFUQnaTRXaeUgmWbJQxCImLO1Ok2WPyabHMKU/xebYjOELwoZ0nYwe21G9\\nGmXfuvYUb4Ri05eBqy23eL+XzHHUtcUwMaP/OH1o3Ph1ybWDWQ/ITju4RENfbuAzQgHdTop+pvmQ\\naglFSesZ5NsyZFjabdEch6GWNNDk2kGvWiQlsxmpdmX3HDM7N3c1kqVGeZIhP+8w5Br9hCyTZEE4\\npTuaQFgd3ls5spRswp1Zes2HfvHhBi4zHIhmGqqVaHZpAJYuPPLTyNbhA6zdFajueqxfldAbBbOh\\nJ01xQrMwHQbq6dWA7GSD5k450v9k76CuGyij6MuhBWymUb67hDy9Aooc7at73MX1CnJwgAPMsoMz\\nEsVjC7M20Bt2FP1Uoj6SSK7pUeM1INvAm1dqdK9UrR0DhftZwp2WZFalV0D1ygzZeQO52EB0Pdy8\\nRHtUUrwVBpv6YgNvNG1mqx4A6YjZ4xXScw2XKLR7Ka5f12h2JcyaHWVzmGBzhzs0s/KY/fGCMYHT\\nBKuHyZj1SiuBnXGorjrOtHRtUd3PkWYauswhzy7hLijaGcU1SkEqhgqLqILUGqPwxnm4xfVIxwNA\\njDkU6JGXHQRAN7t3fjgFOeOv3YM3NJXTi4a7gSW9ThD8YLy1EHkGURTPwhyx+AdTrkhp9KsV5P7e\\nlvIIwFf1lhUTB5dKbdWYnu/tTZYMv+gzS+UnrheiiMOzGEUGSRRxVIcSxalEsrSYvlOPBvOit5BV\\nD59pVK9MIQYPUxGDTD7qYOcloARk1cEnBi7TGKYpqjtRScaill0NY25keeLHoZlLBJavqDDEY0J7\\nsozwA7u05LqD1xJicOjmGjaTSNYWcuAQsC8kIGmwRJ43B6DwgAqwjpcUyYxvg0Jw/ZMjY4b2rLyp\\nx7SfwYfPMQu4TbdJ7pMnHMb2pWbg7w7DmGkTF+Abzx2HVwK2YLdUv74bYrIUgzg6DkI3R3pUiWbX\\n3BXEkI04bGRx49+7uUaysmj3JmN4g01Jy0yXFn0hR+OybkYKneoMzIoBGM2BQTtPkc008o/WUBU/\\noskVdxHd3EBafvg3dwL75Q5d/cyGpkjtTDJ8QxN2inTV6fs8p71vd5i/owJezm7eZsD6PkOdux2F\\nvTcCLNYNMKse1d2MsX7HAsVJDtV5zN/aQF+1cKnB8s/N0f/4LvqCHT4dCBkp56WGS3KYtYVedVCd\\nw+SEBdmdCUw/FBhSYHOPzKV+ImFWgOws+kzBrIl9i447puSiDoM2IOkH+MyMxao/pjuizRRUY1Eu\\nGRUnWkuLgA0dCIeDKYZSQzUW/YwmUdWRwuKHuFPtW4FkpaCaBMVHFZJFwgfYuoNoB6BIoOoBxSmh\\nlSEVuPqKQnEix2jC2beveI6pGSmNoh/GKDMAkGkaOmIyROSNfxsdBiPrI4hqRJZu5fJ9D1/V22Fi\\nhFkiVTDGpiUJc0GtxeKrU/SFQD8tMHvPIr2aIf3GuxysOgcpJfyIg5PfjWEgZg/A1TTHGmGfLIVv\\nW3qEj8PMAMUoBW87Fu+basxQwGUs+jcgmS+Ki78QRVx4j+x8yw+uDyVpfWFbC+/RHOWkr60GaOfH\\nIp0/9rBFgqHkVNwXGdTJBfy0jqyT2AAACTlJREFUhJtmI8alqh7zPw65hkaNw6DNcY7qDrv/GJOm\\n+sDmsMDs/ZB/l9A/hRxu7hCEt/BaoDhp4aVAc8At+WZPBX518EgRgY6YMyW8PLHIn3ZjMndXyrED\\nh2eGZLPv4Q0LuVkpZKcUFMmehlM2FXApoNdbYy2ARbObGJjaoQmGVZu7EnrjUZw76IqMGjlQjq28\\nh9NR3q8gO4++ZHfXzjgLYAdMGEg3jp2MJg7sFHcF1YEKDCMOgSlU8qMgqJsJtLs6XE9i2GZDup9N\\nNcqnDEMoTjo0B3TZs1+eIb0KMWZCELOfylFFmqw8M0RFVHGSVZMtLNJrdp+bY7KTIvQye6dGP0sw\\n5ALrexLdnArc4qnHwbcGZE9rtAcZqiMNZ4DV/UP0E2ax6g1hIa8BWQEXPzqBrkuGLFwNyM88NncT\\nNLsSOsDV9ZHA4sBCthI7byjsf7ODuWqgl7xRlz8wRTcVqA9pxRsH2qol9qwrPuSHiQmDX8r91XqA\\nNwrDfMJos5AvqVctRGthzi5ZwCYFfJbSHmAIw8O6hsqTYMXAe8orgVwD5vclvPB0odTA5Q+lGMos\\nWDR4lCcJVFNgc5cP9/qADc7VVz18OmAoNA6/Ee7ruqWx1aykCZYHMehhCKyMwKn2N2iBwBbmiLaw\\nwNbbZOATeSxzEbK4CXVYO1rcjh250fBVBTErUZySCrtSatxpDj/4EPo7j+llElgzALaF+Wa9isKd\\nyCMH4NcbeqvHCLvwEBk54cAz0FCkEsYdhHcean+PA1IA4snHaTWfvl4MTHz2wH/13/saouQ+LrMi\\nZhu3ssllN277xaaBmxYQzmHYySA6N4a+UuklYZ4s+UHomCriQwfcBTpeP9Uj/htNnIDQIZ8OI/YZ\\nY8tEoK7J3qOdk0rWzJki1E8CjTEwJ2zKwVoU2cQUnNG6FuwOo/Ohl9yNtHOM+ZxxsJgsyV02tUe6\\noLJR9qHbF+zQI0wSwyuyBcMTXODyApTh0wFwQHNAuXfEds06OLcFgUZ9QJgnPrjkEKxtw9/7YB7l\\nFMaoNRHYGJGGCbdlyiRLN1Ic6VHCh5YcyPUvTnukJ2tyv+cZqrspk+Cj6jSqnkOREza8d2AXnV3S\\nZMzpQMvcZYpSdZc7KV1jVJe6JAQlG4znNvkozB4OmWhvE8CsBdJLdvNeAf3MwwQ/nvzcwayG8PsV\\nvAZW9/U417A5xgBoXSMENW8H3xHfzy8sstMW61fyUelo1jZ4sziYx1dwF1fwTQtZ5hC7O7B7E/Tz\\njM6L5xXkxYJxYT1ZLiLP4B7egZ0kkL2FulgDy/W4xZf7e/CTPAw/BeQV7xPfMyRhuLeHbi/D8lWN\\n5Zd5PumVwORDj9kHDYZMMYh7R6K6FyEGoJ87ZCcKKjAM994YUH7nCu6dDyCnk/G+9nUDtwn8WyGg\\n9nYphw8whVtvtli0onVBlK7LvR0OToPfuW+aMdIsDkfj8BAAZGLGGDQhJbov32G+btAzqGrgvKF3\\nkKuKGZfW8b2M3PU0QYxs4wm4UYHpB0vBUtPQq6Vttw+kMMjkDwmznYivh9c//jWyLCCEwG+vfx3X\\nw/nLM9gUQpwB2AA4f97H8j1aB/j+OJfvl/MAbs/lRV235/LJ61Xv/eHn+cIXoogDgBDi97z3/9bz\\nPo7vxfp+OZfvl/MAbs/lRV235/JnX/K7f8ntul2363bdrhd13Rbx23W7btfteonXi1TEf+V5H8D3\\ncH2/nMv3y3kAt+fyoq7bc/kzrhcGE79dt+t23a7b9cXXi9SJ367bdbtu1+36guu5F3EhxF8RQrwp\\nhPiOEOLrz/t4vugSQrwnhPimEOIPhBC/F17bE0L8phDirfD/3ed9nJ+0hBD/gxDiVAjxrRuvfeqx\\nCyH+brhObwoh/vLzOepPXp9yLn9PCPEoXJs/EEL89I1/eyHPRQjxUAjxz4QQ3xZC/JEQ4j8Nr790\\n1+UzzuVlvC6ZEOJ3hRDfCOfyX4bXn/918d4/t/9At9+3AXwJQALgGwB++Hke05/iHN4DcPCx1/5r\\nAF8Pf/46gP/qeR/npxz7XwLw4wC+9d2OHcAPh+uTAng9XDf1vM/hu5zL3wPwn33C176w5wLgLoAf\\nD3+eAviTcLwv3XX5jHN5Ga+LADAJfzZgPOW//SJcl+fdif8EgO9479/x3ncAfg3AzzznY/perJ8B\\n8Kvhz78K4K89x2P51OW9/+cALj/28qcd+88A+DXvfeu9fxfAd8Dr90KsTzmXT1sv7Ll47594738/\\n/HkF4A0A9/ESXpfPOJdPWy/yuXjv/Tr81YT/PF6A6/K8i/h9AB/e+PtH+OyL/CIuD+C3hBD/Sgjx\\n8+G1Y+/9k/DnEwDHz+fQ/lTr0479Zb1W/7EQ4g8D3BK3ui/FuQghXgPwF8Gu76W+Lh87F+AlvC5C\\nCCWE+AMApwB+03v/QlyX513Evx/WT3rvfwzATwH4j4QQf+nmP3rurV5KCtDLfOxh/XcgVPdjAJ4A\\n+G+e7+F8/iWEmAD4RwB+wXu/vPlvL9t1+YRzeSmvi/fehnv9AYCfEEL8yMf+/blcl+ddxB8BeHjj\\n7w/Cay/N8t4/Cv8/BfC/g1ump0KIuwAQ/n/6/I7wC69PO/aX7lp575+GG88B+O+x3c6+0OcihDBg\\n0fuH3vt/HF5+Ka/LJ53Ly3pd4vLeLwD8MwB/BS/AdXneRfxfAviKEOJ1IUQC4GcB/MZzPqbPvYQQ\\npRBiGv8M4N8F8C3wHH4ufNnPAfj153OEf6r1acf+GwB+VgiRCiFeB/AVAL/7HI7vc694c4X1H4DX\\nBniBz0UIIQD8AwBveO9/+cY/vXTX5dPO5SW9LodCiJ3w5xzAvwPgj/EiXJcXYOr70+DU+m0Av/S8\\nj+cLHvuXwAn0NwD8UTx+APsA/imAtwD8FoC9532sn3L8/wu4ne1BzO5vfdaxA/ilcJ3eBPBTz/v4\\nP8e5/E8AvgngD8Gb6u6Lfi4AfhLckv8hgD8I//30y3hdPuNcXsbr8qMA/nU45m8B+C/C68/9utwq\\nNm/X7bpdt+slXs8bTrldt+t23a7b9WdYt0X8dt2u23W7XuJ1W8Rv1+26XbfrJV63Rfx23a7bdbte\\n4nVbxG/X7bpdt+slXrdF/Hbdrtt1u17idVvEb9ftul236yVet0X8dt2u23W7XuL1/wKRl0AIVN0G\\ncwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"from sklearn.datasets import load_sample_images\\n\",\n \"\\n\",\n \"# Load sample images\\n\",\n \"dataset = np.array(load_sample_images().images, dtype=np.float32)\\n\",\n \"batch_size, height, width, channels = dataset.shape\\n\",\n \"\\n\",\n \"# Create 2 filters\\n\",\n \"filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)\\n\",\n \"filters[:, 3, :, 0] = 1 # vertical line\\n\",\n \"filters[3, :, :, 1] = 1 # horizontal line\\n\",\n \"\\n\",\n \"# Create a graph with input X plus a convolutional layer applying the 2 filters\\n\",\n \"X = tf.placeholder(tf.float32, \\n\",\n \" shape=(None, height, width, channels))\\n\",\n \"\\n\",\n \"convolution = tf.nn.conv2d(\\n\",\n \" X, filters, strides=[1,2,2,1], padding=\\\"SAME\\\")\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" output = sess.run(convolution, feed_dict={X: dataset})\\n\",\n \"\\n\",\n \"plt.imshow(output[0, :, :, 1])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%html\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### \\\"Valid\\\" v. \\\"Same\\\" Padding\\n\",\n \"![padding](pics/padding-options.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"x:\\n\",\n \" Tensor(\\\"Const:0\\\", shape=(1, 1, 13, 1), dtype=float32)\\n\",\n \"VALID:\\n\",\n \" [[[[ 184.]\\n\",\n \" [ 389.]]]]\\n\",\n \"SAME:\\n\",\n \" [[[[ 143.]\\n\",\n \" [ 348.]\\n\",\n \" [ 204.]]]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"filter_primes = np.array(\\n\",\n \" [2., 3., 5., 7., 11., 13.], \\n\",\n \" dtype=np.float32)\\n\",\n \"\\n\",\n \"x = tf.constant(\\n\",\n \" np.arange(1, 13+1, dtype=np.float32).reshape([1, 1, 13, 1]))\\n\",\n \"\\n\",\n \"print (\\\"x:\\\\n\\\",x)\\n\",\n \"\\n\",\n \"filters = tf.constant(\\n\",\n \" filter_primes.reshape(1, 6, 1, 1))\\n\",\n \"\\n\",\n \"# conv2d arguments:\\n\",\n \"# x = input minibatch = 4D tensor\\n\",\n \"# filters = 4D tensor\\n\",\n \"# strides = 1D array (1, vstride, hstride, 1)\\n\",\n \"# padding = VALID = no zero padding, may ignore edge rows/cols\\n\",\n \"# padding = SAME = zero padding used if needed\\n\",\n \"\\n\",\n \"valid_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='VALID')\\n\",\n \"same_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='SAME')\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" print(\\\"VALID:\\\\n\\\", valid_conv.eval())\\n\",\n \" print(\\\"SAME:\\\\n\\\", same_conv.eval())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Pooling Layers\\n\",\n \"* Goal: subsample (shrink) input image to reduce loading.\\n\",\n \"* Need to define pool size, stride & padding type.\\n\",\n \"* Result: aggregation function (max, mean)\\n\",\n \"* Below: max pool, 2x2, stride = 2, no padding.\\n\",\n \"![pooling layer](pics/pooling-layer.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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GHY0ucIKa0Qgp/uxSzn27PX1lZv755JKEqfL4u1dXKb+kKYLvqVTtm0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i8PNTmmP+HqKdCqO5vaiKi52sgkk08CuyyzcuUPDdkKtxtWp8/zq6rtMTowQ9QT50qdP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\\nJAuCIAiCIAjCPf4PmNQ5TEZINK8AAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"dataset = np.array([china, flower], dtype=np.float32)\\n\",\n \"\\n\",\n \"batch_size, height, width, channels = dataset.shape\\n\",\n \"\\n\",\n \"filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)\\n\",\n \"filters[:, 3, :, 0] = 1 # vertical line\\n\",\n \"filters[3, :, :, 1] = 1 # horizontal line\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, \\n\",\n \" shape=(None, height, width, channels))\\n\",\n \"\\n\",\n \"# alternative: avg_pool()\\n\",\n \"\\n\",\n \"max_pool = tf.nn.max_pool(\\n\",\n \" X, \\n\",\n \" ksize=[1, 2, 2, 1], \\n\",\n \" strides=[1,2,2,1], \\n\",\n \" padding=\\\"VALID\\\")\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" output = sess.run(max_pool, feed_dict={X: dataset})\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,12))\\n\",\n \"plt.subplot(121)\\n\",\n \"plot_color_image(dataset[0])\\n\",\n \"plt.subplot(122)\\n\",\n \"plot_color_image(output[0])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Memory Requirements\\n\",\n \"* Main memory killer: reverse pass of backprop - needs all intermediate vals computed during forward pass\\n\",\n \"* Example CNN:\\n\",\n \" * 5x5 filters outputting 200 feature maps (size 150,100)\\n\",\n \" * stride = 1, \\\"SAME\\\" padding\\n\",\n \" * If image = 150x100x3 (RGB), then\\n\",\n \" * params_count = (5x5x3+1) * 200 = 15,200 \\n\",\n \" * 200 feature maps contain 150 x 100 neurons => each needs to compute weighted sum of 5x5x3 = 75 inputs => 225M floating-point multiplies.\\n\",\n \" * If using 32b float => output requires 200x150x100x32 = 96M bits = 11.4MB for one instance\\n\",\n \"\\n\",\n \"#### During inference: one layer's memory can be dropped when next layer is computed. (You only need enough memory for two layers).\\n\",\n \"#### During training: all computed values have to preserved for reverse pass (You need enough memory for all layers.)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### CNN Architectures\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### [LeNet-5](http://yann.lecun.com/) (c. 1998, used to solve MNIST digits dataset)\\n\",\n \"![layers](pics/lenet-5.png)\\n\",\n \"* MNIST images zero-padded to 32x32 & normalized\\n\",\n \"* pooling layers: mean x learned coefficient + learnable bias\\n\",\n \"* output layer: output = Euclidian distance (input vect, weight vect)\\n\",\n \"\\n\",\n \"#### [AlexNet](http://goo.gl/mWRBRp) won ILSVRC 2012\\n\",\n \"![layers](pics/alexnet.png)\\n\",\n \"* Uses 50% dropout on layers F8, F9 for regularization\\n\",\n \"* Uses random image shifts/flips/rotates/lighting to augment dataset\\n\",\n \"* Uses *local response normalization* on layers C1, C3.\\n\",\n \"* Hyperparameter settings: r=2, alpha=0.00002, beta=0.75, k=1\\n\",\n \"* ZFNet (tweaked AlexNet) won ILSVRC 2013.\\n\",\n \"\\n\",\n \"#### [GoogLeNet](http://goo.gl/tCFzVs) won ILSVRC 2014\\n\",\n \"* Much deeper than previous nets\\n\",\n \"* Uses *inception modules* to use params much more efficiently. They use 1x1 kernels as \\\"bottleneck layers\\\" (reduces dimensionality). Also: pairs of convo layers act as single more powerful convo layer.\\n\",\n \"* All convo layers use ReLU activation.\\n\",\n \"![inception](pics/inception.png)\\n\",\n \"![googlenet](pics/googlenet.png)\\n\",\n \"\\n\",\n \"#### [ResNet](http://goo.gl/4puHU5) \\n\",\n \"* 152 layers deep\\n\",\n \"* Uses *skip connections* to connect non-adjacent layers in stack\\n\",\n \"* skip connections force learning model f(x) = h(x) - x (*residual learning*). When initialized, weights near zero => network outputs values near-copy of inputs (identity function).\\n\",\n \"![residual](pics/residual-learning.png)\\n\",\n \"* architecture: stack starts & ends like GoogLeNet, stack of residual units in between.\\n\",\n \"![resnet](pics/resnet.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### TF Convolution Ops\\n\",\n \"\\n\",\n \"* conv1d() - 1D layer - good for NLP\\n\",\n \"* conv3d() - 3D layer - good for PET scans\\n\",\n \"* atrous_conv2D() - 2D layer with \\\"holes\\\"\\n\",\n \"* conv2d_transpose() - 2D \\\"deconvolutional layer\\\" - upsamples image by inserting zeroes * between inputs\\n\",\n \"* depthwise_conv2d() - applies every filter to each input channel independently\\n\",\n \"* separable_conv2d() - depthwise convo, then apply 1x1 CNN layer to result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch14-Recurrent-NNs.html", "content": "\n\n\nch14-Recurrent-NNs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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%%html\n<style>\nimg[alt=recurrent_unrolled] { width: 400px; }\n</style>\n<style>\nimg[alt=sequence_vector] { width: 400px; }\n</style>\n<style>\nimg[alt=gru-cell] { width: 400px; }\n</style>\n<style>\nimg[alt=encoder-decoder] { width: 400px; }\n</style>\n
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Intro

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  • Use case: arbitrary-length sequence data analysis - anticipation abilities
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  • RNNs much like feed-forward NNs, but also with backward-facing connections
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  • At time step t each node sees input x(t) plus its previous output y(t-1).
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  • Below: \"unrolling\" a net across a time axis.\n\"recurrent_unrolled\"
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Memory Cells

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  • A network node that preserves state across time is called a cell (memory cell).
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  • h(t) is a cell's \"hidden\" state at time=t.\n\"recurrent-memcells\"
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Input/Output Sequences

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  • RNNs can be used to predict the results of time shifts (sequence-to-sequence), a sentiment score (sequence-to-vector), or image caption (vector-to-sequence).
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  • sequence-to-vector nets = encoders; vector-to-sequence nets = decoders. One use case: language translation.
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    • Top Left: Sequence-to-sequence
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    • Top Right: Sequence-to-vector
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    • Bot Left: Vector-to-sequence
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    • Bot Right: Delayed-sequence-to-sequence\n\"sequence_vector\"
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Basic RNNs in TF

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  • RNN design: layer of 5 recurrent cells with tanh activation; runs over 2 time steps, and uses vectors of size=3 at each step.
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import tensorflow as tf\n\nn_inputs = 3\nn_neurons = 5\n\n# two-layer net\n\nX0 = tf.placeholder(tf.float32, [None, n_inputs])\nX1 = tf.placeholder(tf.float32, [None, n_inputs])\n\nWx = tf.Variable(tf.random_normal(shape=[n_inputs, n_neurons],dtype=tf.float32))\nWy = tf.Variable(tf.random_normal(shape=[n_neurons,n_neurons],dtype=tf.float32))\n\nb = tf.Variable(tf.zeros([1, n_neurons], dtype=tf.float32))\n\nY0 = tf.tanh(tf.matmul(X0, Wx) + b)\nY1 = tf.tanh(tf.matmul(Y0, Wy) + tf.matmul(X1, Wx) + b)\n\ninit = tf.global_variables_initializer()\n
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# to feed inputs at both time steps,\n\nimport numpy as np\n# Mini-batch: instance 0,instance 1,instance 2,instance 3\n\nX0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]]) # t = 0\nX1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]]) # t = 1\n
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# Y0, Y1 = network outputs at both time steps\n\nwith tf.Session() as sess:\n    init.run()\n    Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})\n    \nprint("output at t=0:\\n",Y0_val,"\\n","output at t=1\\n",Y1_val)\n
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output at t=0:\n [[-0.77183092 -0.99924457  0.23752896 -0.63130957 -0.83723265]\n [-0.92028087 -1.          0.99004787 -0.87230623 -0.99995315]\n [-0.97358704 -1.          0.999919   -0.95966864 -1.        ]\n [ 0.99999094 -0.99890459  0.9991411   0.99996841 -0.99999803]] \n output at t=1\n [[ 0.99512661 -1.          0.99997395 -0.99830353 -1.        ]\n [ 0.99977976  0.99013239 -0.96352106 -0.99476629  0.97579277]\n [ 0.99981618 -0.99989575  0.99114233 -0.99827981 -0.99984008]\n [ 0.54805535 -0.84061396 -0.99912792 -0.47432473 -0.99921536]]\n
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Unrolling through Time (Static) using static_rnn()

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tf.reset_default_graph()\n\nn_inputs = 3\nn_neurons = 5\n\nX0 = tf.placeholder(tf.float32, [None, n_inputs])\nX1 = tf.placeholder(tf.float32, [None, n_inputs])\n\n# BasicRNNCell() -- memcell "factory"\n\nbasic_cell = tf.contrib.rnn.BasicRNNCell(\n    num_units=n_neurons)\n\n# static_rnn() -- creates unrolled RNN net by chaining cells.\n# returns 1) python list of output tensors for each time step\n#         2) tensor of final network states\n\noutput_seqs, states = tf.contrib.rnn.static_rnn(\n    basic_cell, \n    [X0, X1], \n    dtype=tf.float32)\n\nY0, Y1 = output_seqs\n\ninit = tf.global_variables_initializer()\n
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# to feed inputs at both time steps,\n\nimport numpy as np\n# Mini-batch: instance 0,instance 1,instance 2,instance 3\n\nX0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]]) # t = 0\nX1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]]) # t = 1\n
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# Y0, Y1 = network outputs at both time steps\n\nwith tf.Session() as sess:\n    init.run()\n    Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})\n    \nprint("output at t=0:\\n",Y0_val,"\\n","output at t=1\\n",Y1_val)\n
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output at t=0:\n [[ 0.42442048  0.92431569 -0.2353479  -0.90074939 -0.94408685]\n [ 0.73783255  0.98977458 -0.72123086 -0.99919385 -0.99999249]\n [ 0.89336294  0.99865782 -0.9186905  -0.99999398 -1.        ]\n [-0.99143326 -0.99993676 -0.37607926  0.88796568 -0.99899191]] \n output at t=1\n [[ 0.81709599  0.48319042 -0.96708876 -0.9998284  -1.        ]\n [-0.18962485 -0.81231028 -0.21763545  0.88739753  0.57306314]\n [ 0.17130674 -0.6411857  -0.86380148 -0.95413983 -0.99999553]\n [-0.07749119 -0.86547101 -0.00461033 -0.91877526 -0.99582738]]\n
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Simplification

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tf.reset_default_graph()\n\nn_steps = 2\nn_inputs = 3\nn_neurons = 5\n\n# this time, use placeholder with add'l dimension for #timesteps\n#X0 = tf.placeholder(tf.float32, [None, n_inputs])\n#X1 = tf.placeholder(tf.float32, [None, n_inputs])\nX =   tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n\n#print(X)\n\n# transpose - make time steps = 1st dimension\n# unstack - extract list of tensors\n\nX_seqs = tf.unstack(\n    tf.transpose(\n        X, perm=[1, 0, 2]))\n\n#print(X_seqs)\n\n# BasicRNNCell() -- memcell "factory"\n\nbasic_cell = tf.contrib.rnn.BasicRNNCell(\n    num_units=n_neurons)\n\n# static_rnn() -- creates unrolled RNN net by chaining cells.\n# returns 1) python list of output tensors for each time step\n#         2) tensor of final network states\n\noutput_seqs, states = tf.contrib.rnn.static_rnn(\n    basic_cell, \n    X_seqs, \n    dtype=tf.float32)\n\n#Y0, Y1 = output_seqs\n\n# stack - merge output tensors\n# transpose - swap 1st two dimensions\n# returns tensor shape [none, #steps, #neurons]\n\noutputs = tf.transpose(\n    tf.stack(output_seqs), \n    perm=[1,0,2])\n\ninit = tf.global_variables_initializer()\n
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X_batch = np.array([\n        # t = 0      t = 1 \n        [[0, 1, 2], [9, 8, 7]], # instance 1\n        [[3, 4, 5], [0, 0, 0]], # instance 2\n        [[6, 7, 8], [6, 5, 4]], # instance 3\n        [[9, 0, 1], [3, 2, 1]], # instance 4\n    ])\n\nwith tf.Session() as sess:\n    init.run()\n    outputs_val = outputs.eval(feed_dict={X: X_batch})\n    \nprint(outputs_val)\n
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[[[ 0.76157701  0.11581181  0.64773971 -0.79434019 -0.86054337]\n  [ 0.99998951 -0.66595364  0.99812627 -1.          0.84574401]]\n\n [[ 0.99683905  0.29572889  0.98365188 -0.99992883 -0.88169324]\n  [ 0.41841054 -0.92049074 -0.64612901 -0.73361856  0.29283327]]\n\n [[ 0.99996316  0.45685658  0.99936479 -1.         -0.89980829]\n  [ 0.99907684 -0.87088716  0.94328976 -0.9999997   0.87934762]]\n\n [[ 0.12318966  0.02264917  0.99982244 -0.99998975  0.99996465]\n  [ 0.9525854  -0.56515652  0.08665188 -0.99705428  0.87525886]]]\n
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  • Above code still not ideal - builds graph with one cell per time step. Ugly & can cause Out Of Memory errors.
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Unrolling through Time using dynamic_rnn()

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In [10]:
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tf.reset_default_graph()\n\nX = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n\nbasic_cell = tf.contrib.rnn.BasicRNNCell(\n    num_units=n_neurons)\n\noutputs, states = tf.nn.dynamic_rnn(\n    basic_cell, X, dtype=tf.float32)\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n    init.run()\n    outputs_val = outputs.eval(feed_dict={X: X_batch})\n    \nprint(outputs_val)\n
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[[[ 0.01341763 -0.10483158 -0.94257653  0.83843452 -0.20272173]\n  [ 0.99978089 -0.63150525 -0.99999148  0.99999386 -0.87993085]]\n\n [[ 0.94205797 -0.13386673 -0.9997741   0.99812031 -0.64444101]\n  [-0.6134249  -0.55738503  0.39783546  0.89031053  0.04465704]]\n\n [[ 0.99817288 -0.16267382 -0.99999928  0.99997997 -0.86824256]\n  [ 0.99097538 -0.61533296 -0.99695957  0.99986053 -0.64558744]]\n\n [[ 0.9963541   0.23641461  0.75174934  0.98267573 -0.97034496]\n  [ 0.85169196 -0.07830215 -0.3604137   0.95550352  0.12307668]]]\n
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Variable-Length Input Sequences

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In [11]:
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tf.reset_default_graph()\n\nX = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n\nseq_length = tf.placeholder(tf.int32, [None])\n\nbasic_cell = tf.contrib.rnn.BasicRNNCell(\n    num_units=n_neurons)\n\noutputs, states = tf.nn.dynamic_rnn(\n    basic_cell, X, dtype=tf.float32,\n    #\n    #\n    sequence_length=seq_length)\n    #\n    #\nX_batch = np.array([\n        [[0, 1, 2], [9, 8, 7]], # instance 1\n        [[3, 4, 5], [0, 0, 0]], # instance 2 -- zero padded\n        [[6, 7, 8], [6, 5, 4]], # instance 3\n        [[9, 0, 1], [3, 2, 1]], # instance 4\n    ])\n\nseq_length_batch = np.array([2,1,2,2])\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n    init.run()\n    outputs_val, states_val = sess.run(\n        [outputs, states], \n        feed_dict={X: X_batch, seq_length: seq_length_batch})\n\n# RNN should output zero vectors for any time step \n# beyond input sequence length\nprint(outputs_val)\n
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[[[ 0.28581977 -0.77421445 -0.34181327 -0.87767971 -0.91387445]\n  [ 0.99970448 -1.          0.79238343 -1.         -0.9997654 ]]\n\n [[ 0.96786171 -0.99937457 -0.03243476 -0.99988878 -0.99875116]\n  [ 0.          0.          0.          0.          0.        ]]\n\n [[ 0.99903995 -0.99999839  0.28328663 -0.99999982 -0.99998271]\n  [ 0.96896154 -0.99999189  0.43341497 -0.99996883 -0.98279852]]\n\n [[ 0.9976812  -0.99999118  0.99979782 -0.99983948  0.84931362]\n  [ 0.57188803 -0.99268627 -0.30526906 -0.99518502  0.109933  ]]]\n
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# states tensor contains final state of each cell\nprint(states_val)\n
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[[ 0.99970448 -1.          0.79238343 -1.         -0.9997654 ]\n [ 0.96786171 -0.99937457 -0.03243476 -0.99988878 -0.99875116]\n [ 0.96896154 -0.99999189  0.43341497 -0.99996883 -0.98279852]\n [ 0.57188803 -0.99268627 -0.30526906 -0.99518502  0.109933  ]]\n
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Variable-Length Output Sequences

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RNN Training

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RNN Training: Classifier

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In [13]:
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# similar to MNIST classifier\n# unrolled RNN replaces hidden layers\n\ntf.reset_default_graph()\n\nfrom tensorflow.contrib.layers import fully_connected\n\nn_steps = 28\nn_inputs = 28\nn_neurons = 150\nn_outputs = 10\nlearning_rate = 0.001\n\n# y = placeholder for target classes\n\nX = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\ny = tf.placeholder(tf.int32, [None])\n\nbasic_cell = tf.contrib.rnn.BasicRNNCell(\n    num_units=n_neurons)\n\noutputs, states = tf.nn.dynamic_rnn(\n    basic_cell, X, dtype=tf.float32)\n\nlogits = fully_connected(\n    states, n_outputs, activation_fn=None)\n\nxentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n    labels=y, logits=logits)\n\nloss = tf.reduce_mean(\n    xentropy)\n\noptimizer = tf.train.AdamOptimizer(\n    learning_rate=learning_rate)\n\ntraining_op = optimizer.minimize(\n    loss)\n\ncorrect = tf.nn.in_top_k(\n    logits, y, 1)\n\naccuracy = tf.reduce_mean(\n    tf.cast(correct, tf.float32))\n\ninit = tf.global_variables_initializer()\n
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# load MNIST data, reshape to [batch_size, n_steps, n_inputs]\n\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nmnist = input_data.read_data_sets("/tmp/data/")\n\nX_test = mnist.test.images.reshape((-1, n_steps, n_inputs))\ny_test = mnist.test.labels\n
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Extracting /tmp/data/train-images-idx3-ubyte.gz\nExtracting /tmp/data/train-labels-idx1-ubyte.gz\nExtracting /tmp/data/t10k-images-idx3-ubyte.gz\nExtracting /tmp/data/t10k-labels-idx1-ubyte.gz\n
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# ready to run. reshape each training batch before feeding to net.\n\nn_epochs = 10\nbatch_size = 150\n\nwith tf.Session() as sess:\n    init.run()\n    for epoch in range(n_epochs):\n        for iteration in range(mnist.train.num_examples // batch_size):\n            \n            X_batch, y_batch = mnist.train.next_batch(batch_size)\n            X_batch = X_batch.reshape(\n                (-1, n_steps, n_inputs))\n\n            sess.run(\n                training_op, \n                feed_dict={X: X_batch, y: y_batch})\n            \n        acc_train = accuracy.eval(\n            feed_dict={X: X_batch, y: y_batch})\n        acc_test = accuracy.eval(\n            feed_dict={X: X_test, y: y_test})\n\n        print(epoch, \n              "Train accuracy:", acc_train, \n              "Test accuracy:",  acc_test)\n
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0 Train accuracy: 0.953333 Test accuracy: 0.8711\n1 Train accuracy: 0.953333 Test accuracy: 0.9417\n2 Train accuracy: 0.953333 Test accuracy: 0.9432\n3 Train accuracy: 0.946667 Test accuracy: 0.9595\n4 Train accuracy: 0.98 Test accuracy: 0.9627\n5 Train accuracy: 0.966667 Test accuracy: 0.9666\n6 Train accuracy: 0.96 Test accuracy: 0.961\n7 Train accuracy: 0.973333 Test accuracy: 0.9729\n8 Train accuracy: 0.986667 Test accuracy: 0.9702\n9 Train accuracy: 0.986667 Test accuracy: 0.9732\n
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RNN Training: Predicting Time Series

\"rnn-timeseries\"

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t_min, t_max = 0, 30\nresolution = 0.1\n\ndef time_series(t):\n    return t * np.sin(t) / 3 + 2 * np.sin(t*5)\n\ndef next_batch(batch_size, n_steps):\n    t0 = np.random.rand(batch_size, 1) * (t_max - t_min - n_steps * resolution)\n    Ts = t0 + np.arange(0., n_steps + 1) * resolution\n    ys = time_series(Ts)\n    return ys[:, :-1].reshape(-1, n_steps, 1), ys[:, 1:].reshape(-1, n_steps, 1)\n\nt = np.linspace(t_min, t_max, (t_max - t_min) // resolution)\n\nn_steps = 20\nt_instance = np.linspace(\n    12.2, 12.2 + resolution * (n_steps + 1), n_steps + 1)\n
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# each training instance = 20 inputs long\n# targets = 20-input sequences\n\ntf.reset_default_graph()\n\nn_steps = 20\nn_inputs = 1\nn_neurons = 100\nn_outputs = 1\n\nX = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\ny = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\n\ncell = tf.contrib.rnn.BasicRNNCell(\n    num_units=n_neurons, \n    activation=tf.nn.relu)\n\noutputs, states = tf.nn.dynamic_rnn(\n    cell, X, dtype=tf.float32)\n\nprint(outputs.shape)\n
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# output at each time step now vector[100],\n# but we want single output value at each step.\n\n# use OutputProjectionWrapper()\n# -- adds FC layer to top of each output\n\ncell = tf.contrib.rnn.OutputProjectionWrapper(\n    tf.contrib.rnn.BasicRNNCell(\n        num_units=n_neurons, \n        activation=tf.nn.relu),\n    output_size=n_outputs)\n
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# define cost function using MSE\n# use Adam optimizer\n\nlearning_rate = 0.001\nloss = tf.reduce_mean(\n    tf.square(outputs - y))\n\noptimizer = tf.train.AdamOptimizer(\n    learning_rate=learning_rate)\n\ntraining_op = optimizer.minimize(loss)\ninit = tf.global_variables_initializer()\n
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# initialize & run\n\ninit = tf.global_variables_initializer()\nn_iterations = 1000\nbatch_size = 50\n\nwith tf.Session() as sess:\n    init.run()\n    for iteration in range(n_iterations):\n        X_batch, y_batch = next_batch(batch_size, n_steps)\n        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n        if iteration % 100 == 0:\n            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n            print(iteration, "\\tMSE:", mse)\n\n    \n    # use trained model to make some predictions\n    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n    y_pred = sess.run(outputs, feed_dict={X: X_new})\n    print(y_pred)\n
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0 \tMSE: 15.3099\n100 \tMSE: 13.5276\n200 \tMSE: 11.0956\n300 \tMSE: 9.91156\n400 \tMSE: 14.0311\n500 \tMSE: 9.73811\n600 \tMSE: 9.23351\n700 \tMSE: 9.64445\n800 \tMSE: 8.98904\n900 \tMSE: 10.849\n[[[ 0.          0.          0.         ...,  0.          0.          0.        ]\n  [ 0.          0.04218276  0.         ...,  0.          0.          0.        ]\n  [ 0.          0.14342034  0.         ...,  0.          0.          0.        ]\n  ..., \n  [ 6.67315388  0.          6.39087296 ...,  6.9017005   6.30435514\n    6.23329258]\n  [ 6.61708975  0.          6.31429434 ...,  6.58116341  6.19745445\n    6.11896658]\n  [ 5.9406209   0.          5.73649979 ...,  5.63920403  5.5386672\n    5.47510672]]]\n
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import matplotlib.pyplot as plt\n\nplt.title("Testing the model", fontsize=14)\n\nplt.plot(\n    t_instance[:-1], \n    time_series(t_instance[:-1]), \n    "bo", markersize=10, label="instance")\n\nplt.plot(\n    t_instance[1:], \n    time_series(t_instance[1:]), \n    "w*", markersize=10, label="target")\n\nplt.plot(\n    t_instance[1:], \n    y_pred[0,:,0], \n    "r.", markersize=10, label="prediction")\n\nplt.legend(loc="upper left")\nplt.xlabel("Time")\n#save_fig("time_series_pred_plot")\nplt.show()\n
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In [22]:
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tf.reset_default_graph()\n\nn_steps = 20\nn_inputs = 1\nn_neurons = 100\nn_outputs = 1\n\nX = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\ny = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\n\ncell = tf.contrib.rnn.BasicRNNCell(\n    num_units=n_neurons, \n    activation=tf.nn.relu)\n\nrnn_outputs, states = tf.nn.dynamic_rnn(\n    cell, X, dtype=tf.float32)\n\n# stack outputs using reshape\nstacked_rnn_outputs = tf.reshape(\n    rnn_outputs, [-1, n_neurons])\n\nprint(stacked_rnn_outputs)\n\n# add FC layer -- just a projection, so no activation fn needed\nstacked_outputs = fully_connected(\n    stacked_rnn_outputs, \n    n_outputs,\n    activation_fn=None)\n\nprint(stacked_outputs)\n\n# unstack outputs using reshape\noutputs = tf.reshape(\n    stacked_outputs, [-1, n_steps, n_outputs])\n\nprint(outputs)\n\nloss = tf.reduce_sum(tf.square(outputs - y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\ntraining_op = optimizer.minimize(loss)\n\n#initialize & run\ninit = tf.global_variables_initializer()\n\nn_iterations = 1000\nbatch_size = 50\n\nwith tf.Session() as sess:\n    init.run()\n    for iteration in range(n_iterations):\n        X_batch, y_batch = next_batch(batch_size, n_steps)\n        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n        if iteration % 100 == 0:\n            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n            print(iteration, "\\tMSE:", mse)\n\n    \n    # use trained model to make some predictions\n    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n    y_pred = sess.run(outputs, feed_dict={X: X_new})\n    print(y_pred)\n
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Tensor("Reshape:0", shape=(?, 100), dtype=float32)\nTensor("fully_connected/BiasAdd:0", shape=(?, 1), dtype=float32)\nTensor("Reshape_1:0", shape=(?, 20, 1), dtype=float32)\n0 \tMSE: 22963.7\n100 \tMSE: 743.444\n200 \tMSE: 276.131\n300 \tMSE: 117.955\n400 \tMSE: 53.3529\n500 \tMSE: 63.4189\n600 \tMSE: 45.1415\n700 \tMSE: 41.5129\n800 \tMSE: 53.4219\n900 \tMSE: 43.2203\n[[[-3.46527553]\n  [-2.46867704]\n  [-1.10144436]\n  [ 0.69717044]\n  [ 2.08823276]\n  [ 3.13628578]\n  [ 3.55210543]\n  [ 3.4186697 ]\n  [ 2.85978389]\n  [ 2.15520501]\n  [ 1.67705297]\n  [ 1.6919663 ]\n  [ 1.93633199]\n  [ 2.70151305]\n  [ 3.87054777]\n  [ 5.11770582]\n  [ 6.15701818]\n  [ 6.71814394]\n  [ 6.69798708]\n  [ 6.08309698]]]\n
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In [23]:
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plt.title("Testing the model", fontsize=14)\nplt.plot(t_instance[:-1], time_series(t_instance[:-1]), "bo", markersize=10, label="instance")\nplt.plot(t_instance[1:], time_series(t_instance[1:]), "w*", markersize=10, label="target")\nplt.plot(t_instance[1:], y_pred[0,:,0], "r.", markersize=10, label="prediction")\nplt.legend(loc="upper left")\nplt.xlabel("Time")\nplt.show()\n
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Creative RNNs

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In [24]:
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n_iterations = 2000\nbatch_size = 50\n\nwith tf.Session() as sess:\n    init.run()\n    for iteration in range(n_iterations):\n        X_batch, y_batch = next_batch(batch_size, n_steps)\n        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n        if iteration % 100 == 0:\n            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n            print(iteration, "\\tMSE:", mse)\n\n    sequence1 = [0. for i in range(n_steps)]\n    for iteration in range(len(t) - n_steps):\n        X_batch = np.array(sequence1[-n_steps:]).reshape(1, n_steps, 1)\n        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n        sequence1.append(y_pred[0, -1, 0])\n\n    sequence2 = [time_series(i * resolution + t_min + (t_max-t_min/3)) for i in range(n_steps)]\n    for iteration in range(len(t) - n_steps):\n        X_batch = np.array(sequence2[-n_steps:]).reshape(1, n_steps, 1)\n        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n        sequence2.append(y_pred[0, -1, 0])\n\nplt.figure(figsize=(11,4))\nplt.subplot(121)\nplt.plot(t, sequence1, "b-")\nplt.plot(t[:n_steps], sequence1[:n_steps], "b-", linewidth=3)\nplt.xlabel("Time")\nplt.ylabel("Value")\n\nplt.subplot(122)\nplt.plot(t, sequence2, "b-")\nplt.plot(t[:n_steps], sequence2[:n_steps], "b-", linewidth=3)\nplt.xlabel("Time")\n#save_fig("creative_sequence_plot")\nplt.show()\n
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0 \tMSE: 14607.1\n100 \tMSE: 505.605\n200 \tMSE: 167.29\n300 \tMSE: 83.1336\n400 \tMSE: 58.9695\n500 \tMSE: 61.0224\n600 \tMSE: 55.8671\n700 \tMSE: 43.7078\n800 \tMSE: 57.2013\n900 \tMSE: 55.3992\n1000 \tMSE: 54.082\n1100 \tMSE: 55.48\n1200 \tMSE: 39.4618\n1300 \tMSE: 40.7414\n1400 \tMSE: 47.8548\n1500 \tMSE: 43.9252\n1600 \tMSE: 47.892\n1700 \tMSE: 42.0762\n1800 \tMSE: 48.2429\n1900 \tMSE: 42.7509\n
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Deep RNNs

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In [25]:
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tf.reset_default_graph()\n\nn_inputs = 2\nn_neurons = 100\nn_layers = 3\nn_steps = 5\nkeep_prob = 0.5\n\nX = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n\nbasic_cell = tf.contrib.rnn.BasicRNNCell(\n    num_units=n_neurons)\n\nprint(basic_cell)\n\nmulti_layer_cell = tf.contrib.rnn.MultiRNNCell(\n    [basic_cell] * n_layers)\n\nprint(multi_layer_cell)\n\n# states = tuple (one tensor/layer, = final state of layer's cell)\n\noutputs, states = tf.nn.dynamic_rnn(\n    multi_layer_cell, X, dtype=tf.float32)\n\ninit = tf.global_variables_initializer()\n\nimport numpy.random as rnd\nX_batch = rnd.rand(2, n_steps, n_inputs)\n\nwith tf.Session() as sess:\n    init.run()\n    outputs_val, states_val = sess.run(\n        [outputs, states], \n        feed_dict={X: X_batch})\n    \nprint(outputs_val.shape)\n
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<tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl.BasicRNNCell object at 0x7fd1ff3dbb00>\n<tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl.MultiRNNCell object at 0x7fd1d9b7c9e8>\n(2, 5, 100)\n
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DRNNs: Multiple GPUs

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Dropout

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In [32]:
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# apply 50% dropout to inputs of RNN layers\n# can apply dropout to outputs via output_keep_prob\n\ntf.reset_default_graph()\nfrom tensorflow.contrib.layers import fully_connected\n\nn_inputs = 1\nn_neurons = 100\nn_layers = 3\nn_steps = 20\nn_outputs = 1\n\nkeep_prob = 0.5\nlearning_rate = 0.001\n\ndef deep_rnn_with_dropout(X, y, is_training):\n\n    # TF implementation of DropoutWrapper doesn't differentiate\n    # between training & testing.\n    \n    cell = tf.contrib.rnn.BasicRNNCell(\n        num_units=n_neurons)\n    \n    if is_training:\n        cell = tf.contrib.rnn.DropoutWrapper(\n            cell, input_keep_prob=keep_prob)\n    \n    #\n    #\n    \n    multi_layer_cell = tf.contrib.rnn.MultiRNNCell(\n        [cell] * n_layers)\n    \n    rnn_outputs, states = tf.nn.dynamic_rnn(\n        multi_layer_cell, X, dtype=tf.float32)\n\n    stacked_rnn_outputs = tf.reshape(\n        rnn_outputs, [-1, n_neurons])\n    \n    stacked_outputs = fully_connected(\n        stacked_rnn_outputs, n_outputs, activation_fn=None)\n    \n    outputs = tf.reshape(\n        stacked_outputs, [-1, n_steps, n_outputs])\n\n    loss = tf.reduce_sum(\n        tf.square(outputs - y))\n    \n    optimizer = tf.train.AdamOptimizer(\n        learning_rate=learning_rate)\n    \n    training_op = optimizer.minimize(loss)\n\n    return outputs, loss, training_op\n\nX = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\ny = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\noutputs, loss, training_op = deep_rnn_with_dropout(X, y, is_training)\ninit = tf.global_variables_initializer()\nsaver = tf.train.Saver()\n
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In [33]:
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n_iterations = 2000\nbatch_size = 50\n\nis_training = True\n\nwith tf.Session() as sess:\n    if is_training:\n        init.run()\n        for iteration in range(n_iterations):\n            X_batch, y_batch = next_batch(batch_size, n_steps)\n            sess.run(\n                training_op, \n                feed_dict={X: X_batch, y: y_batch})\n            \n            if iteration % 100 == 0:\n                mse = loss.eval(\n                    feed_dict={X: X_batch, y: y_batch})\n                \n                print(iteration, "\\tMSE:", mse)\n                \n        save_path = saver.save(sess, "/tmp/my_model.ckpt")\n        \n    else:\n        saver.restore(sess, "/tmp/my_model.ckpt")\n        \n        X_new = time_series(\n            np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n        y_pred = sess.run(\n            outputs, feed_dict={X: X_new})\n        \n        plt.title("Testing the model", fontsize=14)\n        plt.plot(t_instance[:-1], time_series(t_instance[:-1]), "bo", markersize=10, label="instance")\n        plt.plot(t_instance[1:], time_series(t_instance[1:]), "w*", markersize=10, label="target")\n        plt.plot(t_instance[1:], y_pred[0,:,0], "r.", markersize=10, label="prediction")\n        plt.legend(loc="upper left")\n        plt.xlabel("Time")\n        plt.show()\n
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0 \tMSE: 10428.8\n100 \tMSE: 314.521\n200 \tMSE: 152.328\n300 \tMSE: 155.774\n400 \tMSE: 100.226\n500 \tMSE: 80.2064\n600 \tMSE: 92.3898\n700 \tMSE: 55.4301\n800 \tMSE: 50.8537\n900 \tMSE: 47.1413\n1000 \tMSE: 57.1007\n1100 \tMSE: 64.2314\n1200 \tMSE: 51.3272\n1300 \tMSE: 51.1612\n1400 \tMSE: 41.0518\n1500 \tMSE: 42.267\n1600 \tMSE: 29.6838\n1700 \tMSE: 48.4316\n1800 \tMSE: 46.5584\n1900 \tMSE: 40.6252\n
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In [35]:
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# testing\n\nwith tf.Session() as sess:\n\n    saver.restore(sess, "/tmp/my_model.ckpt")\n        \n    X_new = time_series(\n        np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n        \n    y_pred = sess.run(\n        outputs, feed_dict={X: X_new})\n        \n    plt.title("Testing the model", fontsize=14)\n    plt.plot(t_instance[:-1], time_series(t_instance[:-1]), "bo", markersize=10, label="instance")\n    plt.plot(t_instance[1:], time_series(t_instance[1:]), "w*", markersize=10, label="target")\n    plt.plot(t_instance[1:], y_pred[0,:,0], "r.", markersize=10, label="prediction")\n    plt.legend(loc="upper left")\n    plt.xlabel("Time")\n    plt.show()\n
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Training across Many Time Steps

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  • problem #1: RNNs susceptible to vanishing/exploding gradients issues. Previous tricks will work, but training time = prohibitively long for even modest sequences.
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  • solution #1: truncated backprop thru time (unrolling RNN over limited number of timesteps during training). Works, but model will not be able to learn long-term patterns.
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  • problem #2: memory of early inputs fades away - information lost during each transformation.
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  • solution #2: using a long-term memory cell.
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Long Short-Term Memory (LSTM) Cell

\"lstm-cell\"

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  • key feature: net learns what to store (long-term), what to read from, what to throw away.
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    • input gate: controlled by i(t) - decides which parts of g(t) to add to long-term memory
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    • output gate: controlled by o(t) - decides which parts of long-term state should be read & outputted at this time step.
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In [36]:
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tf.reset_default_graph()\n\nfrom tensorflow.contrib.layers import fully_connected\n\nn_steps = 28\nn_inputs = 28\nn_neurons = 150\nn_outputs = 10\n\nlearning_rate = 0.001\n\nX = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\ny = tf.placeholder(tf.int32, [None])\n\nlstm_cell = tf.contrib.rnn.BasicLSTMCell(\n    num_units=n_neurons)\n\nmulti_cell = tf.contrib.rnn.MultiRNNCell(\n    [lstm_cell]*3)\n\noutputs, states = tf.nn.dynamic_rnn(\n    multi_cell, X, dtype=tf.float32)\n\ntop_layer_h_state = states[-1][1]\n\nlogits = fully_connected(\n    top_layer_h_state, \n    n_outputs, \n    activation_fn=None, scope="softmax")\n\nxentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n    labels=y, logits=logits)\n\nloss = tf.reduce_mean(\n    xentropy, name="loss")\n\noptimizer = tf.train.AdamOptimizer(\n    learning_rate=learning_rate)\n\ntraining_op = optimizer.minimize(loss)\n\ncorrect = tf.nn.in_top_k(\n    logits, y, 1)\n\naccuracy = tf.reduce_mean(\n    tf.cast(correct, tf.float32))\n    \ninit = tf.global_variables_initializer()\n
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In [37]:
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Out[37]:
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(LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_2:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_3:0' shape=(?, 150) dtype=float32>),\n LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_4:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_5:0' shape=(?, 150) dtype=float32>),\n LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_6:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>))
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In [38]:
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top_layer_h_state\n
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Out[38]:
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<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>
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In [39]:
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n_epochs = 10\nbatch_size = 150\n\nwith tf.Session() as sess:\n    init.run()\n    for epoch in range(n_epochs):\n        for iteration in range(mnist.train.num_examples // batch_size):\n            X_batch, y_batch = mnist.train.next_batch(batch_size)\n            X_batch = X_batch.reshape((batch_size, n_steps, n_inputs))\n            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n        print("Epoch", epoch, "Train accuracy =", acc_train, "Test accuracy =", acc_test)\n
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Epoch 0 Train accuracy = 0.966667 Test accuracy = 0.9403\nEpoch 1 Train accuracy = 0.98 Test accuracy = 0.9742\nEpoch 2 Train accuracy = 0.993333 Test accuracy = 0.979\nEpoch 3 Train accuracy = 0.993333 Test accuracy = 0.9805\nEpoch 4 Train accuracy = 1.0 Test accuracy = 0.9854\nEpoch 5 Train accuracy = 0.98 Test accuracy = 0.9827\nEpoch 6 Train accuracy = 0.993333 Test accuracy = 0.9851\nEpoch 7 Train accuracy = 1.0 Test accuracy = 0.9865\nEpoch 8 Train accuracy = 1.0 Test accuracy = 0.9887\nEpoch 9 Train accuracy = 0.993333 Test accuracy = 0.9871\n
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Peephole Connections

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  • Improvement: let gate peek at long-term state too. Provided with previous long-term state c(t-1) as inputs to forget gate & input gate; current long-term state c(t) added as input to output gate controller.
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In [40]:
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# Peepholes in TF\nlstm_cell = tf.contrib.rnn.LSTMCell(\n    num_units=n_neurons, \n    use_peepholes=True)\n
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Gated Recurrent Unit (GRU) Cell

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In [41]:
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# in TF\ngru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)\n
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Natural Language Processing (NLP)

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Word Embeddings

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In [42]:
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# create embeddings variable. init with random[-1,+1]\n\nvocabulary_size = 50000\nembedding_size = 150\nembeddings = tf.Variable(\n    tf.random_uniform(\n        [vocabulary_size, embedding_size], \n        -1.0, 1.0))\n
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In [43]:
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train_inputs = tf.placeholder(\n    tf.int32, shape=[None]) # from ids...\n\nembed = tf.nn.embedding_lookup(\n    embeddings, train_inputs) # ...to embeddings\n
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English => French Encoder-Decoder Network (link)

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  • Decoder returns score for each word in output vocabulary - softmax turns them into probabilities. Highest probability word is returned.\n\"encoder-decoder\"
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In [44]:
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from six.moves import urllib\n\nimport errno\nimport os\nimport zipfile\n\nWORDS_PATH = "datasets/words"\nWORDS_URL = 'http://mattmahoney.net/dc/text8.zip'\n\ndef mkdir_p(path):\n    """Create directories, ok if they already exist.\n    \n    This is for python 2 support. In python >=3.2, simply use:\n    >>> os.makedirs(path, exist_ok=True)\n    """\n    try:\n        os.makedirs(path)\n    except OSError as exc:\n        if exc.errno == errno.EEXIST and os.path.isdir(path):\n            pass\n        else:\n            raise\n\ndef fetch_words_data(words_url=WORDS_URL, words_path=WORDS_PATH):\n    os.makedirs(words_path, exist_ok=True)\n    zip_path = os.path.join(words_path, "words.zip")\n    if not os.path.exists(zip_path):\n        urllib.request.urlretrieve(words_url, zip_path)\n    with zipfile.ZipFile(zip_path) as f:\n        data = f.read(f.namelist()[0])\n    return data.decode("ascii").split()\n
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In [45]:
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words = fetch_words_data()\nwords[:5]\n
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Out[45]:
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['anarchism', 'originated', 'as', 'a', 'term']
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Build dictionary

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In [46]:
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from collections import Counter\n\nvocabulary_size = 50000\n\nvocabulary = [("UNK", None)] + Counter(words).most_common(vocabulary_size - 1)\nvocabulary = np.array([word for word, _ in vocabulary])\n\ndictionary = {word: code for code, word in enumerate(vocabulary)}\n\ndata = np.array([dictionary.get(word, 0) for word in words])\n
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In [47]:
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" ".join(words[:9]), data[:9]\n
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Out[47]:
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('anarchism originated as a term of abuse first used',\n array([5244, 3081,   12,    6,  195,    2, 3135,   46,   59]))
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In [48]:
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" ".join([vocabulary[word_index] for word_index in [5241, 3081, 12, 6, 195, 2, 3134, 46, 59]])\n
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Out[48]:
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'anywhere originated as a term of presidency first used'
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In [49]:
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words[24], data[24]\n
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Out[49]:
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('culottes', 0)
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Generate batches

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In [50]:
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import random\nfrom collections import deque\n\ndef generate_batch(batch_size, num_skips, skip_window):\n    global data_index\n    assert batch_size % num_skips == 0\n    assert num_skips <= 2 * skip_window\n    batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n    span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n    buffer = deque(maxlen=span)\n    for _ in range(span):\n        buffer.append(data[data_index])\n        data_index = (data_index + 1) % len(data)\n    for i in range(batch_size // num_skips):\n        target = skip_window  # target label at the center of the buffer\n        targets_to_avoid = [ skip_window ]\n        for j in range(num_skips):\n            while target in targets_to_avoid:\n                target = random.randint(0, span - 1)\n            targets_to_avoid.append(target)\n            batch[i * num_skips + j] = buffer[skip_window]\n            labels[i * num_skips + j, 0] = buffer[target]\n        buffer.append(data[data_index])\n        data_index = (data_index + 1) % len(data)\n    return batch, labels\n
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In [51]:
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data_index=0\nbatch, labels = generate_batch(8, 2, 1)\n
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In [52]:
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batch, [vocabulary[word] for word in batch]\n
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Out[52]:
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(array([3081, 3081,   12,   12,    6,    6,  195,  195], dtype=int32),\n ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term'])
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In [53]:
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labels, [vocabulary[word] for word in labels[:, 0]]\n
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Out[53]:
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(array([[5244],\n        [  12],\n        [   6],\n        [3081],\n        [ 195],\n        [  12],\n        [   6],\n        [   2]], dtype=int32),\n ['anarchism', 'as', 'a', 'originated', 'term', 'as', 'a', 'of'])
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Build the Model

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In [54]:
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batch_size = 128\nembedding_size = 128  # Dimension of the embedding vector.\nskip_window = 1       # How many words to consider left and right.\nnum_skips = 2         # How many times to reuse an input to generate a label.\n\n# We pick a random validation set to sample nearest neighbors. Here we limit the\n# validation samples to the words that have a low numeric ID, which by\n# construction are also the most frequent.\n\nvalid_size = 16     # Random set of words to evaluate similarity on.\nvalid_window = 100  # Only pick dev samples in the head of the distribution.\nvalid_examples = rnd.choice(valid_window, valid_size, replace=False)\nnum_sampled = 64    # Number of negative examples to sample.\n\nlearning_rate = 0.01\n
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In [55]:
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tf.reset_default_graph()\n\n# Input data.\ntrain_inputs = tf.placeholder(tf.int32, shape=[batch_size])\ntrain_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\nvalid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n\n# Look up embeddings for inputs.\ninit_embeddings = tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)\nembeddings = tf.Variable(init_embeddings)\nembed = tf.nn.embedding_lookup(embeddings, train_inputs)\n\n# Construct the variables for the NCE loss\nnce_weights = tf.Variable(\n    tf.truncated_normal([vocabulary_size, embedding_size],\n                        stddev=1.0 / np.sqrt(embedding_size)))\nnce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n\n# Compute the average NCE loss for the batch.\n# tf.nce_loss automatically draws a new sample of the negative labels each\n# time we evaluate the loss.\nloss = tf.reduce_mean(\n    tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed,\n                   num_sampled, vocabulary_size))\n\n# Construct the Adam optimizer\noptimizer = tf.train.AdamOptimizer(learning_rate)\ntraining_op = optimizer.minimize(loss)\n\n# Compute the cosine similarity between minibatch examples and all embeddings.\nnorm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=1, keep_dims=True))\nnormalized_embeddings = embeddings / norm\nvalid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\nsimilarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)\n\n# Add variable initializer.\ninit = tf.global_variables_initializer()\n
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In [56]:
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num_steps = 1000 # was 100000?\n\nwith tf.Session() as session:\n    init.run()\n\n    average_loss = 0\n    for step in range(num_steps):\n        print("\\rIteration: {}".format(step), end="\\t")\n        batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)\n        feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}\n\n        # We perform one update step by evaluating the training op (including it\n        # in the list of returned values for session.run()\n        _, loss_val = session.run([training_op, loss], feed_dict=feed_dict)\n        average_loss += loss_val\n\n        if step % 2000 == 0:\n            if step > 0:\n                average_loss /= 2000\n            # The average loss is an estimate of the loss over the last 2000 batches.\n            print("Average loss at step ", step, ": ", average_loss)\n            average_loss = 0\n\n        # Note that this is expensive (~20% slowdown if computed every 500 steps)\n        if step % 10000 == 0:\n            sim = similarity.eval()\n            for i in range(valid_size):\n                valid_word = vocabulary[valid_examples[i]]\n                top_k = 8 # number of nearest neighbors\n                nearest = (-sim[i, :]).argsort()[1:top_k+1]\n                log_str = "Nearest to %s:" % valid_word\n                for k in range(top_k):\n                    close_word = vocabulary[nearest[k]]\n                    log_str = "%s %s," % (log_str, close_word)\n                print(log_str)\n\n    final_embeddings = normalized_embeddings.eval()\n
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Iteration: 0\tAverage loss at step  0 :  260.603485107\nNearest to and: marsh, sipe, vehement, exercises, einer, mrnas, dancer, grendel,\nNearest to called: innuendo, algerian, synthesizing, montgomery, unspoken, elevating, plankton, monochromatic,\nNearest to many: salinas, fuji, trochaic, rubinstein, eln, tintin, lloyd, carbides,\nNearest to about: moreover, congo, choctaws, accomplished, unwieldy, ks, halifax, pac,\nNearest to than: awake, exact, offutt, gloster, pronunciations, delight, tsarina, hopped,\nNearest to or: long, mage, warriors, adhering, sk, clitoridectomy, parenting, vanguard,\nNearest to of: shakespeare, kemp, relax, cul, breakaway, solemnly, mason, mng,\nNearest to when: tolstoy, courtesan, hashes, coursing, evi, ren, diurnal, stimson,\nNearest to four: supermassive, soviet, palatalization, acclaimed, aided, whitney, filtration, lesbians,\nNearest to most: din, hawaii, loch, necronomicon, sunnah, sh, onager, miracles,\nNearest to on: helpers, tangle, heretical, compulsion, unorganized, rump, intimidating, israeli,\nNearest to but: ohio, rican, politeness, watkins, ingesting, street, hatred, novices,\nNearest to that: xhosa, distressed, continually, fausto, iole, admitted, etsi, gross,\nNearest to all: orissa, persistent, moro, informative, reservation, ren, browne, frobenius,\nNearest to in: chanced, accelerator, sergio, demonstrating, inertia, jarrett, intricate, orange,\nNearest to had: irredentist, kbit, sarris, lactate, bettor, narratives, hui, transpired,\nIteration: 999\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
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Save final embeddings

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In [57]:
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np.save("my_final_embeddings.npy", final_embeddings)\n
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Plot embeddings

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In [60]:
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def plot_with_labels(low_dim_embs, labels):\n    assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"\n    plt.figure(figsize=(18, 18))  #in inches\n    for i, label in enumerate(labels):\n        x, y = low_dim_embs[i,:]\n        plt.scatter(x, y)\n        plt.annotate(label,\n                     xy=(x, y),\n                     xytext=(5, 2),\n                     textcoords='offset points',\n                     ha='right',\n                     va='bottom')\n    plt.show()\n
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In [62]:
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from sklearn.manifold import TSNE\n\ntsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\nplot_only = 500\nlow_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:])\nlabels = [vocabulary[i] for i in range(plot_only)]\nplot_with_labels(low_dim_embs, labels)\n
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In [ ]:
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\n\n\n" }, { "path": "ch14-Recurrent-NNs.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%html\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\\n\",\n \"* Use case: arbitrary-length **sequence** data analysis - *anticipation* abilities\\n\",\n \"* RNNs much like feed-forward NNs, but also with backward-facing connections\\n\",\n \"* At time step *t* each node sees input *x(t)* plus its previous output *y(t-1)*.\\n\",\n \"* Below: \\\"unrolling\\\" a net across a time axis.\\n\",\n \"![recurrent_unrolled](pics/recurrent-neurons-unrolled.png)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Memory Cells\\n\",\n \"* A network node that preserves state across time is called a **cell** (memory cell).\\n\",\n \"* *h(t)* is a cell's \\\"hidden\\\" state at time=t.\\n\",\n \"![recurrent-memcells](pics/recurrent-memcells.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Input/Output Sequences\\n\",\n \"* RNNs can be used to predict the results of time shifts (sequence-to-sequence), a sentiment score (sequence-to-vector), or image caption (vector-to-sequence).\\n\",\n \"* sequence-to-vector nets = **encoders**; vector-to-sequence nets = **decoders**. One use case: language translation.\\n\",\n \"* Below:\\n\",\n \" * Top Left: Sequence-to-sequence\\n\",\n \" * Top Right: Sequence-to-vector\\n\",\n \" * Bot Left: Vector-to-sequence\\n\",\n \" * Bot Right: Delayed-sequence-to-sequence\\n\",\n \"![sequence_vector](pics/sequence-vector.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Basic RNNs in TF\\n\",\n \"* RNN design: layer of **5 recurrent cells** with tanh activation; runs over **2** time steps, and uses **vectors of size=3** at each step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"n_inputs = 3\\n\",\n \"n_neurons = 5\\n\",\n \"\\n\",\n \"# two-layer net\\n\",\n \"\\n\",\n \"X0 = tf.placeholder(tf.float32, [None, n_inputs])\\n\",\n \"X1 = tf.placeholder(tf.float32, [None, n_inputs])\\n\",\n \"\\n\",\n \"Wx = tf.Variable(tf.random_normal(shape=[n_inputs, n_neurons],dtype=tf.float32))\\n\",\n \"Wy = tf.Variable(tf.random_normal(shape=[n_neurons,n_neurons],dtype=tf.float32))\\n\",\n \"\\n\",\n \"b = tf.Variable(tf.zeros([1, n_neurons], dtype=tf.float32))\\n\",\n \"\\n\",\n \"Y0 = tf.tanh(tf.matmul(X0, Wx) + b)\\n\",\n \"Y1 = tf.tanh(tf.matmul(Y0, Wy) + tf.matmul(X1, Wx) + b)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# to feed inputs at both time steps,\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"# Mini-batch: instance 0,instance 1,instance 2,instance 3\\n\",\n \"\\n\",\n \"X0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]]) # t = 0\\n\",\n \"X1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]]) # t = 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"output at t=0:\\n\",\n \" [[-0.77183092 -0.99924457 0.23752896 -0.63130957 -0.83723265]\\n\",\n \" [-0.92028087 -1. 0.99004787 -0.87230623 -0.99995315]\\n\",\n \" [-0.97358704 -1. 0.999919 -0.95966864 -1. ]\\n\",\n \" [ 0.99999094 -0.99890459 0.9991411 0.99996841 -0.99999803]] \\n\",\n \" output at t=1\\n\",\n \" [[ 0.99512661 -1. 0.99997395 -0.99830353 -1. ]\\n\",\n \" [ 0.99977976 0.99013239 -0.96352106 -0.99476629 0.97579277]\\n\",\n \" [ 0.99981618 -0.99989575 0.99114233 -0.99827981 -0.99984008]\\n\",\n \" [ 0.54805535 -0.84061396 -0.99912792 -0.47432473 -0.99921536]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Y0, Y1 = network outputs at both time steps\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})\\n\",\n \" \\n\",\n \"print(\\\"output at t=0:\\\\n\\\",Y0_val,\\\"\\\\n\\\",\\\"output at t=1\\\\n\\\",Y1_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Unrolling through Time (Static) using static_rnn()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_inputs = 3\\n\",\n \"n_neurons = 5\\n\",\n \"\\n\",\n \"X0 = tf.placeholder(tf.float32, [None, n_inputs])\\n\",\n \"X1 = tf.placeholder(tf.float32, [None, n_inputs])\\n\",\n \"\\n\",\n \"# BasicRNNCell() -- memcell \\\"factory\\\"\\n\",\n \"\\n\",\n \"basic_cell = tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons)\\n\",\n \"\\n\",\n \"# static_rnn() -- creates unrolled RNN net by chaining cells.\\n\",\n \"# returns 1) python list of output tensors for each time step\\n\",\n \"# 2) tensor of final network states\\n\",\n \"\\n\",\n \"output_seqs, states = tf.contrib.rnn.static_rnn(\\n\",\n \" basic_cell, \\n\",\n \" [X0, X1], \\n\",\n \" dtype=tf.float32)\\n\",\n \"\\n\",\n \"Y0, Y1 = output_seqs\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# to feed inputs at both time steps,\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"# Mini-batch: instance 0,instance 1,instance 2,instance 3\\n\",\n \"\\n\",\n \"X0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]]) # t = 0\\n\",\n \"X1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]]) # t = 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"output at t=0:\\n\",\n \" [[ 0.42442048 0.92431569 -0.2353479 -0.90074939 -0.94408685]\\n\",\n \" [ 0.73783255 0.98977458 -0.72123086 -0.99919385 -0.99999249]\\n\",\n \" [ 0.89336294 0.99865782 -0.9186905 -0.99999398 -1. ]\\n\",\n \" [-0.99143326 -0.99993676 -0.37607926 0.88796568 -0.99899191]] \\n\",\n \" output at t=1\\n\",\n \" [[ 0.81709599 0.48319042 -0.96708876 -0.9998284 -1. ]\\n\",\n \" [-0.18962485 -0.81231028 -0.21763545 0.88739753 0.57306314]\\n\",\n \" [ 0.17130674 -0.6411857 -0.86380148 -0.95413983 -0.99999553]\\n\",\n \" [-0.07749119 -0.86547101 -0.00461033 -0.91877526 -0.99582738]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Y0, Y1 = network outputs at both time steps\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})\\n\",\n \" \\n\",\n \"print(\\\"output at t=0:\\\\n\\\",Y0_val,\\\"\\\\n\\\",\\\"output at t=1\\\\n\\\",Y1_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Simplification\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_steps = 2\\n\",\n \"n_inputs = 3\\n\",\n \"n_neurons = 5\\n\",\n \"\\n\",\n \"# this time, use placeholder with add'l dimension for #timesteps\\n\",\n \"#X0 = tf.placeholder(tf.float32, [None, n_inputs])\\n\",\n \"#X1 = tf.placeholder(tf.float32, [None, n_inputs])\\n\",\n \"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\\n\",\n \"\\n\",\n \"#print(X)\\n\",\n \"\\n\",\n \"# transpose - make time steps = 1st dimension\\n\",\n \"# unstack - extract list of tensors\\n\",\n \"\\n\",\n \"X_seqs = tf.unstack(\\n\",\n \" tf.transpose(\\n\",\n \" X, perm=[1, 0, 2]))\\n\",\n \"\\n\",\n \"#print(X_seqs)\\n\",\n \"\\n\",\n \"# BasicRNNCell() -- memcell \\\"factory\\\"\\n\",\n \"\\n\",\n \"basic_cell = tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons)\\n\",\n \"\\n\",\n \"# static_rnn() -- creates unrolled RNN net by chaining cells.\\n\",\n \"# returns 1) python list of output tensors for each time step\\n\",\n \"# 2) tensor of final network states\\n\",\n \"\\n\",\n \"output_seqs, states = tf.contrib.rnn.static_rnn(\\n\",\n \" basic_cell, \\n\",\n \" X_seqs, \\n\",\n \" dtype=tf.float32)\\n\",\n \"\\n\",\n \"#Y0, Y1 = output_seqs\\n\",\n \"\\n\",\n \"# stack - merge output tensors\\n\",\n \"# transpose - swap 1st two dimensions\\n\",\n \"# returns tensor shape [none, #steps, #neurons]\\n\",\n \"\\n\",\n \"outputs = tf.transpose(\\n\",\n \" tf.stack(output_seqs), \\n\",\n \" perm=[1,0,2])\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[[ 0.76157701 0.11581181 0.64773971 -0.79434019 -0.86054337]\\n\",\n \" [ 0.99998951 -0.66595364 0.99812627 -1. 0.84574401]]\\n\",\n \"\\n\",\n \" [[ 0.99683905 0.29572889 0.98365188 -0.99992883 -0.88169324]\\n\",\n \" [ 0.41841054 -0.92049074 -0.64612901 -0.73361856 0.29283327]]\\n\",\n \"\\n\",\n \" [[ 0.99996316 0.45685658 0.99936479 -1. -0.89980829]\\n\",\n \" [ 0.99907684 -0.87088716 0.94328976 -0.9999997 0.87934762]]\\n\",\n \"\\n\",\n \" [[ 0.12318966 0.02264917 0.99982244 -0.99998975 0.99996465]\\n\",\n \" [ 0.9525854 -0.56515652 0.08665188 -0.99705428 0.87525886]]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"X_batch = np.array([\\n\",\n \" # t = 0 t = 1 \\n\",\n \" [[0, 1, 2], [9, 8, 7]], # instance 1\\n\",\n \" [[3, 4, 5], [0, 0, 0]], # instance 2\\n\",\n \" [[6, 7, 8], [6, 5, 4]], # instance 3\\n\",\n \" [[9, 0, 1], [3, 2, 1]], # instance 4\\n\",\n \" ])\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" outputs_val = outputs.eval(feed_dict={X: X_batch})\\n\",\n \" \\n\",\n \"print(outputs_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Above code still not ideal - builds graph with one cell per time step. Ugly & can cause Out Of Memory errors.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Unrolling through Time using dynamic_rnn()\\n\",\n \"* uses while_loop() to iterate over the memcell\\n\",\n \"* set swap_memory=True to move GPU memory to CPU during backprop if needed\\n\",\n \"* accepts single tensor, outputs single tensor - no stack/unstack/transpose ops required.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[[ 0.01341763 -0.10483158 -0.94257653 0.83843452 -0.20272173]\\n\",\n \" [ 0.99978089 -0.63150525 -0.99999148 0.99999386 -0.87993085]]\\n\",\n \"\\n\",\n \" [[ 0.94205797 -0.13386673 -0.9997741 0.99812031 -0.64444101]\\n\",\n \" [-0.6134249 -0.55738503 0.39783546 0.89031053 0.04465704]]\\n\",\n \"\\n\",\n \" [[ 0.99817288 -0.16267382 -0.99999928 0.99997997 -0.86824256]\\n\",\n \" [ 0.99097538 -0.61533296 -0.99695957 0.99986053 -0.64558744]]\\n\",\n \"\\n\",\n \" [[ 0.9963541 0.23641461 0.75174934 0.98267573 -0.97034496]\\n\",\n \" [ 0.85169196 -0.07830215 -0.3604137 0.95550352 0.12307668]]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\\n\",\n \"\\n\",\n \"basic_cell = tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons)\\n\",\n \"\\n\",\n \"outputs, states = tf.nn.dynamic_rnn(\\n\",\n \" basic_cell, X, dtype=tf.float32)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" outputs_val = outputs.eval(feed_dict={X: X_batch})\\n\",\n \" \\n\",\n \"print(outputs_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Variable-Length Input Sequences\\n\",\n \"* Most problems will have variable length inputs (like sentences).\\n\",\n \"* This option uses **sequence_length** param (1D tensor)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[[ 0.28581977 -0.77421445 -0.34181327 -0.87767971 -0.91387445]\\n\",\n \" [ 0.99970448 -1. 0.79238343 -1. -0.9997654 ]]\\n\",\n \"\\n\",\n \" [[ 0.96786171 -0.99937457 -0.03243476 -0.99988878 -0.99875116]\\n\",\n \" [ 0. 0. 0. 0. 0. ]]\\n\",\n \"\\n\",\n \" [[ 0.99903995 -0.99999839 0.28328663 -0.99999982 -0.99998271]\\n\",\n \" [ 0.96896154 -0.99999189 0.43341497 -0.99996883 -0.98279852]]\\n\",\n \"\\n\",\n \" [[ 0.9976812 -0.99999118 0.99979782 -0.99983948 0.84931362]\\n\",\n \" [ 0.57188803 -0.99268627 -0.30526906 -0.99518502 0.109933 ]]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\\n\",\n \"\\n\",\n \"seq_length = tf.placeholder(tf.int32, [None])\\n\",\n \"\\n\",\n \"basic_cell = tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons)\\n\",\n \"\\n\",\n \"outputs, states = tf.nn.dynamic_rnn(\\n\",\n \" basic_cell, X, dtype=tf.float32,\\n\",\n \" #\\n\",\n \" #\\n\",\n \" sequence_length=seq_length)\\n\",\n \" #\\n\",\n \" #\\n\",\n \"X_batch = np.array([\\n\",\n \" [[0, 1, 2], [9, 8, 7]], # instance 1\\n\",\n \" [[3, 4, 5], [0, 0, 0]], # instance 2 -- zero padded\\n\",\n \" [[6, 7, 8], [6, 5, 4]], # instance 3\\n\",\n \" [[9, 0, 1], [3, 2, 1]], # instance 4\\n\",\n \" ])\\n\",\n \"\\n\",\n \"seq_length_batch = np.array([2,1,2,2])\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" outputs_val, states_val = sess.run(\\n\",\n \" [outputs, states], \\n\",\n \" feed_dict={X: X_batch, seq_length: seq_length_batch})\\n\",\n \"\\n\",\n \"# RNN should output zero vectors for any time step \\n\",\n \"# beyond input sequence length\\n\",\n \"print(outputs_val)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0.99970448 -1. 0.79238343 -1. -0.9997654 ]\\n\",\n \" [ 0.96786171 -0.99937457 -0.03243476 -0.99988878 -0.99875116]\\n\",\n \" [ 0.96896154 -0.99999189 0.43341497 -0.99996883 -0.98279852]\\n\",\n \" [ 0.57188803 -0.99268627 -0.30526906 -0.99518502 0.109933 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# states tensor contains final state of each cell\\n\",\n \"print(states_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Variable-Length Output Sequences\\n\",\n \"* Typical output sequence lengths not equal to input lengths\\n\",\n \"* Most common solution: use *end-of-sequence (EOS) token*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RNN Training\\n\",\n \"* Unroll through time (as shown above) then use backprop through time (*BPTT*).\\n\",\n \"![rnn_training](pics/rnn-training.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RNN Training: Classifier\\n\",\n \"* Example: use MNIST (CNN would be better, but lets keep it simple)\\n\",\n \"* Treat images as 28 rows of 28 pixels each\\n\",\n \"* Use 150 rnn cells + fully-connected layer of 10 cells (1 per class)\\n\",\n \"* Followed by softmax layer\\n\",\n \"![sequence-classifier](pics/sequence-classifier.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# similar to MNIST classifier\\n\",\n \"# unrolled RNN replaces hidden layers\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"from tensorflow.contrib.layers import fully_connected\\n\",\n \"\\n\",\n \"n_steps = 28\\n\",\n \"n_inputs = 28\\n\",\n \"n_neurons = 150\\n\",\n \"n_outputs = 10\\n\",\n \"learning_rate = 0.001\\n\",\n \"\\n\",\n \"# y = placeholder for target classes\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\\n\",\n \"y = tf.placeholder(tf.int32, [None])\\n\",\n \"\\n\",\n \"basic_cell = tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons)\\n\",\n \"\\n\",\n \"outputs, states = tf.nn.dynamic_rnn(\\n\",\n \" basic_cell, X, dtype=tf.float32)\\n\",\n \"\\n\",\n \"logits = fully_connected(\\n\",\n \" states, n_outputs, activation_fn=None)\\n\",\n \"\\n\",\n \"xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\\n\",\n \" labels=y, logits=logits)\\n\",\n \"\\n\",\n \"loss = tf.reduce_mean(\\n\",\n \" xentropy)\\n\",\n \"\\n\",\n \"optimizer = tf.train.AdamOptimizer(\\n\",\n \" learning_rate=learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(\\n\",\n \" loss)\\n\",\n \"\\n\",\n \"correct = tf.nn.in_top_k(\\n\",\n \" logits, y, 1)\\n\",\n \"\\n\",\n \"accuracy = tf.reduce_mean(\\n\",\n \" tf.cast(correct, tf.float32))\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\"\n ]\n }\n ],\n \"source\": [\n \"# load MNIST data, reshape to [batch_size, n_steps, n_inputs]\\n\",\n \"\\n\",\n \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n \"\\n\",\n \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\")\\n\",\n \"\\n\",\n \"X_test = mnist.test.images.reshape((-1, n_steps, n_inputs))\\n\",\n \"y_test = mnist.test.labels\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 Train accuracy: 0.953333 Test accuracy: 0.8711\\n\",\n \"1 Train accuracy: 0.953333 Test accuracy: 0.9417\\n\",\n \"2 Train accuracy: 0.953333 Test accuracy: 0.9432\\n\",\n \"3 Train accuracy: 0.946667 Test accuracy: 0.9595\\n\",\n \"4 Train accuracy: 0.98 Test accuracy: 0.9627\\n\",\n \"5 Train accuracy: 0.966667 Test accuracy: 0.9666\\n\",\n \"6 Train accuracy: 0.96 Test accuracy: 0.961\\n\",\n \"7 Train accuracy: 0.973333 Test accuracy: 0.9729\\n\",\n \"8 Train accuracy: 0.986667 Test accuracy: 0.9702\\n\",\n \"9 Train accuracy: 0.986667 Test accuracy: 0.9732\\n\"\n ]\n }\n ],\n \"source\": [\n \"# ready to run. reshape each training batch before feeding to net.\\n\",\n \"\\n\",\n \"n_epochs = 10\\n\",\n \"batch_size = 150\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" for iteration in range(mnist.train.num_examples // batch_size):\\n\",\n \" \\n\",\n \" X_batch, y_batch = mnist.train.next_batch(batch_size)\\n\",\n \" X_batch = X_batch.reshape(\\n\",\n \" (-1, n_steps, n_inputs))\\n\",\n \"\\n\",\n \" sess.run(\\n\",\n \" training_op, \\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \" \\n\",\n \" acc_train = accuracy.eval(\\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \" acc_test = accuracy.eval(\\n\",\n \" feed_dict={X: X_test, y: y_test})\\n\",\n \"\\n\",\n \" print(epoch, \\n\",\n \" \\\"Train accuracy:\\\", acc_train, \\n\",\n \" \\\"Test accuracy:\\\", acc_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RNN Training: Predicting Time Series\\n\",\n \"![rnn-timeseries](pics/rnn-timeseries.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"t_min, t_max = 0, 30\\n\",\n \"resolution = 0.1\\n\",\n \"\\n\",\n \"def time_series(t):\\n\",\n \" return t * np.sin(t) / 3 + 2 * np.sin(t*5)\\n\",\n \"\\n\",\n \"def next_batch(batch_size, n_steps):\\n\",\n \" t0 = np.random.rand(batch_size, 1) * (t_max - t_min - n_steps * resolution)\\n\",\n \" Ts = t0 + np.arange(0., n_steps + 1) * resolution\\n\",\n \" ys = time_series(Ts)\\n\",\n \" return ys[:, :-1].reshape(-1, n_steps, 1), ys[:, 1:].reshape(-1, n_steps, 1)\\n\",\n \"\\n\",\n \"t = np.linspace(t_min, t_max, (t_max - t_min) // resolution)\\n\",\n \"\\n\",\n \"n_steps = 20\\n\",\n \"t_instance = np.linspace(\\n\",\n \" 12.2, 12.2 + resolution * (n_steps + 1), n_steps + 1)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(?, 20, 100)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# each training instance = 20 inputs long\\n\",\n \"# targets = 20-input sequences\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_steps = 20\\n\",\n \"n_inputs = 1\\n\",\n \"n_neurons = 100\\n\",\n \"n_outputs = 1\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\\n\",\n \"y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\\n\",\n \"\\n\",\n \"cell = tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons, \\n\",\n \" activation=tf.nn.relu)\\n\",\n \"\\n\",\n \"outputs, states = tf.nn.dynamic_rnn(\\n\",\n \" cell, X, dtype=tf.float32)\\n\",\n \"\\n\",\n \"print(outputs.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# output at each time step now vector[100],\\n\",\n \"# but we want single output value at each step.\\n\",\n \"\\n\",\n \"# use OutputProjectionWrapper()\\n\",\n \"# -- adds FC layer to top of each output\\n\",\n \"\\n\",\n \"cell = tf.contrib.rnn.OutputProjectionWrapper(\\n\",\n \" tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons, \\n\",\n \" activation=tf.nn.relu),\\n\",\n \" output_size=n_outputs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# define cost function using MSE\\n\",\n \"# use Adam optimizer\\n\",\n \"\\n\",\n \"learning_rate = 0.001\\n\",\n \"loss = tf.reduce_mean(\\n\",\n \" tf.square(outputs - y))\\n\",\n \"\\n\",\n \"optimizer = tf.train.AdamOptimizer(\\n\",\n \" learning_rate=learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(loss)\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 \\tMSE: 15.3099\\n\",\n \"100 \\tMSE: 13.5276\\n\",\n \"200 \\tMSE: 11.0956\\n\",\n \"300 \\tMSE: 9.91156\\n\",\n \"400 \\tMSE: 14.0311\\n\",\n \"500 \\tMSE: 9.73811\\n\",\n \"600 \\tMSE: 9.23351\\n\",\n \"700 \\tMSE: 9.64445\\n\",\n \"800 \\tMSE: 8.98904\\n\",\n \"900 \\tMSE: 10.849\\n\",\n \"[[[ 0. 0. 0. ..., 0. 0. 0. ]\\n\",\n \" [ 0. 0.04218276 0. ..., 0. 0. 0. ]\\n\",\n \" [ 0. 0.14342034 0. ..., 0. 0. 0. ]\\n\",\n \" ..., \\n\",\n \" [ 6.67315388 0. 6.39087296 ..., 6.9017005 6.30435514\\n\",\n \" 6.23329258]\\n\",\n \" [ 6.61708975 0. 6.31429434 ..., 6.58116341 6.19745445\\n\",\n \" 6.11896658]\\n\",\n \" [ 5.9406209 0. 5.73649979 ..., 5.63920403 5.5386672\\n\",\n \" 5.47510672]]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# initialize & run\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"n_iterations = 1000\\n\",\n \"batch_size = 50\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" for iteration in range(n_iterations):\\n\",\n \" X_batch, y_batch = next_batch(batch_size, n_steps)\\n\",\n \" sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n \" if iteration % 100 == 0:\\n\",\n \" mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n \" print(iteration, \\\"\\\\tMSE:\\\", mse)\\n\",\n \"\\n\",\n \" \\n\",\n \" # use trained model to make some predictions\\n\",\n \" X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\\n\",\n \" y_pred = sess.run(outputs, feed_dict={X: X_new})\\n\",\n \" print(y_pred)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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qP9TTroMjgZ4g7s7v4P4HUzOzG6aDKQcX3YHmpgd3f2799/yOdRYBc5\\ndK0f7T/hhqnM+tstnDh3Kgtuzqe2Nj0f7W8you0ugykrS8rpEtUq5svAEjPbBJQB30nQcXvMggUL\\nqK6upqysjOuvv57JkyczcuRIhg0b1vTE5/bt2znxxBP5/Oc/z9ChQ3n99dd54IEHOOGEExg7dixX\\nXXUV1113HQA7d+7koosuYsyYMYwZM4Y///nPbN++nfvuu48777yTsrIynnrqqVReskhGycRH+5uU\\nl4cy9WZdBjNuXPIS3ZWnmBI9xfPkabJs27bNTzrpJHd3b2ho8N27d7u7+86dO33gwIG+f/9+37Zt\\nm5uZP/vss+7u/sYbb/hxxx3nu3bt8n379vmpp57q10b775wxY4Y/9dRT7u7+2muv+aBBg9zd/dZb\\nb/Xbb7+9x64r1fdVRKIaG91XrHBfuDDMGxsP+RB08cnTjOrdsae4OzfddBN/+tOfyMvL44033mjq\\nYve4447j5JNPBuD555/n9NNP54gjjgDg4osv5tVXXwVCV7zNR1V6//33qUlEf8Iikpny80ObzalT\\nk34qBfY2LFmyhJ07d7J+/XoKCgro378/e/fuBaCodTlZO/bv389zzz1H7969k5lUEZGDZFxfMcnS\\nvOvd3bt385GPfISCggL++Mc/8tprr7W5z5gxY1i3bh3vvvsujY2N/OY3v2lad/bZZ3Pvvfc2vY/1\\nq966i18RkURTYI868sgjGT9+PEOHDmXjxo1UVVUxbNgw/uu//qvdrnc/9rGPcdNNNzF27FjGjx9P\\n//79OfzwwwG45557qKqqorS0lCFDhnDfffcBcP7557N8+XJVnopI0ph3MIpPsowePdqrqqpaLNu8\\neTODBw/u8bTEq6amhuLiYhobG5k2bRqzZs1i2rRpqU5Wk0y9ryJyMDNb7+6jO9tOOfY4feMb36Cs\\nrIyhQ4cyYMAAPv3pT6c6SSKS41R5GqfYaEciIulCOXYRkSyjwC4iPSqecUulaxTYRaTHVFRAaSnc\\nfz9MmgTTpsHEieF9aWnSOjvMOQrsItIjmo9b2tAAs2aF5bNmhfcatzRxFNiTqLi4GIA333yT6dOn\\nd7jtXXfdRV1dXdP7c889l/fe00BUkj1i45a6h+mUU8Ly8eMPLKutDUFe4pO5gb0Hh5lqedpDP89H\\nP/pRli1b1uE2rQP7qlWrKCkpOeRziaSrpnFLow47rOUc0LilCZKZgT02zNSMGXDrrWF+zjlxB/ft\\n27czaNAgLr30UgYPHsz06dOpq6ujf//+zJ8/n5EjR7J06VKqq6uZMmUKo0aNYsKECWyJDo2+bds2\\nPvWpTzFs2DBuueWWFscdOnRoNOkR5s2bx9ChQyktLeXee+/lnnvu4c0332TSpElMmjQJgP79+/P2\\n228DsGjRIoYOHcrQoUO56667mo45ePBgrrrqKk466STOPvts6uvr47p+kWSKjVtaW0ubGbPaWtJ/\\n3NJM0ZUuIBM9xd1t74oV7sXFsV9vYSouDsvjsG3bNgf86aefdnf3K664wm+//XY/7rjj/Lbbbmva\\n7owzzvBXX33V3d2fe+45nzRpkru7n3/++f7zn//c3d0XL17sRUVFTceNdQn8wx/+0C+66CJvaGhw\\nd/ddu3a5u/txxx3nO3fubDpH7H1VVZUPHTrUa2pqfM+ePT5kyBD/y1/+4tu2bfP8/HzfsGGDu7tf\\nfPHF/tBDDx10Teq2V9JFnz7hX/X8cxu9ceLk8D9r5l5c7I0TJ/v55zY6uPftm+qUpi+62G1vZubY\\nkzjM1LHHHsv48eMBmDlzJk8//TQAl1xyCRC6EHjmmWe4+OKLKSsr4+qrr+att94C4M9//jMzZswA\\n4LJ2Bl9cs2YNV199Nb16hWfDYl3+tufpp59m2rRpFBUVUVxczGc+85mmPmYGDBhAWXQEllGjRrF9\\n+/Y4rlwkuWLjlp5WW0HeCy3H/8x7oZLTaivSe9zSDJKZgT2Jw0yZWZvvY9317t+/n5KSEjZu3Ng0\\nbd68ud39k+mwZoWT+fn5NDY29ti5RQ5VbNzSz3xiA9S1ypjV1fKZT2xM73FLM0hmBvYkDjP197//\\nnWeffRaAX/7yl5x66qkt1vft25cBAwawdOlSIBRlvfjiiwCMHz+eX/3qV0Do070tZ511Fj/60Y+a\\ngvA777wDtN+d74QJE3jkkUeoq6ujtraW5cuXM2HChLivU6SnxcYt3dlvBPt6tcyY7etVxM5+Zek9\\nbmkGyczAnp8Pq1fDww/DN78Z5qtXh+VxOvHEE/nBD37A4MGDeffdd7nmmmsO2mbJkiU88MADDB8+\\nnJNOOqlpTNS7776bH/zgBwwbNow33nijzeN/8Ytf5OMf/zilpaUMHz6cX/7ylwDMnj2bKVOmNFWe\\nxowcOZLLL7+csWPHMm7cOL74xS8yYsSIuK9TJBXKy+GomeVs+8g4aigmglFDMds+Mo6jZpan97il\\nGUTd9jazfft2pk6dyksvvZTSdCRSOtxXkYNEIqGZzMaNoQi1vDwhGbNs19Vue9W7o4j0vB4c/zMX\\nZWZRTJL0798/q3LrIpKb0iqwp6JYKJvpforkprQJ7L1792bXrl0KRgni7uzatYvevXunOiki0sPS\\npoy9X79+7Nixg507d6Y6KVmjd+/e9OvXL9XJEJEeljaBvaCggAEDBqQ6GSIiGS9timJERCQxFNhF\\nRLKMAruISJZRYBcRyTIK7CIiWSZhgd3M8s1sg5mtTNQxRUTk0CUyx/5VYHOnW4lIRquuhjlzoF8/\\nWL48zOfMCcslPSQksJtZP+A84P5EHE9E0lNFBZSWwv33w6RJMG0aTJwY3peWhvWSeonKsd8FfA3Y\\n394GZjbbzKrMrEpPl4pknupqmD4d6uqgoQFmzQrLZ80K7+vqwnrl3FMv7sBuZlOB/3X39R1t5+4/\\ndvfR7j766KOPjve0ItLD9u0LQwvHRpA/5ZSwfPz4A8tqa0OQl9RKRI59PHCBmW0HfgWcYWa/SMBx\\nRSSNzJvXcgz52JC7zYbepbY2jG0qqRV3YHf3f3f3fu7eH/gc8KS7z4w7ZSKSVioqwrgYtbVtr6+t\\nhfPOg8ce69l0ycHUjl1EuqS4GNauhUsugfr6luvq68PydevCdpJaCQ3s7r7W3TXWVQ5TU7jsNXMm\\nFBRASQk0Noapru7A65KSsP6yy1KdUlGOXRJGTeGy29y5IXBfeSUUFsKmTXDhhWFeWBhaxxQUwPXX\\npzqlosAuCdG8KVykIcKNg1fCwoXcOHglkYZIl5rCKbef3gYOhGXLoKYG5s+H0aNhzRoYMwYWLAhl\\n7MuWhe0ktdJmoA3JbLGmcEQicM45+Hcqoa6W0sIiIpPHwerVkJ/Pli1t719REQJ/ZF+Eb59awbSX\\nNlD9yRHc8pNyfv7zfJYtg/LyHr0kaUN5efiifbwiwvTeFQzau4Eth41gb205Q4bkK6inC3fv8WnU\\nqFEu2eXcc91ratx9xQr34uJYs+YwFRe7r1jhNTVhu9a2bnUvLHTPo9H/wGRv+HCxu5k3fLjY/8Bk\\nz6PRCwvDdpIGGhvdJ08Of1ezMJ88OSyXpAKqvAsxVkUxkhCxpnD7Kjcc3B6utpZ9z29stylcLLcf\\nWVHBmcWV9KqvAXd61ddwZnElkRUVevAlnVRUQGVlKJNxD/PKSlWipBEFdkmIWFO47z42Ai8sarHO\\nC4v4bkVZu03hmh582dD2lwIbN+rBl3TSwd9J0oMCuyRErCnc1uPLiYwehxcV42Z4UTGR0ePYenx5\\nu03hYrn9vYNHQFHLLwWKitg7qEwPvqSTEW3/nSgrS0165CAK7NJCd1umxJrCzboqH3t8NdXfepif\\nDfgm1d96GHt8NVd8Mb/dpnCx3P5nHwxfChQXgxkUhy+Fzz5Yrgdf0kl5OYxr+Xdi3DjVbqcRtYqR\\nJrGWKQ0N4SnCadPgN78J7dB//nM6bJnSoincTfksWjQV96nkzYUb3oDTTmu/KdzMmeEcff8ln7ol\\nqylcV0HkLxvJH1lG3enl9L0uXw++pJP8/NDKqaIiFL+UlYUPRn5+qlMmMV2pYU30pFYx6SfWMiXW\\nkOXJJ8PyJ544sKwrLVO2bnW/9lr3vn3d8/LC/NprO94vdu4nnwwNK9avdz/zzDBvbAxpUKsYka63\\nirGwbc8aPXq0V1VV9fh5pX2bN8PgwdE3kQgNj1ZQ8NIGGoaOoOCCA7mxLVtg0KDEn7+iIjyWvm4d\\nLFoUvkry8uCGG0Juv1cv/dIXMbP17j66s+1Uxi5As5Yp0QeMCj4/A269NczPOQcikaS2TCkvhyFD\\nYO9e6NMnBPXi4tC51JAhnQd1PbUq0kxXsvWJnlQUk37M3CdOdK9f2vYDRvVLV/jpp4filXSzalUo\\nqikocJ85Myy79NLwvrAwrBfJBugBJTkUsZYpy27egLdqo+y1tSy9eWNatkzRcG0iB1NgF+BAO/R/\\nHDMCWj1gRGER/zymLC1bpmi4NpGDKbALcKAd+qhbQhvlyIeL2Y8R+XBoozzy5vK07JJVw7V1j+ok\\nspsCuwDN2qHX5/O14av5dP3DfJ1vMm3vw8wvW03t3vy07JJVw7UdOvWbn/0U2KVJrGVK/b58/tR3\\nKt/Nu4V1faZS90F+l1qmpIKGazs0qpPIDXryVFoYOBAWLw5TJog9tVpSAo0fRIg8XkFk/QbyR42g\\n8fRySkr01GpzTf3mR33wQZjH6iRi2us3XzKDcuyS0ZqGa7s8QvFF58CMGfRaeCvMmEHxRecw6wuR\\ntKwbSBXVSeQGBXbJaLG6gcI/VbDvqUry62vIw8mvr2HfU5UUPVWRlnUDqaI6idygwC4Zr7wcjt+z\\ngYKGltGqoKGWT9ZsTMu6gVRRnURuUGCXrHDE5BHkFbdsf59XXMSRZ6iP8OZizyuUlIS+eRobQ4Vp\\n7HVJCaqTyAIK7JId4ugjPJfadDfVSVwJhYWwaRNceGGYFxaG1jGqk8h86t1Rskckcsh9hLfug/6h\\nh0Ku9te/DgGuoz7oM5V60sxc6t1Rck9+fqgZvOWWMO8kqOdqm+54e9KU9KfAnoVyqWghHrncz0zs\\neYXdu8MPnd27w3u1HsoOCuxZRo+Ld53adEu2UmDPIrlatNBdatMt2UqBPYvkctFCd+R8m+5IBFau\\nhIULwzwSSXWKJEHiDuxmdqyZ/dHMXjGzl83sq4lImBw6FS0cmkxv0x1XXUp0CERmhCEQmXFgCETJ\\nAl0ZZqmjCTgGGBl93Qd4FRjS0T4aGi85YsPb1dS0vb6mxtN2eLtU2Lo1DJ335JPujY3u69e7n3lm\\nmDc2uj/xRFi/dWuqU3qwuIcDXNH2EIi+YkXS0y7dR08Njefub7n7X6Kv9wCbgY/Fe1w5dDlftHCI\\nmvqgr4H582H0aFizBsaMgQULwq+bdOxnJiF1KRs2HFy5UFsbngGQjJfQMnYz6w+MACrbWDfbzKrM\\nrGrnzp2JPK1EZXrRQipkYpvuhNSljBgBRa2GQCwqCg92SebrSra+KxNQDKwHPtPZtiqKSY5MLlrI\\nZFu3ul9zjfvHPub+29+G+TXXJO8+n3tu+8VtMTU1Ybt2NTa6T54cil/Mwnzy5LBc0hZdLIpJSJcC\\nZlYArARWu/uizrZXlwLJo8fFe1YquiTIy4PTTw8NWVpnuuFAM82nnuqkLrQbXTBIavVYlwJmZsAD\\nwOauBHVJrkwsWshUqXpuIGF1KYfYBYNkjkSUsY8HLgPOMLON0encBBxXukmPi3dDN9p0J6KsuztN\\nFlWXIp3qSnlNoieVsUta6WZ5c7xl3d1tsqi6lNxFTzV3FMl4FRVQWRnaPbqHeWVlpx3rxNMlQTzF\\nOJnaTFN6jgK7SDfbdMdT1h1vMY7qUqQjCuwi3WzTHU9ZdyK6f1BdirRHgV2km8PqxTPMnHqWlGTS\\n0Hgi0O023d19bqBvX9izJwTvpUvhwx8+sK6+Hi6+GH7/+7Dd7t0JvE7JaF1tx96rJxIjkvZibbqn\\nTj2k3crLQwXn6tWhrLumpmVZd3vFIjNnhsFPmhfj7NsHH/qQmixK/JRjz1axHOiGDaEMWU8VppXq\\n6jCi1cqVIWf/4ouhhcttt8Hw4eEXwPnnh2KdDsvM9XfOKcqx57JYX9uVlaGwtqgolBmvXq1/+jTR\\nuslirBhnzJgDxTidNlnU31naocrTbNTNdtnSs+Jusqi/s7RDgT0bqa/tntfNYebiarKov7O0Q0Ux\\n2SjWLrsxwQFFAAALe0lEQVSm5sAy9bWdPKkqEtHfWdqhHHs26ma7bOmmeItEujuotP7O0g7l2LNR\\nfn7ILaqv7Z7RUZFIZ80n48nt6+8s7VCOPU3FNQI9qK/tnhTPMHPx5vb1d5Y2KLCnoYqK0Mb5/vth\\n0iSYNg0mTgzvS0vV6CHtxFMkogpQSQIF9jSTqlF5JA6xIpGHH4ZvfjPMu1pxqkGlJQlUxp5mYt25\\nxnzwQZjHunON2bKlZ9MlnehmlwRNuf3WZeyqAJU4KMeeZhLRnatkkHhy+yLtUF8xaSZhI9CLSNbp\\nal8xyrGnmYSNQC8iOUuBPc1oBHoRiZcCe5qJZ1QeERFQYE87GoFeROKlwJ6GNAK9iMRDrWJERDKE\\nRlDKBhr2TES6QYE9XWnYMxHpJpWxpysNeyYi3aTAnq7U65+IdJMCe7pSr38i0k0JCexmNsXM/sfM\\ntprZgkQcM6G6O/RYIvbXsGci0sPirjw1s3zgB8BZwA7gBTN71N1fiffYCRFvJWQ8+2vYMxFJgUTk\\n2McCW939b+6+D/gVcGECjpsY8VZCxrO/hj0TkRRIRGD/GPB6s/c7ostaMLPZZlZlZlU7d+5MwGm7\\nKN5KyHj2VwWoiKRAj1WeuvuP3X20u48++uije+q08VdCxrO/KkBFJAUSEdjfAI5t9r5fdFl6iLcS\\nMp79VQEqIikQd18xZtYLeBWYTAjoLwD/x91fbm+fHu8rJvZofncrIePZP95zi4hEdbWvmIR0AmZm\\n5wJ3AfnAT9392x1tr07AREQOXY92Aubuq4BViTiWiIjER0+eiohkGQV2EZEso8AuIpJlFNhFRLKM\\nAnsSVVfDnDnQrx8sXx7mc+aE5SIiyaLAniQVFVBaCvffD5MmwbRpMHFieF9aqvEyRCR5FNiToLoa\\npk+HujpoaIBZs8LyWbPC+7q6sF45dxFJBo15mgT79rXs++uDD8J8/PjQyWPMli09my4RyQ3KsSfB\\nvHktA/thh7WcQ1g/d27PpktEcoMCexJUVITu01v32BtTWwvnnQePPdaz6RKR3KDAngTFxbB2LVxy\\nCdTXt1xXXx+Wr1sXthMRSTQF9iSYORMKCqCkBBobw1RXd+B1SUlYf9llqU6piGQjBfYkmDs3BO4r\\nr4TCQti0CS68MMwLC0PrmIICuP76VKdURLKRAnsSDBwIy5aFIU7nz4fRo2HNGhgzBhYsCGXsy5aF\\n7UREEk2BPUnKy2HIENi7F/r0gby8UKZeXx+WaxAlEUmWhAy0cag00IaIyKHr6kAbyrGLiGQZBXYR\\nkSyjwC4ikmUU2EVEsowCu4hIllFgFxHJMgrsIiJZRoFdRCTLKLCLiGQZBXYRkSyjwC4ikmUU2EVE\\nsowCu4hIllFgFxHJMnEFdjO73cy2mNkmM1tuZiWJSli6qK6GOXOgXz9YvjzM58wJy0VE0lG8OfY/\\nAEPdvRR4Ffj3+JOUPioqoLQU7r8fJk2CadNg4sTwvrQ0rBcRSTdxBXZ3f9zdG6NvnwP6xZ+k9FBd\\nDdOnh0GoGxrCOKUQ5g0NYfn06cq5i0j66ZXAY80C/juBx0upffvC2KQxH3wQ5uPHQ/NBp7Zs6dl0\\niYh0ptMcu5mtMbOX2pgubLbNzUAjsKSD48w2syozq9q5c2diUp9E8+a1DOyHHdZyDmH93Lk9my4R\\nkc7EPeapmV0OXA1Mdve6ruyTCWOe5uXB6afDypVQVHTw+tpaOO88eOopiER6Pn0iknt6ZMxTM5sC\\nfA24oKtBPVMUF8PatXDJJVBf33JdfX1Yvm5d2E5EJJ3E2ypmMdAH+IOZbTSz+xKQprQwcyYUFEBJ\\nCTQ2hqmu7sDrkpKw/rLLUp1SEZGW4m0V80l3P9bdy6LTlxKVsFSbOzcE7iuvhMJC2LQJLrwwzAsL\\nQ+uYggK4/vpUp1REpCU9edqOgQNh2TKoqYH582H0aFizBsaMgQULQhn7smVhOxGRdKLA3oHychgy\\nBPbuhT59QoVqcXEoYx8yJKwXEUk3cbeK6Y5MaBUjIpJueqRVjIiIpB8FdhGRLKPALiKSZRTYRUSy\\njAK7iEiWUWAXEckyWR/YNQKSiOSarA7sGgFJRHJR1gZ2jYAkIrkqkSMopRWNgCQiuSprc+waAUlE\\nclXWBvaKCpg6tWVwby42AtJjj/VsukREki1rA7tGQBKRXJW1gV0jIIlIrsqIwB5ri963b+gTvW/f\\nztuiawQkEclVad8fe0VFaJbY0BCmmIKCMC1b1v6AFxUVIXe+bh0sWhRaw+TlwQ03wGmnQa9eGixD\\nRDJHVvTH3rotenNdaYuuEZBEJBeldWC/446DA3prDQ1w553trx84EBYvht27IRIJ88WLNVapiGSv\\ntA7sv/hF1wL7Qw/1THpERDJBWgf2mprEbicikgvSOrB3tY252qKLiByQ1oE91ha9I2qLLiLSUloH\\n9lhb9I6oLbqISEtpHdgHDgzt1AsLDw7wBQVh+bJlauEiItJcWgd2CG3NN22C2bNbPnk6e3ZYrrbo\\nIiItpf2TpyIiEmTFk6ciInLoFNhFRLKMAruISJZJSRm7me0EXuvxE6fGUcDbqU5EGtP96ZzuUcdy\\n6f4c5+5Hd7ZRSgJ7LjGzqq5UduQq3Z/O6R51TPfnYCqKERHJMgrsIiJZRoE9+X6c6gSkOd2fzuke\\ndUz3pxWVsYuIZBnl2EVEsowCu4hIllFg7yYz+6mZ/a+ZvdRs2e1mtsXMNpnZcjMraWff7Wb2VzPb\\naGZZ2WlOO/dnYfTebDSzx83so+3sO8XM/sfMtprZgp5Ldc+K8x7l5Geo2bq5ZuZmdlQ7++bEZ6hd\\n7q6pGxNwGjASeKnZsrOBXtHXtwG3tbPvduCoVF9DCu5P32avvwLc18Z++UA18AngQ8CLwJBUX086\\n3aNc/gxFlx8LrCY85HjQPcilz1B7k3Ls3eTufwLeabXscXdvjL59DujX4wlLE+3cn/ebvS0C2qq5\\nHwtsdfe/ufs+4FfAhUlLaArFcY9yQlv3J+pO4Gu0f29y5jPUHgX25JkFVLSzzoE1ZrbezGb3YJpS\\nzsy+bWavA5cCX29jk48Brzd7vyO6LGd04R5Bjn6GzOxC4A13f7GDzXL+M6TAngRmdjPQCCxpZ5NT\\n3b0MKAeuNbPTeixxKebuN7v7sYR7c12q05OOuniPcu4zZGaFwE20/2UnUQrsCWZmlwNTgUs9WuDX\\nmru/EZ3/L7Cc8NMx1ywBLmpj+RuEMtSYftFluai9e5Srn6GBwADgRTPbTvhs/MXM/q3Vdjn/GVJg\\nTyAzm0Io+7vA3eva2abIzPrEXhMqXA+q9c9GZnZ8s7cXAlva2OwF4HgzG2BmHwI+BzzaE+lLB125\\nR7n6GXL3v7r7R9y9v7v3JxSxjHT3f7TaNKc/Q6DA3m1m9jDwLHCime0wsyuBxUAf4A/RZmj3Rbf9\\nqJmtiu76r8DTZvYi8Dzwe3d/LAWXkFTt3J/vmdlLZraJEIy+Gt226f5EK5+vI7R62Az82t1fTslF\\nJFl37xG5/Rlqb9uc/Ay1R10KiIhkGeXYRUSyjAK7iEiWUWAXEckyCuwiIllGgV1EJMv0SnUCRJLJ\\nzI4Enoi+/TcgAuyMvq9z91NSkjCRJFJzR8kZZvYNoMbdv5/qtIgkk4piJGeZWU10PtHM1pnZ78zs\\nb2b2PTO71Myej/Z5PjC63dFm9hszeyE6jU/tFYi0TYFdJBgOfAkYDFwGnODuY4H7gS9Ht7kbuNPd\\nxxD6cLk/FQkV6YzK2EWCF9z9LQAzqwYejy7/KzAp+vpMYIiZxfbpa2bF7l7ToykV6YQCu0jwQbPX\\n+5u938+B/5M84GR339uTCRM5VCqKEem6xzlQLIOZlaUwLSLtUmAX6bqvAKOjg02/QiiTF0k7au4o\\nIpJllGMXEckyCuwiIllGgV1EJMsosIuIZBkFdhGRLKPALiKSZRTYRUSyzP8HRrkJiuJisKwAAAAA\\nSUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"plt.title(\\\"Testing the model\\\", fontsize=14)\\n\",\n \"\\n\",\n \"plt.plot(\\n\",\n \" t_instance[:-1], \\n\",\n \" time_series(t_instance[:-1]), \\n\",\n \" \\\"bo\\\", markersize=10, label=\\\"instance\\\")\\n\",\n \"\\n\",\n \"plt.plot(\\n\",\n \" t_instance[1:], \\n\",\n \" time_series(t_instance[1:]), \\n\",\n \" \\\"w*\\\", markersize=10, label=\\\"target\\\")\\n\",\n \"\\n\",\n \"plt.plot(\\n\",\n \" t_instance[1:], \\n\",\n \" y_pred[0,:,0], \\n\",\n \" \\\"r.\\\", markersize=10, label=\\\"prediction\\\")\\n\",\n \"\\n\",\n \"plt.legend(loc=\\\"upper left\\\")\\n\",\n \"plt.xlabel(\\\"Time\\\")\\n\",\n \"#save_fig(\\\"time_series_pred_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* **OutputProjectionWrapper()** = simplest solution for reducing output sequences to one value/timestep, but not most efficient.\\n\",\n \"* More efficient solution shown below - **signficant speed boost**.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tensor(\\\"Reshape:0\\\", shape=(?, 100), dtype=float32)\\n\",\n \"Tensor(\\\"fully_connected/BiasAdd:0\\\", shape=(?, 1), dtype=float32)\\n\",\n \"Tensor(\\\"Reshape_1:0\\\", shape=(?, 20, 1), dtype=float32)\\n\",\n \"0 \\tMSE: 22963.7\\n\",\n \"100 \\tMSE: 743.444\\n\",\n \"200 \\tMSE: 276.131\\n\",\n \"300 \\tMSE: 117.955\\n\",\n \"400 \\tMSE: 53.3529\\n\",\n \"500 \\tMSE: 63.4189\\n\",\n \"600 \\tMSE: 45.1415\\n\",\n \"700 \\tMSE: 41.5129\\n\",\n \"800 \\tMSE: 53.4219\\n\",\n \"900 \\tMSE: 43.2203\\n\",\n \"[[[-3.46527553]\\n\",\n \" [-2.46867704]\\n\",\n \" [-1.10144436]\\n\",\n \" [ 0.69717044]\\n\",\n \" [ 2.08823276]\\n\",\n \" [ 3.13628578]\\n\",\n \" [ 3.55210543]\\n\",\n \" [ 3.4186697 ]\\n\",\n \" [ 2.85978389]\\n\",\n \" [ 2.15520501]\\n\",\n \" [ 1.67705297]\\n\",\n \" [ 1.6919663 ]\\n\",\n \" [ 1.93633199]\\n\",\n \" [ 2.70151305]\\n\",\n \" [ 3.87054777]\\n\",\n \" [ 5.11770582]\\n\",\n \" [ 6.15701818]\\n\",\n \" [ 6.71814394]\\n\",\n \" [ 6.69798708]\\n\",\n \" [ 6.08309698]]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_steps = 20\\n\",\n \"n_inputs = 1\\n\",\n \"n_neurons = 100\\n\",\n \"n_outputs = 1\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\\n\",\n \"y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\\n\",\n \"\\n\",\n \"cell = tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons, \\n\",\n \" activation=tf.nn.relu)\\n\",\n \"\\n\",\n \"rnn_outputs, states = tf.nn.dynamic_rnn(\\n\",\n \" cell, X, dtype=tf.float32)\\n\",\n \"\\n\",\n \"# stack outputs using reshape\\n\",\n \"stacked_rnn_outputs = tf.reshape(\\n\",\n \" rnn_outputs, [-1, n_neurons])\\n\",\n \"\\n\",\n \"print(stacked_rnn_outputs)\\n\",\n \"\\n\",\n \"# add FC layer -- just a projection, so no activation fn needed\\n\",\n \"stacked_outputs = fully_connected(\\n\",\n \" stacked_rnn_outputs, \\n\",\n \" n_outputs,\\n\",\n \" activation_fn=None)\\n\",\n \"\\n\",\n \"print(stacked_outputs)\\n\",\n \"\\n\",\n \"# unstack outputs using reshape\\n\",\n \"outputs = tf.reshape(\\n\",\n \" stacked_outputs, [-1, n_steps, n_outputs])\\n\",\n \"\\n\",\n \"print(outputs)\\n\",\n \"\\n\",\n \"loss = tf.reduce_sum(tf.square(outputs - y))\\n\",\n \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\\n\",\n \"training_op = optimizer.minimize(loss)\\n\",\n \"\\n\",\n \"#initialize & run\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"n_iterations = 1000\\n\",\n \"batch_size = 50\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" for iteration in range(n_iterations):\\n\",\n \" X_batch, y_batch = next_batch(batch_size, n_steps)\\n\",\n \" sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n \" if iteration % 100 == 0:\\n\",\n \" mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n \" print(iteration, \\\"\\\\tMSE:\\\", mse)\\n\",\n \"\\n\",\n \" \\n\",\n \" # use trained model to make some predictions\\n\",\n \" X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\\n\",\n \" y_pred = sess.run(outputs, feed_dict={X: X_new})\\n\",\n \" print(y_pred)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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nAzkdllRDzUZVqAjFDa8V8aqerGl4zAvgfY4+7Vsfl1BIE+qxxtYHd3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\\nRHKMAruISI5RYBcRyTH/H+iR+UZEn7pXAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.title(\\\"Testing the model\\\", fontsize=14)\\n\",\n \"plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \\\"bo\\\", markersize=10, label=\\\"instance\\\")\\n\",\n \"plt.plot(t_instance[1:], time_series(t_instance[1:]), \\\"w*\\\", markersize=10, label=\\\"target\\\")\\n\",\n \"plt.plot(t_instance[1:], y_pred[0,:,0], \\\"r.\\\", markersize=10, label=\\\"prediction\\\")\\n\",\n \"plt.legend(loc=\\\"upper left\\\")\\n\",\n \"plt.xlabel(\\\"Time\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Creative RNNs\\n\",\n \"* Use model to generate creative sequences\\n\",\n \"* Provide seed sequence of length = n_steps, zero-filled\\n\",\n \"* use model to append predicted new value to sequence\\n\",\n \"* feed last n_steps values to model to predict next value, etc.\\n\",\n \"* should get new sequence resembling original time series\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 \\tMSE: 14607.1\\n\",\n \"100 \\tMSE: 505.605\\n\",\n \"200 \\tMSE: 167.29\\n\",\n \"300 \\tMSE: 83.1336\\n\",\n \"400 \\tMSE: 58.9695\\n\",\n \"500 \\tMSE: 61.0224\\n\",\n \"600 \\tMSE: 55.8671\\n\",\n \"700 \\tMSE: 43.7078\\n\",\n \"800 \\tMSE: 57.2013\\n\",\n \"900 \\tMSE: 55.3992\\n\",\n \"1000 \\tMSE: 54.082\\n\",\n \"1100 \\tMSE: 55.48\\n\",\n \"1200 \\tMSE: 39.4618\\n\",\n \"1300 \\tMSE: 40.7414\\n\",\n \"1400 \\tMSE: 47.8548\\n\",\n \"1500 \\tMSE: 43.9252\\n\",\n \"1600 \\tMSE: 47.892\\n\",\n \"1700 \\tMSE: 42.0762\\n\",\n \"1800 \\tMSE: 48.2429\\n\",\n \"1900 \\tMSE: 42.7509\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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OS6X5J6LHJhVUq7dAH22y9/Q2lxai/Drl2tUuq3vNXHH9u6rCedBMyd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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"n_iterations = 2000\\n\",\n \"batch_size = 50\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" for iteration in range(n_iterations):\\n\",\n \" X_batch, y_batch = next_batch(batch_size, n_steps)\\n\",\n \" sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n \" if iteration % 100 == 0:\\n\",\n \" mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n \" print(iteration, \\\"\\\\tMSE:\\\", mse)\\n\",\n \"\\n\",\n \" sequence1 = [0. for i in range(n_steps)]\\n\",\n \" for iteration in range(len(t) - n_steps):\\n\",\n \" X_batch = np.array(sequence1[-n_steps:]).reshape(1, n_steps, 1)\\n\",\n \" y_pred = sess.run(outputs, feed_dict={X: X_batch})\\n\",\n \" sequence1.append(y_pred[0, -1, 0])\\n\",\n \"\\n\",\n \" sequence2 = [time_series(i * resolution + t_min + (t_max-t_min/3)) for i in range(n_steps)]\\n\",\n \" for iteration in range(len(t) - n_steps):\\n\",\n \" X_batch = np.array(sequence2[-n_steps:]).reshape(1, n_steps, 1)\\n\",\n \" y_pred = sess.run(outputs, feed_dict={X: X_batch})\\n\",\n \" sequence2.append(y_pred[0, -1, 0])\\n\",\n \"\\n\",\n \"plt.figure(figsize=(11,4))\\n\",\n \"plt.subplot(121)\\n\",\n \"plt.plot(t, sequence1, \\\"b-\\\")\\n\",\n \"plt.plot(t[:n_steps], sequence1[:n_steps], \\\"b-\\\", linewidth=3)\\n\",\n \"plt.xlabel(\\\"Time\\\")\\n\",\n \"plt.ylabel(\\\"Value\\\")\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"plt.plot(t, sequence2, \\\"b-\\\")\\n\",\n \"plt.plot(t[:n_steps], sequence2[:n_steps], \\\"b-\\\", linewidth=3)\\n\",\n \"plt.xlabel(\\\"Time\\\")\\n\",\n \"#save_fig(\\\"creative_sequence_plot\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Deep RNNs\\n\",\n \"![deep-rnn](pics/deep-rnn.png)\\n\",\n \"* Built by stacking cells into a *MultiRNNCell()*.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"(2, 5, 100)\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_inputs = 2\\n\",\n \"n_neurons = 100\\n\",\n \"n_layers = 3\\n\",\n \"n_steps = 5\\n\",\n \"keep_prob = 0.5\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\\n\",\n \"\\n\",\n \"basic_cell = tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons)\\n\",\n \"\\n\",\n \"print(basic_cell)\\n\",\n \"\\n\",\n \"multi_layer_cell = tf.contrib.rnn.MultiRNNCell(\\n\",\n \" [basic_cell] * n_layers)\\n\",\n \"\\n\",\n \"print(multi_layer_cell)\\n\",\n \"\\n\",\n \"# states = tuple (one tensor/layer, = final state of layer's cell)\\n\",\n \"\\n\",\n \"outputs, states = tf.nn.dynamic_rnn(\\n\",\n \" multi_layer_cell, X, dtype=tf.float32)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"import numpy.random as rnd\\n\",\n \"X_batch = rnd.rand(2, n_steps, n_inputs)\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" outputs_val, states_val = sess.run(\\n\",\n \" [outputs, states], \\n\",\n \" feed_dict={X: X_batch})\\n\",\n \" \\n\",\n \"print(outputs_val.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### DRNNs: Multiple GPUs\\n\",\n \"* **TO DO**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dropout\\n\",\n \"* Very deep RNNs = danger of overfit. Use dropout to avoid problem.\\n\",\n \"* Can apply before or after RNN\\n\",\n \"* If applying dropout between RNN layers, need to use *DropoutWrapper*.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# apply 50% dropout to inputs of RNN layers\\n\",\n \"# can apply dropout to outputs via output_keep_prob\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"from tensorflow.contrib.layers import fully_connected\\n\",\n \"\\n\",\n \"n_inputs = 1\\n\",\n \"n_neurons = 100\\n\",\n \"n_layers = 3\\n\",\n \"n_steps = 20\\n\",\n \"n_outputs = 1\\n\",\n \"\\n\",\n \"keep_prob = 0.5\\n\",\n \"learning_rate = 0.001\\n\",\n \"\\n\",\n \"def deep_rnn_with_dropout(X, y, is_training):\\n\",\n \"\\n\",\n \" # TF implementation of DropoutWrapper doesn't differentiate\\n\",\n \" # between training & testing.\\n\",\n \" \\n\",\n \" cell = tf.contrib.rnn.BasicRNNCell(\\n\",\n \" num_units=n_neurons)\\n\",\n \" \\n\",\n \" if is_training:\\n\",\n \" cell = tf.contrib.rnn.DropoutWrapper(\\n\",\n \" cell, input_keep_prob=keep_prob)\\n\",\n \" \\n\",\n \" #\\n\",\n \" #\\n\",\n \" \\n\",\n \" multi_layer_cell = tf.contrib.rnn.MultiRNNCell(\\n\",\n \" [cell] * n_layers)\\n\",\n \" \\n\",\n \" rnn_outputs, states = tf.nn.dynamic_rnn(\\n\",\n \" multi_layer_cell, X, dtype=tf.float32)\\n\",\n \"\\n\",\n \" stacked_rnn_outputs = tf.reshape(\\n\",\n \" rnn_outputs, [-1, n_neurons])\\n\",\n \" \\n\",\n \" stacked_outputs = fully_connected(\\n\",\n \" stacked_rnn_outputs, n_outputs, activation_fn=None)\\n\",\n \" \\n\",\n \" outputs = tf.reshape(\\n\",\n \" stacked_outputs, [-1, n_steps, n_outputs])\\n\",\n \"\\n\",\n \" loss = tf.reduce_sum(\\n\",\n \" tf.square(outputs - y))\\n\",\n \" \\n\",\n \" optimizer = tf.train.AdamOptimizer(\\n\",\n \" learning_rate=learning_rate)\\n\",\n \" \\n\",\n \" training_op = optimizer.minimize(loss)\\n\",\n \"\\n\",\n \" return outputs, loss, training_op\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\\n\",\n \"y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\\n\",\n \"outputs, loss, training_op = deep_rnn_with_dropout(X, y, is_training)\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"saver = tf.train.Saver()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Dropout, in this code, works during both training & testing (don't want).\\n\",\n \"* *dropout_wrapper()* doesn't know how to handle this, so you need one graph for training, another for testing.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 \\tMSE: 10428.8\\n\",\n \"100 \\tMSE: 314.521\\n\",\n \"200 \\tMSE: 152.328\\n\",\n \"300 \\tMSE: 155.774\\n\",\n \"400 \\tMSE: 100.226\\n\",\n \"500 \\tMSE: 80.2064\\n\",\n \"600 \\tMSE: 92.3898\\n\",\n \"700 \\tMSE: 55.4301\\n\",\n \"800 \\tMSE: 50.8537\\n\",\n \"900 \\tMSE: 47.1413\\n\",\n \"1000 \\tMSE: 57.1007\\n\",\n \"1100 \\tMSE: 64.2314\\n\",\n \"1200 \\tMSE: 51.3272\\n\",\n \"1300 \\tMSE: 51.1612\\n\",\n \"1400 \\tMSE: 41.0518\\n\",\n \"1500 \\tMSE: 42.267\\n\",\n \"1600 \\tMSE: 29.6838\\n\",\n \"1700 \\tMSE: 48.4316\\n\",\n \"1800 \\tMSE: 46.5584\\n\",\n \"1900 \\tMSE: 40.6252\\n\"\n ]\n }\n ],\n \"source\": [\n \"n_iterations = 2000\\n\",\n \"batch_size = 50\\n\",\n \"\\n\",\n \"is_training = True\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" if is_training:\\n\",\n \" init.run()\\n\",\n \" for iteration in range(n_iterations):\\n\",\n \" X_batch, y_batch = next_batch(batch_size, n_steps)\\n\",\n \" sess.run(\\n\",\n \" training_op, \\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \" \\n\",\n \" if iteration % 100 == 0:\\n\",\n \" mse = loss.eval(\\n\",\n \" feed_dict={X: X_batch, y: y_batch})\\n\",\n \" \\n\",\n \" print(iteration, \\\"\\\\tMSE:\\\", mse)\\n\",\n \" \\n\",\n \" save_path = saver.save(sess, \\\"/tmp/my_model.ckpt\\\")\\n\",\n \" \\n\",\n \" else:\\n\",\n \" saver.restore(sess, \\\"/tmp/my_model.ckpt\\\")\\n\",\n \" \\n\",\n \" X_new = time_series(\\n\",\n \" np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\\n\",\n \" y_pred = sess.run(\\n\",\n \" outputs, feed_dict={X: X_new})\\n\",\n \" \\n\",\n \" plt.title(\\\"Testing the model\\\", fontsize=14)\\n\",\n \" plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \\\"bo\\\", markersize=10, label=\\\"instance\\\")\\n\",\n \" plt.plot(t_instance[1:], time_series(t_instance[1:]), \\\"w*\\\", markersize=10, label=\\\"target\\\")\\n\",\n \" plt.plot(t_instance[1:], y_pred[0,:,0], \\\"r.\\\", markersize=10, label=\\\"prediction\\\")\\n\",\n \" plt.legend(loc=\\\"upper left\\\")\\n\",\n \" plt.xlabel(\\\"Time\\\")\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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lgE9h3ADncvj8yvIAj0KeXjjz/m5z//edyPs3bt2i73IimSqaLpFiAp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\\nEZE0o8AuIpJm/j9JUAFO0b1t8QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# testing\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \"\\n\",\n \" saver.restore(sess, \\\"/tmp/my_model.ckpt\\\")\\n\",\n \" \\n\",\n \" X_new = time_series(\\n\",\n \" np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\\n\",\n \" \\n\",\n \" y_pred = sess.run(\\n\",\n \" outputs, feed_dict={X: X_new})\\n\",\n \" \\n\",\n \" plt.title(\\\"Testing the model\\\", fontsize=14)\\n\",\n \" plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \\\"bo\\\", markersize=10, label=\\\"instance\\\")\\n\",\n \" plt.plot(t_instance[1:], time_series(t_instance[1:]), \\\"w*\\\", markersize=10, label=\\\"target\\\")\\n\",\n \" plt.plot(t_instance[1:], y_pred[0,:,0], \\\"r.\\\", markersize=10, label=\\\"prediction\\\")\\n\",\n \" plt.legend(loc=\\\"upper left\\\")\\n\",\n \" plt.xlabel(\\\"Time\\\")\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training across Many Time Steps\\n\",\n \"* problem #1: RNNs susceptible to vanishing/exploding gradients issues. Previous tricks will work, but training time = prohibitively long for even modest sequences.\\n\",\n \"* solution #1: *truncated backprop thru time* (unrolling RNN over limited number of timesteps during training). Works, but *model will not be able to learn long-term patterns*.\\n\",\n \"* problem #2: memory of early inputs fades away - information lost during each transformation.\\n\",\n \"* solution #2: using a *long-term memory cell*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Long Short-Term Memory (LSTM) Cell\\n\",\n \"![lstm-cell](pics/lstm-cell.png)\\n\",\n \"* implemented via *BasicLSTMCell()* instead of *BasicRNNCell()*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* key feature: net learns what to store (long-term), what to read from, what to throw away.\\n\",\n \"* Four FC layers - each with unique purposes:\\n\",\n \" * main layer: outputs g(t)\\n\",\n \" * forget gate: controlled by f(t) - decides which parts of long-term memory to erase\\n\",\n \" * input gate: controlled by i(t) - decides which parts of g(t) to add to long-term memory\\n\",\n \" * output gate: controlled by o(t) - decides which parts of long-term state should be read & outputted at this time step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"from tensorflow.contrib.layers import fully_connected\\n\",\n \"\\n\",\n \"n_steps = 28\\n\",\n \"n_inputs = 28\\n\",\n \"n_neurons = 150\\n\",\n \"n_outputs = 10\\n\",\n \"\\n\",\n \"learning_rate = 0.001\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\\n\",\n \"y = tf.placeholder(tf.int32, [None])\\n\",\n \"\\n\",\n \"lstm_cell = tf.contrib.rnn.BasicLSTMCell(\\n\",\n \" num_units=n_neurons)\\n\",\n \"\\n\",\n \"multi_cell = tf.contrib.rnn.MultiRNNCell(\\n\",\n \" [lstm_cell]*3)\\n\",\n \"\\n\",\n \"outputs, states = tf.nn.dynamic_rnn(\\n\",\n \" multi_cell, X, dtype=tf.float32)\\n\",\n \"\\n\",\n \"top_layer_h_state = states[-1][1]\\n\",\n \"\\n\",\n \"logits = fully_connected(\\n\",\n \" top_layer_h_state, \\n\",\n \" n_outputs, \\n\",\n \" activation_fn=None, scope=\\\"softmax\\\")\\n\",\n \"\\n\",\n \"xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\\n\",\n \" labels=y, logits=logits)\\n\",\n \"\\n\",\n \"loss = tf.reduce_mean(\\n\",\n \" xentropy, name=\\\"loss\\\")\\n\",\n \"\\n\",\n \"optimizer = tf.train.AdamOptimizer(\\n\",\n \" learning_rate=learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(loss)\\n\",\n \"\\n\",\n \"correct = tf.nn.in_top_k(\\n\",\n \" logits, y, 1)\\n\",\n \"\\n\",\n \"accuracy = tf.reduce_mean(\\n\",\n \" tf.cast(correct, tf.float32))\\n\",\n \" \\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(LSTMStateTuple(c=, h=),\\n\",\n \" LSTMStateTuple(c=, h=),\\n\",\n \" LSTMStateTuple(c=, h=))\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"states\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"top_layer_h_state\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 0 Train accuracy = 0.966667 Test accuracy = 0.9403\\n\",\n \"Epoch 1 Train accuracy = 0.98 Test accuracy = 0.9742\\n\",\n \"Epoch 2 Train accuracy = 0.993333 Test accuracy = 0.979\\n\",\n \"Epoch 3 Train accuracy = 0.993333 Test accuracy = 0.9805\\n\",\n \"Epoch 4 Train accuracy = 1.0 Test accuracy = 0.9854\\n\",\n \"Epoch 5 Train accuracy = 0.98 Test accuracy = 0.9827\\n\",\n \"Epoch 6 Train accuracy = 0.993333 Test accuracy = 0.9851\\n\",\n \"Epoch 7 Train accuracy = 1.0 Test accuracy = 0.9865\\n\",\n \"Epoch 8 Train accuracy = 1.0 Test accuracy = 0.9887\\n\",\n \"Epoch 9 Train accuracy = 0.993333 Test accuracy = 0.9871\\n\"\n ]\n }\n ],\n \"source\": [\n \"n_epochs = 10\\n\",\n \"batch_size = 150\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" for iteration in range(mnist.train.num_examples // batch_size):\\n\",\n \" X_batch, y_batch = mnist.train.next_batch(batch_size)\\n\",\n \" X_batch = X_batch.reshape((batch_size, n_steps, n_inputs))\\n\",\n \" sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\\n\",\n \" acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\\n\",\n \" acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\\n\",\n \" print(\\\"Epoch\\\", epoch, \\\"Train accuracy =\\\", acc_train, \\\"Test accuracy =\\\", acc_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Peephole Connections\\n\",\n \"* Basic LSTM cell: gate controllers only see input x(t) & prev short-term state h(t-1).\\n\",\n \"* Improvement: let gate peek at long-term state too. Provided with previous long-term state c(t-1) as inputs to forget gate & input gate; current long-term state c(t) added as input to output gate controller.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Peepholes in TF\\n\",\n \"lstm_cell = tf.contrib.rnn.LSTMCell(\\n\",\n \" num_units=n_neurons, \\n\",\n \" use_peepholes=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Gated Recurrent Unit (GRU) Cell\\n\",\n \"* Simplified version of LSTM cell\\n\",\n \"* State vectors merged into single h(t).\\n\",\n \"* Single gate controller manages forget gate & input gate. (if a memory is to be stored, its location is erased first.)\\n\",\n \"* No output gate - full state vector output on \\n\",\n \"![gru-cell](pics/gru-cell.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# in TF\\n\",\n \"gru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Natural Language Processing (NLP)\\n\",\n \"* Mostly based on RNNs\\n\",\n \"* See [Word2Vec](https://goo.gl/edArdi) and [Seq2Seq](https://goo.gl/L82gvS) tutorials!\\n\",\n \"* More: [Chris Olah](https://goo.gl/5rLNTj), [Sebastian Ruder](https://goo.gl/ojJjiE)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Word Embeddings\\n\",\n \"* First: need a word representation. Similar words should have similar representations.\\n\",\n \"* Common sol'n: each word in vocab = small, dense vector of embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create embeddings variable. init with random[-1,+1]\\n\",\n \"\\n\",\n \"vocabulary_size = 50000\\n\",\n \"embedding_size = 150\\n\",\n \"embeddings = tf.Variable(\\n\",\n \" tf.random_uniform(\\n\",\n \" [vocabulary_size, embedding_size], \\n\",\n \" -1.0, 1.0))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Feeding new sentences to net: replace unknown words, numbers, URLs, etc with predefined tokens. Once a word is known, you can look it up in a dictionary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"train_inputs = tf.placeholder(\\n\",\n \" tf.int32, shape=[None]) # from ids...\\n\",\n \"\\n\",\n \"embed = tf.nn.embedding_lookup(\\n\",\n \" embeddings, train_inputs) # ...to embeddings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### English => French Encoder-Decoder Network ([link](https://goo.gl/0g9zWP))\\n\",\n \"* English inputs, French outputs\\n\",\n \"* French translations also fed, pushed back one step\\n\",\n \"* English sentences **reversed** before entry (ensures beginning of sentence is fed last = best for decoder translation)\\n\",\n \"* Decoder returns score for each word in output vocabulary - softmax turns them into probabilities. Highest probability word is returned.\\n\",\n \"![encoder-decoder](pics/encoder-decoder.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from six.moves import urllib\\n\",\n \"\\n\",\n \"import errno\\n\",\n \"import os\\n\",\n \"import zipfile\\n\",\n \"\\n\",\n \"WORDS_PATH = \\\"datasets/words\\\"\\n\",\n \"WORDS_URL = 'http://mattmahoney.net/dc/text8.zip'\\n\",\n \"\\n\",\n \"def mkdir_p(path):\\n\",\n \" \\\"\\\"\\\"Create directories, ok if they already exist.\\n\",\n \" \\n\",\n \" This is for python 2 support. In python >=3.2, simply use:\\n\",\n \" >>> os.makedirs(path, exist_ok=True)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" try:\\n\",\n \" os.makedirs(path)\\n\",\n \" except OSError as exc:\\n\",\n \" if exc.errno == errno.EEXIST and os.path.isdir(path):\\n\",\n \" pass\\n\",\n \" else:\\n\",\n \" raise\\n\",\n \"\\n\",\n \"def fetch_words_data(words_url=WORDS_URL, words_path=WORDS_PATH):\\n\",\n \" os.makedirs(words_path, exist_ok=True)\\n\",\n \" zip_path = os.path.join(words_path, \\\"words.zip\\\")\\n\",\n \" if not os.path.exists(zip_path):\\n\",\n \" urllib.request.urlretrieve(words_url, zip_path)\\n\",\n \" with zipfile.ZipFile(zip_path) as f:\\n\",\n \" data = f.read(f.namelist()[0])\\n\",\n \" return data.decode(\\\"ascii\\\").split()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['anarchism', 'originated', 'as', 'a', 'term']\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"words = fetch_words_data()\\n\",\n \"words[:5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Build dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from collections import Counter\\n\",\n \"\\n\",\n \"vocabulary_size = 50000\\n\",\n \"\\n\",\n \"vocabulary = [(\\\"UNK\\\", None)] + Counter(words).most_common(vocabulary_size - 1)\\n\",\n \"vocabulary = np.array([word for word, _ in vocabulary])\\n\",\n \"\\n\",\n \"dictionary = {word: code for code, word in enumerate(vocabulary)}\\n\",\n \"\\n\",\n \"data = np.array([dictionary.get(word, 0) for word in words])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"('anarchism originated as a term of abuse first used',\\n\",\n \" array([5244, 3081, 12, 6, 195, 2, 3135, 46, 59]))\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\" \\\".join(words[:9]), data[:9]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'anywhere originated as a term of presidency first used'\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\" \\\".join([vocabulary[word_index] for word_index in [5241, 3081, 12, 6, 195, 2, 3134, 46, 59]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"('culottes', 0)\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"words[24], data[24]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Generate batches\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import random\\n\",\n \"from collections import deque\\n\",\n \"\\n\",\n \"def generate_batch(batch_size, num_skips, skip_window):\\n\",\n \" global data_index\\n\",\n \" assert batch_size % num_skips == 0\\n\",\n \" assert num_skips <= 2 * skip_window\\n\",\n \" batch = np.ndarray(shape=(batch_size), dtype=np.int32)\\n\",\n \" labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\\n\",\n \" span = 2 * skip_window + 1 # [ skip_window target skip_window ]\\n\",\n \" buffer = deque(maxlen=span)\\n\",\n \" for _ in range(span):\\n\",\n \" buffer.append(data[data_index])\\n\",\n \" data_index = (data_index + 1) % len(data)\\n\",\n \" for i in range(batch_size // num_skips):\\n\",\n \" target = skip_window # target label at the center of the buffer\\n\",\n \" targets_to_avoid = [ skip_window ]\\n\",\n \" for j in range(num_skips):\\n\",\n \" while target in targets_to_avoid:\\n\",\n \" target = random.randint(0, span - 1)\\n\",\n \" targets_to_avoid.append(target)\\n\",\n \" batch[i * num_skips + j] = buffer[skip_window]\\n\",\n \" labels[i * num_skips + j, 0] = buffer[target]\\n\",\n \" buffer.append(data[data_index])\\n\",\n \" data_index = (data_index + 1) % len(data)\\n\",\n \" return batch, labels\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"data_index=0\\n\",\n \"batch, labels = generate_batch(8, 2, 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([3081, 3081, 12, 12, 6, 6, 195, 195], dtype=int32),\\n\",\n \" ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term'])\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"batch, [vocabulary[word] for word in batch]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([[5244],\\n\",\n \" [ 12],\\n\",\n \" [ 6],\\n\",\n \" [3081],\\n\",\n \" [ 195],\\n\",\n \" [ 12],\\n\",\n \" [ 6],\\n\",\n \" [ 2]], dtype=int32),\\n\",\n \" ['anarchism', 'as', 'a', 'originated', 'term', 'as', 'a', 'of'])\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"labels, [vocabulary[word] for word in labels[:, 0]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Build the Model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"batch_size = 128\\n\",\n \"embedding_size = 128 # Dimension of the embedding vector.\\n\",\n \"skip_window = 1 # How many words to consider left and right.\\n\",\n \"num_skips = 2 # How many times to reuse an input to generate a label.\\n\",\n \"\\n\",\n \"# We pick a random validation set to sample nearest neighbors. Here we limit the\\n\",\n \"# validation samples to the words that have a low numeric ID, which by\\n\",\n \"# construction are also the most frequent.\\n\",\n \"\\n\",\n \"valid_size = 16 # Random set of words to evaluate similarity on.\\n\",\n \"valid_window = 100 # Only pick dev samples in the head of the distribution.\\n\",\n \"valid_examples = rnd.choice(valid_window, valid_size, replace=False)\\n\",\n \"num_sampled = 64 # Number of negative examples to sample.\\n\",\n \"\\n\",\n \"learning_rate = 0.01\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"# Input data.\\n\",\n \"train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\\n\",\n \"train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\\n\",\n \"valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\\n\",\n \"\\n\",\n \"# Look up embeddings for inputs.\\n\",\n \"init_embeddings = tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)\\n\",\n \"embeddings = tf.Variable(init_embeddings)\\n\",\n \"embed = tf.nn.embedding_lookup(embeddings, train_inputs)\\n\",\n \"\\n\",\n \"# Construct the variables for the NCE loss\\n\",\n \"nce_weights = tf.Variable(\\n\",\n \" tf.truncated_normal([vocabulary_size, embedding_size],\\n\",\n \" stddev=1.0 / np.sqrt(embedding_size)))\\n\",\n \"nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\\n\",\n \"\\n\",\n \"# Compute the average NCE loss for the batch.\\n\",\n \"# tf.nce_loss automatically draws a new sample of the negative labels each\\n\",\n \"# time we evaluate the loss.\\n\",\n \"loss = tf.reduce_mean(\\n\",\n \" tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed,\\n\",\n \" num_sampled, vocabulary_size))\\n\",\n \"\\n\",\n \"# Construct the Adam optimizer\\n\",\n \"optimizer = tf.train.AdamOptimizer(learning_rate)\\n\",\n \"training_op = optimizer.minimize(loss)\\n\",\n \"\\n\",\n \"# Compute the cosine similarity between minibatch examples and all embeddings.\\n\",\n \"norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=1, keep_dims=True))\\n\",\n \"normalized_embeddings = embeddings / norm\\n\",\n \"valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\\n\",\n \"similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)\\n\",\n \"\\n\",\n \"# Add variable initializer.\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Iteration: 0\\tAverage loss at step 0 : 260.603485107\\n\",\n \"Nearest to and: marsh, sipe, vehement, exercises, einer, mrnas, dancer, grendel,\\n\",\n \"Nearest to called: innuendo, algerian, synthesizing, montgomery, unspoken, elevating, plankton, monochromatic,\\n\",\n \"Nearest to many: salinas, fuji, trochaic, rubinstein, eln, tintin, lloyd, carbides,\\n\",\n \"Nearest to about: moreover, congo, choctaws, accomplished, unwieldy, ks, halifax, pac,\\n\",\n \"Nearest to than: awake, exact, offutt, gloster, pronunciations, delight, tsarina, hopped,\\n\",\n \"Nearest to or: long, mage, warriors, adhering, sk, clitoridectomy, parenting, vanguard,\\n\",\n \"Nearest to of: shakespeare, kemp, relax, cul, breakaway, solemnly, mason, mng,\\n\",\n \"Nearest to when: tolstoy, courtesan, hashes, coursing, evi, ren, diurnal, stimson,\\n\",\n \"Nearest to four: supermassive, soviet, palatalization, acclaimed, aided, whitney, filtration, lesbians,\\n\",\n \"Nearest to most: din, hawaii, loch, necronomicon, sunnah, sh, onager, miracles,\\n\",\n \"Nearest to on: helpers, tangle, heretical, compulsion, unorganized, rump, intimidating, israeli,\\n\",\n \"Nearest to but: ohio, rican, politeness, watkins, ingesting, street, hatred, novices,\\n\",\n \"Nearest to that: xhosa, distressed, continually, fausto, iole, admitted, etsi, gross,\\n\",\n \"Nearest to all: orissa, persistent, moro, informative, reservation, ren, browne, frobenius,\\n\",\n \"Nearest to in: chanced, accelerator, sergio, demonstrating, inertia, jarrett, intricate, orange,\\n\",\n \"Nearest to had: irredentist, kbit, sarris, lactate, bettor, narratives, hui, transpired,\\n\",\n \"Iteration: 999\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\"\n ]\n }\n ],\n \"source\": [\n \"num_steps = 1000 # was 100000?\\n\",\n \"\\n\",\n \"with tf.Session() as session:\\n\",\n \" init.run()\\n\",\n \"\\n\",\n \" average_loss = 0\\n\",\n \" for step in range(num_steps):\\n\",\n \" print(\\\"\\\\rIteration: {}\\\".format(step), end=\\\"\\\\t\\\")\\n\",\n \" batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)\\n\",\n \" feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}\\n\",\n \"\\n\",\n \" # We perform one update step by evaluating the training op (including it\\n\",\n \" # in the list of returned values for session.run()\\n\",\n \" _, loss_val = session.run([training_op, loss], feed_dict=feed_dict)\\n\",\n \" average_loss += loss_val\\n\",\n \"\\n\",\n \" if step % 2000 == 0:\\n\",\n \" if step > 0:\\n\",\n \" average_loss /= 2000\\n\",\n \" # The average loss is an estimate of the loss over the last 2000 batches.\\n\",\n \" print(\\\"Average loss at step \\\", step, \\\": \\\", average_loss)\\n\",\n \" average_loss = 0\\n\",\n \"\\n\",\n \" # Note that this is expensive (~20% slowdown if computed every 500 steps)\\n\",\n \" if step % 10000 == 0:\\n\",\n \" sim = similarity.eval()\\n\",\n \" for i in range(valid_size):\\n\",\n \" valid_word = vocabulary[valid_examples[i]]\\n\",\n \" top_k = 8 # number of nearest neighbors\\n\",\n \" nearest = (-sim[i, :]).argsort()[1:top_k+1]\\n\",\n \" log_str = \\\"Nearest to %s:\\\" % valid_word\\n\",\n \" for k in range(top_k):\\n\",\n \" close_word = vocabulary[nearest[k]]\\n\",\n \" log_str = \\\"%s %s,\\\" % (log_str, close_word)\\n\",\n \" print(log_str)\\n\",\n \"\\n\",\n \" final_embeddings = normalized_embeddings.eval()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Save final embeddings\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"np.save(\\\"my_final_embeddings.npy\\\", final_embeddings)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Plot embeddings\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_with_labels(low_dim_embs, labels):\\n\",\n \" assert low_dim_embs.shape[0] >= len(labels), \\\"More labels than embeddings\\\"\\n\",\n \" plt.figure(figsize=(18, 18)) #in inches\\n\",\n \" for i, label in enumerate(labels):\\n\",\n \" x, y = low_dim_embs[i,:]\\n\",\n \" plt.scatter(x, y)\\n\",\n \" plt.annotate(label,\\n\",\n \" xy=(x, y),\\n\",\n \" xytext=(5, 2),\\n\",\n \" textcoords='offset points',\\n\",\n \" ha='right',\\n\",\n \" va='bottom')\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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VoLFizgwoULvPDCC2arT66uW7du3HHHHVhZWeHt7U1OTs5lx3z55Zdk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\\nPyZOnEjv3r0JCgoq3MG10dWZbhRt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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.manifold import TSNE\\n\",\n \"\\n\",\n \"tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\\n\",\n \"plot_only = 500\\n\",\n \"low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:])\\n\",\n \"labels = [vocabulary[i] for i in range(plot_only)]\\n\",\n \"plot_with_labels(low_dim_embs, labels)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch15-autoencoders.html", "content": "\n\n\nch15-autoencoders\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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import matplotlib.pyplot as plt\nimport numpy as np\nimport numpy.random as rnd\nimport tensorflow as tf\nimport sys\n
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Data Representations

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  • Much easier to remember sequence patterns than to remember exact lists. First studied as chess game positions (1970s).
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  • Autoencoder converts inputs to internal shorthand, then returns best-guess similarity. Two parts: encoder (recognizer) & decoder (generator, aka reconstructor).
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  • Reconstruction loss - penalizes model when reconstructions /= inputs.
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  • Internal representation = lower dimensionality, so AE is forced to learn most important features in inputs.
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PCA with Undercomplete Linear Autoencoder

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# lets build a 3D dataset\n\nrnd.seed(4)\nm = 100\nw1, w2 = 0.1, 0.3\nnoise = 0.1\n\nangles = rnd.rand(m) * 3 * np.pi / 2 - 0.5\nX_train = np.empty((m, 3))\nX_train[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * rnd.randn(m) / 2\nX_train[:, 1] = np.sin(angles) * 0.7 + noise * rnd.randn(m) / 2\nX_train[:, 2] = X_train[:, 0] * w1 + X_train[:, 1] * w2 + noise * rnd.randn(m)\n\n# normalize it\n\nfrom sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\n\nplt.plot(X_train)\nplt.show()\n
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# build AE\n\nfrom tensorflow.contrib.layers import fully_connected\n\nn_inputs = 3 # 3D inputs\nn_hidden = 2 # 2D codings\nn_outputs = n_inputs\n\nlearning_rate = 0.01\n\nX = tf.placeholder(\n    tf.float32, shape=[None, n_inputs])\n\n#\n# set activation_fn=None & use MSE for cost function\n# to perform simple PCA.\n#\n\nhidden = fully_connected(\n    X, \n    n_hidden, \n    activation_fn=None)\n\noutputs = fully_connected(\n    hidden, \n    n_outputs, \n    activation_fn=None)\n\n# MSE\nreconstruction_loss = tf.reduce_mean(\n    tf.square(outputs - X))\n\noptimizer = tf.train.AdamOptimizer(\n    learning_rate)\n\ntraining_op = optimizer.minimize(\n    reconstruction_loss)\n\ninit = tf.global_variables_initializer()\n
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# run the AE\n\nn_iterations = 10000\ncodings = hidden\n\nwith tf.Session() as sess:\n    init.run()\n    for iteration in range(n_iterations):\n        training_op.run(feed_dict={X: X_train})\n    codings_val = codings.eval(feed_dict={X: X_train})\n
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fig = plt.figure(figsize=(4,3))\nplt.plot(codings_val[:,0], codings_val[:, 1], "b.")\nplt.xlabel("$z_1$", fontsize=18)\nplt.ylabel("$z_2$", fontsize=18, rotation=0)\n#ave_fig("linear_autoencoder_pca_plot")\nplt.show()\n\n# plot: 2D projection with max variance\n
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Stacked Autoencoders

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  • AEs with multiple hidden layers - for more complex model learning\n\"stacked-AE\"
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tf.reset_default_graph()\n\nn_inputs      = 28 * 28 # for MNIST\nn_hidden1     = 300\nn_hidden2     = 150 # codings\nn_hidden3     = n_hidden1\nn_outputs     = n_inputs\nlearning_rate = 0.01\nl2_reg        = 0.0001\n\nX = tf.placeholder(tf.float32, \n                   shape=[None, n_inputs])\n\nwith tf.contrib.framework.arg_scope(\n    [fully_connected],\n    activation_fn=tf.nn.elu,\n    weights_initializer=tf.contrib.layers.variance_scaling_initializer(),\n    weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg)):\n\n    hidden1 = fully_connected(X,       n_hidden1)\n    hidden2 = fully_connected(hidden1, n_hidden2) # codings\n    hidden3 = fully_connected(hidden2, n_hidden3)\n    outputs = fully_connected(hidden3, n_outputs, activation_fn=None)\n\n# MSE\nreconstruction_loss = tf.reduce_mean(\n    tf.square(outputs - X))\n\nreg_losses = tf.get_collection(\n    tf.GraphKeys.REGULARIZATION_LOSSES)\n\nloss = tf.add_n(\n    [reconstruction_loss] + reg_losses)\n\noptimizer = tf.train.AdamOptimizer(\n    learning_rate)\n\ntraining_op = optimizer.minimize(loss)\n\ninit = tf.global_variables_initializer()\nsaver = tf.train.Saver()\n
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# use MNIST dataset \n\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nmnist = input_data.read_data_sets("/tmp/data/")\n\n# train the net. digit labels (y_batch) = unused.\n\nn_epochs = 4\nbatch_size = 150\n\nwith tf.Session() as sess:\n    init.run()\n    for epoch in range(n_epochs):\n        n_batches = mnist.train.num_examples // batch_size\n        for iteration in range(n_batches):\n            print("\\r{}%".format(100 * iteration // n_batches), end="")\n            sys.stdout.flush()\n            X_batch, y_batch = mnist.train.next_batch(batch_size)\n            sess.run(training_op, feed_dict={X: X_batch})\n        mse_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n        print("\\r{}".format(epoch), "Train MSE:", mse_train)\n        saver.save(sess, "./my_model_all_layers.ckpt")\n
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Extracting /tmp/data/train-images-idx3-ubyte.gz\nExtracting /tmp/data/train-labels-idx1-ubyte.gz\nExtracting /tmp/data/t10k-images-idx3-ubyte.gz\nExtracting /tmp/data/t10k-labels-idx1-ubyte.gz\n0 Train MSE: 0.02705\n1 Train MSE: 0.0137857\n2 Train MSE: 0.0113694\n3 Train MSE: 0.0107478\n
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# utility: plot grayscale 28x28 image\n\ndef plot_image(image, shape=[28, 28]):\n    plt.imshow(image.reshape(shape), cmap="Greys", interpolation="nearest")\n    plt.axis("off")\n    \n
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# load model, eval on test set (measure reconstruction error, display original & reconstruction)\n\ndef show_reconstructed_digits(X, outputs, model_path = None, n_test_digits = 2):\n    with tf.Session() as sess:\n        if model_path:\n            saver.restore(sess, model_path)\n        X_test = mnist.test.images[:n_test_digits]\n        outputs_val = outputs.eval(feed_dict={X: X_test})\n\n    fig = plt.figure(figsize=(8, 3 * n_test_digits))\n    for digit_index in range(n_test_digits):\n        plt.subplot(n_test_digits, 2, digit_index * 2 + 1)\n        plot_image(X_test[digit_index])\n        plt.subplot(n_test_digits, 2, digit_index * 2 + 2)\n        plot_image(outputs_val[digit_index])\n
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show_reconstructed_digits(X, outputs, "./my_model_all_layers.ckpt")\nplt.show()\n
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Tying Weights

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In [11]:
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tf.reset_default_graph()\n\nactivation = tf.nn.elu\n\nregularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n\ninitializer = tf.contrib.layers.variance_scaling_initializer()\n\nX = tf.placeholder(tf.float32, shape=[None, n_inputs])\n\nweights1_init = initializer([n_inputs, n_hidden1])\nweights2_init = initializer([n_hidden1, n_hidden2])\n\nweights1 = tf.Variable(weights1_init, dtype=tf.float32, name="weights1")\nweights2 = tf.Variable(weights2_init, dtype=tf.float32, name="weights2")\n# weights 3,4 not vars!\nweights3 = tf.transpose(weights2, name="weights3") # tied weights\nweights4 = tf.transpose(weights1, name="weights4") # tied weights\n\nbiases1 = tf.Variable(tf.zeros(n_hidden1),name="biases1")\nbiases2 = tf.Variable(tf.zeros(n_hidden2),name="biases2")\nbiases3 = tf.Variable(tf.zeros(n_hidden3),name="biases3")\nbiases4 = tf.Variable(tf.zeros(n_outputs),name="biases4")\n\nhidden1 = activation(tf.matmul(X, weights1) + biases1)\nhidden2 = activation(tf.matmul(hidden1, weights2) + biases2)\nhidden3 = activation(tf.matmul(hidden2, weights3) + biases3)\noutputs = tf.matmul(hidden3, weights4) + biases4\n\nreconstruction_loss = tf.reduce_mean(\n    tf.square(outputs - X))\n\nreg_loss = regularizer(weights1) + regularizer(weights2)\n\nloss = reconstruction_loss + reg_loss\n\noptimizer = tf.train.AdamOptimizer(learning_rate)\n\ntraining_op = optimizer.minimize(loss)\n\ninit = tf.global_variables_initializer()\n
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Training one Autoencoder at a time

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\"one-ae-atatime\"

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def train_autoencoder(\n    X_train, \n    n_neurons, \n    n_epochs, \n    batch_size, \n    learning_rate = 0.01, \n    l2_reg = 0.0005, \n    activation_fn=tf.nn.elu):\n    \n    graph = tf.Graph()\n    with graph.as_default():\n        n_inputs = X_train.shape[1]\n\n        X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n        \n        with tf.contrib.framework.arg_scope(\n            [fully_connected],\n            activation_fn=activation_fn,\n            weights_initializer=tf.contrib.layers.variance_scaling_initializer(),\n            weights_regularizer=tf.contrib.layers.l2_regularizer(\n                l2_reg)):\n            hidden = fully_connected(\n                X, n_neurons, scope="hidden")\n            outputs = fully_connected(\n                hidden, n_inputs, activation_fn=None, scope="outputs")\n\n        mse = tf.reduce_mean(tf.square(outputs - X))\n\n        reg_losses = tf.get_collection(\n            tf.GraphKeys.REGULARIZATION_LOSSES)\n        \n        loss = tf.add_n([mse] + reg_losses)\n\n        optimizer = tf.train.AdamOptimizer(learning_rate)\n        \n        training_op = optimizer.minimize(loss)\n\n        init = tf.global_variables_initializer()\n\n    with tf.Session(graph=graph) as sess:\n        init.run()\n        \n        for epoch in range(n_epochs):\n            n_batches = len(X_train) // batch_size\n            \n            for iteration in range(n_batches):\n                print("\\r{}%".format(100 * iteration // n_batches), end="")\n                sys.stdout.flush()\n                \n                indices = rnd.permutation(\n                    len(X_train))[:batch_size]\n                \n                X_batch = X_train[indices]\n                \n                sess.run(\n                    training_op, feed_dict={X: X_batch})\n                \n            mse_train = mse.eval(\n                feed_dict={X: X_batch})\n            \n            print("\\r{}".format(epoch), "Train MSE:", mse_train)\n            \n        params = dict(\n            [(var.name, var.eval()) for var in tf.get_collection(\n                tf.GraphKeys.TRAINABLE_VARIABLES)])\n        \n        hidden_val = hidden.eval(\n            feed_dict={X: X_train})\n        \n        return hidden_val, params["hidden/weights:0"], params["hidden/biases:0"], params["outputs/weights:0"], params["outputs/biases:0"]\n
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In [13]:
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# train two AEs\n\nhidden_output, W1, b1, W4, b4 = train_autoencoder(\n    mnist.train.images, \n    n_neurons=300, \n    n_epochs=4, \n    batch_size=150)\n\n_, W2, b2, W3, b3 = train_autoencoder(\n    hidden_output, \n    n_neurons=150, \n    n_epochs=4, \n    batch_size=150)\n
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0 Train MSE: 0.0193591\n1 Train MSE: 0.0190697\n2 Train MSE: 0.0188801\n3 Train MSE: 0.0192353\n0 Train MSE: 0.00428287\n1 Train MSE: 0.00438113\n2 Train MSE: 0.00464872\n3 Train MSE: 0.00457076\n
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In [15]:
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# create stacked AE by reusing weights &and biases from above\n\ntf.reset_default_graph()\n\nn_inputs = 28*28\n\nX = tf.placeholder(tf.float32, shape=[None, n_inputs])\nhidden1 = tf.nn.elu(tf.matmul(X, W1) + b1)\nhidden2 = tf.nn.elu(tf.matmul(hidden1, W2) + b2)\nhidden3 = tf.nn.elu(tf.matmul(hidden2, W3) + b3)\noutputs = tf.matmul(hidden3, W4) + b4\n
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Visualizing Reconstructions

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In [19]:
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# Load model, evaluates it on test set (reconstruction error)\n# display original & reconstructed images\n\ndef show_reconstructed_digits(\n    X, \n    outputs, \n    model_path = None, \n    n_test_digits = 2):\n    \n    with tf.Session() as sess:\n        if model_path:\n            saver.restore(sess, model_path)\n            \n        X_test = mnist.test.images[:n_test_digits]\n        outputs_val = outputs.eval(feed_dict={X: X_test})\n\n    fig = plt.figure(figsize=(8, 3 * n_test_digits))\n    \n    for digit_index in range(n_test_digits):\n        \n        plt.subplot(n_test_digits, 2, digit_index * 2 + 1)\n        plot_image(X_test[digit_index])\n        plt.subplot(n_test_digits, 2, digit_index * 2 + 2)\n        plot_image(outputs_val[digit_index])\n        plt.show()\n        \n#show_reconstructed_digits(X, outputs, "./my_model_all_layers.ckpt")\nshow_reconstructed_digits(X, outputs)\n
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Visualizing Features

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Unsupervised Pretraining with Stacked Autoencoders

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Denoising Autoencoders

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Sparse Autoencoders

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Variational Autoencoders

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Other Autoencoders

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\n\n\n" }, { "path": "ch15-autoencoders.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import numpy.random as rnd\\n\",\n \"import tensorflow as tf\\n\",\n \"import sys\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Data Representations\\n\",\n \"* Much easier to remember *sequence patterns* than to remember exact lists. First studied as chess game positions (1970s).\\n\",\n \"* Autoencoder converts inputs to internal shorthand, then returns best-guess similarity. Two parts: *encoder* (recognizer) & *decoder* (generator, aka *reconstructor*).\\n\",\n \"* Reconstruction loss - penalizes model when reconstructions /= inputs.\\n\",\n \"* Internal representation = lower dimensionality, so AE is forced to learn most important features in inputs.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### PCA with Undercomplete Linear Autoencoder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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OENQMD2u0+k2Aup6CcjWl6Dx26+RC4EV1ZOwbh+zpFR7HFxdxS7kh6D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\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# lets build a 3D dataset\\n\",\n \"\\n\",\n \"rnd.seed(4)\\n\",\n \"m = 100\\n\",\n \"w1, w2 = 0.1, 0.3\\n\",\n \"noise = 0.1\\n\",\n \"\\n\",\n \"angles = rnd.rand(m) * 3 * np.pi / 2 - 0.5\\n\",\n \"X_train = np.empty((m, 3))\\n\",\n \"X_train[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * rnd.randn(m) / 2\\n\",\n \"X_train[:, 1] = np.sin(angles) * 0.7 + noise * rnd.randn(m) / 2\\n\",\n \"X_train[:, 2] = X_train[:, 0] * w1 + X_train[:, 1] * w2 + noise * rnd.randn(m)\\n\",\n \"\\n\",\n \"# normalize it\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"scaler = StandardScaler()\\n\",\n \"X_train = scaler.fit_transform(X_train)\\n\",\n \"\\n\",\n \"plt.plot(X_train)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# build AE\\n\",\n \"\\n\",\n \"from tensorflow.contrib.layers import fully_connected\\n\",\n \"\\n\",\n \"n_inputs = 3 # 3D inputs\\n\",\n \"n_hidden = 2 # 2D codings\\n\",\n \"n_outputs = n_inputs\\n\",\n \"\\n\",\n \"learning_rate = 0.01\\n\",\n \"\\n\",\n \"X = tf.placeholder(\\n\",\n \" tf.float32, shape=[None, n_inputs])\\n\",\n \"\\n\",\n \"#\\n\",\n \"# set activation_fn=None & use MSE for cost function\\n\",\n \"# to perform simple PCA.\\n\",\n \"#\\n\",\n \"\\n\",\n \"hidden = fully_connected(\\n\",\n \" X, \\n\",\n \" n_hidden, \\n\",\n \" activation_fn=None)\\n\",\n \"\\n\",\n \"outputs = fully_connected(\\n\",\n \" hidden, \\n\",\n \" n_outputs, \\n\",\n \" activation_fn=None)\\n\",\n \"\\n\",\n \"# MSE\\n\",\n \"reconstruction_loss = tf.reduce_mean(\\n\",\n \" tf.square(outputs - X))\\n\",\n \"\\n\",\n \"optimizer = tf.train.AdamOptimizer(\\n\",\n \" learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(\\n\",\n \" reconstruction_loss)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# run the AE\\n\",\n \"\\n\",\n \"n_iterations = 10000\\n\",\n \"codings = hidden\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" for iteration in range(n_iterations):\\n\",\n \" training_op.run(feed_dict={X: X_train})\\n\",\n \" codings_val = codings.eval(feed_dict={X: X_train})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAARMAAADbCAYAAABOZXXVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAFPZJREFUeJzt3X2MZXV9x/H3d2d2hqSlVXctWHVcjabRSkPJhDrWthsh\\nPBiVCvGpwVnEMBCXJhhb49bSkpiG1iZkhVXLILvupD6k6YKiQlgwTNVkfNhVfKQ+1MoKQcFtq9LK\\n4u58+8fvHubM5T6cO/d3z++cez6vZDL34cy937kP3/P9PZzfMXdHRGRYm1IHICLjQclERKJQMhGR\\nKJRMRCQKJRMRiULJRESiUDIRkSiUTEQkCiUTEYliMnUARW3dutW3bduWOgyRxjl8+PBP3f3p/bar\\nTTLZtm0bhw4dSh2GSOOY2f1FtlMzR0SiUDIRkSiUTEQkirFLJisrcO214beIlKc2HbBFrKzAWWfB\\n44/D1BR85jMwN5c6KpFmGKvKZGkJHnsMTpwICWV5OXVEIs0xNslkZQX27YNs4biJCdi+PWlIIo0y\\nNslkeRmOHw+XzeDSS9XEESnT2CST7dtDP8nEBJx0EszPp45IpFnGpgN2bi50uC4vh8SiqkSkXGOT\\nTCAkECURkTTGppkjImkpmYhIFEomaNasSAxj1WeyEZo1KxJH4yuT5eWQSDRrVmQ4jU8m+fkpU1Oa\\nNSuyUY1v5mh+ikgcjU8moPkpIjE0vpkjInEomUSgoWWRBjdzVlbi9JNoaFkkSJJMzOzZwBJwCuDA\\noru/t4znXlkJiyjt2xeWLBg2AXQaWlYykSZKVZkcB97u7l8xs5OBw2Z2l7t/e5RPmlURjz22tojS\\nsAkgG1rOKhMNLUtTJUkm7v4Q8FDr8i/M7D7gmcBIk0lWRWSJxGz4BKChZZEgeZ+JmW0Dfh/4Yof7\\nFoAFgJmZmaGfK19FTEyE1djm54dPABpaFgHzbDed4snNfh34N+Dv3P2WXtvOzs56jNODxup4FWkK\\nMzvs7rP9tktWmZjZZuAA8OF+iSQmVREio5FknomZGXAzcJ+7X5cihqI0h0SkmFSVyR8CbwK+YWb3\\ntm77K3e/PVE8HWkOiUhxqUZzPg9YiucehOaQiBSn6fQ9aHkCkeKSDw1XmeaQiBSnZNJB+/CxkohI\\nf0ombWJ3umpeizSFkkmbmJ2uGg2SJlEHLOvnksTsdNVi1dIkja9MOlUPsTpddUSxNEnjk0mn6mHX\\nrjjNEY0GSZM0PpmMunrQaFC51OGdTuOTiaqH8aEO77Qan0xguOohvycEJaWUdPhDWkomHfQrlbP7\\nt2yBq64KH9zJybCC24kTG9srqjwfnjq801IyaZMvlScn4c1vXr8aW/7+TZtC8lhdDT8QEsqge0WV\\n53GoyZqW5pm0yZfKx47BjTeGL3q2nkn+/hMnQkLJ5qRs3ryx+SmajxLP3Fy80TgZjCqTNlmpnK1g\\nn680AI4cCRULhO1274ajR4frM1F5LuNAyaRNViovLcHevWt9IFu2rDVFJibgsss6L0a9kT2iyvNy\\ndOuXUn9VHEomHWSjO/Pzax+yfFMEYGYm7gdP81FGq1u/1OIiXHlleF+np9VfNQwlkx7av+BqitRX\\nt36pnTvDmR0h9JFpOHnjlEwKUlOk3jr1Sy0vr43CQWi+aiexcUomA8gqlewoYyWV+ui2M5ieDhXJ\\npk2wZ4/ez2EomQxIc0Lqq73ZqmozLiWTAWnK9nhRx3c8SiYD2rIllMTu6ogVydMM2AGsrIRjcbKZ\\nr7t3a68mklEyGUDWxFldDZXJ0aOpI5JUdNrYJ0t54vK9wCuBh939xaniGISmvQuoE76blJXJh4Dz\\nEj7/wLLe/3e/Wx+gJtOBmZ0lq0zc/bNmti3V82+Uev9FFWpnlR7NMbMFYAFgZmYmcTQigeandGbu\\nnu7JQ2XyqSJ9JrOzs37o0KGRx7RROvJUxpWZHXb32X7bVboyqQt1yAloh6JkEkG3Drkmf7CaRjuU\\ntEPDHwW2A1vN7AHgb9395lTxDKO9Qy6/kFJTP1hN075DWVpq3s4k5WjOG1M9d2ztHXLdjt9pehlc\\nd73ev/wOZXJy/Sp9TdmZFEomZjYFPAps7rLJre5+YbSoaqjfQkoqg+ut3/uX36EcOQI33dS8g0GL\\nViabgUs73P424Azgk9EiGgOdhg6vvVZHG9dZkaPF8+vd7N/fvHkohZKJu/8v8M/528zsPYRE8nZ3\\n3zeC2GqtvVLRRKd6G+T9a+o8lIH7TMzMgOuBncBOd39/9KjG1I4d4XenVe2l2gZNEE2cKT1QMjGz\\nTcCNhCbPW7KKxMymgT3AWcDTgYeAG9z9hrjh1lN7e3t+PnVEshEbSRBN6nQvfKCfmU0AS8AlwMVt\\nTZtJ4MfAOcBvAq8D/trMXhcv1Prqd2DY4iKce274LeMj24lcffX6s0KOq6KjOZuBjwCvBl7v7rfk\\n72/1qVydu+leM7sNeBnwL5Fira1e7e3FRbj88nD54MHwe2Gh7AhlFJq2xGffyqTVhLmFsPbIhe2J\\npMvfbAb+CPj60BGOgV5LFxw4sH7b9utSX9lOZCPnn66jIpXJEiGRfAh4qpld3Hb/be7+87bb9gC/\\naP2t0L29fdFFaxVJdl3GQ9NGdXomk9bIzfmtq5e0fvJWgZPb/uY6YA54ubs/HiXKMZY1aQ4cCIlE\\nTRypq6hLEJjZbsKIzsvd/ZFoD0z1lyAQaTcus56LLkEQbdlGM7seOJsRJBKROmra8o5RkomZPQf4\\nc+D5wH+a2aOtnztiPL5IHakDdgPc/X7AYjyWyLhQB6yIRNOkafU6CZdICZpw0i5VJiIjNi6jOv2o\\nMqmwJuzNmqApozqqTCokf4QpNGNv1gRNWctGyaQi2kvhHTuadZDYONvoqE7dli9QMqmI9lIYmrE3\\na4pBR3Xq2M+iPpOKaJ/gND+vk6Q3WR37WVSZVES3UnjQvVmdyuImGfS9qWM/i5JJhQwzwamOZXFT\\n9HtvOiWaOs6eTXlGv/OA9wITwAfd/e9TxTIOmraqV530em96JZq6zZ5N0mfSWk/2fYS1Ul4EvNHM\\nXpQilnHRtIPK6qTXezNI30jV5x2lqkzOBL7v7j8AMLOPARcA304UT+31KovVl5JWr/emaN9IHZqx\\nqZLJM4Ef5a4/APxB+0ZmtgAsAMzMzJQTWY11Kovr8CFsgm5NlqJ9I3Voxla6A9bdF4FFCCutJQ6n\\nlurwIWy6In0jdRjdSZVMHgSenbv+rNZtElkdPoTSX76C2bJlfd9KdtvRo2mbsqmSyZeBF5jZcwlJ\\n5A3AnyWKZazVcYhROsveu6zZOjEBZvCrX8HqKmzaBNPT6ZqySZKJux83syuBOwlDw3vd/VspYmmC\\nug0xSnf5ZuvqargtWxN+dTVtUzZZn4m73w7cnur5ZY1Ge+oj32ztVJmkbMpWugNWRk+jPfXS3mwF\\n9ZlIRbSP9iwtqUqpuvZma1XeJyWThmsvm/ftg+PH11cpagZJEUomDZcvm48cgZtuClXKsWNwzTXh\\nlKVXXaVmkPSn9UyEuTnYtSusoTI1FTryVlfh7rvhrW+FX/6yXutqSBpKJvKErEo5++y1hHLixNr9\\nk5Oho6/KB5tJOkomss7cXGjeTE+HYceMGZx/fmjyXH11GAFSQqmGxUU499zwOyX1mciTZBXK0hLs\\n3Ruqk6kpOPVUjfxUzeIiXH55uHzwYPi9sJAmFnOvx/Fzs7OzfujQodRhNE63029MToaZl8ePh6rl\\nVa+Cd7xDSWUUeo2mnXvuWhIBOOccuPPOuM9vZofdfbbfdqpMpKf2OQ35kZ/FxbUp3R//ONxxB9xz\\njxJKTP0mFV500fpkctFF5ceYUTKRQvJ7x127wvWbb15LJrB+tEfNnzj6LSGRNWkOHAiJJFUTB5RM\\npIBue8c9e8LQcTbiMzUVRns0PT+eIktILCykTSIZjeZIX93WKV1YgM99Dq64Ivzcc084PmSj53up\\n+hqnKWSd4XU4f5IqE+mr196x0/IGgyzGtLgYSvTTT4cbblBF00ldlpBQMpG+uq3yNcyaptB5WBO0\\nvGRdKZlIIe2rfPWqHnrtSfMduQcOdN5mYkLLS9aR+kyksGHPf5t15GYzaE8//cnbmMGll45PVdKk\\nfiBVJlLYsItTtyejpzwFbrwxDDF/9athmDk7afs4qNLCU2UsI6FkIoUNuzh1p2Q0NxdGhcZxzZRY\\npxkZ9rUpK6kpmchAhhlZ6JWMNvK4VU9AMU4zEiMRlHXuJCUTKVWsYc5UTYhBEliM04zESARlnTtJ\\nyURqKcWZCjeSwIZNnjESQaekNoqqTslEShXrQ5ziTIUpElisk6jlk9qoqjolEylNzA9xijMVpjrV\\nauwZsKNKikomUprYH+JBv2S9qqIiFdO4nGp1VEmx9GRiZq8FrgFeCJzp7lrxqCHK3rN3W9ipvSoa\\npGIapkookrBiNAP7PcaokmKKyuSbwIXAjQmeWxIqc8/eniB27OheFY2q7C+azLrFvJFmYNHHGMXB\\ng6UnE3e/D8DyqxVLY5R1BGx7goDuVdEoKqZBklm3mDeS1FJ0Emcq3WdiZgvAAsDMzEziaKRO2hPE\\n/Hz46TZhLnbFNEgy6xbzRpJaqk5iGNGC0mZ2N3Bqh7ve5e6faG2zDPxF0T4TLSgtg0o5Q7ZTcwOq\\n0WcyqKILSidbnV7JRMbdMF/qKh0qoNXpRRLbaP9QlY42HkTp65mY2WvM7AFgDvi0mUU+y4dIWu1r\\nmAy6pkm+v+Wxx8LJzuogxWjOrcCtZT+vSBnaq4rdu8MpVfPXjx7t3XzZvj2sNnfiRDjR2b59ofO4\\n6tWJVlqTxhnl6mdLS6GayEZxDhxYqzKOHYOdO/ufq3luLqw2l82eOH588FXtUlAykUZpXzoyZkJZ\\nWQlVRDamMTERTow1NRUuT0yE1eSKLHs5Pw8nnRT+puwh3o1SB6w0yigndS0vhyoC1tayXViA005b\\nW9k/3+TplCDyozh1Ow5IyUQapdOkrlhzO44cCSd0h/Vr2eZHdU47rXuHaqdRnF27ej9nlZKNkok0\\nSvtsV4h7PMzEBFx2We8O0/37w7b7969/vkGqpioOH6vPRBpnbi7s8efm1n+Bjx2Da64ZvB8l/xgn\\nTsDMTPcvdqeEkcmqpiL9JL0eJxUlE2m07Au8aVPoHD14cK3pM+hjFEkCvbYd5LzCgzxnWZJNpx+U\\nptPLqKyshI7RL31p7bYrroAPfGCwxyjafxGrr6OsPhNNpxcpaG4OzjhjfTLZyGMU/ULHWoahaic0\\nVzNHhNBhOj0dhnSnp9dGYpp0es9hqTIRIezh77nnyaeDGMWISdWGdGNRMhFpyZoNWTVy5Ej8CW5V\\nHNKNRclEJCf/ZZ+cDKMlEG/EJOWyiqOmZCKSk/+yQ5iANjMTr0mSclnFUVMyEcnptHZszMphXM69\\n04mSiUhOGV/2qg3pxqJkItJmXL/so6Z5JiIShZKJiEShZCISSVVmy6aKQ30mIhFUZTJayjhUmYhE\\nUJX1RVLGoWQiEkFV1hdJGYeaOSIRVGUyWso4Sl8cycz+EXgV8DjwH8Cb3f1/+v2dFkcSSaPo4kgp\\nmjl3AS92998Dvgv0WH9bROqi9GTi7gfdvXV2Eb4APKvsGEQkvtQdsJcCd3S708wWzOyQmR165JFH\\nSgxLRAY1kg5YM7sbOLXDXe9y90+0tnkXcBz4cLfHcfdFYBFCn8kIQhWRSJKsTm9mlwCXA2e5+/8V\\n/JtHgPv7bLYV+Olw0Y2E4hpMVeOC6sY2yrie4+5P77dRitGc84DrgD9x96htFzM7VKTXuWyKazBV\\njQuqG1sV4krRZ7IHOBm4y8zuNbN/ShCDiERW+qQ1d39+2c8pIqOXejQntsXUAXShuAZT1bigurEl\\nj6s2pwcVkWobt8pERBJRMhGRKGqbTMzstWb2LTNbNbOuQ2Jm9kMz+0Zr5KiUIwUHiO08M/uOmX3f\\nzN5ZQlxPM7O7zOx7rd9P7bJdKa9Zv//fgutb93/dzM4YVSwDxrXdzH7Wen3uNbO/KSmuvWb2sJl9\\ns8v9SV6vJ7h7LX+AFwK/AywDsz22+yGwtWqxAROEo6afB0wBXwNeNOK43gO8s3X5ncA/pHrNivz/\\nwCsIh1sY8BLgiyW8d0Xi2g58qszPVOt5/xg4A/hml/tLf73yP7WtTNz9Pnf/Tuo4OikY25nA9939\\nB+7+OPAx4IIRh3YBsL91eT/wpyN+vl6K/P8XAEsefAF4ipk9owJxJeHunwX+q8cmKV6vJ9Q2mQzA\\ngbvN7LCZLaQOJueZwI9y1x9o3TZKp7j7Q63LPwZO6bJdGa9Zkf8/xWtU9Dlf2mpK3GFmvzvimIpK\\n8Xo9odIrrRU5YLCAl7n7g2b2W4RZt//eyvBViC26XnHlr7i7m1m3eQEjec3GyFeAGXd/1MxeAXwc\\neEHimJKrdDJx97MjPMaDrd8Pm9mthDJ26C9GhNgeBJ6du/6s1m1D6RWXmf3EzJ7h7g+1yt+HuzzG\\nSF6zNkX+/5G8RsPG5e4/z12+3czeb2Zb3T31AYApXq8njHUzx8x+zcxOzi4D5wAde8IT+DLwAjN7\\nrplNAW8Abhvxc94G7Ghd3gE8qYIq8TUr8v/fBsy3RileAvws10wblb5xmdmpZmaty2cSvkdHRxxX\\nESlerzVl90hH7Nl+DaFNeAz4CXBn6/bfBm5vXX4eoTf+a8C3CE2QSsTma73v3yWMHow8NmAL8Bng\\ne8DdwNNSvmad/n/gCuCK1mUD3te6/xv0GLUrOa4rW6/N1wirBb60pLg+CjwE/Kr1+XpLFV6v7EfT\\n6UUkirFu5ohIeZRMRCQKJRMRiULJRESiUDIRkSiUTEQkCiUTEYlCyUREolAykSjMbMrMHjcz7/Jz\\nS+oYZbQqfaCf1Mpmwrmj272NsKDPJ8sNR8qm6fQyMmb2HuAvgbe7+3Wp45HRUmUi0bWOqL0e2Ans\\ndPf3Jw5JSqA+E4nKzDYRTgj1VuAt+URiZq8zs8+b2aNm9sNUMcpoqDKRaMxsgrC27OuBi939o22b\\n/DfhXNOnEPpSZIwomUgUZrYZ+AjwauD17v6k0Rt3v6u1bcqFrGVElExkaGY2DfwrcDZwobt/OnFI\\nkoCSicSwBLwS+BDwVDO7uO3+2zy3bqqMJyUTGUpr5Ob81tVLWj95q8DJJYYkiSiZyFA8TFT6jdRx\\nSHpKJlKa1mjP5taPmdlJhHx0LG1kEoOSiZTpTcC+3PVfAvcD25JEI1FpOr2IRKEZsCIShZKJiESh\\nZCIiUSiZiEgUSiYiEoWSiYhEoWQiIlH8P2qZkrL9cDGnAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(4,3))\\n\",\n \"plt.plot(codings_val[:,0], codings_val[:, 1], \\\"b.\\\")\\n\",\n \"plt.xlabel(\\\"$z_1$\\\", fontsize=18)\\n\",\n \"plt.ylabel(\\\"$z_2$\\\", fontsize=18, rotation=0)\\n\",\n \"#ave_fig(\\\"linear_autoencoder_pca_plot\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# plot: 2D projection with max variance\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Stacked Autoencoders\\n\",\n \"* AEs with multiple hidden layers - for more complex model learning\\n\",\n \"![stacked-AE](pics/stacked-AE.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_inputs = 28 * 28 # for MNIST\\n\",\n \"n_hidden1 = 300\\n\",\n \"n_hidden2 = 150 # codings\\n\",\n \"n_hidden3 = n_hidden1\\n\",\n \"n_outputs = n_inputs\\n\",\n \"learning_rate = 0.01\\n\",\n \"l2_reg = 0.0001\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, \\n\",\n \" shape=[None, n_inputs])\\n\",\n \"\\n\",\n \"with tf.contrib.framework.arg_scope(\\n\",\n \" [fully_connected],\\n\",\n \" activation_fn=tf.nn.elu,\\n\",\n \" weights_initializer=tf.contrib.layers.variance_scaling_initializer(),\\n\",\n \" weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg)):\\n\",\n \"\\n\",\n \" hidden1 = fully_connected(X, n_hidden1)\\n\",\n \" hidden2 = fully_connected(hidden1, n_hidden2) # codings\\n\",\n \" hidden3 = fully_connected(hidden2, n_hidden3)\\n\",\n \" outputs = fully_connected(hidden3, n_outputs, activation_fn=None)\\n\",\n \"\\n\",\n \"# MSE\\n\",\n \"reconstruction_loss = tf.reduce_mean(\\n\",\n \" tf.square(outputs - X))\\n\",\n \"\\n\",\n \"reg_losses = tf.get_collection(\\n\",\n \" tf.GraphKeys.REGULARIZATION_LOSSES)\\n\",\n \"\\n\",\n \"loss = tf.add_n(\\n\",\n \" [reconstruction_loss] + reg_losses)\\n\",\n \"\\n\",\n \"optimizer = tf.train.AdamOptimizer(\\n\",\n \" learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(loss)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"saver = tf.train.Saver()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Extracting /tmp/data/train-images-idx3-ubyte.gz\\n\",\n \"Extracting /tmp/data/train-labels-idx1-ubyte.gz\\n\",\n \"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\\n\",\n \"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\\n\",\n \"0 Train MSE: 0.02705\\n\",\n \"1 Train MSE: 0.0137857\\n\",\n \"2 Train MSE: 0.0113694\\n\",\n \"3 Train MSE: 0.0107478\\n\"\n ]\n }\n ],\n \"source\": [\n \"# use MNIST dataset \\n\",\n \"\\n\",\n \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n \"\\n\",\n \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\")\\n\",\n \"\\n\",\n \"# train the net. digit labels (y_batch) = unused.\\n\",\n \"\\n\",\n \"n_epochs = 4\\n\",\n \"batch_size = 150\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" init.run()\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" n_batches = mnist.train.num_examples // batch_size\\n\",\n \" for iteration in range(n_batches):\\n\",\n \" print(\\\"\\\\r{}%\\\".format(100 * iteration // n_batches), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \" X_batch, y_batch = mnist.train.next_batch(batch_size)\\n\",\n \" sess.run(training_op, feed_dict={X: X_batch})\\n\",\n \" mse_train = reconstruction_loss.eval(feed_dict={X: X_batch})\\n\",\n \" print(\\\"\\\\r{}\\\".format(epoch), \\\"Train MSE:\\\", mse_train)\\n\",\n \" saver.save(sess, \\\"./my_model_all_layers.ckpt\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# utility: plot grayscale 28x28 image\\n\",\n \"\\n\",\n \"def plot_image(image, shape=[28, 28]):\\n\",\n \" plt.imshow(image.reshape(shape), cmap=\\\"Greys\\\", interpolation=\\\"nearest\\\")\\n\",\n \" plt.axis(\\\"off\\\")\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# load model, eval on test set (measure reconstruction error, display original & reconstruction)\\n\",\n \"\\n\",\n \"def show_reconstructed_digits(X, outputs, model_path = None, n_test_digits = 2):\\n\",\n \" with tf.Session() as sess:\\n\",\n \" if model_path:\\n\",\n \" saver.restore(sess, model_path)\\n\",\n \" X_test = mnist.test.images[:n_test_digits]\\n\",\n \" outputs_val = outputs.eval(feed_dict={X: X_test})\\n\",\n \"\\n\",\n \" fig = plt.figure(figsize=(8, 3 * n_test_digits))\\n\",\n \" for digit_index in range(n_test_digits):\\n\",\n \" plt.subplot(n_test_digits, 2, digit_index * 2 + 1)\\n\",\n \" plot_image(X_test[digit_index])\\n\",\n \" plt.subplot(n_test_digits, 2, digit_index * 2 + 2)\\n\",\n \" plot_image(outputs_val[digit_index])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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JoWACAMmhYAIAyaFgAgDJoWACAMmhYAIAyaFgAgDJoWACAMmhYAIAya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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_reconstructed_digits(X, outputs, \\\"./my_model_all_layers.ckpt\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tying Weights\\n\",\n \"* Used when AE is symmetrical. Tying decoder layer weights to encoder layers' weights cuts number of weights by 50% (speedup & less memory).\\n\",\n \"* Tied weights in TF is cumbersome. Easier to define layers manually.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"activation = tf.nn.elu\\n\",\n \"\\n\",\n \"regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\\n\",\n \"\\n\",\n \"initializer = tf.contrib.layers.variance_scaling_initializer()\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, shape=[None, n_inputs])\\n\",\n \"\\n\",\n \"weights1_init = initializer([n_inputs, n_hidden1])\\n\",\n \"weights2_init = initializer([n_hidden1, n_hidden2])\\n\",\n \"\\n\",\n \"weights1 = tf.Variable(weights1_init, dtype=tf.float32, name=\\\"weights1\\\")\\n\",\n \"weights2 = tf.Variable(weights2_init, dtype=tf.float32, name=\\\"weights2\\\")\\n\",\n \"# weights 3,4 not vars!\\n\",\n \"weights3 = tf.transpose(weights2, name=\\\"weights3\\\") # tied weights\\n\",\n \"weights4 = tf.transpose(weights1, name=\\\"weights4\\\") # tied weights\\n\",\n \"\\n\",\n \"biases1 = tf.Variable(tf.zeros(n_hidden1),name=\\\"biases1\\\")\\n\",\n \"biases2 = tf.Variable(tf.zeros(n_hidden2),name=\\\"biases2\\\")\\n\",\n \"biases3 = tf.Variable(tf.zeros(n_hidden3),name=\\\"biases3\\\")\\n\",\n \"biases4 = tf.Variable(tf.zeros(n_outputs),name=\\\"biases4\\\")\\n\",\n \"\\n\",\n \"hidden1 = activation(tf.matmul(X, weights1) + biases1)\\n\",\n \"hidden2 = activation(tf.matmul(hidden1, weights2) + biases2)\\n\",\n \"hidden3 = activation(tf.matmul(hidden2, weights3) + biases3)\\n\",\n \"outputs = tf.matmul(hidden3, weights4) + biases4\\n\",\n \"\\n\",\n \"reconstruction_loss = tf.reduce_mean(\\n\",\n \" tf.square(outputs - X))\\n\",\n \"\\n\",\n \"reg_loss = regularizer(weights1) + regularizer(weights2)\\n\",\n \"\\n\",\n \"loss = reconstruction_loss + reg_loss\\n\",\n \"\\n\",\n \"optimizer = tf.train.AdamOptimizer(learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(loss)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training one Autoencoder at a time\\n\",\n \"* Often faster to train each shallow AE individually, then stack them.\\n\",\n \"* Simplest approach = use separate TF graph for each phase\\n\",\n \"\\n\",\n \"![one-ae-atatime](pics/training-one-AE-atatime.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def train_autoencoder(\\n\",\n \" X_train, \\n\",\n \" n_neurons, \\n\",\n \" n_epochs, \\n\",\n \" batch_size, \\n\",\n \" learning_rate = 0.01, \\n\",\n \" l2_reg = 0.0005, \\n\",\n \" activation_fn=tf.nn.elu):\\n\",\n \" \\n\",\n \" graph = tf.Graph()\\n\",\n \" with graph.as_default():\\n\",\n \" n_inputs = X_train.shape[1]\\n\",\n \"\\n\",\n \" X = tf.placeholder(tf.float32, shape=[None, n_inputs])\\n\",\n \" \\n\",\n \" with tf.contrib.framework.arg_scope(\\n\",\n \" [fully_connected],\\n\",\n \" activation_fn=activation_fn,\\n\",\n \" weights_initializer=tf.contrib.layers.variance_scaling_initializer(),\\n\",\n \" weights_regularizer=tf.contrib.layers.l2_regularizer(\\n\",\n \" l2_reg)):\\n\",\n \" hidden = fully_connected(\\n\",\n \" X, n_neurons, scope=\\\"hidden\\\")\\n\",\n \" outputs = fully_connected(\\n\",\n \" hidden, n_inputs, activation_fn=None, scope=\\\"outputs\\\")\\n\",\n \"\\n\",\n \" mse = tf.reduce_mean(tf.square(outputs - X))\\n\",\n \"\\n\",\n \" reg_losses = tf.get_collection(\\n\",\n \" tf.GraphKeys.REGULARIZATION_LOSSES)\\n\",\n \" \\n\",\n \" loss = tf.add_n([mse] + reg_losses)\\n\",\n \"\\n\",\n \" optimizer = tf.train.AdamOptimizer(learning_rate)\\n\",\n \" \\n\",\n \" training_op = optimizer.minimize(loss)\\n\",\n \"\\n\",\n \" init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \" with tf.Session(graph=graph) as sess:\\n\",\n \" init.run()\\n\",\n \" \\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" n_batches = len(X_train) // batch_size\\n\",\n \" \\n\",\n \" for iteration in range(n_batches):\\n\",\n \" print(\\\"\\\\r{}%\\\".format(100 * iteration // n_batches), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \" \\n\",\n \" indices = rnd.permutation(\\n\",\n \" len(X_train))[:batch_size]\\n\",\n \" \\n\",\n \" X_batch = X_train[indices]\\n\",\n \" \\n\",\n \" sess.run(\\n\",\n \" training_op, feed_dict={X: X_batch})\\n\",\n \" \\n\",\n \" mse_train = mse.eval(\\n\",\n \" feed_dict={X: X_batch})\\n\",\n \" \\n\",\n \" print(\\\"\\\\r{}\\\".format(epoch), \\\"Train MSE:\\\", mse_train)\\n\",\n \" \\n\",\n \" params = dict(\\n\",\n \" [(var.name, var.eval()) for var in tf.get_collection(\\n\",\n \" tf.GraphKeys.TRAINABLE_VARIABLES)])\\n\",\n \" \\n\",\n \" hidden_val = hidden.eval(\\n\",\n \" feed_dict={X: X_train})\\n\",\n \" \\n\",\n \" return hidden_val, params[\\\"hidden/weights:0\\\"], params[\\\"hidden/biases:0\\\"], params[\\\"outputs/weights:0\\\"], params[\\\"outputs/biases:0\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 Train MSE: 0.0193591\\n\",\n \"1 Train MSE: 0.0190697\\n\",\n \"2 Train MSE: 0.0188801\\n\",\n \"3 Train MSE: 0.0192353\\n\",\n \"0 Train MSE: 0.00428287\\n\",\n \"1 Train MSE: 0.00438113\\n\",\n \"2 Train MSE: 0.00464872\\n\",\n \"3 Train MSE: 0.00457076\\n\"\n ]\n }\n ],\n \"source\": [\n \"# train two AEs\\n\",\n \"\\n\",\n \"hidden_output, W1, b1, W4, b4 = train_autoencoder(\\n\",\n \" mnist.train.images, \\n\",\n \" n_neurons=300, \\n\",\n \" n_epochs=4, \\n\",\n \" batch_size=150)\\n\",\n \"\\n\",\n \"_, W2, b2, W3, b3 = train_autoencoder(\\n\",\n \" hidden_output, \\n\",\n \" n_neurons=150, \\n\",\n \" n_epochs=4, \\n\",\n \" batch_size=150)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create stacked AE by reusing weights &and biases from above\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n_inputs = 28*28\\n\",\n \"\\n\",\n \"X = tf.placeholder(tf.float32, shape=[None, n_inputs])\\n\",\n \"hidden1 = tf.nn.elu(tf.matmul(X, W1) + b1)\\n\",\n \"hidden2 = tf.nn.elu(tf.matmul(hidden1, W2) + b2)\\n\",\n \"hidden3 = tf.nn.elu(tf.matmul(hidden2, W3) + b3)\\n\",\n \"outputs = tf.matmul(hidden3, W4) + b4\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Visualizing Reconstructions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Load model, evaluates it on test set (reconstruction error)\\n\",\n \"# display original & reconstructed images\\n\",\n \"\\n\",\n \"def show_reconstructed_digits(\\n\",\n \" X, \\n\",\n \" outputs, \\n\",\n \" model_path = None, \\n\",\n \" n_test_digits = 2):\\n\",\n \" \\n\",\n \" with tf.Session() as sess:\\n\",\n \" if model_path:\\n\",\n \" saver.restore(sess, model_path)\\n\",\n \" \\n\",\n \" X_test = mnist.test.images[:n_test_digits]\\n\",\n \" outputs_val = outputs.eval(feed_dict={X: X_test})\\n\",\n \"\\n\",\n \" fig = plt.figure(figsize=(8, 3 * n_test_digits))\\n\",\n \" \\n\",\n \" for digit_index in range(n_test_digits):\\n\",\n \" \\n\",\n \" plt.subplot(n_test_digits, 2, digit_index * 2 + 1)\\n\",\n \" plot_image(X_test[digit_index])\\n\",\n \" plt.subplot(n_test_digits, 2, digit_index * 2 + 2)\\n\",\n \" plot_image(outputs_val[digit_index])\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"#show_reconstructed_digits(X, outputs, \\\"./my_model_all_layers.ckpt\\\")\\n\",\n \"show_reconstructed_digits(X, outputs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Visualizing Features\\n\",\n \"* simplest method: find training instances that activate each hidden node the most. (best on upper layers, given their tendency to capture high-level features.)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Unsupervised Pretraining with Stacked Autoencoders\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Denoising Autoencoders\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Sparse Autoencoders\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Variational Autoencoders\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Other Autoencoders\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ch16-reinforcement-learning.html", "content": "\n\n\nch16-reinforcement-learning\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n\n
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Intro & Resources

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Learning to Optimize Rewards

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  • Definitions: software agents make observations & take actions within an environment. In return they can receive rewards (positive or negative).
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Policy Search

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OpenAI Gym (link:)

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  • A toolkit for various simulated environments.
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In [1]:
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!pip3 install --upgrade gym\n
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Requirement already up-to-date: gym in /home/bjpcjp/anaconda3/lib/python3.5/site-packages\nRequirement already up-to-date: requests>=2.0 in /home/bjpcjp/anaconda3/lib/python3.5/site-packages (from gym)\nRequirement already up-to-date: pyglet>=1.2.0 in /home/bjpcjp/anaconda3/lib/python3.5/site-packages (from gym)\nRequirement already up-to-date: six in /home/bjpcjp/anaconda3/lib/python3.5/site-packages (from gym)\nRequirement already up-to-date: numpy>=1.10.4 in /home/bjpcjp/anaconda3/lib/python3.5/site-packages (from gym)\n
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In [2]:
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import gym\nenv = gym.make("CartPole-v0")\nobs = env.reset()\nobs\nenv.render()\n
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[2017-04-27 13:05:47,311] Making new env: CartPole-v0\n
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  • make() creates environment
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  • reset() returns a 1st env't
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  • CartPole() - each observation = 1D numpy array (hposition, velocity, angle, angularvelocity)\n\"cartpole\"
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img = env.render(mode="rgb_array")\nimg.shape\n
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(1, 1, 3)
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In [4]:
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# what actions are possible?\n# in this case: 0 = accelerate left, 1 = accelerate right\nenv.action_space\n
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Discrete(2)
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In [5]:
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# pole is leaning right. let's go further to the right.\naction = 1\nobs, reward, done, info = env.step(action)\nobs, reward, done, info\n
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Out[5]:
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(array([-0.04061536,  0.1486962 , -0.01966318, -0.29249162]), 1.0, False, {})
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  • new observation:
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    • hpos = obs[0]<0
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    • velocity = obs[1]>0 = moving to the right
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    • angle = obs[2]>0 = leaning right
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    • ang velocity = obs[3]<0 = slowing down?
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  • reward = 1.0
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  • done = False (episode not over)
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  • info = (empty)
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In [6]:
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# example policy: \n# (1) accelerate left when leaning left, (2) accelerate right when leaning right\n# average reward over 500 episodes?\n\ndef basic_policy(obs):\n    angle = obs[2]\n    return 0 if angle < 0 else 1\n\ntotals = []\nfor episode in range(500):\n    episode_rewards = 0\n    obs = env.reset()\n    for step in range(1000): # 1000 steps max, we don't want to run forever\n        action = basic_policy(obs)\n        obs, reward, done, info = env.step(action)\n        episode_rewards += reward\n        if done:\n            break\n    totals.append(episode_rewards)\n\nimport numpy as np\nnp.mean(totals), np.std(totals), np.min(totals), np.max(totals)\n
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Out[6]:
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(41.579999999999998, 8.5249985337242151, 25.0, 62.0)
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NN Policies

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  • observations as inputs - actions to be executed as outputs - determined by p(action)
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  • approach lets agent find best balance between exploring new actions & reusing known good actions.
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Evaluating Actions: Credit Assignment problem

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  • Reinforcement Learning (RL) training not like supervised learning.
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  • RL feedback is via rewards (often sparse & delayed)
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  • How to determine which previous steps were \"good\" or \"bad\"? (aka \"credit assigmnment problem\")
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  • Common tactic: applying a discount rate to older rewards.

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  • Use normalization across many episodes to increase score reliability.

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NN PolicyDiscounts & Rewards
\"nn-policy\"\"discount-rewards\"
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In [7]:
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import tensorflow as tf\nfrom tensorflow.contrib.layers import fully_connected\n\n# 1. Specify the neural network architecture\nn_inputs = 4                            # == env.observation_space.shape[0]\nn_hidden = 4                            # simple task, don't need more hidden neurons\nn_outputs = 1                           # only output prob(accelerating left)\ninitializer = tf.contrib.layers.variance_scaling_initializer()\n\n# 2. Build the neural network\nX = tf.placeholder(\n    tf.float32, shape=[None, n_inputs])\n\nhidden = fully_connected(\n    X, n_hidden, \n    activation_fn=tf.nn.elu,\n    weights_initializer=initializer)\n\nlogits = fully_connected(\n    hidden, n_outputs, \n    activation_fn=None,\n    weights_initializer=initializer)\n\noutputs = tf.nn.sigmoid(logits)          # logistic (sigmoid) ==> return 0.0-1.0\n\n# 3. Select a random action based on the estimated probabilities\np_left_and_right = tf.concat(\n    axis=1, values=[outputs, 1 - outputs])\n\naction = tf.multinomial(\n    tf.log(p_left_and_right), \n    num_samples=1)\n\ninit = tf.global_variables_initializer()\n
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Policy Gradient (PG) algorithms

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Markov Decision processes (MDPs)

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  • Markov chains = stochastic processes, no memory, fixed #states, random transitions
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  • Markov decision processes = similar to MCs - agent can choose action; transition probabilities depend on the action; transitions can return reward/punishment.
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  • Goal: find policy with maximum rewards over time.
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Markov ChainMarkov Decision Process
\"markov-chain\"\"alt\"
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  • Bellman Optimality Equation: a method to estimate optimal state value of any state s.
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  • Knowing optimal states = useful, but doesn't tell agent what to do. Q-Value algorithm helps solve this problem. Optimal Q-Value of a state-action pair = sum of discounted future rewards the agent can expect on average.
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In [8]:
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# Define MDP:\n\nnan=np.nan # represents impossible actions\nT = np.array([ # shape=[s, a, s']\n        [[0.7, 0.3, 0.0], [1.0, 0.0, 0.0], [0.8, 0.2, 0.0]],\n        [[0.0, 1.0, 0.0], [nan, nan, nan], [0.0, 0.0, 1.0]],\n        [[nan, nan, nan], [0.8, 0.1, 0.1], [nan, nan, nan]],\n        ])\n\nR = np.array([ # shape=[s, a, s']\n        [[10., 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],\n        [[10., 0.0, 0.0], [nan, nan, nan], [0.0, 0.0, -50.]],\n        [[nan, nan, nan], [40., 0.0, 0.0], [nan, nan, nan]],\n        ])\n\npossible_actions = [[0, 1, 2], [0, 2], [1]]\n\n# run Q-Value Iteration algo\n\nQ = np.full((3, 3), -np.inf)\nfor state, actions in enumerate(possible_actions):\n    Q[state, actions] = 0.0 # Initial value = 0.0, for all possible actions\n\nlearning_rate = 0.01\ndiscount_rate = 0.95\nn_iterations = 100\n\nfor iteration in range(n_iterations):\n    Q_prev = Q.copy()\n    for s in range(3):\n        for a in possible_actions[s]:\n            Q[s, a] = np.sum([\n                T[s, a, sp] * (R[s, a, sp] + discount_rate * np.max(Q_prev[sp]))\n                for sp in range(3)\n                ])\n            \nprint("Q: \\n",Q)\nprint("Optimal action for each state:\\n",np.argmax(Q, axis=1))\n
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Q: \n [[ 21.88646117  20.79149867  16.854807  ]\n [  1.10804034         -inf   1.16703135]\n [        -inf  53.8607061          -inf]]\nOptimal action for each state:\n [0 2 1]\n
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In [9]:
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# change discount rate to 0.9, see how policy changes:\n\ndiscount_rate = 0.90\n\nfor iteration in range(n_iterations):\n    Q_prev = Q.copy()\n    for s in range(3):\n        for a in possible_actions[s]:\n            Q[s, a] = np.sum([\n                T[s, a, sp] * (R[s, a, sp] + discount_rate * np.max(Q_prev[sp]))\n                for sp in range(3)\n                ])\n            \nprint("Q: \\n",Q)\nprint("Optimal action for each state:\\n",np.argmax(Q, axis=1))\n
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Q: \n [[  1.89189499e+01   1.70270580e+01   1.36216526e+01]\n [  3.09979853e-05             -inf  -4.87968388e+00]\n [            -inf   5.01336811e+01             -inf]]\nOptimal action for each state:\n [0 0 1]\n
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Temporal Difference Learning & Q-Learning

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  • In general - agent has no knowledge of transition probabilities or rewards
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  • Temporal Difference Learning (TD Learning) similar to value iteration, but accounts for this lack of knowlege.
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  • Algorithm tracks running average of most recent awards & anticipated rewards.

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  • Q-Learning algorithm adaptation of Q-Value Iteration where initial transition probabilities & rewards are unknown.

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In [10]:
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import numpy.random as rnd\n\nlearning_rate0 = 0.05\nlearning_rate_decay = 0.1\nn_iterations = 20000\n\ns = 0                         # start in state 0\nQ = np.full((3, 3), -np.inf)  # -inf for impossible actions\n\nfor state, actions in enumerate(possible_actions):\n    Q[state, actions] = 0.0 # Initial value = 0.0, for all possible actions\n    for iteration in range(n_iterations):\n        a = rnd.choice(possible_actions[s]) # choose an action (randomly)\n        sp = rnd.choice(range(3), p=T[s, a]) # pick next state using T[s, a]\n        reward = R[s, a, sp]\n        \n        learning_rate = learning_rate0 / (1 + iteration * learning_rate_decay)\n        \n        Q[s, a] = learning_rate * Q[s, a] + (1 - learning_rate) * (reward + discount_rate * np.max(Q[sp]))\n\ns = sp # move to next state\n\nprint("Q: \\n",Q)\nprint("Optimal action for each state:\\n",np.argmax(Q, axis=1))\n
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Q: \n [[             -inf   2.47032823e-323              -inf]\n [  0.00000000e+000              -inf   0.00000000e+000]\n [             -inf   0.00000000e+000              -inf]]\nOptimal action for each state:\n [1 0 1]\n
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Exploration Policies

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  • Q-Learning works only if exploration is thorough - not always possible.
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  • Better alternative: explore more interesting routes using a sigma probability
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Approximate Q-Learning

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Ms Pac-Man with Deep Q-Learning

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In [11]:
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env = gym.make('MsPacman-v0')\nobs = env.reset()\nobs.shape, env.action_space\n\n# action_space = 9 possible joystick actions\n# observations = atari screenshots as 3D NumPy arrays\n
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[2017-04-27 13:06:21,861] Making new env: MsPacman-v0\n
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Out[11]:
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((210, 160, 3), Discrete(9))
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In [12]:
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mspacman_color = np.array([210, 164, 74]).mean()\n\n# crop image, shrink to 88x80 pixels, convert to grayscale, improve contrast\n\ndef preprocess_observation(obs):\n    img = obs[1:176:2, ::2] # crop and downsize\n    img = img.mean(axis=2) # to greyscale\n    img[img==mspacman_color] = 0 # improve contrast\n    img = (img - 128) / 128 - 1 # normalize from -1. to 1.\n    return img.reshape(88, 80, 1)\n
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Ms PacMan ObservationDeep-Q net
\"observation\"\"alt\"
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# Create DQN\n# 3 convo layers, then 2 FC layers including output layer\n\nfrom tensorflow.contrib.layers import convolution2d, fully_connected\n\ninput_height      = 88\ninput_width       = 80\ninput_channels    = 1\nconv_n_maps       = [32, 64, 64]\nconv_kernel_sizes = [(8,8), (4,4), (3,3)]\nconv_strides      = [4, 2, 1]\nconv_paddings     = ["SAME"]*3\nconv_activation   = [tf.nn.relu]*3\nn_hidden_in       = 64 * 11 * 10 # conv3 has 64 maps of 11x10 each\nn_hidden          = 512\nhidden_activation = tf.nn.relu\nn_outputs         = env.action_space.n # 9 discrete actions are available\n\ninitializer = tf.contrib.layers.variance_scaling_initializer()\n\n# training will need ***TWO*** DQNs:\n# one to train the actor\n# another to learn from trials & errors (critic)\n# q_network is our net builder.\n\ndef q_network(X_state, scope):\n    prev_layer = X_state\n    conv_layers = []\n\n    with tf.variable_scope(scope) as scope:\n    \n        for n_maps, kernel_size, stride, padding, activation in zip(\n            conv_n_maps, \n            conv_kernel_sizes, \n            conv_strides,\n            conv_paddings, \n            conv_activation):\n            \n            prev_layer = convolution2d(\n                prev_layer, \n                num_outputs=n_maps, \n                kernel_size=kernel_size,\n                stride=stride, \n                padding=padding, \n                activation_fn=activation,\n                weights_initializer=initializer)\n            \n            conv_layers.append(prev_layer)\n\n        last_conv_layer_flat = tf.reshape(\n            prev_layer, \n            shape=[-1, n_hidden_in])\n            \n        hidden = fully_connected(\n            last_conv_layer_flat, \n            n_hidden, \n            activation_fn=hidden_activation,\n            weights_initializer=initializer)\n        \n        outputs = fully_connected(\n            hidden, \n            n_outputs, \n            activation_fn=None,\n            weights_initializer=initializer)\n        \n    trainable_vars = tf.get_collection(\n        tf.GraphKeys.TRAINABLE_VARIABLES,\n        scope=scope.name)\n    \n    trainable_vars_by_name = {var.name[len(scope.name):]: var\n        for var in trainable_vars}\n\n    return outputs, trainable_vars_by_name\n
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In [14]:
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# create input placeholders & two DQNs\n\nX_state = tf.placeholder(\n    tf.float32, \n    shape=[None, input_height, input_width,\n    input_channels])\n\nactor_q_values, actor_vars   = q_network(X_state, scope="q_networks/actor")\ncritic_q_values, critic_vars = q_network(X_state, scope="q_networks/critic")\n\ncopy_ops = [actor_var.assign(critic_vars[var_name])\n            for var_name, actor_var in actor_vars.items()]\n\n\n# op to copy all trainable vars of critic DQN to actor DQN...\n# use tf.group() to group all assignment ops together\n\ncopy_critic_to_actor = tf.group(*copy_ops)\n
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In [15]:
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# Critic DQN learns by matching Q-Value predictions \n# to actor's Q-Value estimations during game play\n\n# Actor will use a "replay memory" (5 tuples):\n# state, action, next-state, reward, (0=over/1=continue)\n\n# use normal supervised training ops\n# occasionally copy critic DQN to actor DQN\n\n# DQN normally returns one Q-Value for every poss. action\n# only need Q-Value of action actually chosen\n# So, convert action to one-hot vector [0...1...0], multiple by Q-values\n# then sum over 1st axis.\n\nX_action = tf.placeholder(\n    tf.int32, shape=[None])\n\nq_value = tf.reduce_sum(\n    critic_q_values * tf.one_hot(X_action, n_outputs),\n    axis=1, keep_dims=True)\n
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In [54]:
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# training setup\n\ntf.reset_default_graph()\n\ny = tf.placeholder(\n    tf.float32, shape=[None, 1])\n\ncost = tf.reduce_mean(\n    tf.square(y - q_value))\n\n# non-trainable. minimize() op will manage incrementing it\nglobal_step = tf.Variable(\n    0, \n    trainable=False, \n    name='global_step')\n\noptimizer = tf.train.AdamOptimizer(learning_rate)\n\ntraining_op = optimizer.minimize(cost, global_step=global_step)\n\ninit = tf.global_variables_initializer()\n\nsaver = tf.train.Saver()\n
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\n---------------------------------------------------------------------------\nValueError                                Traceback (most recent call last)\n<ipython-input-54-ae5a849b8026> in <module>()\n      7 \n      8 cost = tf.reduce_mean(\n----> 9     tf.square(y - q_value))\n     10 \n     11 # non-trainable. minimize() op will manage incrementing it\n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py in binary_op_wrapper(x, y)\n    879 \n    880   def binary_op_wrapper(x, y):\n--> 881     with ops.name_scope(None, op_name, [x, y]) as name:\n    882       if not isinstance(y, sparse_tensor.SparseTensor):\n    883         y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")\n\n/home/bjpcjp/anaconda3/lib/python3.5/contextlib.py in __enter__(self)\n     57     def __enter__(self):\n     58         try:\n---> 59             return next(self.gen)\n     60         except StopIteration:\n     61             raise RuntimeError("generator didn't yield") from None\n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in name_scope(name, default_name, values)\n   4217   if values is None:\n   4218     values = []\n-> 4219   g = _get_graph_from_inputs(values)\n   4220   with g.as_default(), g.name_scope(n) as scope:\n   4221     yield scope\n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in _get_graph_from_inputs(op_input_list, graph)\n   3966         graph = graph_element.graph\n   3967       elif original_graph_element is not None:\n-> 3968         _assert_same_graph(original_graph_element, graph_element)\n   3969       elif graph_element.graph is not graph:\n   3970         raise ValueError(\n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in _assert_same_graph(original_item, item)\n   3905   if original_item.graph is not item.graph:\n   3906     raise ValueError(\n-> 3907         "%s must be from the same graph as %s." % (item, original_item))\n   3908 \n   3909 \n\nValueError: Tensor("Sum_1:0", shape=(?, 1), dtype=float32) must be from the same graph as Tensor("Placeholder:0", shape=(?, 1), dtype=float32).
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In [37]:
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# use a deque list to build the replay memory\n\nfrom collections import deque\n\nreplay_memory_size = 10000\nreplay_memory = deque(\n    [], maxlen=replay_memory_size)\n\ndef sample_memories(batch_size):\n    indices = rnd.permutation(\n        len(replay_memory))[:batch_size]\n    cols = [[], [], [], [], []] # state, action, reward, next_state, continue\n\n    for idx in indices:\n        memory = replay_memory[idx]\n        for col, value in zip(cols, memory):\n            col.append(value)\n\n    cols = [np.array(col) for col in cols]\n    return (cols[0], cols[1], cols[2].reshape(-1, 1), cols[3], cols[4].reshape(-1, 1))\n
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In [38]:
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# create an actor\n# use epsilon-greedy policy\n# gradually decrease epsilon from 1.0 to 0.05 across 50K training steps\n\neps_min = 0.05\neps_max = 1.0\neps_decay_steps = 50000\n\ndef epsilon_greedy(q_values, step):\n    epsilon = max(eps_min, eps_max - (eps_max-eps_min) * step/eps_decay_steps)\n    if rnd.rand() < epsilon:\n        return rnd.randint(n_outputs) # random action\n    else:\n        return np.argmax(q_values) # optimal action\n
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In [39]:
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# training setup: the variables\n\nn_steps = 100000 # total number of training steps\ntraining_start = 1000 # start training after 1,000 game iterations\ntraining_interval = 3 # run a training step every 3 game iterations\nsave_steps = 50 # save the model every 50 training steps\ncopy_steps = 25 # copy the critic to the actor every 25 training steps\ndiscount_rate = 0.95\nskip_start = 90 # skip the start of every game (it's just waiting time)\nbatch_size = 50\niteration = 0 # game iterations\ncheckpoint_path = "./my_dqn.ckpt"\ndone = True # env needs to be reset\n
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In [44]:
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# let's get busy\nimport os\n\nwith tf.Session() as sess:\n    \n    # restore models if checkpoint file exists\n    if os.path.isfile(checkpoint_path):\n        saver.restore(sess, checkpoint_path)\n        \n        # otherwise normally initialize variables\n    else:\n        init.run()\n        \n    while True:\n        step = global_step.eval()\n        if step >= n_steps:\n            break\n\n        # iteration = total number of game steps from beginning\n        \n        iteration += 1\n        if done: # game over, start again\n            obs = env.reset()\n\n            for skip in range(skip_start): # skip the start of each game\n                obs, reward, done, info = env.step(0)\n            state = preprocess_observation(obs)\n\n        # Actor evaluates what to do\n        q_values = actor_q_values.eval(feed_dict={X_state: [state]})\n        action   = epsilon_greedy(q_values, step)\n\n        # Actor plays\n        obs, reward, done, info = env.step(action)\n        next_state = preprocess_observation(obs)\n\n        # Let's memorize what just happened\n        replay_memory.append((state, action, reward, next_state, 1.0 - done))\n        state = next_state\n        if iteration < training_start or iteration % training_interval != 0:\n            continue\n\n        # Critic learns\n        X_state_val, X_action_val, rewards, X_next_state_val, continues = (\n            sample_memories(batch_size))\n\n        next_q_values = actor_q_values.eval(\n            feed_dict={X_state: X_next_state_val})\n\n        max_next_q_values = np.max(\n            next_q_values, axis=1, keepdims=True)\n\n        y_val = rewards + continues * discount_rate * max_next_q_values\n\n        training_op.run(\n            feed_dict={X_state: X_state_val, X_action: X_action_val, y: y_val})\n\n        # Regularly copy critic to actor\n        if step % copy_steps == 0:\n            copy_critic_to_actor.run()\n\n        # And save regularly\n        if step % save_steps == 0:\n            saver.save(sess, checkpoint_path)\n            \n        print("\\n",np.average(y_val))\n
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\n 1.09000234097\n\n 1.35392784142\n\n 1.56906713688\n\n 2.5765440191\n\n 1.57079289043\n\n 1.75170834792\n\n 1.97005553639\n\n 1.97246688247\n\n 2.16126081383\n\n 1.550295331\n\n 1.75750140131\n\n 1.56052656734\n\n 1.7519523176\n\n 1.74495741558\n\n 1.95223849511\n\n 1.35289915931\n\n 1.56913152564\n\n 2.96387254691\n\n 1.76067311585\n\n 1.35536773229\n\n 1.54768545294\n\n 1.53594982147\n\n 1.56104325151\n\n 1.96987313104\n\n 2.35546155441\n\n 1.5688166486\n\n 3.08286282682\n\n 3.28864161086\n\n 3.2878398273\n\n 3.09510449028\n\n 3.09807873964\n\n 3.90697311211\n\n 3.07757974195\n\n 3.09214673901\n\n 3.28402029777\n\n 3.28337000942\n\n 3.4255889504\n\n 3.49763186431\n\n 2.85764229989\n\n 3.04482784653\n\n 2.68228099513\n\n 3.28635532999\n\n 3.29647485089\n\n 3.07898310328\n\n 3.10530596256\n\n 3.27691918874\n\n 3.09561720395\n\n 2.67830030346\n\n 3.09576807404\n\n 3.288335078\n\n 3.0956065948\n\n 5.21222548962\n\n 4.21721751595\n\n 4.7905973649\n\n 4.59864345837\n\n 4.39875211382\n\n 4.51839643717\n\n 4.59503188992\n\n 5.01186150789\n\n 4.77968219852\n\n 4.78787856865\n\n 4.20382899523\n\n 4.20432999897\n\n 5.0028930707\n\n 5.20069698572\n\n 4.80375980473\n\n 5.19750945711\n\n 4.20367767668\n\n 4.19593407536\n\n 4.40061367989\n\n 4.6054182477\n\n 4.79921974087\n\n 4.38844807434\n\n 4.20397897291\n\n 4.60095557356\n\n 4.59488785553\n\n 5.75924422598\n\n 5.75949315596\n\n 5.16320213652\n\n 5.36019721937\n\n 5.56076610899\n\n 5.16949163198\n\n 5.75895399189\n\n 5.96050115204\n\n 5.97032629395\n
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\n---------------------------------------------------------------------------\nKeyboardInterrupt                         Traceback (most recent call last)\n<ipython-input-44-d0da605267f3> in <module>()\n     46 \n     47         next_q_values = actor_q_values.eval(\n---> 48             feed_dict={X_state: X_next_state_val})\n     49 \n     50         max_next_q_values = np.max(\n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in eval(self, feed_dict, session)\n    579 \n    580     """\n--> 581     return _eval_using_default_session(self, feed_dict, self.graph, session)\n    582 \n    583 \n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in _eval_using_default_session(tensors, feed_dict, graph, session)\n   3795                        "the tensor's graph is different from the session's "\n   3796                        "graph.")\n-> 3797   return session.run(tensors, feed_dict)\n   3798 \n   3799 \n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)\n    765     try:\n    766       result = self._run(None, fetches, feed_dict, options_ptr,\n--> 767                          run_metadata_ptr)\n    768       if run_metadata:\n    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)\n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)\n    963     if final_fetches or final_targets:\n    964       results = self._do_run(handle, final_targets, final_fetches,\n--> 965                              feed_dict_string, options, run_metadata)\n    966     else:\n    967       results = []\n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\n   1013     if handle is None:\n   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n-> 1015                            target_list, options, run_metadata)\n   1016     else:\n   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)\n   1020   def _do_call(self, fn, *args):\n   1021     try:\n-> 1022       return fn(*args)\n   1023     except errors.OpError as e:\n   1024       message = compat.as_text(e.message)\n\n/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)\n   1002         return tf_session.TF_Run(session, options,\n   1003                                  feed_dict, fetch_list, target_list,\n-> 1004                                  status, run_metadata)\n   1005 \n   1006     def _prun_fn(session, handle, feed_dict, fetch_list):\n\nKeyboardInterrupt:
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\n\n\n" }, { "path": "ch16-reinforcement-learning.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro & Resources\\n\",\n \"* [Sutton/Barto ebook](https://goo.gl/7utZaz); [Silver online course](https://goo.gl/AWcMFW)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Learning to Optimize Rewards\\n\",\n \"* Definitions: software *agents* make *observations* & take *actions* within an *environment*. In return they can receive *rewards* (positive or negative).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Policy Search\\n\",\n \"* **Policy**: the algorithm used by an agent to determine a next action.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### OpenAI Gym ([link:](https://gym.openai.com/))\\n\",\n \"* A toolkit for various simulated environments.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already up-to-date: gym in /home/bjpcjp/anaconda3/lib/python3.5/site-packages\\n\",\n \"Requirement already up-to-date: requests>=2.0 in /home/bjpcjp/anaconda3/lib/python3.5/site-packages (from gym)\\n\",\n \"Requirement already up-to-date: pyglet>=1.2.0 in /home/bjpcjp/anaconda3/lib/python3.5/site-packages (from gym)\\n\",\n \"Requirement already up-to-date: six in /home/bjpcjp/anaconda3/lib/python3.5/site-packages (from gym)\\n\",\n \"Requirement already up-to-date: numpy>=1.10.4 in /home/bjpcjp/anaconda3/lib/python3.5/site-packages (from gym)\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip3 install --upgrade gym\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2017-04-27 13:05:47,311] Making new env: CartPole-v0\\n\"\n ]\n }\n ],\n \"source\": [\n \"import gym\\n\",\n \"env = gym.make(\\\"CartPole-v0\\\")\\n\",\n \"obs = env.reset()\\n\",\n \"obs\\n\",\n \"env.render()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* **make()** creates environment\\n\",\n \"* **reset()** returns a 1st env't\\n\",\n \"* **CartPole()** - each observation = 1D numpy array (hposition, velocity, angle, angularvelocity)\\n\",\n \"![cartpole](pics/cartpole.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1, 1, 3)\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"img = env.render(mode=\\\"rgb_array\\\")\\n\",\n \"img.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Discrete(2)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# what actions are possible?\\n\",\n \"# in this case: 0 = accelerate left, 1 = accelerate right\\n\",\n \"env.action_space\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([-0.04061536, 0.1486962 , -0.01966318, -0.29249162]), 1.0, False, {})\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# pole is leaning right. let's go further to the right.\\n\",\n \"action = 1\\n\",\n \"obs, reward, done, info = env.step(action)\\n\",\n \"obs, reward, done, info\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* new observation:\\n\",\n \" * hpos = obs[0]<0\\n\",\n \" * velocity = obs[1]>0 = moving to the right\\n\",\n \" * angle = obs[2]>0 = leaning right\\n\",\n \" * ang velocity = obs[3]<0 = slowing down?\\n\",\n \"* reward = 1.0\\n\",\n \"* done = False (episode not over)\\n\",\n \"* info = (empty)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(41.579999999999998, 8.5249985337242151, 25.0, 62.0)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# example policy: \\n\",\n \"# (1) accelerate left when leaning left, (2) accelerate right when leaning right\\n\",\n \"# average reward over 500 episodes?\\n\",\n \"\\n\",\n \"def basic_policy(obs):\\n\",\n \" angle = obs[2]\\n\",\n \" return 0 if angle < 0 else 1\\n\",\n \"\\n\",\n \"totals = []\\n\",\n \"for episode in range(500):\\n\",\n \" episode_rewards = 0\\n\",\n \" obs = env.reset()\\n\",\n \" for step in range(1000): # 1000 steps max, we don't want to run forever\\n\",\n \" action = basic_policy(obs)\\n\",\n \" obs, reward, done, info = env.step(action)\\n\",\n \" episode_rewards += reward\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" totals.append(episode_rewards)\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"np.mean(totals), np.std(totals), np.min(totals), np.max(totals)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### NN Policies\\n\",\n \"* observations as inputs - actions to be executed as outputs - determined by p(action)\\n\",\n \"* approach lets agent find best balance between **exploring new actions** & **reusing known good actions**.\\n\",\n \"\\n\",\n \"### Evaluating Actions: Credit Assignment problem\\n\",\n \"* Reinforcement Learning (RL) training not like supervised learning. \\n\",\n \"* RL feedback is via rewards (often sparse & delayed)\\n\",\n \"* How to determine which previous steps were \\\"good\\\" or \\\"bad\\\"? (aka \\\"*credit assigmnment problem*\\\")\\n\",\n \"* Common tactic: applying a **discount rate** to older rewards.\\n\",\n \"\\n\",\n \"* Use normalization across many episodes to increase score reliability. \\n\",\n \"\\n\",\n \"NN Policy | Discounts & Rewards\\n\",\n \"- | -\\n\",\n \"![nn-policy](pics/nn-policy.png) | ![discount-rewards](pics/discount-rewards.png)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"from tensorflow.contrib.layers import fully_connected\\n\",\n \"\\n\",\n \"# 1. Specify the neural network architecture\\n\",\n \"n_inputs = 4 # == env.observation_space.shape[0]\\n\",\n \"n_hidden = 4 # simple task, don't need more hidden neurons\\n\",\n \"n_outputs = 1 # only output prob(accelerating left)\\n\",\n \"initializer = tf.contrib.layers.variance_scaling_initializer()\\n\",\n \"\\n\",\n \"# 2. Build the neural network\\n\",\n \"X = tf.placeholder(\\n\",\n \" tf.float32, shape=[None, n_inputs])\\n\",\n \"\\n\",\n \"hidden = fully_connected(\\n\",\n \" X, n_hidden, \\n\",\n \" activation_fn=tf.nn.elu,\\n\",\n \" weights_initializer=initializer)\\n\",\n \"\\n\",\n \"logits = fully_connected(\\n\",\n \" hidden, n_outputs, \\n\",\n \" activation_fn=None,\\n\",\n \" weights_initializer=initializer)\\n\",\n \"\\n\",\n \"outputs = tf.nn.sigmoid(logits) # logistic (sigmoid) ==> return 0.0-1.0\\n\",\n \"\\n\",\n \"# 3. Select a random action based on the estimated probabilities\\n\",\n \"p_left_and_right = tf.concat(\\n\",\n \" axis=1, values=[outputs, 1 - outputs])\\n\",\n \"\\n\",\n \"action = tf.multinomial(\\n\",\n \" tf.log(p_left_and_right), \\n\",\n \" num_samples=1)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Policy Gradient (PG) algorithms\\n\",\n \"* example: [\\\"reinforce\\\" algo, 1992](https://goo.gl/tUe4Sh)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Markov Decision processes (MDPs)\\n\",\n \"\\n\",\n \"* Markov chains = stochastic processes, no memory, fixed #states, random transitions\\n\",\n \"* Markov decision processes = similar to MCs - agent can choose action; transition probabilities depend on the action; transitions can return reward/punishment.\\n\",\n \"* Goal: find policy with maximum rewards over time.\\n\",\n \"\\n\",\n \"Markov Chain | Markov Decision Process\\n\",\n \"- | -\\n\",\n \"![markov-chain](pics/markov-chain.png) | ![alt](pics/markov-decision-process.png)\\n\",\n \"\\n\",\n \"* **Bellman Optimality Equation**: a method to estimate optimal state value of any state *s*.\\n\",\n \"* Knowing optimal states = useful, but doesn't tell agent what to do. **Q-Value algorithm** helps solve this problem. Optimal Q-Value of a state-action pair = sum of discounted future rewards the agent can expect on average.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Q: \\n\",\n \" [[ 21.88646117 20.79149867 16.854807 ]\\n\",\n \" [ 1.10804034 -inf 1.16703135]\\n\",\n \" [ -inf 53.8607061 -inf]]\\n\",\n \"Optimal action for each state:\\n\",\n \" [0 2 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Define MDP:\\n\",\n \"\\n\",\n \"nan=np.nan # represents impossible actions\\n\",\n \"T = np.array([ # shape=[s, a, s']\\n\",\n \" [[0.7, 0.3, 0.0], [1.0, 0.0, 0.0], [0.8, 0.2, 0.0]],\\n\",\n \" [[0.0, 1.0, 0.0], [nan, nan, nan], [0.0, 0.0, 1.0]],\\n\",\n \" [[nan, nan, nan], [0.8, 0.1, 0.1], [nan, nan, nan]],\\n\",\n \" ])\\n\",\n \"\\n\",\n \"R = np.array([ # shape=[s, a, s']\\n\",\n \" [[10., 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],\\n\",\n \" [[10., 0.0, 0.0], [nan, nan, nan], [0.0, 0.0, -50.]],\\n\",\n \" [[nan, nan, nan], [40., 0.0, 0.0], [nan, nan, nan]],\\n\",\n \" ])\\n\",\n \"\\n\",\n \"possible_actions = [[0, 1, 2], [0, 2], [1]]\\n\",\n \"\\n\",\n \"# run Q-Value Iteration algo\\n\",\n \"\\n\",\n \"Q = np.full((3, 3), -np.inf)\\n\",\n \"for state, actions in enumerate(possible_actions):\\n\",\n \" Q[state, actions] = 0.0 # Initial value = 0.0, for all possible actions\\n\",\n \"\\n\",\n \"learning_rate = 0.01\\n\",\n \"discount_rate = 0.95\\n\",\n \"n_iterations = 100\\n\",\n \"\\n\",\n \"for iteration in range(n_iterations):\\n\",\n \" Q_prev = Q.copy()\\n\",\n \" for s in range(3):\\n\",\n \" for a in possible_actions[s]:\\n\",\n \" Q[s, a] = np.sum([\\n\",\n \" T[s, a, sp] * (R[s, a, sp] + discount_rate * np.max(Q_prev[sp]))\\n\",\n \" for sp in range(3)\\n\",\n \" ])\\n\",\n \" \\n\",\n \"print(\\\"Q: \\\\n\\\",Q)\\n\",\n \"print(\\\"Optimal action for each state:\\\\n\\\",np.argmax(Q, axis=1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Q: \\n\",\n \" [[ 1.89189499e+01 1.70270580e+01 1.36216526e+01]\\n\",\n \" [ 3.09979853e-05 -inf -4.87968388e+00]\\n\",\n \" [ -inf 5.01336811e+01 -inf]]\\n\",\n \"Optimal action for each state:\\n\",\n \" [0 0 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# change discount rate to 0.9, see how policy changes:\\n\",\n \"\\n\",\n \"discount_rate = 0.90\\n\",\n \"\\n\",\n \"for iteration in range(n_iterations):\\n\",\n \" Q_prev = Q.copy()\\n\",\n \" for s in range(3):\\n\",\n \" for a in possible_actions[s]:\\n\",\n \" Q[s, a] = np.sum([\\n\",\n \" T[s, a, sp] * (R[s, a, sp] + discount_rate * np.max(Q_prev[sp]))\\n\",\n \" for sp in range(3)\\n\",\n \" ])\\n\",\n \" \\n\",\n \"print(\\\"Q: \\\\n\\\",Q)\\n\",\n \"print(\\\"Optimal action for each state:\\\\n\\\",np.argmax(Q, axis=1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Temporal Difference Learning & Q-Learning\\n\",\n \"* In general - agent has no knowledge of transition probabilities or rewards\\n\",\n \"* **Temporal Difference Learning** (TD Learning) similar to value iteration, but accounts for this lack of knowlege.\\n\",\n \"* Algorithm tracks running average of most recent awards & anticipated rewards.\\n\",\n \"\\n\",\n \"* **Q-Learning** algorithm adaptation of Q-Value Iteration where initial transition probabilities & rewards are unknown.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Q: \\n\",\n \" [[ -inf 2.47032823e-323 -inf]\\n\",\n \" [ 0.00000000e+000 -inf 0.00000000e+000]\\n\",\n \" [ -inf 0.00000000e+000 -inf]]\\n\",\n \"Optimal action for each state:\\n\",\n \" [1 0 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy.random as rnd\\n\",\n \"\\n\",\n \"learning_rate0 = 0.05\\n\",\n \"learning_rate_decay = 0.1\\n\",\n \"n_iterations = 20000\\n\",\n \"\\n\",\n \"s = 0 # start in state 0\\n\",\n \"Q = np.full((3, 3), -np.inf) # -inf for impossible actions\\n\",\n \"\\n\",\n \"for state, actions in enumerate(possible_actions):\\n\",\n \" Q[state, actions] = 0.0 # Initial value = 0.0, for all possible actions\\n\",\n \" for iteration in range(n_iterations):\\n\",\n \" a = rnd.choice(possible_actions[s]) # choose an action (randomly)\\n\",\n \" sp = rnd.choice(range(3), p=T[s, a]) # pick next state using T[s, a]\\n\",\n \" reward = R[s, a, sp]\\n\",\n \" \\n\",\n \" learning_rate = learning_rate0 / (1 + iteration * learning_rate_decay)\\n\",\n \" \\n\",\n \" Q[s, a] = learning_rate * Q[s, a] + (1 - learning_rate) * (reward + discount_rate * np.max(Q[sp]))\\n\",\n \"\\n\",\n \"s = sp # move to next state\\n\",\n \"\\n\",\n \"print(\\\"Q: \\\\n\\\",Q)\\n\",\n \"print(\\\"Optimal action for each state:\\\\n\\\",np.argmax(Q, axis=1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Exploration Policies\\n\",\n \"* Q-Learning works only if exploration is thorough - not always possible.\\n\",\n \"* Better alternative: explore more interesting routes using a *sigma* probability\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Approximate Q-Learning\\n\",\n \"* TODO\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Ms Pac-Man with Deep Q-Learning\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2017-04-27 13:06:21,861] Making new env: MsPacman-v0\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"((210, 160, 3), Discrete(9))\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"env = gym.make('MsPacman-v0')\\n\",\n \"obs = env.reset()\\n\",\n \"obs.shape, env.action_space\\n\",\n \"\\n\",\n \"# action_space = 9 possible joystick actions\\n\",\n \"# observations = atari screenshots as 3D NumPy arrays\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"mspacman_color = np.array([210, 164, 74]).mean()\\n\",\n \"\\n\",\n \"# crop image, shrink to 88x80 pixels, convert to grayscale, improve contrast\\n\",\n \"\\n\",\n \"def preprocess_observation(obs):\\n\",\n \" img = obs[1:176:2, ::2] # crop and downsize\\n\",\n \" img = img.mean(axis=2) # to greyscale\\n\",\n \" img[img==mspacman_color] = 0 # improve contrast\\n\",\n \" img = (img - 128) / 128 - 1 # normalize from -1. to 1.\\n\",\n \" return img.reshape(88, 80, 1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Ms PacMan Observation | Deep-Q net\\n\",\n \"- | -\\n\",\n \"![observation](pics/mspacman-before-after.png) | ![alt](pics/mspacman-deepq.png)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Create DQN\\n\",\n \"# 3 convo layers, then 2 FC layers including output layer\\n\",\n \"\\n\",\n \"from tensorflow.contrib.layers import convolution2d, fully_connected\\n\",\n \"\\n\",\n \"input_height = 88\\n\",\n \"input_width = 80\\n\",\n \"input_channels = 1\\n\",\n \"conv_n_maps = [32, 64, 64]\\n\",\n \"conv_kernel_sizes = [(8,8), (4,4), (3,3)]\\n\",\n \"conv_strides = [4, 2, 1]\\n\",\n \"conv_paddings = [\\\"SAME\\\"]*3\\n\",\n \"conv_activation = [tf.nn.relu]*3\\n\",\n \"n_hidden_in = 64 * 11 * 10 # conv3 has 64 maps of 11x10 each\\n\",\n \"n_hidden = 512\\n\",\n \"hidden_activation = tf.nn.relu\\n\",\n \"n_outputs = env.action_space.n # 9 discrete actions are available\\n\",\n \"\\n\",\n \"initializer = tf.contrib.layers.variance_scaling_initializer()\\n\",\n \"\\n\",\n \"# training will need ***TWO*** DQNs:\\n\",\n \"# one to train the actor\\n\",\n \"# another to learn from trials & errors (critic)\\n\",\n \"# q_network is our net builder.\\n\",\n \"\\n\",\n \"def q_network(X_state, scope):\\n\",\n \" prev_layer = X_state\\n\",\n \" conv_layers = []\\n\",\n \"\\n\",\n \" with tf.variable_scope(scope) as scope:\\n\",\n \" \\n\",\n \" for n_maps, kernel_size, stride, padding, activation in zip(\\n\",\n \" conv_n_maps, \\n\",\n \" conv_kernel_sizes, \\n\",\n \" conv_strides,\\n\",\n \" conv_paddings, \\n\",\n \" conv_activation):\\n\",\n \" \\n\",\n \" prev_layer = convolution2d(\\n\",\n \" prev_layer, \\n\",\n \" num_outputs=n_maps, \\n\",\n \" kernel_size=kernel_size,\\n\",\n \" stride=stride, \\n\",\n \" padding=padding, \\n\",\n \" activation_fn=activation,\\n\",\n \" weights_initializer=initializer)\\n\",\n \" \\n\",\n \" conv_layers.append(prev_layer)\\n\",\n \"\\n\",\n \" last_conv_layer_flat = tf.reshape(\\n\",\n \" prev_layer, \\n\",\n \" shape=[-1, n_hidden_in])\\n\",\n \" \\n\",\n \" hidden = fully_connected(\\n\",\n \" last_conv_layer_flat, \\n\",\n \" n_hidden, \\n\",\n \" activation_fn=hidden_activation,\\n\",\n \" weights_initializer=initializer)\\n\",\n \" \\n\",\n \" outputs = fully_connected(\\n\",\n \" hidden, \\n\",\n \" n_outputs, \\n\",\n \" activation_fn=None,\\n\",\n \" weights_initializer=initializer)\\n\",\n \" \\n\",\n \" trainable_vars = tf.get_collection(\\n\",\n \" tf.GraphKeys.TRAINABLE_VARIABLES,\\n\",\n \" scope=scope.name)\\n\",\n \" \\n\",\n \" trainable_vars_by_name = {var.name[len(scope.name):]: var\\n\",\n \" for var in trainable_vars}\\n\",\n \"\\n\",\n \" return outputs, trainable_vars_by_name\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create input placeholders & two DQNs\\n\",\n \"\\n\",\n \"X_state = tf.placeholder(\\n\",\n \" tf.float32, \\n\",\n \" shape=[None, input_height, input_width,\\n\",\n \" input_channels])\\n\",\n \"\\n\",\n \"actor_q_values, actor_vars = q_network(X_state, scope=\\\"q_networks/actor\\\")\\n\",\n \"critic_q_values, critic_vars = q_network(X_state, scope=\\\"q_networks/critic\\\")\\n\",\n \"\\n\",\n \"copy_ops = [actor_var.assign(critic_vars[var_name])\\n\",\n \" for var_name, actor_var in actor_vars.items()]\\n\",\n \"\\n\",\n \"\\n\",\n \"# op to copy all trainable vars of critic DQN to actor DQN...\\n\",\n \"# use tf.group() to group all assignment ops together\\n\",\n \"\\n\",\n \"copy_critic_to_actor = tf.group(*copy_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Critic DQN learns by matching Q-Value predictions \\n\",\n \"# to actor's Q-Value estimations during game play\\n\",\n \"\\n\",\n \"# Actor will use a \\\"replay memory\\\" (5 tuples):\\n\",\n \"# state, action, next-state, reward, (0=over/1=continue)\\n\",\n \"\\n\",\n \"# use normal supervised training ops\\n\",\n \"# occasionally copy critic DQN to actor DQN\\n\",\n \"\\n\",\n \"# DQN normally returns one Q-Value for every poss. action\\n\",\n \"# only need Q-Value of action actually chosen\\n\",\n \"# So, convert action to one-hot vector [0...1...0], multiple by Q-values\\n\",\n \"# then sum over 1st axis.\\n\",\n \"\\n\",\n \"X_action = tf.placeholder(\\n\",\n \" tf.int32, shape=[None])\\n\",\n \"\\n\",\n \"q_value = tf.reduce_sum(\\n\",\n \" critic_q_values * tf.one_hot(X_action, n_outputs),\\n\",\n \" axis=1, keep_dims=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"Tensor(\\\"Sum_1:0\\\", shape=(?, 1), dtype=float32) must be from the same graph as Tensor(\\\"Placeholder:0\\\", shape=(?, 1), dtype=float32).\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 7\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 8\\u001b[0m cost = tf.reduce_mean(\\n\\u001b[0;32m----> 9\\u001b[0;31m tf.square(y - q_value))\\n\\u001b[0m\\u001b[1;32m 10\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 11\\u001b[0m \\u001b[0;31m# non-trainable. minimize() op will manage incrementing it\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py\\u001b[0m in \\u001b[0;36mbinary_op_wrapper\\u001b[0;34m(x, y)\\u001b[0m\\n\\u001b[1;32m 879\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 880\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mbinary_op_wrapper\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 881\\u001b[0;31m \\u001b[0;32mwith\\u001b[0m \\u001b[0mops\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mname_scope\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mop_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mas\\u001b[0m \\u001b[0mname\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 882\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0misinstance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0my\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0msparse_tensor\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mSparseTensor\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 883\\u001b[0m \\u001b[0my\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mops\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mconvert_to_tensor\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0my\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdtype\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdtype\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mbase_dtype\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mname\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m\\\"y\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/contextlib.py\\u001b[0m in \\u001b[0;36m__enter__\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 57\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m__enter__\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 58\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 59\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mnext\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgen\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 60\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mStopIteration\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 61\\u001b[0m \\u001b[0;32mraise\\u001b[0m \\u001b[0mRuntimeError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"generator didn't yield\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mfrom\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\\u001b[0m in \\u001b[0;36mname_scope\\u001b[0;34m(name, default_name, values)\\u001b[0m\\n\\u001b[1;32m 4217\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mvalues\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4218\\u001b[0m \\u001b[0mvalues\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 4219\\u001b[0;31m \\u001b[0mg\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0m_get_graph_from_inputs\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mvalues\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 4220\\u001b[0m \\u001b[0;32mwith\\u001b[0m \\u001b[0mg\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mas_default\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mg\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mname_scope\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mn\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mas\\u001b[0m \\u001b[0mscope\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4221\\u001b[0m \\u001b[0;32myield\\u001b[0m \\u001b[0mscope\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\\u001b[0m in \\u001b[0;36m_get_graph_from_inputs\\u001b[0;34m(op_input_list, graph)\\u001b[0m\\n\\u001b[1;32m 3966\\u001b[0m \\u001b[0mgraph\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgraph_element\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgraph\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3967\\u001b[0m \\u001b[0;32melif\\u001b[0m \\u001b[0moriginal_graph_element\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 3968\\u001b[0;31m \\u001b[0m_assert_same_graph\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moriginal_graph_element\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mgraph_element\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3969\\u001b[0m \\u001b[0;32melif\\u001b[0m \\u001b[0mgraph_element\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgraph\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mgraph\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3970\\u001b[0m raise ValueError(\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\\u001b[0m in \\u001b[0;36m_assert_same_graph\\u001b[0;34m(original_item, item)\\u001b[0m\\n\\u001b[1;32m 3905\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0moriginal_item\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgraph\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mitem\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgraph\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3906\\u001b[0m raise ValueError(\\n\\u001b[0;32m-> 3907\\u001b[0;31m \\\"%s must be from the same graph as %s.\\\" % (item, original_item))\\n\\u001b[0m\\u001b[1;32m 3908\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3909\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mValueError\\u001b[0m: Tensor(\\\"Sum_1:0\\\", shape=(?, 1), dtype=float32) must be from the same graph as Tensor(\\\"Placeholder:0\\\", shape=(?, 1), dtype=float32).\"\n ]\n }\n ],\n \"source\": [\n \"# training setup\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"y = tf.placeholder(\\n\",\n \" tf.float32, shape=[None, 1])\\n\",\n \"\\n\",\n \"cost = tf.reduce_mean(\\n\",\n \" tf.square(y - q_value))\\n\",\n \"\\n\",\n \"# non-trainable. minimize() op will manage incrementing it\\n\",\n \"global_step = tf.Variable(\\n\",\n \" 0, \\n\",\n \" trainable=False, \\n\",\n \" name='global_step')\\n\",\n \"\\n\",\n \"optimizer = tf.train.AdamOptimizer(learning_rate)\\n\",\n \"\\n\",\n \"training_op = optimizer.minimize(cost, global_step=global_step)\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\\n\",\n \"\\n\",\n \"saver = tf.train.Saver()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# use a deque list to build the replay memory\\n\",\n \"\\n\",\n \"from collections import deque\\n\",\n \"\\n\",\n \"replay_memory_size = 10000\\n\",\n \"replay_memory = deque(\\n\",\n \" [], maxlen=replay_memory_size)\\n\",\n \"\\n\",\n \"def sample_memories(batch_size):\\n\",\n \" indices = rnd.permutation(\\n\",\n \" len(replay_memory))[:batch_size]\\n\",\n \" cols = [[], [], [], [], []] # state, action, reward, next_state, continue\\n\",\n \"\\n\",\n \" for idx in indices:\\n\",\n \" memory = replay_memory[idx]\\n\",\n \" for col, value in zip(cols, memory):\\n\",\n \" col.append(value)\\n\",\n \"\\n\",\n \" cols = [np.array(col) for col in cols]\\n\",\n \" return (cols[0], cols[1], cols[2].reshape(-1, 1), cols[3], cols[4].reshape(-1, 1))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create an actor\\n\",\n \"# use epsilon-greedy policy\\n\",\n \"# gradually decrease epsilon from 1.0 to 0.05 across 50K training steps\\n\",\n \"\\n\",\n \"eps_min = 0.05\\n\",\n \"eps_max = 1.0\\n\",\n \"eps_decay_steps = 50000\\n\",\n \"\\n\",\n \"def epsilon_greedy(q_values, step):\\n\",\n \" epsilon = max(eps_min, eps_max - (eps_max-eps_min) * step/eps_decay_steps)\\n\",\n \" if rnd.rand() < epsilon:\\n\",\n \" return rnd.randint(n_outputs) # random action\\n\",\n \" else:\\n\",\n \" return np.argmax(q_values) # optimal action\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# training setup: the variables\\n\",\n \"\\n\",\n \"n_steps = 100000 # total number of training steps\\n\",\n \"training_start = 1000 # start training after 1,000 game iterations\\n\",\n \"training_interval = 3 # run a training step every 3 game iterations\\n\",\n \"save_steps = 50 # save the model every 50 training steps\\n\",\n \"copy_steps = 25 # copy the critic to the actor every 25 training steps\\n\",\n \"discount_rate = 0.95\\n\",\n \"skip_start = 90 # skip the start of every game (it's just waiting time)\\n\",\n \"batch_size = 50\\n\",\n \"iteration = 0 # game iterations\\n\",\n \"checkpoint_path = \\\"./my_dqn.ckpt\\\"\\n\",\n \"done = True # env needs to be reset\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \" 1.09000234097\\n\",\n \"\\n\",\n \" 1.35392784142\\n\",\n \"\\n\",\n \" 1.56906713688\\n\",\n \"\\n\",\n \" 2.5765440191\\n\",\n \"\\n\",\n \" 1.57079289043\\n\",\n \"\\n\",\n \" 1.75170834792\\n\",\n \"\\n\",\n \" 1.97005553639\\n\",\n \"\\n\",\n \" 1.97246688247\\n\",\n \"\\n\",\n \" 2.16126081383\\n\",\n \"\\n\",\n \" 1.550295331\\n\",\n \"\\n\",\n \" 1.75750140131\\n\",\n \"\\n\",\n \" 1.56052656734\\n\",\n \"\\n\",\n \" 1.7519523176\\n\",\n \"\\n\",\n \" 1.74495741558\\n\",\n \"\\n\",\n \" 1.95223849511\\n\",\n \"\\n\",\n \" 1.35289915931\\n\",\n \"\\n\",\n \" 1.56913152564\\n\",\n \"\\n\",\n \" 2.96387254691\\n\",\n \"\\n\",\n \" 1.76067311585\\n\",\n \"\\n\",\n \" 1.35536773229\\n\",\n \"\\n\",\n \" 1.54768545294\\n\",\n \"\\n\",\n \" 1.53594982147\\n\",\n \"\\n\",\n \" 1.56104325151\\n\",\n \"\\n\",\n \" 1.96987313104\\n\",\n \"\\n\",\n \" 2.35546155441\\n\",\n \"\\n\",\n \" 1.5688166486\\n\",\n \"\\n\",\n \" 3.08286282682\\n\",\n \"\\n\",\n \" 3.28864161086\\n\",\n \"\\n\",\n \" 3.2878398273\\n\",\n \"\\n\",\n \" 3.09510449028\\n\",\n \"\\n\",\n \" 3.09807873964\\n\",\n \"\\n\",\n \" 3.90697311211\\n\",\n \"\\n\",\n \" 3.07757974195\\n\",\n \"\\n\",\n \" 3.09214673901\\n\",\n \"\\n\",\n \" 3.28402029777\\n\",\n \"\\n\",\n \" 3.28337000942\\n\",\n \"\\n\",\n \" 3.4255889504\\n\",\n \"\\n\",\n \" 3.49763186431\\n\",\n \"\\n\",\n \" 2.85764229989\\n\",\n \"\\n\",\n \" 3.04482784653\\n\",\n \"\\n\",\n \" 2.68228099513\\n\",\n \"\\n\",\n \" 3.28635532999\\n\",\n \"\\n\",\n \" 3.29647485089\\n\",\n \"\\n\",\n \" 3.07898310328\\n\",\n \"\\n\",\n \" 3.10530596256\\n\",\n \"\\n\",\n \" 3.27691918874\\n\",\n \"\\n\",\n \" 3.09561720395\\n\",\n \"\\n\",\n \" 2.67830030346\\n\",\n \"\\n\",\n \" 3.09576807404\\n\",\n \"\\n\",\n \" 3.288335078\\n\",\n \"\\n\",\n \" 3.0956065948\\n\",\n \"\\n\",\n \" 5.21222548962\\n\",\n \"\\n\",\n \" 4.21721751595\\n\",\n \"\\n\",\n \" 4.7905973649\\n\",\n \"\\n\",\n \" 4.59864345837\\n\",\n \"\\n\",\n \" 4.39875211382\\n\",\n \"\\n\",\n \" 4.51839643717\\n\",\n \"\\n\",\n \" 4.59503188992\\n\",\n \"\\n\",\n \" 5.01186150789\\n\",\n \"\\n\",\n \" 4.77968219852\\n\",\n \"\\n\",\n \" 4.78787856865\\n\",\n \"\\n\",\n \" 4.20382899523\\n\",\n \"\\n\",\n \" 4.20432999897\\n\",\n \"\\n\",\n \" 5.0028930707\\n\",\n \"\\n\",\n \" 5.20069698572\\n\",\n \"\\n\",\n \" 4.80375980473\\n\",\n \"\\n\",\n \" 5.19750945711\\n\",\n \"\\n\",\n \" 4.20367767668\\n\",\n \"\\n\",\n \" 4.19593407536\\n\",\n \"\\n\",\n \" 4.40061367989\\n\",\n \"\\n\",\n \" 4.6054182477\\n\",\n \"\\n\",\n \" 4.79921974087\\n\",\n \"\\n\",\n \" 4.38844807434\\n\",\n \"\\n\",\n \" 4.20397897291\\n\",\n \"\\n\",\n \" 4.60095557356\\n\",\n \"\\n\",\n \" 4.59488785553\\n\",\n \"\\n\",\n \" 5.75924422598\\n\",\n \"\\n\",\n \" 5.75949315596\\n\",\n \"\\n\",\n \" 5.16320213652\\n\",\n \"\\n\",\n \" 5.36019721937\\n\",\n \"\\n\",\n \" 5.56076610899\\n\",\n \"\\n\",\n \" 5.16949163198\\n\",\n \"\\n\",\n \" 5.75895399189\\n\",\n \"\\n\",\n \" 5.96050115204\\n\",\n \"\\n\",\n \" 5.97032629395\\n\"\n ]\n },\n {\n \"ename\": \"KeyboardInterrupt\",\n \"evalue\": \"\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 46\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 47\\u001b[0m next_q_values = actor_q_values.eval(\\n\\u001b[0;32m---> 48\\u001b[0;31m feed_dict={X_state: X_next_state_val})\\n\\u001b[0m\\u001b[1;32m 49\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 50\\u001b[0m max_next_q_values = np.max(\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\\u001b[0m in \\u001b[0;36meval\\u001b[0;34m(self, feed_dict, session)\\u001b[0m\\n\\u001b[1;32m 579\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 580\\u001b[0m \\\"\\\"\\\"\\n\\u001b[0;32m--> 581\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0m_eval_using_default_session\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgraph\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0msession\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 582\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 583\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\\u001b[0m in \\u001b[0;36m_eval_using_default_session\\u001b[0;34m(tensors, feed_dict, graph, session)\\u001b[0m\\n\\u001b[1;32m 3795\\u001b[0m \\u001b[0;34m\\\"the tensor's graph is different from the session's \\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3796\\u001b[0m \\\"graph.\\\")\\n\\u001b[0;32m-> 3797\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0msession\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mrun\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3798\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3799\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36mrun\\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\\u001b[0m\\n\\u001b[1;32m 765\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 766\\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\\n\\u001b[0;32m--> 767\\u001b[0;31m run_metadata_ptr)\\n\\u001b[0m\\u001b[1;32m 768\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mrun_metadata\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 769\\u001b[0m \\u001b[0mproto_data\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtf_session\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mTF_GetBuffer\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrun_metadata_ptr\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36m_run\\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\\u001b[0m\\n\\u001b[1;32m 963\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mfinal_fetches\\u001b[0m \\u001b[0;32mor\\u001b[0m \\u001b[0mfinal_targets\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 964\\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\\n\\u001b[0;32m--> 965\\u001b[0;31m feed_dict_string, options, run_metadata)\\n\\u001b[0m\\u001b[1;32m 966\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 967\\u001b[0m \\u001b[0mresults\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36m_do_run\\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\\u001b[0m\\n\\u001b[1;32m 1013\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhandle\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1014\\u001b[0m return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\\n\\u001b[0;32m-> 1015\\u001b[0;31m target_list, options, run_metadata)\\n\\u001b[0m\\u001b[1;32m 1016\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1017\\u001b[0m return self._do_call(_prun_fn, self._session, handle, feed_dict,\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36m_do_call\\u001b[0;34m(self, fn, *args)\\u001b[0m\\n\\u001b[1;32m 1020\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m_do_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfn\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1021\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1022\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mfn\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1023\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0merrors\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mOpError\\u001b[0m \\u001b[0;32mas\\u001b[0m \\u001b[0me\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1024\\u001b[0m \\u001b[0mmessage\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcompat\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mas_text\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0me\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmessage\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/home/bjpcjp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36m_run_fn\\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\\u001b[0m\\n\\u001b[1;32m 1002\\u001b[0m return tf_session.TF_Run(session, options,\\n\\u001b[1;32m 1003\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfetch_list\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtarget_list\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1004\\u001b[0;31m status, run_metadata)\\n\\u001b[0m\\u001b[1;32m 1005\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1006\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m_prun_fn\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0msession\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mhandle\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfetch_list\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n ]\n }\n ],\n \"source\": [\n \"# let's get busy\\n\",\n \"import os\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" \\n\",\n \" # restore models if checkpoint file exists\\n\",\n \" if os.path.isfile(checkpoint_path):\\n\",\n \" saver.restore(sess, checkpoint_path)\\n\",\n \" \\n\",\n \" # otherwise normally initialize variables\\n\",\n \" else:\\n\",\n \" init.run()\\n\",\n \" \\n\",\n \" while True:\\n\",\n \" step = global_step.eval()\\n\",\n \" if step >= n_steps:\\n\",\n \" break\\n\",\n \"\\n\",\n \" # iteration = total number of game steps from beginning\\n\",\n \" \\n\",\n \" iteration += 1\\n\",\n \" if done: # game over, start again\\n\",\n \" obs = env.reset()\\n\",\n \"\\n\",\n \" for skip in range(skip_start): # skip the start of each game\\n\",\n \" obs, reward, done, info = env.step(0)\\n\",\n \" state = preprocess_observation(obs)\\n\",\n \"\\n\",\n \" # Actor evaluates what to do\\n\",\n \" q_values = actor_q_values.eval(feed_dict={X_state: [state]})\\n\",\n \" action = epsilon_greedy(q_values, step)\\n\",\n \"\\n\",\n \" # Actor plays\\n\",\n \" obs, reward, done, info = env.step(action)\\n\",\n \" next_state = preprocess_observation(obs)\\n\",\n \"\\n\",\n \" # Let's memorize what just happened\\n\",\n \" replay_memory.append((state, action, reward, next_state, 1.0 - done))\\n\",\n \" state = next_state\\n\",\n \" if iteration < training_start or iteration % training_interval != 0:\\n\",\n \" continue\\n\",\n \"\\n\",\n \" # Critic learns\\n\",\n \" X_state_val, X_action_val, rewards, X_next_state_val, continues = (\\n\",\n \" sample_memories(batch_size))\\n\",\n \"\\n\",\n \" next_q_values = actor_q_values.eval(\\n\",\n \" feed_dict={X_state: X_next_state_val})\\n\",\n \"\\n\",\n \" max_next_q_values = np.max(\\n\",\n \" next_q_values, axis=1, keepdims=True)\\n\",\n \"\\n\",\n \" y_val = rewards + continues * discount_rate * max_next_q_values\\n\",\n \"\\n\",\n \" training_op.run(\\n\",\n \" feed_dict={X_state: X_state_val, X_action: X_action_val, y: y_val})\\n\",\n \"\\n\",\n \" # Regularly copy critic to actor\\n\",\n \" if step % copy_steps == 0:\\n\",\n \" copy_critic_to_actor.run()\\n\",\n \"\\n\",\n \" # And save regularly\\n\",\n \" if step % save_steps == 0:\\n\",\n \" saver.save(sess, checkpoint_path)\\n\",\n \" \\n\",\n \" print(\\\"\\\\n\\\",np.average(y_val))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "index.md", "content": "## Welcome to GitHub Pages\n\nYou can use the [editor on GitHub](https://github.com/bjpcjp/scikit-and-tensorflow-workbooks/edit/master/index.md) to maintain and preview the content for your website in Markdown files.\n\nWhenever you commit to this repository, GitHub Pages will run [Jekyll](https://jekyllrb.com/) to rebuild the pages in your site, from the content in your Markdown files.\n\n### Markdown\n\nMarkdown is a lightweight and easy-to-use syntax for styling your writing. It includes conventions for\n\n```markdown\nSyntax highlighted code block\n\n# Header 1\n## Header 2\n### Header 3\n\n- Bulleted\n- List\n\n1. Numbered\n2. List\n\n**Bold** and _Italic_ and `Code` text\n\n[Link](url) and ![Image](src)\n```\n\nFor more details see [GitHub Flavored Markdown](https://guides.github.com/features/mastering-markdown/).\n\n### Jekyll Themes\n\nYour Pages site will use the layout and styles from the Jekyll theme you have selected in your [repository settings](https://github.com/bjpcjp/scikit-and-tensorflow-workbooks/settings). The name of this theme is saved in the Jekyll `_config.yml` configuration file.\n\n### Support or Contact\n\nHaving trouble with Pages? Check out our [documentation](https://help.github.com/categories/github-pages-basics/) or [contact support](https://github.com/contact) and we’ll help you sort it out.\n" } ]