\n", "1. Group A proceed w/o pre-processing datasets.\n", "2. Group B proceed w/ normalizing datasets into the range of [0, 1].\n", "3. Group C proceed w/ standardizing datasets by z-scoring (de-mean, uni-variance).\n", "\n", "[See results](#Observe-Training-Process)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def PreprocessDataset():\n", " from sklearn import preprocessing\n", " ## Load dataset\n", " (x_train, y_train), (x_test, y_test) = mnist.load_data()\n", " ## Transform labels to one-hot\n", " ## i.e., from '7' to [0,0,0,0,0,0,0,1,0,0]\n", " y_train = np_utils.to_categorical(y_train, 10)\n", " y_test = np_utils.to_categorical(y_test, 10)\n", " \n", " ## Process features. Set numeric type\n", " x_train = x_train.astype('float32')\n", " x_test = x_test.astype('float32')\n", " ## Reshape from a matrix of 28 x 28 pixels to 1-D vector of 784 dimensions\n", " x_train = np.reshape(x_train, (60000, 784))\n", " x_test = np.reshape(x_test, (10000, 784))\n", "\n", " ################################################################\n", " # Activity 1 (Pre-processing):\n", " # Group A: w/o pre-processing datasets.\n", " #\n", " # Group B: Min-Max Normalize value to [0, 1]\n", " # x_train /= 255\n", " # x_test /= 255\n", " #\n", " # Group C: proceed w/ standardizing datasets by z-scoring (de-mean, uni-variance).\n", " # x_train = preprocessing.scale(x_train)\n", " # x_test = preprocessing.scale(x_test)\n", " ################################################################ \n", " ## YOUR TURN: CHANGE HERE\n", " x_train /= 255\n", " x_test /= 255\n", "\n", " return x_train, x_test, y_train, y_test\n", "\n", "x_train, x_test, y_train, y_test = PreprocessDataset()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Show part of training data: features and labels\n", "## Each row is a sample, and each column represents a feature.\n", "print(\"{:^43}\".format(\"x\"), \"|\", \"{:^4}\".format(\"y\"))\n", "print(\"=\"*50)\n", "for sample_id in range(10):\n", " print(\"{:.2f} {:.2f} ... {:.2f} {:.2f} {:.2f} ... {:.2f} {:.2f}\".format(\n", " x_train[sample_id][0], x_train[sample_id][1],\n", " x_train[sample_id][156], x_train[sample_id][157], x_train[sample_id][158],\n", " x_train[sample_id][-2], x_train[sample_id][-1]), \"| \",\n", " \"{:.0f}\".format(y_train[sample_id][0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Recipes for Neural Nets\n", "\n", "In this section you will play with useful recipes for designing a neural net.\n", "### Tutorial/workshop activity 2: Network Structure\n", "\n", "Each group uses different types of ingredients:
\n", "1. Group A uses **1** layer.\n", "2. Group B uses **2** layers of a **tower-shaped** (same width) network. \n", "3. Group C uses **2** layers of a **pyramid-shaped** (shrink width) network.\n", "\n", "[See results](#Observe-Training-Process)\n", "\n", "### Tutorial/workshop activity 3: Activation Function\n", "\n", "Each group uses different types of ingredients:\n", "1. Group A uses Relu.\n", "2. Group B uses Sigmoid.\n", "3. Group C uses Tanh.\n", "\n", "[See results](#Observe-Training-Process)\n", "\n", "### Tutorial/workshop activity 4: Loss Function\n", "\n", "Each group uses different types of ingredients:\n", "1. Group A uses cross entropy.\n", "2. Group B uses cross entropy.\n", "3. Group C uses squared error.\n", "\n", "[See results](#Observe-Training-Process)\n", "\n", "### Tutorial/workshop activity 5: Dropout\n", "\n", "Each group uses different types of ingredients:\n", "1. Group A uses 0% dropout.\n", "2. Group B uses 50% dropout.\n", "3. Group C uses 90% dropout.\n", "\n", "[See results](#Observe-Training-Process)\n", "\n", "### Tutorial/workshop activity 6: Regularization\n", "\n", "Each group uses different types of ingredients:\n", "1. Group A uses L1-norm.\n", "2. Group B uses L2-norm.\n", "3. Group C uses no regularization.\n", "\n", "[See results](#Observe-Training-Process)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def DefineModel():\n", "\n", " ################################################################\n", " # Activity 2 (Network Structure):\n", " # Group A: uses only 1 layer\n", " # second_layer_width = 0\n", " #\n", " # Group B: uses 2 layers of a tower-shaped (same width) network.\n", " # second_layer_width = 128\n", " #\n", " # Group C: uses 2 layers of a pyramid-shaped (shrink width) network.\n", " # second_layer_width = 64\n", " ################################################################\n", " first_layer_width = 128\n", " second_layer_width = 64 \n", " \n", " ################################################################\n", " # Activity 3 (Activation Function):\n", " # Group A uses ReLU.\n", " # activation_func = 'relu' \n", " # \n", " # Group B uses Sigmoid.\n", " # activation_func = 'sigmoid'\n", " #\n", " # Group C uses Tanh.\n", " # activation_func = 'tanh'\n", " ################################################################\n", " activation_func = 'relu' \n", "\n", " ################################################################ \n", " # Activity 4 (Loss Function):\n", " # Group A uses cross entropy.\n", " # loss_function = 'categorical_crossentropy'\n", " # \n", " # Group B uses cross entropy.\n", " # loss_function = 'categorical_crossentropy'\n", " # \n", " # Group C uses squared error.\n", " # loss_function = 'mean_squared_error'\n", " ################################################################ \n", " loss_function = 'categorical_crossentropy'\n", " \n", " ################################################################# \n", " # Activity 5 (Dropout):\n", " # Group A uses 0% dropout.\n", " #\n", " # Group B uses 50% dropout.\n", " # dropout_rate = 0.5\n", " #\n", " # Group C uses 90% dropout.\n", " # dropout_rate = 0.9\n", " ################################################################# \n", " dropout_rate = 0.0\n", " \n", " ################################################################ \n", " # Activity 6 (Regularization):\n", " # Group A uses L1 regularizer\n", " # weight_regularizer = l1(0.01)\n", " #\n", " # Group B uses L2 regularizer\n", " # weight_regularizer = l2(0.01)\n", " # \n", " # Group C uses no regularizer\n", " # weight_regularizer = None\n", " ################################################################\n", " weight_regularizer = None\n", "\n", " ################################################################ \n", " # Activity 8 (Learning Rate):\n", " # Group A uses learning rate of 0.1.\n", " # learning_rate = 0.1\n", " # \n", " # Group B uses learning rate of 0.01.\n", " # learning_rate = 0.01\n", " #\n", " # Group C uses learning rate of 0.5. \n", " # learning_rate = 0.5\n", " ################################################################\n", " learning_rate = 0.1\n", " \n", " ## Initialize model.\n", " model = Sequential()\n", "\n", " ## First hidden layer with 'first_layer_width' neurons. \n", " ## Also need to specify input dimension.\n", " ## 'Dense' means fully-connected.\n", " model.add(Dense(first_layer_width, input_dim=784, W_regularizer=weight_regularizer))\n", " model.add(Activation(activation_func))\n", " if dropout_rate > 0:\n", " model.add(Dropout(0.5))\n", "\n", " ## Second hidden layer.\n", " if second_layer_width > 0:\n", " model.add(Dense(second_layer_width))\n", " model.add(Activation(activation_func))\n", " if dropout_rate > 0:\n", " model.add(Dropout(0.5)) \n", " \n", " ## Last layer has the same dimension as the number of classes\n", " model.add(Dense(10))\n", " ## For classification, the activation is softmax\n", " model.add(Activation('softmax'))\n", " ## Define optimizer. In this tutorial/codelab, we select SGD.\n", " ## You can also use other methods, e.g., opt = RMSprop()\n", " opt = SGD(lr=learning_rate, clipnorm=5.)\n", " ## Define loss function = 'categorical_crossentropy' or 'mean_squared_error'\n", " model.compile(loss=loss_function, optimizer=opt, metrics=[\"accuracy\"])\n", "\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Training Procedure\n", "### Tutorial/workshop activity 7: Mini-batch\n", "\n", "Each group uses different types of ingredients:\n", "1. Group A uses mini-batch of size 128.\n", "2. Group B uses mini-batch of size 256.\n", "3. Group C uses mini-batch of size 512.\n", "\n", "[See results](#Observe-Training-Process)\n", "\n", "### Tutorial/workshop activity 8: Learning Rate\n", "\n", "Each group uses different types of ingredients:\n", "1. Group A uses learning rate of 0.1.\n", "2. Group B uses learning rate of 0.01.\n", "3. Group C uses learning rate of 0.5.\n", "\n", "[See results](#Observe-Training-Process)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def TrainModel(data=None, epochs=20):\n", " ################################################################\n", " # Activity 7 (Mini-batch):\n", " # Group A uses mini-batch of size 128.\n", " # batch = 128\n", " #\n", " # Group B uses mini-batch of size 256.\n", " # batch = 256\n", " # \n", " # Group C uses mini-batch of size 512.\n", " # batch = 512\n", " ################################################################\n", " batch=128\n", " start_time = time.time()\n", " model = DefineModel()\n", " if data is None:\n", " print(\"Must provide data.\")\n", " return\n", " x_train, x_test, y_train, y_test = data\n", " print('Start training.')\n", " ## Use the first 55,000 (out of 60,000) samples to train, last 5,500 samples to validate.\n", " history = model.fit(x_train[:55000], y_train[:55000], nb_epoch=epochs, batch_size=batch,\n", " validation_data=(x_train[55000:], y_train[55000:]))\n", " print(\"Training took {0} seconds.\".format(time.time() - start_time))\n", " return model, history" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Start Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "trained_model, training_history = TrainModel(data=[x_train, x_test, y_train, y_test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Plotting" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def PlotHistory(train_value, test_value, value_is_loss_or_acc):\n", " f, ax = plt.subplots()\n", " ax.plot([None] + train_value, 'o-')\n", " ax.plot([None] + test_value, 'x-')\n", " ## Plot legend and use the best location automatically: loc = 0.\n", " ax.legend(['Train ' + value_is_loss_or_acc, 'Validation ' + value_is_loss_or_acc], loc = 0) \n", " ax.set_title('Training/Validation ' + value_is_loss_or_acc + ' per Epoch')\n", " ax.set_xlabel('Epoch')\n", " ax.set_ylabel(value_is_loss_or_acc) \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Observe Training Process" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PlotHistory(training_history.history['loss'], training_history.history['val_loss'], 'Loss')\n", "PlotHistory(training_history.history['acc'], training_history.history['val_acc'], 'Accuracy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Observe Regularization Effects\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def drawWeightHistogram(x):\n", " ## the histogram of the data\n", " fig = plt.subplots()\n", " n, bins, patches = plt.hist(x, 50)\n", " plt.xlim(-0.5, 0.5)\n", " plt.xlabel('Weight')\n", " plt.ylabel('Count')\n", " zero_counts = (x == 0.0).sum()\n", " plt.title(\"Weight Histogram. Num of '0's: %d\" % zero_counts)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "w1 = trained_model.layers[0].get_weights()[0].flatten()\n", "drawWeightHistogram(w1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Testing Procedure\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def TestModel(model=None, data=None):\n", " if model is None:\n", " print(\"Must provide a trained model.\")\n", " return\n", " if data is None:\n", " print(\"Must provide data.\")\n", " return\n", " x_test, y_test = data\n", " scores = model.evaluate(x_test, y_test)\n", " return scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Trained Model\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test_score = TestModel(model=trained_model, data=[x_test, y_test])\n", "print(\"Test loss {:.4f}, accuracy {:.2f}%\".format(test_score[0], test_score[1] * 100))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ShowInputImage(data):\n", " \"\"\"Visualize input image.\"\"\"\n", " plot = plt.figure()\n", " plot.set_size_inches(2,2)\n", " plt.imshow(np.reshape(-data, (28,28)), cmap='Greys_r')\n", " plt.title(\"Input\")\n", " plt.axis('off')\n", " plt.show()\n", " \n", "def ShowHiddenLayerOutput(input_data, target_layer_num):\n", " \"\"\"Visualize output from the target hidden layer.\"\"\"\n", " from keras import backend as K\n", " ## Backend converter: to TensorFlow\n", " target_layer = K.function(trained_model.inputs, [trained_model.layers[target_layer_num].output])\n", " ## Extract output from the target hidden layer.\n", " target_layer_out = target_layer([input_data])\n", " plot = plt.figure()\n", " plot.set_size_inches(2,2)\n", " plt.imshow(np.reshape(-target_layer_out[0][0], (16,-1)), cmap='Greys_r')\n", " plt.title(\"Hidden layer \" + str(target_layer_num))\n", " plt.axis('off')\n", " plt.show()\n", "\n", "def ShowFinalOutput(input_data):\n", " \"\"\"Calculate final prediction.\"\"\"\n", " from keras import backend as K\n", " ## Backend converter: to TensorFlow\n", " ## Calculate final prediction.\n", " last_layer = K.function(trained_model.inputs, [trained_model.layers[-1].output])\n", " last_layer_out = last_layer([input_data])\n", " print(\"Final prediction: \" + str(np.argmax(last_layer_out[0][0])) )\n", "\n", "ShowInputImage(x_test[0])\n", "ShowHiddenLayerOutput(x_test, 1)\n", "ShowFinalOutput(x_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: Quora_questions_similar/.gitignore ================================================ # Created by .ignore support plugin (hsz.mobi) *.npy *.json *.h5 *.csv ================================================ FILE: Quora_questions_similar/CNN_Question_pair.py ================================================ # coding: utf-8 # # Predicting Duplicate Questions # In[5]: import pandas as pd import numpy as np import nltk from nltk.corpus import stopwords from nltk.stem import SnowballStemmer import re from sklearn.metrics import accuracy_score import matplotlib.pyplot as plt import datetime, time, json from string import punctuation from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.models import Sequential from keras.layers import Embedding, Dense, Dropout, Reshape, Merge, BatchNormalization, TimeDistributed, Lambda, \ Activation, LSTM, Flatten, Convolution1D, GRU, MaxPooling1D from keras.regularizers import l2 from keras.callbacks import Callback, ModelCheckpoint, EarlyStopping from keras import initializers from keras import backend as K from keras.optimizers import SGD from collections import defaultdict # In[6]: train = pd.read_csv("train.csv")[:100] test = pd.read_csv("test.csv")[:100] # In[7]: train.head(6) # In[8]: test.head() # In[9]: print(train.shape) print(test.shape) # In[10]: # Check for any null values print(train.isnull().sum()) print(test.isnull().sum()) # In[11]: # Add the string 'empty' to empty strings train = train.fillna('empty') test = test.fillna('empty') # In[12]: print(train.isnull().sum()) print(test.isnull().sum()) # In[13]: # Preview some of the pairs of questions for i in range(6): print(train.question1[i]) print(train.question2[i]) print() # In[14]: stop_words = ['the', 'a', 'an', 'and', 'but', 'if', 'or', 'because', 'as', 'what', 'which', 'this', 'that', 'these', 'those', 'then', 'just', 'so', 'than', 'such', 'both', 'through', 'about', 'for', 'is', 'of', 'while', 'during', 'to', 'What', 'Which', 'Is', 'If', 'While', 'This'] # In[191]: def text_to_wordlist(text, remove_stop_words=True, stem_words=False): # Clean the text, with the option to remove stop_words and to stem words. # Convert words to lower case and split them # text = text.lower() # Clean the text text = re.sub(r"[^A-Za-z0-9]", " ", text) text = re.sub(r"what's", "", text) text = re.sub(r"What's", "", text) text = re.sub(r"\'s", " ", text) text = re.sub(r"\'ve", " have ", text) text = re.sub(r"can't", "cannot ", text) text = re.sub(r"n't", " not ", text) text = re.sub(r"I'm", "I am", text) text = re.sub(r" m ", " am ", text) text = re.sub(r"\'re", " are ", text) text = re.sub(r"\'d", " would ", text) text = re.sub(r"\'ll", " will ", text) text = re.sub(r"\0k ", "0000 ", text) text = re.sub(r" e g ", " eg ", text) text = re.sub(r" b g ", " bg ", text) text = re.sub(r"\0s", "0", text) text = re.sub(r" 9 11 ", "911", text) text = re.sub(r"e-mail", "email", text) text = re.sub(r"\s{2,}", " ", text) text = re.sub(r"quikly", "quickly", text) text = re.sub(r" usa ", " America ", text) text = re.sub(r" USA ", " America ", text) text = re.sub(r" u s ", " America ", text) text = re.sub(r" uk ", " England ", text) text = re.sub(r" UK ", " England ", text) text = re.sub(r"india", "India", text) text = re.sub(r"china", "China", text) text = re.sub(r"chinese", "Chinese", text) text = re.sub(r"imrovement", "improvement", text) text = re.sub(r"intially", "initially", text) text = re.sub(r"quora", "Quora", text) text = re.sub(r" dms ", "direct messages ", text) text = re.sub(r"demonitization", "demonetization", text) text = re.sub(r"actived", "active", text) text = re.sub(r"kms", " kilometers ", text) text = re.sub(r"KMs", " kilometers ", text) text = re.sub(r" cs ", " computer science ", text) text = re.sub(r" upvotes ", " up votes ", text) text = re.sub(r" iPhone ", " phone ", text) text = re.sub(r"\0rs ", " rs ", text) text = re.sub(r"calender", "calendar", text) text = re.sub(r"ios", "operating system", text) text = re.sub(r"gps", "GPS", text) text = re.sub(r"gst", "GST", text) text = re.sub(r"programing", "programming", text) text = re.sub(r"bestfriend", "best friend", text) text = re.sub(r"dna", "DNA", text) text = re.sub(r"III", "3", text) text = re.sub(r"the US", "America", text) text = re.sub(r"Astrology", "astrology", text) text = re.sub(r"Method", "method", text) text = re.sub(r"Find", "find", text) text = re.sub(r"banglore", "Banglore", text) text = re.sub(r" J K ", " JK ", text) # Remove punctuation from text text = ''.join([c for c in text if c not in punctuation]) # Optionally, remove stop words if remove_stop_words: text = text.split() text = [w for w in text if not w in stop_words] text = " ".join(text) # Optionally, shorten words to their stems if stem_words: text = text.split() stemmer = SnowballStemmer('english') stemmed_words = [stemmer.stem(word) for word in text] text = " ".join(stemmed_words) # Return a list of words return (text) # In[192]: def process_questions(question_list, questions, question_list_name, dataframe): '''transform questions and display progress''' for question in questions: question_list.append(text_to_wordlist(question)) if len(question_list) % 100000 == 0: progress = len(question_list) / len(dataframe) * 100 print("{} is {}% complete.".format(question_list_name, round(progress, 1))) # In[193]: train_question1 = [] process_questions(train_question1, train.question1, 'train_question1', train) # In[194]: train_question2 = [] process_questions(train_question2, train.question2, 'train_question2', train) # In[165]: test_question1 = [] process_questions(test_question1, test.question1, 'test_question1', test) # In[166]: test_question2 = [] process_questions(test_question2, test.question2, 'test_question2', test) # In[195]: # Preview some transformed pairs of questions i = 0 for i in range(i, i + 10): print(train_question1[i]) print(train_question2[i]) print() # In[168]: # Find the length of questions lengths = [] for question in train_question1: lengths.append(len(question.split())) for question in train_question2: lengths.append(len(question.split())) # Create a dataframe so that the values can be inspected lengths = pd.DataFrame(lengths, columns=['counts']) # In[169]: lengths.counts.describe() # In[170]: print(np.percentile(lengths.counts, 99.0)) print(np.percentile(lengths.counts, 99.4)) print(np.percentile(lengths.counts, 99.5)) print(np.percentile(lengths.counts, 99.9)) # In[171]: # tokenize the words for all of the questions all_questions = train_question1 + train_question2 + test_question1 + test_question2 tokenizer = Tokenizer() tokenizer.fit_on_texts(all_questions) print("Fitting is complete.") train_question1_word_sequences = tokenizer.texts_to_sequences(train_question1) print("train_question1 is complete.") train_question2_word_sequences = tokenizer.texts_to_sequences(train_question2) print("train_question2 is complete") # In[172]: test_question1_word_sequences = tokenizer.texts_to_sequences(test_question1) print("test_question1 is complete.") test_question2_word_sequences = tokenizer.texts_to_sequences(test_question2) print("test_question2 is complete.") # In[173]: word_index = tokenizer.word_index print("Words in index: %d" % len(word_index)) # In[174]: # Pad the questions so that they all have the same length. max_question_len = 36 train_q1 = pad_sequences(train_question1_word_sequences, maxlen=max_question_len) print("train_q1 is complete.") train_q2 = pad_sequences(train_question2_word_sequences, maxlen=max_question_len) print("train_q2 is complete.") # In[175]: test_q1 = pad_sequences(test_question1_word_sequences, maxlen=max_question_len, padding='post', truncating='post') print("test_q1 is complete.") test_q2 = pad_sequences(test_question2_word_sequences, maxlen=max_question_len, padding='post', truncating='post') print("test_q2 is complete.") # In[30]: y_train = train.is_duplicate # In[31]: # Load GloVe to use pretrained vectors # Note for Kaggle users: Uncomment this - it couldn't be used on Kaggle # From this link: https://nlp.stanford.edu/projects/glove/ # embeddings_index = {} # with open('glove.840B.300d.txt', encoding='utf-8') as f: # for line in f: # values = line.split(' ') # word = values[0] # embedding = np.asarray(values[1:], dtype='float32') # embeddings_index[word] = embedding # # print('Word embeddings:', len(embeddings_index)) #151,250 # In[176]: # Need to use 300 for embedding dimensions to match GloVe's vectors. embedding_dim = 300 # Note for Kaggle users: Uncomment this too, because it relate to the code for GloVe. nb_words = len(word_index) # word_embedding_matrix = np.zeros((nb_words + 1, embedding_dim)) # for word, i in word_index.items(): # embedding_vector = embeddings_index.get(word) # if embedding_vector is not None: # # words not found in embedding index will be all-zeros. # word_embedding_matrix[i] = embedding_vector # # print('Null word embeddings: %d' % np.sum(np.sum(word_embedding_matrix, axis=1) == 0)) #75,334 # In[177]: units = 128 # Number of nodes in the Dense layers dropout = 0.25 # Percentage of nodes to drop nb_filter = 32 # Number of filters to use in Convolution1D filter_length = 3 # Length of filter for Convolution1D # Initialize weights and biases for the Dense layers weights = initializers.TruncatedNormal(mean=0.0, stddev=0.05, seed=2) bias = bias_initializer = 'zeros' model1 = Sequential() model1.add(Embedding(nb_words + 1, embedding_dim, # weights = [word_embedding_matrix], Commented out for Kaggle input_length=max_question_len, trainable=False)) model1.add(Convolution1D(filters=nb_filter, kernel_size=filter_length, padding='same')) model1.add(BatchNormalization()) model1.add(Activation('relu')) model1.add(Dropout(dropout)) model1.add(Convolution1D(filters=nb_filter, kernel_size=filter_length, padding='same')) model1.add(BatchNormalization()) model1.add(Activation('relu')) model1.add(Dropout(dropout)) model1.add(Flatten()) model2 = Sequential() model2.add(Embedding(nb_words + 1, embedding_dim, # weights = [word_embedding_matrix], input_length=max_question_len, trainable=False)) model2.add(Convolution1D(filters=nb_filter, kernel_size=filter_length, padding='same')) model2.add(BatchNormalization()) model2.add(Activation('relu')) model2.add(Dropout(dropout)) model2.add(Convolution1D(filters=nb_filter, kernel_size=filter_length, padding='same')) model2.add(BatchNormalization()) model2.add(Activation('relu')) model2.add(Dropout(dropout)) model2.add(Flatten()) model3 = Sequential() model3.add(Embedding(nb_words + 1, embedding_dim, # weights = [word_embedding_matrix], input_length=max_question_len, trainable=False)) model3.add(TimeDistributed(Dense(embedding_dim))) model3.add(BatchNormalization()) model3.add(Activation('relu')) model3.add(Dropout(dropout)) model3.add(Lambda(lambda x: K.max(x, axis=1), output_shape=(embedding_dim,))) model4 = Sequential() model4.add(Embedding(nb_words + 1, embedding_dim, # weights = [word_embedding_matrix], input_length=max_question_len, trainable=False)) model4.add(TimeDistributed(Dense(embedding_dim))) model4.add(BatchNormalization()) model4.add(Activation('relu')) model4.add(Dropout(dropout)) model4.add(Lambda(lambda x: K.max(x, axis=1), output_shape=(embedding_dim,))) modela = Sequential() modela.add(Merge([model1, model2], mode='concat')) modela.add(Dense(units * 2, kernel_initializer=weights, bias_initializer=bias)) modela.add(BatchNormalization()) modela.add(Activation('relu')) modela.add(Dropout(dropout)) modela.add(Dense(units, kernel_initializer=weights, bias_initializer=bias)) modela.add(BatchNormalization()) modela.add(Activation('relu')) modela.add(Dropout(dropout)) modelb = Sequential() modelb.add(Merge([model3, model4], mode='concat')) modelb.add(Dense(units * 2, kernel_initializer=weights, bias_initializer=bias)) modelb.add(BatchNormalization()) modelb.add(Activation('relu')) modelb.add(Dropout(dropout)) modelb.add(Dense(units, kernel_initializer=weights, bias_initializer=bias)) modelb.add(BatchNormalization()) modelb.add(Activation('relu')) modelb.add(Dropout(dropout)) model = Sequential() model.add(Merge([modela, modelb], mode='concat')) model.add(Dense(units * 2, kernel_initializer=weights, bias_initializer=bias)) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dropout(dropout)) model.add(Dense(units, kernel_initializer=weights, bias_initializer=bias)) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dropout(dropout)) model.add(Dense(units, kernel_initializer=weights, bias_initializer=bias)) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dropout(dropout)) model.add(Dense(1, kernel_initializer=weights, bias_initializer=bias)) model.add(BatchNormalization()) model.add(Activation('sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # In[178]: # save the best weights for predicting the test question pairs save_best_weights = 'question_pairs_weights.h5' t0 = time.time() callbacks = [ModelCheckpoint(save_best_weights, monitor='val_loss', save_best_only=True), EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='auto')] history = model.fit([train_q1, train_q2, train_q1, train_q2], y_train, batch_size=256, epochs=2, # Use 100, I reduce it for Kaggle, validation_split=0.15, verbose=True, shuffle=True, callbacks=callbacks) t1 = time.time() print("Minutes elapsed: %f" % ((t1 - t0) / 60.)) # In[179]: # Aggregate the summary statistics summary_stats = pd.DataFrame({'epoch': [i + 1 for i in history.epoch], 'train_acc': history.history['acc'], 'valid_acc': history.history['val_acc'], 'train_loss': history.history['loss'], 'valid_loss': history.history['val_loss']}) # In[180]: summary_stats # In[181]: plt.plot(summary_stats.train_loss) # blue plt.plot(summary_stats.valid_loss) # green plt.show() # In[182]: # Find the minimum validation loss during the training min_loss, idx = min((loss, idx) for (idx, loss) in enumerate(history.history['val_loss'])) print('Minimum loss at epoch', '{:d}'.format(idx + 1), '=', '{:.4f}'.format(min_loss)) min_loss = round(min_loss, 4) # In[183]: # Make predictions with the best weights model.load_weights(save_best_weights) predictions = model.predict([test_q1, test_q2, test_q1, test_q2], verbose=True) # In[184]: # Create submission submission = pd.DataFrame(predictions, columns=['is_duplicate']) submission.insert(0, 'test_id', test.test_id) file_name = 'submission_{}.csv'.format(min_loss) submission.to_csv(file_name, index=False) # In[185]: submission.head(10) ================================================ FILE: Quora_questions_similar/README.md ================================================ **Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)* - [Reference](#reference) # Reference https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs https://engineering.quora.com/Semantic-Question-Matching-with-Deep-Learning https://github.com/bradleypallen/keras-quora-question-pairs ================================================ FILE: Quora_questions_similar/lstm_embedding.py ================================================ ''' Single model may achieve LB scores at around 0.29+ ~ 0.30+ Average ensembles can easily get 0.28+ or less Don't need to be an expert of feature engineering All you need is a GPU!!!!!!! The code is tested on Keras 2.0.0 using Tensorflow backend, and Python 2.7 According to experiments by kagglers, Theano backend with GPU may give bad LB scores while the val_loss seems to be fine, so try Tensorflow backend first please ''' ######################################## ## import packages ######################################## import os import re import csv import codecs import numpy as np import pandas as pd from nltk.corpus import stopwords from nltk.stem import SnowballStemmer from string import punctuation from gensim.models import KeyedVectors from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation from keras.layers.merge import concatenate from keras.models import Model from keras.layers.normalization import BatchNormalization from keras.callbacks import EarlyStopping, ModelCheckpoint import sys reload(sys) sys.setdefaultencoding('utf-8') ######################################## ## set directories and parameters ######################################## BASE_DIR = '/Users/ruizhang/Documents/NLP_dataset/quora' GoolgeNews = '/Users/ruizhang/Documents/NLP_dataset' EMBEDDING_FILE = GoolgeNews + 'GoogleNews-vectors-negative300.bin' TRAIN_DATA_FILE = BASE_DIR + 'train.csv' TEST_DATA_FILE = BASE_DIR + 'test.csv' MAX_SEQUENCE_LENGTH = 30 MAX_NB_WORDS = 200000 EMBEDDING_DIM = 300 VALIDATION_SPLIT = 0.1 num_lstm = np.random.randint(175, 275) num_dense = np.random.randint(100, 150) rate_drop_lstm = 0.15 + np.random.rand() * 0.25 rate_drop_dense = 0.15 + np.random.rand() * 0.25 act = 'relu' re_weight = True # whether to re-weight classes to fit the 17.5% share in test set STAMP = 'lstm_%d_%d_%.2f_%.2f' % (num_lstm, num_dense, rate_drop_lstm, \ rate_drop_dense) ######################################## ## index word vectors ######################################## print('Indexing word vectors') word2vec = KeyedVectors.load_word2vec_format(EMBEDDING_FILE, \ binary=True) print('Found %s word vectors of word2vec' % len(word2vec.vocab)) ######################################## ## process texts in datasets ######################################## print('Processing text dataset') # The function "text_to_wordlist" is from # https://www.kaggle.com/currie32/quora-question-pairs/the-importance-of-cleaning-text def text_to_wordlist(text, remove_stopwords=False, stem_words=False): # Clean the text, with the option to remove stopwords and to stem words. # Convert words to lower case and split them text = text.lower().split() # Optionally, remove stop words if remove_stopwords: stops = set(stopwords.words("english")) text = [w for w in text if not w in stops] text = " ".join(text) # Clean the text text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ", text) text = re.sub(r"what's", "what is ", text) text = re.sub(r"\'s", " ", text) text = re.sub(r"\'ve", " have ", text) text = re.sub(r"can't", "cannot ", text) text = re.sub(r"n't", " not ", text) text = re.sub(r"i'm", "i am ", text) text = re.sub(r"\'re", " are ", text) text = re.sub(r"\'d", " would ", text) text = re.sub(r"\'ll", " will ", text) text = re.sub(r",", " ", text) text = re.sub(r"\.", " ", text) text = re.sub(r"!", " ! ", text) text = re.sub(r"\/", " ", text) text = re.sub(r"\^", " ^ ", text) text = re.sub(r"\+", " + ", text) text = re.sub(r"\-", " - ", text) text = re.sub(r"\=", " = ", text) text = re.sub(r"'", " ", text) text = re.sub(r"(\d+)(k)", r"\g<1>000", text) text = re.sub(r":", " : ", text) text = re.sub(r" e g ", " eg ", text) text = re.sub(r" b g ", " bg ", text) text = re.sub(r" u s ", " american ", text) text = re.sub(r"\0s", "0", text) text = re.sub(r" 9 11 ", "911", text) text = re.sub(r"e - mail", "email", text) text = re.sub(r"j k", "jk", text) text = re.sub(r"\s{2,}", " ", text) # Optionally, shorten words to their stems if stem_words: text = text.split() stemmer = SnowballStemmer('english') stemmed_words = [stemmer.stem(word) for word in text] text = " ".join(stemmed_words) # Return a list of words return (text) texts_1 = [] texts_2 = [] labels = [] with codecs.open(TRAIN_DATA_FILE, encoding='utf-8') as f: reader = csv.reader(f, delimiter=',') header = next(reader) for values in reader: texts_1.append(text_to_wordlist(values[3])) texts_2.append(text_to_wordlist(values[4])) labels.append(int(values[5])) print('Found %s texts in train.csv' % len(texts_1)) test_texts_1 = [] test_texts_2 = [] test_ids = [] with codecs.open(TEST_DATA_FILE, encoding='utf-8') as f: reader = csv.reader(f, delimiter=',') header = next(reader) for values in reader: test_texts_1.append(text_to_wordlist(values[1])) test_texts_2.append(text_to_wordlist(values[2])) test_ids.append(values[0]) print('Found %s texts in test.csv' % len(test_texts_1)) tokenizer = Tokenizer(num_words=MAX_NB_WORDS) tokenizer.fit_on_texts(texts_1 + texts_2 + test_texts_1 + test_texts_2) sequences_1 = tokenizer.texts_to_sequences(texts_1) sequences_2 = tokenizer.texts_to_sequences(texts_2) test_sequences_1 = tokenizer.texts_to_sequences(test_texts_1) test_sequences_2 = tokenizer.texts_to_sequences(test_texts_2) word_index = tokenizer.word_index print('Found %s unique tokens' % len(word_index)) data_1 = pad_sequences(sequences_1, maxlen=MAX_SEQUENCE_LENGTH) data_2 = pad_sequences(sequences_2, maxlen=MAX_SEQUENCE_LENGTH) labels = np.array(labels) print('Shape of data tensor:', data_1.shape) print('Shape of label tensor:', labels.shape) test_data_1 = pad_sequences(test_sequences_1, maxlen=MAX_SEQUENCE_LENGTH) test_data_2 = pad_sequences(test_sequences_2, maxlen=MAX_SEQUENCE_LENGTH) test_ids = np.array(test_ids) ######################################## ## prepare embeddings ######################################## print('Preparing embedding matrix') nb_words = min(MAX_NB_WORDS, len(word_index)) + 1 embedding_matrix = np.zeros((nb_words, EMBEDDING_DIM)) for word, i in word_index.items(): if word in word2vec.vocab: embedding_matrix[i] = word2vec.word_vec(word) print('Null word embeddings: %d' % np.sum(np.sum(embedding_matrix, axis=1) == 0)) ######################################## ## sample train/validation data ######################################## # np.random.seed(1234) perm = np.random.permutation(len(data_1)) idx_train = perm[:int(len(data_1) * (1 - VALIDATION_SPLIT))] idx_val = perm[int(len(data_1) * (1 - VALIDATION_SPLIT)):] data_1_train = np.vstack((data_1[idx_train], data_2[idx_train])) data_2_train = np.vstack((data_2[idx_train], data_1[idx_train])) labels_train = np.concatenate((labels[idx_train], labels[idx_train])) data_1_val = np.vstack((data_1[idx_val], data_2[idx_val])) data_2_val = np.vstack((data_2[idx_val], data_1[idx_val])) labels_val = np.concatenate((labels[idx_val], labels[idx_val])) weight_val = np.ones(len(labels_val)) if re_weight: weight_val *= 0.472001959 weight_val[labels_val == 0] = 1.309028344 ######################################## ## define the model structure ######################################## embedding_layer = Embedding(nb_words, EMBEDDING_DIM, weights=[embedding_matrix], input_length=MAX_SEQUENCE_LENGTH, trainable=False) lstm_layer = LSTM(num_lstm, dropout=rate_drop_lstm, recurrent_dropout=rate_drop_lstm) sequence_1_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') embedded_sequences_1 = embedding_layer(sequence_1_input) x1 = lstm_layer(embedded_sequences_1) sequence_2_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') embedded_sequences_2 = embedding_layer(sequence_2_input) y1 = lstm_layer(embedded_sequences_2) merged = concatenate([x1, y1]) merged = Dropout(rate_drop_dense)(merged) merged = BatchNormalization()(merged) merged = Dense(num_dense, activation=act)(merged) merged = Dropout(rate_drop_dense)(merged) merged = BatchNormalization()(merged) preds = Dense(1, activation='sigmoid')(merged) ######################################## ## add class weight ######################################## if re_weight: class_weight = {0: 1.309028344, 1: 0.472001959} else: class_weight = None ######################################## ## train the model ######################################## model = Model(inputs=[sequence_1_input, sequence_2_input], \ outputs=preds) model.compile(loss='binary_crossentropy', optimizer='nadam', metrics=['acc']) # model.summary() print(STAMP) early_stopping = EarlyStopping(monitor='val_loss', patience=3) bst_model_path = STAMP + '.h5' model_checkpoint = ModelCheckpoint(bst_model_path, save_best_only=True, save_weights_only=True) hist = model.fit([data_1_train, data_2_train], labels_train, \ validation_data=([data_1_val, data_2_val], labels_val, weight_val), \ epochs=200, batch_size=2048, shuffle=True, \ class_weight=class_weight, callbacks=[early_stopping, model_checkpoint]) model.load_weights(bst_model_path) bst_val_score = min(hist.history['val_loss']) ######################################## ## make the submission ######################################## print('Start making the submission before fine-tuning') preds = model.predict([test_data_1, test_data_2], batch_size=8192, verbose=1) preds += model.predict([test_data_2, test_data_1], batch_size=8192, verbose=1) preds /= 2 submission = pd.DataFrame({'test_id':test_ids, 'is_duplicate':preds.ravel()}) submission.to_csv('%.4f_'%(bst_val_score)+STAMP+'.csv', index=False) ================================================ FILE: Quora_questions_similar/similar_questions.py ================================================ from __future__ import print_function import numpy as np import csv, datetime, time, json from zipfile import ZipFile from os.path import expanduser, exists from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.models import Model from keras.layers import Input, TimeDistributed, Dense, Lambda, concatenate,Dropout, BatchNormalization from keras.layers.embeddings import Embedding from keras.regularizers import l2 from keras.callbacks import Callback, ModelCheckpoint from keras.utils.data_utils import get_file from keras import backend as K from sklearn.model_selection import train_test_split # Initialize global variables KERAS_DATASETS_DIR = expanduser('~/.keras/datasets/') QUESTION_PAIRS_FILE_URL = 'http://qim.ec.quoracdn.net/quora_duplicate_questions.tsv' QUESTION_PAIRS_FILE = 'quora_duplicate_questions.tsv' GLOVE_ZIP_FILE_PATH = '/Users/ruizhang/Documents/NLP_dataset/' GLOVE_ZIP_FILE = 'glove.840B.300d.zip' GLOVE_FILE = 'glove.840B.300d.txt' Q1_TRAINING_DATA_FILE = 'q1_train.npy' Q2_TRAINING_DATA_FILE = 'q2_train.npy' LABEL_TRAINING_DATA_FILE = 'label_train.npy' WORD_EMBEDDING_MATRIX_FILE = 'word_embedding_matrix.npy' NB_WORDS_DATA_FILE = 'nb_words.json' MAX_NB_WORDS = 200000 MAX_SEQUENCE_LENGTH = 25 EMBEDDING_DIM = 300 MODEL_WEIGHTS_FILE = 'question_pairs_weights.h5' VALIDATION_SPLIT = 0.1 TEST_SPLIT = 0.1 RNG_SEED = 13371447 NB_EPOCHS = 5 DROPOUT = 0.1 BATCH_SIZE = 32 OPTIMIZER = 'adam' # If the dataset, embedding matrix and word count exist in the local directory if exists(Q1_TRAINING_DATA_FILE) and exists(Q2_TRAINING_DATA_FILE) and exists(LABEL_TRAINING_DATA_FILE) and exists( NB_WORDS_DATA_FILE) and exists(WORD_EMBEDDING_MATRIX_FILE): # Then load them q1_data = np.load(open(Q1_TRAINING_DATA_FILE, 'rb')) q2_data = np.load(open(Q2_TRAINING_DATA_FILE, 'rb')) labels = np.load(open(LABEL_TRAINING_DATA_FILE, 'rb')) word_embedding_matrix = np.load(open(WORD_EMBEDDING_MATRIX_FILE, 'rb')) with open(NB_WORDS_DATA_FILE, 'r') as f: nb_words = json.load(f)['nb_words'] else: # Else download and extract questions pairs data if not exists(KERAS_DATASETS_DIR + QUESTION_PAIRS_FILE): get_file(QUESTION_PAIRS_FILE, QUESTION_PAIRS_FILE_URL) print("Processing", QUESTION_PAIRS_FILE) question1 = [] question2 = [] is_duplicate = [] with open(KERAS_DATASETS_DIR + QUESTION_PAIRS_FILE, encoding='utf-8') as csvfile: reader = csv.DictReader(csvfile, delimiter='\t') for row in reader: question1.append(row['question1']) question2.append(row['question2']) is_duplicate.append(row['is_duplicate']) print('Question pairs: %d' % len(question1)) # Build tokenized word index questions = question1 + question2 tokenizer = Tokenizer(nb_words=MAX_NB_WORDS) tokenizer.fit_on_texts(questions) question1_word_sequences = tokenizer.texts_to_sequences(question1) question2_word_sequences = tokenizer.texts_to_sequences(question2) word_index = tokenizer.word_index print("Words in index: %d" % len(word_index)) # Download and process GloVe embeddings if not exists(KERAS_DATASETS_DIR + GLOVE_ZIP_FILE): zipfile = ZipFile(GLOVE_ZIP_FILE_PATH+GLOVE_ZIP_FILE) zipfile.extract(GLOVE_FILE, path=KERAS_DATASETS_DIR) print("Processing", GLOVE_FILE) embeddings_index = {} with open(KERAS_DATASETS_DIR + GLOVE_FILE, encoding='utf-8') as f: for line in f: values = line.split(' ') word = values[0] embedding = np.asarray(values[1:], dtype='float32') embeddings_index[word] = embedding print('Word embeddings: %d' % len(embeddings_index)) # Prepare word embedding matrix nb_words = min(MAX_NB_WORDS, len(word_index)) word_embedding_matrix = np.zeros((nb_words + 1, EMBEDDING_DIM)) for word, i in word_index.items(): if i > MAX_NB_WORDS: continue embedding_vector = embeddings_index.get(word) if embedding_vector is not None: word_embedding_matrix[i] = embedding_vector print('Null word embeddings: %d' % np.sum(np.sum(word_embedding_matrix, axis=1) == 0)) # Prepare training data tensors q1_data = pad_sequences(question1_word_sequences, maxlen=MAX_SEQUENCE_LENGTH) q2_data = pad_sequences(question2_word_sequences, maxlen=MAX_SEQUENCE_LENGTH) labels = np.array(is_duplicate, dtype=int) print('Shape of question1 data tensor:', q1_data.shape) print('Shape of question2 data tensor:', q2_data.shape) print('Shape of label tensor:', labels.shape) # Persist training and configuration data to files np.save(open(Q1_TRAINING_DATA_FILE, 'wb'), q1_data) np.save(open(Q2_TRAINING_DATA_FILE, 'wb'), q2_data) np.save(open(LABEL_TRAINING_DATA_FILE, 'wb'), labels) np.save(open(WORD_EMBEDDING_MATRIX_FILE, 'wb'), word_embedding_matrix) with open(NB_WORDS_DATA_FILE, 'w') as f: json.dump({'nb_words': nb_words}, f) # Partition the dataset into train and test sets X = np.stack((q1_data, q2_data), axis=1) y = labels X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SPLIT, random_state=RNG_SEED) Q1_train = X_train[:, 0] Q2_train = X_train[:, 1] Q1_test = X_test[:, 0] Q2_test = X_test[:, 1] # Define the model question1 = Input(shape=(MAX_SEQUENCE_LENGTH,)) question2 = Input(shape=(MAX_SEQUENCE_LENGTH,)) print(question1.shape) print(question2.shape) q1 = Embedding(nb_words + 1, EMBEDDING_DIM, weights=[word_embedding_matrix], input_length=MAX_SEQUENCE_LENGTH, trainable=False)(question1) print(word_embedding_matrix.shape) print(q1.shape) q1 = TimeDistributed(Dense(EMBEDDING_DIM, activation='relu'))(q1) q1 = Lambda(lambda x: K.max(x, axis=1), output_shape=(EMBEDDING_DIM,))(q1) q2 = Embedding(nb_words + 1, EMBEDDING_DIM, weights=[word_embedding_matrix], input_length=MAX_SEQUENCE_LENGTH, trainable=False)(question2) print(q2.shape) print(nb_words) q2 = TimeDistributed(Dense(EMBEDDING_DIM, activation='relu'))(q2) q2 = Lambda(lambda x: K.max(x, axis=1), output_shape=(EMBEDDING_DIM,))(q2) merged = concatenate([q1,q2]) print(merged.shape) merged = Dense(128, activation='relu')(merged) merged = Dropout(DROPOUT)(merged) merged = BatchNormalization()(merged) merged = Dense(64, activation='relu')(merged) merged = Dropout(DROPOUT)(merged) merged = BatchNormalization()(merged) # merged = Dense(200, activation='relu')(merged) # merged = Dropout(DROPOUT)(merged) # merged = BatchNormalization()(merged) # merged = Dense(200, activation='relu')(merged) # merged = Dropout(DROPOUT)(merged) # merged = BatchNormalization()(merged) is_duplicate = Dense(1, activation='sigmoid')(merged) model = Model(inputs=[question1, question2], outputs=is_duplicate) model.compile(loss='binary_crossentropy', optimizer=OPTIMIZER, metrics=['accuracy']) # Train the model, checkpointing weights with best validation accuracy print("Starting training at", datetime.datetime.now()) t0 = time.time() callbacks = [ModelCheckpoint(MODEL_WEIGHTS_FILE, monitor='val_acc', save_best_only=True)] history = model.fit([Q1_train, Q2_train], y_train, epochs=NB_EPOCHS, validation_split=VALIDATION_SPLIT, verbose=2, batch_size=BATCH_SIZE, callbacks=callbacks) t1 = time.time() print("Training ended at", datetime.datetime.now()) print("Minutes elapsed: %f" % ((t1 - t0) / 60.)) # Print best validation accuracy and epoch max_val_acc, idx = max((val, idx) for (idx, val) in enumerate(history.history['val_acc'])) print('Maximum validation accuracy = {0:.4f} (epoch {1:d})'.format(max_val_acc, idx + 1)) # Evaluate the model with best validation accuracy on the test partition model.load_weights(MODEL_WEIGHTS_FILE) loss, accuracy = model.evaluate([Q1_test, Q2_test], y_test, verbose=0) print('Test loss = {0:.4f}, test accuracy = {1:.4f}'.format(loss, accuracy)) ================================================ FILE: README.md ================================================ **Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)* - [Basic algorithm/Framework](#basic-algorithmframework) - [Basics Machine learning](#basics-machine-learning) - [CNN](#cnn) - [RNN](#rnn) - [TensorFlow](#tensorflow) - [Pytorch](#pytorch) - [GBDT](#gbdt) - [SVM](#svm) - [Applications](#applications) - [NLP](#nlp) - [Search/Rank/CTR](#searchrankctr) - [Text Classification](#text-classification) - [Recommendations](#recommendations) - [Industrial machine Learning Application design](#industrial-machine-learning-application-design) - [Machine learning implementation in large scale system](#machine-learning-implementation-in-large-scale-system) - [Deep Learning/AI Chip Design](#deep-learningai-chip-design) - [Reference Materails](#reference-materails) - [Kaggle](#kaggle) - [Book](#book) - [Tutorial](#tutorial) - [Courses](#courses) - [Workshop](#workshop) - [Blog](#blog) - [Blog Posts](#blog-posts) - [Code and Framework](#code-and-framework) - [Open Source](#open-source) # Basic algorithm/Framework ## Basics Machine learning [Basics Machine learning](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/Basics_Machine_learning.md) ## CNN [CNN](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/CNN.md) ## RNN [RNN](https://github.com/zhangruiskyline/DeepLearning/blob/master/doc/RNN.md) ## TensorFlow [TensorFlow and Keras](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/Tensorflow_Keras.md) ## Pytorch [Pytorch](https://github.com/zhangruiskyline/DeepLearning/blob/master/doc/pytorch.md) ## GBDT [Tree(decision_tree,xgboost)](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/decision_tree.md) ## SVM [SVM](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/SVM.md) # Applications ## NLP * [NLP](https://github.com/zhangruiskyline/DeepLearning/blob/master/doc/NLP.md) ## Search/Rank/CTR * [Search/Rank/CTR](https://github.com/zhangruiskyline/DeepLearning/blob/master/doc/Search.md) ## Text Classification * [Summary of Text Classification](https://github.com/zhangruiskyline/text_classification) * Convolutional Neural Networks for Sentence Classification. [Paper](https://www.cs.cmu.edu/%7Ediyiy/docs/naacl16.pdf) and [blog post](https://richliao.github.io/supervised/classification/2016/11/26/textclassifier-convolutional/) * Bidirectional LSTM and one level attentional RNN. [blog](https://richliao.github.io/supervised/classification/2016/12/26/textclassifier-RNN/) ## Recommendations * [Recommendation using DL](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/Recommendation.md) ## Industrial machine Learning Application design [Industrial machine Learning Application design](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/industrial_design.md) # Machine learning implementation in large scale system [Machine learning implementation in large scale system](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/system.md) ## Deep Learning/AI Chip Design [Deep Learning/AI Chip Design](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/AI_chip.md) # Reference Materails ## Kaggle * [Kaggle experience](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/Kaggle.md) ## Book * [Deep Learning: An MIT Press Book](http://www.deeplearningbook.org) * By Ian Goodfellow and Yoshua Bengio and Aaron Courville ## Tutorial * [Stanford Deep Learning Tutorial](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial) * [A Guide to Deep Learning](http://yerevann.com/a-guide-to-deep-learning/) * [Reinforcement Learning- OpenAI Gym](https://gym.openai.com/) * [Fast AI Tutorial](http://course.fast.ai) * [Introduction to Boosted Trees](http://xgboost.readthedocs.io/en/latest/model.html) ## Courses * [Stanford: CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu) * [CS231n Chinese Translation](https://zhuanlan.zhihu.com/p/21930884?refer=intelligentunit) * [Berkeley: CS 294: Deep Reinforcement Learning](http://rll.berkeley.edu/deeprlcourse/) * [MIT: Deep Learning Self Driving Car](http://selfdrivingcars.mit.edu/) * [Oxford: Deep Learning on NLP](https://github.com/oxford-cs-deepnlp-2017) * [UCL: Reinforcement learning (By D.Silver)](http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html) * [Stanford: CS224n: Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/) * [CMU:Scalable Machine Learning (By A. Smola)](http://alex.smola.org/teaching/berkeley2012/) ## Workshop * [Applied Deep Learning Workshop London 2017](https://github.com/telecombcn-dl/2017-persontyle) * [Deep Learning course: lecture slides and lab notebooks](https://github.com/m2dsupsdlclass/lectures-labs) ## Blog * [Simple Reinforcement Learning: Reinforcement Learning With Tensorflow Series(On Medium)](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.qyt0m7oro) * [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/) * [Word2Vec Resource (by Chris McCormick)](http://mccormickml.com/2016/04/27/word2vec-resources/#alex-minnaars-tutorials) * [AI, Deep Learning and NLP (By Denny Britz in Google Brain)](http://www.wildml.com) ### Blog Posts * [Understand CNN on NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) * [IMPLEMENTING A CNN FOR TEXT CLASSIFICATION IN TENSORFLOW](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/) * [Understanding RNN](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/) * [A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN](https://blog.athelas.com/a-brief-history-of-cnns-in-image-segmentation-from-r-cnn-to-mask-r-cnn-34ea83205de4) * [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) and [LSTM by Example using Tensorflow](https://medium.com/towards-data-science/lstm-by-example-using-tensorflow-feb0c1968537) ## Code and Framework * [LEARNING REINFORCEMENT LEARNING (WITH CODE, EXERCISES AND SOLUTIONS)](http://www.wildml.com/2016/10/learning-reinforcement-learning/) * [Github link](https://github.com/dennybritz/reinforcement-learning) ### Open Source * Prophet: forecasting at scale by Facebook Facebook is open sourcing [Prophet](https://research.fb.com/prophet-forecasting-at-scale/), a forecasting tool available some interesting project based on it 1. [Forecasting iPad sales using Facebook's Prophet package](https://github.com/natebrix/prophet-ipad) ================================================ FILE: TensorFlow_Examples/.gitignore ================================================ # random crap *~ *.swp \#*\# .\#* # python crap *.pyc __pycache__/ evaluations/*.pdf # Dataset files and folders ml-100k ml-100k.zip MNIST_data/ images_resize labs/04_conv_nets_2/VOCdevkit/ labs/04_conv_nets_2/VOCtrainval_06-Nov-2007.tar labs/04_conv_nets_2/voc_representaions.h5 labs/04_conv_nets_2/voc_representations.h5 glove100* simple_seq2seq_pretrained.h5 simple_seq2seq_partially_pretrained.h5 # generated data results tsne.png simple_seq2seq_checkpoint.h5 # editors crap .vscode .ipynb_checkpoints/ # tf summary folders tensorflow_summaries # VS Code jupyter temp files kernel-*.json tags installation_instructions.md ================================================ FILE: TensorFlow_Examples/LICENSE ================================================ MIT License Copyright (c) 2017 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: TensorFlow_Examples/_config.yml ================================================ theme: jekyll-theme-slate ================================================ FILE: TensorFlow_Examples/assets/css/style.scss ================================================ --- --- @import "{{ site.theme }}"; #header_wrap { display: none; } ================================================ FILE: TensorFlow_Examples/labs/02_recommendation/Explicit_Feedback_Neural_Recommender_System.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Explicit Feedback Neural Recommender Systems\n", "\n", "Goals:\n", "- Understand recommender data\n", "- Build different models architectures using Keras\n", "- Retrieve Embeddings and visualize them\n", "- Add metadata information as input to the model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os.path as op\n", "\n", "from zipfile import ZipFile\n", "try:\n", " from urllib.request import urlretrieve\n", "except ImportError: # Python 2 compat\n", " from urllib import urlretrieve\n", "\n", "\n", "ML_100K_URL = \"http://files.grouplens.org/datasets/movielens/ml-100k.zip\"\n", "ML_100K_FILENAME = ML_100K_URL.rsplit('/', 1)[1]\n", "ML_100K_FOLDER = 'ml-100k'\n", "\n", "if not op.exists(ML_100K_FILENAME):\n", " print('Downloading %s to %s...' % (ML_100K_URL, ML_100K_FILENAME))\n", " urlretrieve(ML_100K_URL, ML_100K_FILENAME)\n", "\n", "if not op.exists(ML_100K_FOLDER):\n", " print('Extracting %s to %s...' % (ML_100K_FILENAME, ML_100K_FOLDER))\n", " ZipFile(ML_100K_FILENAME).extractall('.')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Ratings file\n", "\n", "Each line contains a rated movie: \n", "- a user\n", "- an item\n", "- a rating from 1 to 5 stars" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(100000, 4)\n" ] }, { "data": { "text/html": [ "
\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" user_id item_id rating timestamp\n",
"0 196 242 3 881250949\n",
"1 186 302 3 891717742\n",
"2 22 377 1 878887116\n",
"3 244 51 2 880606923\n",
"4 166 346 1 886397596"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"all_ratings = pd.read_csv(op.join(ML_100K_FOLDER, 'u.data'), sep='\\t',\n",
" names=[\"user_id\", \"item_id\", \"rating\", \"timestamp\"])\n",
"print(all_ratings.shape)\n",
"all_ratings.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Item metadata file\n",
"\n",
"The item metadata file contains metadata like the name of the movie or the date it was released"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"| \n", " | user_id | \n", "item_id | \n", "rating | \n", "timestamp | \n", "
|---|---|---|---|---|
| 0 | \n", "196 | \n", "242 | \n", "3 | \n", "881250949 | \n", "
| 1 | \n", "186 | \n", "302 | \n", "3 | \n", "891717742 | \n", "
| 2 | \n", "22 | \n", "377 | \n", "1 | \n", "878887116 | \n", "
| 3 | \n", "244 | \n", "51 | \n", "2 | \n", "880606923 | \n", "
| 4 | \n", "166 | \n", "346 | \n", "1 | \n", "886397596 | \n", "
\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" name date genre \\\n",
"1 Toy Story (1995) 01-Jan-1995 01-Jan-1997 \n",
"2 GoldenEye (1995) 01-Jan-1995 01-Jan-1997 \n",
"3 Four Rooms (1995) 01-Jan-1995 01-Jan-1997 \n",
"4 Get Shorty (1995) 01-Jan-1995 01-Jan-1997 \n",
"5 Copycat (1995) 01-Jan-1995 01-Jan-1997 \n",
"\n",
" url f0 f1 f2 f3 f4 f5 \\\n",
"1 http://us.imdb.com/M/title-exact?Toy%20Story%2... 0 0 0 1 1 1 \n",
"2 http://us.imdb.com/M/title-exact?GoldenEye%20(... 0 1 1 0 0 0 \n",
"3 http://us.imdb.com/M/title-exact?Four%20Rooms%... 0 0 0 0 0 0 \n",
"4 http://us.imdb.com/M/title-exact?Get%20Shorty%... 0 1 0 0 0 1 \n",
"5 http://us.imdb.com/M/title-exact?Copycat%20(1995) 0 0 0 0 0 0 \n",
"\n",
" ... f9 f10 f11 f12 f13 f14 f15 f16 f17 f18 \n",
"1 ... 0 0 0 0 0 0 0 0 0 0 \n",
"2 ... 0 0 0 0 0 0 0 1 0 0 \n",
"3 ... 0 0 0 0 0 0 0 1 0 0 \n",
"4 ... 0 0 0 0 0 0 0 0 0 0 \n",
"5 ... 0 0 0 0 0 0 0 1 0 0 \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names = [\"name\", \"date\", \"genre\", \"url\"]\n",
"names += [\"f\" + str(x) for x in range(19)] # unused feature names\n",
"\n",
"items = pd.read_csv(op.join(ML_100K_FOLDER, 'u.item'), sep='|', encoding='latin-1',\n",
" names=names)\n",
"# fix a missing value\n",
"items.fillna(value=\"01-Jan-1997\", inplace=True)\n",
"items.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Data preprocessing\n",
"\n",
"To understand well the distribution of the data, the following statistics are computed:\n",
"- the number of users\n",
"- the number of items\n",
"- the rating distribution"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(100000, 4)\n"
]
},
{
"data": {
"text/plain": [
"count 100000.000000\n",
"mean 3.529860\n",
"std 1.125674\n",
"min 1.000000\n",
"25% 3.000000\n",
"50% 4.000000\n",
"75% 4.000000\n",
"max 5.000000\n",
"Name: rating, dtype: float64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(all_ratings.shape)\n",
"\n",
"all_ratings['rating'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"943"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_user_id = all_ratings['user_id'].max()\n",
"max_user_id"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"1682"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_item_id = all_ratings['item_id'].max()\n",
"max_item_id"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 80000.000000\n",
"mean 425.538538\n",
"std 331.169279\n",
"min 1.000000\n",
"25% 174.000000\n",
"50% 322.000000\n",
"75% 631.000000\n",
"max 1682.000000\n",
"Name: item_id, dtype: float64\n",
"count 20000.000000\n",
"mean 425.496500\n",
"std 329.318736\n",
"min 1.000000\n",
"25% 176.000000\n",
"50% 321.000000\n",
"75% 632.000000\n",
"max 1680.000000\n",
"Name: item_id, dtype: float64\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"ratings_train, ratings_test = train_test_split(\n",
" all_ratings, test_size=0.2, random_state=0)\n",
"\n",
"user_id_train = ratings_train['user_id']\n",
"item_id_train = ratings_train['item_id']\n",
"rating_train = ratings_train['rating']\n",
"\n",
"print(item_id_train.describe())\n",
"\n",
"\n",
"\n",
"user_id_test = ratings_test['user_id']\n",
"item_id_test = ratings_test['item_id']\n",
"rating_test = ratings_test['rating']\n",
"\n",
"print(item_id_test.describe())\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Explicit feedback: supervised ratings prediction\n",
"\n",
"For each pair of (user, item) try to predict the rating the user would give to the item.\n",
"\n",
"This is the classical setup for building recommender systems from offline data with explicit supervision signal. "
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Predictive ratings as a regression problem\n",
"\n",
"The following code implements the following architecture:\n",
"\n",
"| \n", " | name | \n", "date | \n", "genre | \n", "url | \n", "f0 | \n", "f1 | \n", "f2 | \n", "f3 | \n", "f4 | \n", "f5 | \n", "... | \n", "f9 | \n", "f10 | \n", "f11 | \n", "f12 | \n", "f13 | \n", "f14 | \n", "f15 | \n", "f16 | \n", "f17 | \n", "f18 | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | \n", "Toy Story (1995) | \n", "01-Jan-1995 | \n", "01-Jan-1997 | \n", "http://us.imdb.com/M/title-exact?Toy%20Story%2... | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "1 | \n", "1 | \n", "... | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "
| 2 | \n", "GoldenEye (1995) | \n", "01-Jan-1995 | \n", "01-Jan-1997 | \n", "http://us.imdb.com/M/title-exact?GoldenEye%20(... | \n", "0 | \n", "1 | \n", "1 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "0 | \n", "0 | \n", "
| 3 | \n", "Four Rooms (1995) | \n", "01-Jan-1995 | \n", "01-Jan-1997 | \n", "http://us.imdb.com/M/title-exact?Four%20Rooms%... | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "0 | \n", "0 | \n", "
| 4 | \n", "Get Shorty (1995) | \n", "01-Jan-1995 | \n", "01-Jan-1997 | \n", "http://us.imdb.com/M/title-exact?Get%20Shorty%... | \n", "0 | \n", "1 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "... | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "
| 5 | \n", "Copycat (1995) | \n", "01-Jan-1995 | \n", "01-Jan-1997 | \n", "http://us.imdb.com/M/title-exact?Copycat%20(1995) | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "... | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "0 | \n", "0 | \n", "
5 rows × 23 columns
\n", "![](\"images/rec_archi_1.svg\")
"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.layers import Input, Embedding, Flatten, merge, Dense, Dropout, Lambda\n",
"from keras.models import Model\n",
"import keras.backend as K\n",
"from keras_fixes import dot_mode"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[1 2 3]]]\n",
"[[[4 5 6]]]\n",
"[[[ 1. 2. 3. 4. 5. 6.]]]\n",
"[[[ 32.]]]\n",
"[[[[ 0.97463191]]]]\n"
]
}
],
"source": [
"# test the merge in keras\n",
"from keras.layers import Input, merge\n",
"from keras.models import Model\n",
"import numpy as np\n",
"\n",
"input_a = np.reshape([1, 2, 3], (1, 1, 3))\n",
"input_b = np.reshape([4, 5, 6], (1, 1, 3))\n",
"\n",
"print(input_a)\n",
"print(input_b)\n",
"\n",
"a = Input(shape=(1, 3))\n",
"b = Input(shape=(1, 3))\n",
"\n",
"\n",
"concat = merge([a, b], mode='concat', concat_axis=-1)\n",
"dot = merge([a, b], mode='dot', dot_axes=2)\n",
"cos = merge([a, b], mode='cos', dot_axes=2)\n",
"\n",
"model_concat = Model(input=[a, b], output=concat)\n",
"model_dot = Model(input=[a, b], output=dot)\n",
"model_cos = Model(input=[a, b], output=cos)\n",
"\n",
"\n",
"print(model_concat.predict([input_a, input_b]))\n",
"print(model_dot.predict([input_a, input_b]))\n",
"print(model_cos.predict([input_a, input_b]))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# For each sample we input the integer identifiers\n",
"# of a single user and a single item\n",
"user_id_input = Input(shape=[1], name='user')\n",
"item_id_input = Input(shape=[1], name='item')\n",
"\n",
"\n",
"embedding_size = 32\n",
"user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,\n",
" input_length=1, name='user_embedding')(user_id_input)\n",
"item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,\n",
" input_length=1, name='item_embedding')(item_id_input)\n",
"\n",
"\n",
"\n",
"# reshape from shape: (batch_size, input_length, embedding_size)\n",
"# to shape: (batch_size, input_length * embedding_size) which is\n",
"# equal to shape: (batch_size, embedding_size)\n",
"user_vecs = Flatten()(user_embedding)\n",
"item_vecs = Flatten()(item_embedding)\n",
"\n",
"\n",
"y = merge([user_vecs, item_vecs], mode=dot_mode, output_shape=(1,))\n",
"\n",
"\n",
"model = Model(input=[user_id_input, item_id_input], output=y)\n",
"model.compile(optimizer='adam', loss='mae')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.00425739 0.00101695 0.00587618 ..., -0.00157842 0.00947668\n",
" 0.00450012]\n"
]
}
],
"source": [
"# Useful for debugging the output shape of model\n",
"initial_train_preds = model.predict([user_id_train, item_id_train])\n",
"initial_train_preds.shape\n",
"\n",
"print(initial_train_preds)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Model error\n",
"\n",
"Using `initial_train_preds`, compute the model errors:\n",
"- mean absolute error\n",
"- mean squared error\n",
"\n",
"Converting a pandas Series to numpy array is usually implicit, but you may use `rating_train.values` to do so explicitely. Be sure to monitor the shapes of each object you deal with by using `object.shape`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random init MSE: 13.732\n",
"Random init MAE: 3.531\n"
]
}
],
"source": [
"# %load solutions/compute_errors.py\n",
"squared_differences = np.square(initial_train_preds - rating_train.values)\n",
"absolute_differences = np.abs(initial_train_preds - rating_train.values)\n",
"\n",
"print(\"Random init MSE: %0.3f\" % np.mean(squared_differences))\n",
"print(\"Random init MAE: %0.3f\" % np.mean(absolute_differences))\n",
"\n",
"# You may also use sklearn metrics to do so with less numpy engineering \n",
"#from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
"#\n",
"#print(\"Random init MSE: %0.3f\" % mean_squared_error(initial_train_preds, rating_train))\n",
"#print(\"Random init MAE: %0.3f\" % mean_absolute_error(initial_train_preds, rating_train))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Monitoring runs\n",
"\n",
"Keras enables to monitor various variables during training. \n",
"\n",
"`history.history` returned by the `model.fit` function is a dictionary\n",
"containing the `'loss'` and validation loss `'val_loss'` after each epoch"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 72000 samples, validate on 8000 samples\n",
"Epoch 1/6\n",
"3s - loss: 2.9310 - val_loss: 1.4521\n",
"Epoch 2/6\n",
"3s - loss: 1.1063 - val_loss: 0.9898\n",
"Epoch 3/6\n",
"3s - loss: 0.9439 - val_loss: 0.9467\n",
"Epoch 4/6\n",
"2s - loss: 0.9201 - val_loss: 0.9336\n",
"Epoch 5/6\n",
"2s - loss: 0.9123 - val_loss: 0.9291\n",
"Epoch 6/6\n",
"2s - loss: 0.9088 - val_loss: 0.9264\n",
"CPU times: user 24.6 s, sys: 5.59 s, total: 30.2 s\n",
"Wall time: 19.5 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# Training the model\n",
"history = model.fit([user_id_train, item_id_train], rating_train,\n",
" batch_size=64, nb_epoch=6, validation_split=0.1,\n",
" shuffle=True, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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FbQOALQnzVIdtLTKz2Wa21MyW5uIDGy4aXkJBNKLLOkUk7VIR/G8Cg919DPBD4KmTWYm7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\nmuLJVkIeMQojMfI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"text/plain": [
"![](\"images/rec_archi_2.svg\")
\n",
"\n",
"\n",
"### Exercise\n",
"\n",
"- The following code has **4 errors** that prevent it from working correctly. **Correct them and explain** why they are critical."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# %load solutions/deep_explicit_feedback_recsys.py\n",
"# For each sample we input the integer identifiers\n",
"# of a single user and a single item\n",
"user_id_input = Input(shape=[1], name='user')\n",
"item_id_input = Input(shape=[1], name='item')\n",
"\n",
"embedding_size = 50\n",
"user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,\n",
" input_length=1, name='user_embedding')(user_id_input)\n",
"item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,\n",
" input_length=1, name='item_embedding')(item_id_input)\n",
"\n",
"# reshape from shape: (batch_size, input_length, embedding_size)\n",
"# to shape: (batch_size, input_length * embedding_size) which is\n",
"# equal to shape: (batch_size, embedding_size)\n",
"user_vecs = Flatten()(user_embedding)\n",
"item_vecs = Flatten()(item_embedding)\n",
"\n",
"input_vecs = merge([user_vecs, item_vecs], mode='concat')\n",
"\n",
"input_vecs = Dropout(0.2)(input_vecs)\n",
"\n",
"x = Dense(64, activation='relu')(input_vecs)\n",
"## output dimension for 1-d rating\n",
"## tanh activation squashes the outputs between -1 and 1\n",
"## when we want to predict values between 1 and 5\n",
"y = Dense(1)(x)\n",
"model = Model(input=[user_id_input, item_id_input], output=y)\n",
"## A binary crossentropy loss is only useful for binary\n",
"## classification, while we are in regression (use mse or mae)\n",
"model.compile(optimizer='adam', loss='mae')\n",
"\n",
"initial_train_preds = model.predict([user_id_train, item_id_train])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 72000 samples, validate on 8000 samples\n",
"Epoch 1/5\n",
"4s - loss: 0.9496 - val_loss: 0.7715\n",
"Epoch 2/5\n",
"4s - loss: 0.7486 - val_loss: 0.7603\n",
"Epoch 3/5\n",
"3s - loss: 0.7347 - val_loss: 0.7496\n",
"Epoch 4/5\n",
"5s - loss: 0.7220 - val_loss: 0.7460\n",
"Epoch 5/5\n",
"5s - loss: 0.7118 - val_loss: 0.7397\n",
"CPU times: user 33.4 s, sys: 9.85 s, total: 43.3 s\n",
"Wall time: 24 s\n"
]
}
],
"source": [
"%%time\n",
"history = model.fit([user_id_train, item_id_train], rating_train,\n",
" batch_size=64, nb_epoch=5, validation_split=0.1,\n",
" shuffle=True, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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KSW2SevyidJXcK2mzpHWSFpTVLZG0Kam7s5qBm5nZ8ankiP+HwJJj1F8NTEtey4DvAUjK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\nQmCC\n",
"text/plain": [
"![](\"images/rec_archi_3.svg\")
\n"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"(80000, 20000)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# transform the date (string) into an int representing the release year\n",
"parsed_dates = [int(film_date[-4:])\n",
" for film_date in items[\"date\"].tolist()]\n",
"\n",
"items['parsed_date'] = pd.Series(parsed_dates, index=items.index)\n",
"max_date = max(items['parsed_date'])\n",
"min_date = min(items['parsed_date'])\n",
"\n",
"from sklearn.preprocessing import scale\n",
"\n",
"items['scaled_date'] = scale(items['parsed_date'].astype('float64'))\n",
"item_meta_train = items[\"scaled_date\"][item_id_train]\n",
"item_meta_test = items[\"scaled_date\"][item_id_test]\n",
"\n",
"len(item_meta_train), len(item_meta_test)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"count 1.682000e+03\n",
"mean -2.415758e-15\n",
"std 1.000297e+00\n",
"min -4.730390e+00\n",
"25% 2.533563e-01\n",
"50% 3.937435e-01\n",
"75% 4.639372e-01\n",
"max 6.043244e-01\n",
"Name: scaled_date, dtype: float64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"items[\"scaled_date\"].describe()"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# For each sample we input the integer identifiers\n",
"# of a single user and a single item\n",
"user_id_input = Input(shape=[1], name='user')\n",
"item_id_input = Input(shape=[1], name='item')\n",
"meta_input = Input(shape=[1], name='meta_item')\n",
"\n",
"embedding_size = 50\n",
"user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,\n",
" input_length=1, name='user_embedding')(user_id_input)\n",
"item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,\n",
" input_length=1, name='item_embedding')(item_id_input)\n",
"\n",
"\n",
"# reshape from shape: (batch_size, input_length, embedding_size)\n",
"# to shape: (batch_size, input_length * embedding_size) which is\n",
"# equal to shape: (batch_size, embedding_size)\n",
"user_vecs = Flatten()(user_embedding)\n",
"item_vecs = Flatten()(item_embedding)\n",
"\n",
"input_vecs = merge([user_vecs, item_vecs, meta_input], mode='concat')\n",
"\n",
"x = Dense(64, activation='relu')(input_vecs)\n",
"x = Dropout(0.5)(x)\n",
"x = Dense(32, activation='relu')(x)\n",
"y = Dense(1)(x)\n",
"\n",
"model = Model(input=[user_id_input, item_id_input, meta_input], output=y)\n",
"model.compile(optimizer='adam', loss='mae')\n",
"\n",
"initial_train_preds = model.predict([user_id_train, item_id_train, item_meta_train])"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 72000 samples, validate on 8000 samples\n",
"Epoch 1/15\n",
"5s - loss: 0.9475 - val_loss: 0.7691\n",
"Epoch 2/15\n",
"5s - loss: 0.7587 - val_loss: 0.7587\n",
"Epoch 3/15\n",
"4s - loss: 0.7363 - val_loss: 0.7465\n",
"Epoch 4/15\n",
"4s - loss: 0.7168 - val_loss: 0.7383\n",
"Epoch 5/15\n",
"4s - loss: 0.7025 - val_loss: 0.7347\n",
"Epoch 6/15\n",
"5s - loss: 0.6885 - val_loss: 0.7349\n",
"Epoch 7/15\n",
"7s - loss: 0.6772 - val_loss: 0.7380\n",
"Epoch 8/15\n",
"6s - loss: 0.6691 - val_loss: 0.7381\n",
"Epoch 9/15\n",
"4s - loss: 0.6621 - val_loss: 0.7315\n",
"Epoch 10/15\n",
"4s - loss: 0.6560 - val_loss: 0.7317\n",
"Epoch 11/15\n",
"5s - loss: 0.6519 - val_loss: 0.7316\n",
"Epoch 12/15\n",
"5s - loss: 0.6458 - val_loss: 0.7324\n",
"Epoch 13/15\n",
"5s - loss: 0.6404 - val_loss: 0.7299\n",
"Epoch 14/15\n",
"5s - loss: 0.6374 - val_loss: 0.7306\n",
"Epoch 15/15\n",
"5s - loss: 0.6315 - val_loss: 0.7400\n",
"CPU times: user 1min 50s, sys: 35.8 s, total: 2min 26s\n",
"Wall time: 1min 20s\n"
]
}
],
"source": [
"%%time\n",
"history = model.fit([user_id_train, item_id_train, item_meta_train], rating_train,\n",
" batch_size=64, nb_epoch=15, validation_split=0.1,\n",
" shuffle=True, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final test Loss: 0.973\n",
"Final test Loss: 0.723\n"
]
}
],
"source": [
"test_preds = model.predict([user_id_test, item_id_test, item_meta_test])\n",
"print(\"Final test Loss: %0.3f\" % mean_squared_error(test_preds, rating_test))\n",
"print(\"Final test Loss: %0.3f\" % mean_absolute_error(test_preds, rating_test))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### A recommendation function for a given user\n",
"\n",
"Once the model is trained, the system can be used to recommend a few items for a user, that he/she hasn't already seen:\n",
"- we use the `model.predict` to compute the ratings a user would have given to all items\n",
"- we build a reco function that sorts these items and exclude those the user has already seen"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [],
"source": [
"def recommend(user_id, top_n=10):\n",
" item_ids = range(1, max_item_id)\n",
" seen_movies = list(all_ratings[all_ratings[\"user_id\"]==user_id][\"item_id\"])\n",
" item_ids = list(filter(lambda x: x not in seen_movies, item_ids))\n",
" \n",
" print(\"user \"+str(user_id) +\" has seen \"+str(len(seen_movies)) + \" movies. \"+\n",
" \"Computing ratings for \"+str(len(item_ids))+ \" other movies\")\n",
" \n",
" item_ids = np.array(item_ids)\n",
" user = np.zeros_like(item_ids)\n",
" user[:]=user_id\n",
" items_meta = items[\"scaled_date\"][item_ids].values\n",
"\n",
" rating_preds = model.predict([user, item_ids, items_meta])\n",
"\n",
" item_ids = np.argsort(rating_preds[:,0])[::-1].tolist()\n",
" rec_items = item_ids[:top_n]\n",
" return [(items[\"name\"][movie], rating_preds[movie][0]) for movie in rec_items]"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"user 3 has seen 54 movies. Computing ratings for 1627 other movies\n"
]
},
{
"data": {
"text/plain": [
"[('A Chef in Love (1996)', 5.0031729),\n",
" ('Hurricane Streets (1998)', 4.7923875),\n",
" (\"Pharaoh's Army (1995)\", 4.7363672),\n",
" ('Ladybird Ladybird (1994)', 4.4955535),\n",
" ('Swept from the Sea (1997)', 4.2269187),\n",
" ('Spitfire Grill, The (1996)', 4.187326),\n",
" ('NeverEnding Story III, The (1994)', 4.1698661),\n",
" ('Nixon (1995)', 4.1500397),\n",
" ('Mary Poppins (1964)', 4.1487379),\n",
" ('Palmetto (1998)', 4.1305041)]"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recommend(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Home assignment: Predicting ratings as a classification problem\n",
"\n",
"In this dataset, the ratings all belong to a finite set of possible values:"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5])"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"np.unique(rating_train)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Maybe we can help the model by forcing it to predict those values by treating the problem as a multiclassification problem. The only required changes are:\n",
"\n",
"- setting the final layer to output class membership probabities using a softmax activation with 5 outputs;\n",
"- optimize the categorical cross-entropy classification loss instead of a regression loss such as MSE or MAE."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random init MSE: 4.012\n",
"Random init MAE: 1.706\n",
"Train on 72000 samples, validate on 8000 samples\n",
"Epoch 1/6\n",
"7s - loss: 1.3425 - val_loss: 1.2857\n",
"Epoch 2/6\n",
"6s - loss: 1.2584 - val_loss: 1.2631\n",
"Epoch 3/6\n",
"5s - loss: 1.2348 - val_loss: 1.2545\n",
"Epoch 4/6\n",
"5s - loss: 1.2191 - val_loss: 1.2574\n",
"Epoch 5/6\n",
"5s - loss: 1.2086 - val_loss: 1.2557\n",
"Epoch 6/6\n",
"5s - loss: 1.1997 - val_loss: 1.2546\n",
"Final test MSE: 1.198\n",
"Final test MAE: 0.736\n"
]
},
{
"data": {
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4zcxSZtwEfxpvDSFptaSdkjZWuy9jQdJMSWslvShpk6QvVbtPo01SvaTfS3o+\nOeavV7tPY0VSVtL/k/SLavdlLEjaKukFSc9Jah3V1xoPc/zJrSH+CLyf4pfE1gE3RcSLVe3YKJP0\nXuAgxW9Nz692f0Zb8m3wsyPiD5ImAuuBD4/nf2dJApoi4qCkHPA08KXkG/LjmqS/BVqASRHxwWr3\nZ7RJ2gq0RMSof2ltvJzxp/LWEBHxJLC32v0YKxHxWkT8IVk+ALxE8dvh41ZyG5SDyWoueZz6Z2vD\nkDQD+ADww2r3ZTwaL8E/1C0jbJySNBtYBPyuuj0ZfcmUx3PATuDXETHujxm4C/ivQKHaHRlDATwh\naX1yC5tRM16C31JE0gTgIeDLEbG/2v0ZbRGRj4iFFL/5vkTSuJ7Wk/RBYGdErK92X8bYZcm/8zLg\ntmQqd1SMl+Av57YSNg4k89wPAT+NiJ9Vuz9jKSLeAtYCS6vdl1F2KfChZM77fuBvJP17dbs0+iJi\ne/K8E1hDcQp7VIyX4PetIVIg+aDz34CXIuLOavdnLEhqlnR6stxA8QKGl6vbq9EVEf8YETMiYjbF\n/8v/JyI+WeVujSpJTckFC0hqAq4BRu1qvXER/BHRDfTcGuIl4IExuDVE1Um6D3gGOE9Sm6TPVrtP\no+xS4FMUzwCfSx7XVrtTo+xsYK2kDRRPcH4dEam4vDFlpgFPS3oe+D3wHxHxy9F6sXFxOaeZmZVv\nXJzxm5lZ+Rz8ZmYp4+A3M0sZB7+ZWco4+M3MUsbBb2aWMg5+M7OU+f/fRsTM4QkT8gAAAABJRU5E\nrkJggg==\n",
"text/plain": [
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" user_id item_id rating timestamp\n",
"count 90570.000000 90570.000000 90570.000000 9.057000e+04\n",
"mean 461.494038 428.104891 3.523827 8.835073e+08\n",
"std 266.004364 333.088029 1.126073 5.341684e+06\n",
"min 1.000000 1.000000 1.000000 8.747247e+08\n",
"25% 256.000000 174.000000 3.000000 8.794484e+08\n",
"50% 442.000000 324.000000 4.000000 8.828143e+08\n",
"75% 682.000000 636.000000 4.000000 8.882049e+08\n",
"max 943.000000 1682.000000 5.000000 8.932866e+08"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_train = pd.read_csv(op.join(ML_100K_FOLDER, 'ua.base'), sep='\\t',\n",
" names=[\"user_id\", \"item_id\", \"rating\", \"timestamp\"])\n",
"data_test = pd.read_csv(op.join(ML_100K_FOLDER, 'ua.test'), sep='\\t',\n",
" names=[\"user_id\", \"item_id\", \"rating\", \"timestamp\"])\n",
"\n",
"data_train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"| \n", " | user_id | \n", "item_id | \n", "rating | \n", "timestamp | \n", "
|---|---|---|---|---|
| count | \n", "90570.000000 | \n", "90570.000000 | \n", "90570.000000 | \n", "9.057000e+04 | \n", "
| mean | \n", "461.494038 | \n", "428.104891 | \n", "3.523827 | \n", "8.835073e+08 | \n", "
| std | \n", "266.004364 | \n", "333.088029 | \n", "1.126073 | \n", "5.341684e+06 | \n", "
| min | \n", "1.000000 | \n", "1.000000 | \n", "1.000000 | \n", "8.747247e+08 | \n", "
| 25% | \n", "256.000000 | \n", "174.000000 | \n", "3.000000 | \n", "8.794484e+08 | \n", "
| 50% | \n", "442.000000 | \n", "324.000000 | \n", "4.000000 | \n", "8.828143e+08 | \n", "
| 75% | \n", "682.000000 | \n", "636.000000 | \n", "4.000000 | \n", "8.882049e+08 | \n", "
| max | \n", "943.000000 | \n", "1682.000000 | \n", "5.000000 | \n", "8.932866e+08 | \n", "
\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" user_id item_id rating timestamp\n",
"0 1 1 5 874965758\n",
"1 1 2 3 876893171\n",
"2 1 3 4 878542960\n",
"3 1 4 3 876893119\n",
"4 1 5 3 889751712"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# data_test.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"n_users=944, n_items=1683\n"
]
}
],
"source": [
"max_user_id = max(data_train['user_id'].max(), data_test['user_id'].max())\n",
"max_item_id = max(data_train['item_id'].max(), data_test['item_id'].max())\n",
"\n",
"n_users = max_user_id + 1\n",
"n_items = max_item_id + 1\n",
"\n",
"print('n_users=%d, n_items=%d' % (n_users, n_items))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Implicit feedback data\n",
"\n",
"Consider ratings >= 4 as positive feed back and ignore the rest:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"pos_data_train = data_train.query(\"rating >= 4\")\n",
"pos_data_test = data_test.query(\"rating >= 4\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Because the median rating is around 3.5, this cut will remove approximately half of the ratings from the datasets:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"49906"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pos_data_train['rating'].count()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"5469"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pos_data_test['rating'].count()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## The Triplet Loss\n",
"\n",
"The following section demonstrates how to build a low-rank quadratic interaction model between users and items. The similarity score between a user and an item is defined by the unormalized dot products of their respective embeddings.\n",
"\n",
"The matching scores can be use to rank items to recommend to a specific user.\n",
"\n",
"Training of the model parameters is achieved by randomly sampling negative items not seen by a pre-selected anchor user. We want the model embedding matrices to be such that the similarity between the user vector and the negative vector is smaller than the similarity between the user vector and the positive item vector. Furthermore we use a margin to further move appart the negative from the anchor user.\n",
"\n",
"Here is the architecture of such a triplet architecture. The triplet name comes from the fact that the loss to optimize is defined for triple `(anchor_user, positive_item, negative_item)`:\n",
"\n",
"| \n", " | user_id | \n", "item_id | \n", "rating | \n", "timestamp | \n", "
|---|---|---|---|---|
| 0 | \n", "1 | \n", "1 | \n", "5 | \n", "874965758 | \n", "
| 1 | \n", "1 | \n", "2 | \n", "3 | \n", "876893171 | \n", "
| 2 | \n", "1 | \n", "3 | \n", "4 | \n", "878542960 | \n", "
| 3 | \n", "1 | \n", "4 | \n", "3 | \n", "876893119 | \n", "
| 4 | \n", "1 | \n", "5 | \n", "3 | \n", "889751712 | \n", "
![](\"images/rec_archi_implicit_2.svg\")
\n",
"\n",
"We call this model a triplet model with bi-linear interactions because the similarity between a user and an item is captured by a dot product of the first level embedding vectors. This is therefore not a deep architecture."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n",
"\n",
"def identity_loss(y_true, y_pred):\n",
" \"\"\"Ignore y_true and return the mean of y_pred\n",
" \n",
" This is a hack to work-around the design of the Keras API that is\n",
" not really suited to train networks with a triplet loss by default.\n",
" \"\"\"\n",
" return tf.reduce_mean(y_pred + 0 * y_true)\n",
"\n",
"\n",
"def margin_comparator_loss(inputs, margin=1.):\n",
" \"\"\"Comparator loss for a pair of precomputed similarities\n",
" \n",
" If the inputs are cosine similarities, they each have range in\n",
" (-1, 1), therefore their difference have range in (-2, 2). Using\n",
" a margin of 1. can therefore make sense.\n",
"\n",
" If the input similarities are not normalized, it can be beneficial\n",
" to use larger values for the margin of the comparator loss.\n",
" \"\"\"\n",
" positive_pair_sim, negative_pair_sim = inputs\n",
" return tf.maximum(negative_pair_sim - positive_pair_sim + margin, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Here is the actual code that builds the model(s) with shared weights. Note that here we use the cosine similarity instead of unormalized dot products (both seems to yield comparable results)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.models import Model\n",
"from keras.layers import Embedding, Flatten, Input, Dense, merge\n",
"from keras.regularizers import l2\n",
"from keras_fixes import dot_mode, cos_mode\n",
"\n",
"\n",
"def build_models(n_users, n_items, latent_dim=64, l2_reg=0):\n",
" \"\"\"Build a triplet model and its companion similarity model\n",
" \n",
" The triplet model is used to train the weights of the companion\n",
" similarity model. The triplet model takes 1 user, 1 positive item\n",
" (relative to the selected user) and one negative item and is\n",
" trained with comparator loss.\n",
" \n",
" The similarity model takes one user and one item as input and return\n",
" compatibility score (aka the match score).\n",
" \"\"\"\n",
" # Common architectural components for the two models:\n",
" # - symbolic input placeholders\n",
" user_input = Input((1,), name='user_input')\n",
" positive_item_input = Input((1,), name='positive_item_input')\n",
" negative_item_input = Input((1,), name='negative_item_input')\n",
"\n",
" # - embeddings\n",
" l2_reg = None if l2_reg == 0 else l2(l2_reg)\n",
" user_layer = Embedding(n_users, latent_dim, input_length=1,\n",
" name='user_embedding', W_regularizer=l2_reg)\n",
" \n",
" # The following embedding parameters will be shared to encode both\n",
" # the positive and negative items.\n",
" item_layer = Embedding(n_items, latent_dim, input_length=1,\n",
" name=\"item_embedding\", W_regularizer=l2_reg)\n",
"\n",
" user_embedding = Flatten()(user_layer(user_input))\n",
" positive_item_embedding = Flatten()(item_layer(positive_item_input))\n",
" negative_item_embedding = Flatten()(item_layer(negative_item_input))\n",
"\n",
" # - similarity computation between embeddings\n",
" positive_similarity = merge([user_embedding, positive_item_embedding],\n",
" mode=cos_mode, output_shape=(1,),\n",
" name=\"positive_similarity\")\n",
" negative_similarity = merge([user_embedding, negative_item_embedding],\n",
" mode=cos_mode, output_shape=(1,),\n",
" name=\"negative_similarity\")\n",
"\n",
" # The triplet network model, only used for training\n",
" triplet_loss = merge([positive_similarity, negative_similarity],\n",
" mode=margin_comparator_loss, output_shape=(1,),\n",
" name='comparator_loss')\n",
"\n",
" triplet_model = Model(input=[user_input,\n",
" positive_item_input,\n",
" negative_item_input],\n",
" output=triplet_loss)\n",
" \n",
" # The match-score model, only use at inference to rank items for a given\n",
" # model: the model weights are shared with the triplet_model therefore\n",
" # we do not need to train it and therefore we do not need to plug a loss\n",
" # and an optimizer.\n",
" match_model = Model(input=[user_input, positive_item_input],\n",
" output=positive_similarity)\n",
" \n",
" return triplet_model, match_model\n",
"\n",
"\n",
"triplet_model, match_model = build_models(n_users, n_items, latent_dim=64,\n",
" l2_reg=1e-6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Exercise:\n",
"\n",
"How many trainable parameters does each model. Count the shared parameters only once per model."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"user_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"positive_item_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"user_embedding (Embedding) (None, 1, 64) 60416 user_input[0][0] \n",
"____________________________________________________________________________________________________\n",
"item_embedding (Embedding) (None, 1, 64) 107712 positive_item_input[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_1 (Flatten) (None, 64) 0 user_embedding[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_2 (Flatten) (None, 64) 0 item_embedding[0][0] \n",
"____________________________________________________________________________________________________\n",
"positive_similarity (Merge) (None, 1) 0 flatten_1[0][0] \n",
" flatten_2[0][0] \n",
"====================================================================================================\n",
"Total params: 168,128\n",
"Trainable params: 168,128\n",
"Non-trainable params: 0\n",
"____________________________________________________________________________________________________\n",
"None\n"
]
}
],
"source": [
"print(match_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"user_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"positive_item_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"negative_item_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"user_embedding (Embedding) (None, 1, 64) 60416 user_input[0][0] \n",
"____________________________________________________________________________________________________\n",
"item_embedding (Embedding) (None, 1, 64) 107712 positive_item_input[0][0] \n",
" negative_item_input[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_1 (Flatten) (None, 64) 0 user_embedding[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_2 (Flatten) (None, 64) 0 item_embedding[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_3 (Flatten) (None, 64) 0 item_embedding[1][0] \n",
"____________________________________________________________________________________________________\n",
"positive_similarity (Merge) (None, 1) 0 flatten_1[0][0] \n",
" flatten_2[0][0] \n",
"____________________________________________________________________________________________________\n",
"negative_similarity (Merge) (None, 1) 0 flatten_1[0][0] \n",
" flatten_3[0][0] \n",
"____________________________________________________________________________________________________\n",
"comparator_loss (Merge) (None, 1) 0 positive_similarity[0][0] \n",
" negative_similarity[0][0] \n",
"====================================================================================================\n",
"Total params: 168,128\n",
"Trainable params: 168,128\n",
"Non-trainable params: 0\n",
"____________________________________________________________________________________________________\n",
"None\n"
]
}
],
"source": [
"print(triplet_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# %load solutions/triplet_parameter_count.py\n",
"# Analysis:\n",
"#\n",
"# Both models have exactly the same number of parameters,\n",
"# namely the parameters of the 2 embeddings:\n",
"#\n",
"# - user embedding: n_users x embedding_dim\n",
"# - item embedding: n_items x embedding_dim\n",
"#\n",
"# The triplet model uses the same item embedding twice,\n",
"# once to compute the positive similarity and the other\n",
"# time to compute the negative similarity. However because\n",
"# those two nodes in the computation graph share the same\n",
"# instance of the item embedding layer, the item embedding\n",
"# weight matrix is shared by the two branches of the\n",
"# graph and therefore the total number of parameters for\n",
"# each model is in both cases:\n",
"#\n",
"# (n_users x embedding_dim) + (n_items x embedding_dim)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Quality of Ranked Recommendations\n",
"\n",
"Now that we have a randomly initialized model we can start computing random recommendations. To assess their quality we do the following for each user:\n",
"\n",
"- compute matching scores for items (except the movies that the user has already seen in the training set),\n",
"- compare to the positive feedback actually collected on the test set using the ROC AUC ranking metric,\n",
"- average ROC AUC scores across users to get the average performance of the recommender model on the test set."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.metrics import roc_auc_score\n",
"\n",
"\n",
"def average_roc_auc(match_model, data_train, data_test):\n",
" \"\"\"Compute the ROC AUC for each user and average over users\"\"\"\n",
" max_user_id = max(data_train['user_id'].max(), data_test['user_id'].max())\n",
" max_item_id = max(data_train['item_id'].max(), data_test['item_id'].max())\n",
" user_auc_scores = []\n",
" for user_id in range(1, max_user_id + 1):\n",
" pos_item_train = data_train[data_train['user_id'] == user_id]\n",
" pos_item_test = data_test[data_test['user_id'] == user_id]\n",
" \n",
" # Consider all the items already seen in the training set\n",
" all_item_ids = np.arange(1, max_item_id + 1)\n",
" items_to_rank = np.setdiff1d(all_item_ids, pos_item_train['item_id'].values)\n",
" \n",
" # Ground truth: return 1 for each item positively present in the test set\n",
" # and 0 otherwise.\n",
" expected = np.in1d(items_to_rank, pos_item_test['item_id'].values)\n",
" \n",
" if np.sum(expected) >= 1:\n",
" # At least one positive test value to rank\n",
" repeated_user_id = np.empty_like(items_to_rank)\n",
" repeated_user_id.fill(user_id)\n",
"\n",
" predicted = match_model.predict([repeated_user_id, items_to_rank],\n",
" batch_size=4096)\n",
" user_auc_scores.append(roc_auc_score(expected, predicted))\n",
"\n",
" return sum(user_auc_scores) / len(user_auc_scores)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"By default the model should make predictions that rank the items in random order. The **ROC AUC score** is a ranking score that represents the **expected value of correctly ordering uniformly sampled pairs of recommendations**.\n",
"\n",
"A random (untrained) model should yield 0.50 ROC AUC on average. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.49808857713281779"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"average_roc_auc(match_model, pos_data_train, pos_data_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Training the Triplet Model\n",
"\n",
"Let's now fit the parameters of the model by sampling triplets: for each user, select a movie in the positive feedback set of that user and randomly sample another movie to serve as negative item.\n",
"\n",
"Note that this sampling scheme could be improved by removing items that are marked as positive in the data to remove some label noise. In practice this does not seem to be a problem though."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def sample_triplets(pos_data, max_item_id, random_seed=0):\n",
" \"\"\"Sample negatives at random\"\"\"\n",
" rng = np.random.RandomState(random_seed)\n",
" user_ids = pos_data['user_id'].values\n",
" pos_item_ids = pos_data['item_id'].values\n",
"\n",
" neg_item_ids = rng.randint(low=1, high=max_item_id + 1,\n",
" size=len(user_ids))\n",
"\n",
" return [user_ids, pos_item_ids, neg_item_ids]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's train the triplet model:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"5s - loss: 0.7700\n",
"Epoch 1/15: test ROC AUC: 0.8427\n",
"Epoch 1/1\n",
"4s - loss: 0.3884\n",
"Epoch 2/15: test ROC AUC: 0.8787\n",
"Epoch 1/1\n",
"4s - loss: 0.3588\n",
"Epoch 3/15: test ROC AUC: 0.8972\n",
"Epoch 1/1\n",
"4s - loss: 0.3440\n",
"Epoch 4/15: test ROC AUC: 0.9042\n",
"Epoch 1/1\n",
"4s - loss: 0.3358\n",
"Epoch 5/15: test ROC AUC: 0.9098\n",
"Epoch 1/1\n",
"4s - loss: 0.3288\n",
"Epoch 6/15: test ROC AUC: 0.9130\n",
"Epoch 1/1\n",
"4s - loss: 0.3306\n",
"Epoch 7/15: test ROC AUC: 0.9157\n",
"Epoch 1/1\n",
"6s - loss: 0.3246\n",
"Epoch 8/15: test ROC AUC: 0.9167\n",
"Epoch 1/1\n",
"6s - loss: 0.3225\n",
"Epoch 9/15: test ROC AUC: 0.9167\n",
"Epoch 1/1\n",
"5s - loss: 0.3204\n",
"Epoch 10/15: test ROC AUC: 0.9178\n",
"Epoch 1/1\n",
"4s - loss: 0.3190\n",
"Epoch 11/15: test ROC AUC: 0.9194\n",
"Epoch 1/1\n",
"4s - loss: 0.3191\n",
"Epoch 12/15: test ROC AUC: 0.9193\n",
"Epoch 1/1\n",
"4s - loss: 0.3185\n",
"Epoch 13/15: test ROC AUC: 0.9198\n",
"Epoch 1/1\n",
"4s - loss: 0.3218\n",
"Epoch 14/15: test ROC AUC: 0.9216\n",
"Epoch 1/1\n",
"4s - loss: 0.3170\n",
"Epoch 15/15: test ROC AUC: 0.9230\n"
]
}
],
"source": [
"# we plug the identity loss and the a fake target variable ignored by\n",
"# the model to be able to use the Keras API to train the triplet model\n",
"triplet_model.compile(loss=identity_loss, optimizer=\"adam\")\n",
"fake_y = np.ones_like(pos_data_train['user_id'])\n",
"\n",
"n_epochs = 15\n",
"\n",
"for i in range(n_epochs):\n",
" # Sample new negatives to build different triplets at each epoch\n",
" triplet_inputs = sample_triplets(pos_data_train, max_item_id,\n",
" random_seed=i)\n",
"\n",
" # Fit the model incrementally by doing a single pass over the\n",
" # sampled triplets.\n",
" triplet_model.fit(triplet_inputs, fake_y, shuffle=True,\n",
" batch_size=64, nb_epoch=1, verbose=2)\n",
" \n",
" # Monitor the convergence of the model\n",
" test_auc = average_roc_auc(match_model, pos_data_train, pos_data_test)\n",
" print(\"Epoch %d/%d: test ROC AUC: %0.4f\"\n",
" % (i + 1, n_epochs, test_auc))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"## Training a Deep Matching Model on Implicit Feedback\n",
"\n",
"\n",
"Instead of using hard-coded cosine similarities to predict the match of a `(user_id, item_id)` pair, we can instead specify a deep neural network based parametrisation of the similarity. The parameters of that matching model are also trained with the margin comparator loss:\n",
"\n",
"![](\"images/rec_archi_implicit_1.svg\")
\n",
"\n",
"\n",
"### Exercise to complete as a home assignment:\n",
"\n",
"- Implement a `deep_match_model`, `deep_triplet_model` pair of models\n",
" for the architecture described in the schema. The last layer of\n",
" the embedded Multi Layer Perceptron outputs a single scalar that\n",
" encodes the similarity between a user and a candidate item.\n",
"\n",
"- Evaluate the resulting model by computing the per-user average\n",
" ROC AUC score on the test feedback data.\n",
" \n",
" - Check that the AUC ROC score is close to 0.50 for a randomly\n",
" initialized model.\n",
" \n",
" - Check that you can reach at least 0.91 ROC AUC with this deep\n",
" model (you might need to adjust the hyperparameters).\n",
" \n",
" \n",
"Hints:\n",
"\n",
"- it is possible to reuse the code to create embeddings from the previous model\n",
" definition;\n",
"\n",
"- the concatenation between user and the positive item embedding can be\n",
" obtained with:\n",
"\n",
"```py\n",
" positive_embeddings_pair = merge([user_embedding, positive_item_embedding],\n",
" mode='concat',\n",
" name=\"positive_embeddings_pair\")\n",
" negative_embeddings_pair = merge([user_embedding, negative_item_embedding],\n",
" mode='concat',\n",
" name=\"negative_embeddings_pair\")\n",
"```\n",
"\n",
"- those embedding pairs should be fed to a shared MLP instance to compute the similarity scores."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"5s - loss: 0.4747\n",
"Epoch 1/15: test ROC AUC: 0.8554\n",
"Epoch 1/1\n",
"4s - loss: 0.3814\n",
"Epoch 2/15: test ROC AUC: 0.8623\n",
"Epoch 1/1\n",
"4s - loss: 0.3742\n",
"Epoch 3/15: test ROC AUC: 0.8637\n",
"Epoch 1/1\n",
"4s - loss: 0.3668\n",
"Epoch 4/15: test ROC AUC: 0.8662\n",
"Epoch 1/1\n",
"4s - loss: 0.3480\n",
"Epoch 5/15: test ROC AUC: 0.8740\n",
"Epoch 1/1\n",
"4s - loss: 0.3282\n",
"Epoch 6/15: test ROC AUC: 0.8871\n",
"Epoch 1/1\n",
"4s - loss: 0.3113\n",
"Epoch 7/15: test ROC AUC: 0.8986\n",
"Epoch 1/1\n",
"4s - loss: 0.2918\n",
"Epoch 8/15: test ROC AUC: 0.9048\n",
"Epoch 1/1\n",
"4s - loss: 0.2824\n",
"Epoch 9/15: test ROC AUC: 0.9098\n",
"Epoch 1/1\n",
"4s - loss: 0.2735\n",
"Epoch 10/15: test ROC AUC: 0.9116\n",
"Epoch 1/1\n",
"4s - loss: 0.2691\n",
"Epoch 11/15: test ROC AUC: 0.9137\n",
"Epoch 1/1\n",
"4s - loss: 0.2659\n",
"Epoch 12/15: test ROC AUC: 0.9158\n",
"Epoch 1/1\n",
"4s - loss: 0.2597\n",
"Epoch 13/15: test ROC AUC: 0.9161\n",
"Epoch 1/1\n",
"78s - loss: 0.2609\n",
"Epoch 14/15: test ROC AUC: 0.9159\n",
"Epoch 1/1\n",
"4s - loss: 0.2544\n",
"Epoch 15/15: test ROC AUC: 0.9189\n"
]
}
],
"source": [
"# %load solutions/deep_implicit_feedback_recsys.py\n",
"from keras.models import Model, Sequential\n",
"from keras.layers import Embedding, Flatten, Input, Dense, Dropout, merge\n",
"from keras.regularizers import l2\n",
"\n",
"\n",
"def make_interaction_mlp(input_dim, n_hidden=1, hidden_size=64,\n",
" dropout=0, l2_reg=None):\n",
" \"\"\"Build the shared multi layer perceptron\"\"\"\n",
" mlp = Sequential()\n",
" if n_hidden == 0:\n",
" # Plug the output unit directly: this is a simple\n",
" # linear regression model. Not dropout required.\n",
" mlp.add(Dense(1, input_dim=input_dim,\n",
" activation='relu', W_regularizer=l2_reg))\n",
" else:\n",
" mlp.add(Dense(hidden_size, input_dim=input_dim,\n",
" activation='relu', W_regularizer=l2_reg))\n",
" mlp.add(Dropout(dropout))\n",
" for i in range(n_hidden - 1):\n",
" mlp.add(Dense(hidden_size, activation='relu',\n",
" W_regularizer=l2_reg))\n",
" mlp.add(Dropout(dropout))\n",
" mlp.add(Dense(1, activation='relu', W_regularizer=l2_reg))\n",
" return mlp\n",
"\n",
"\n",
"def build_models(n_users, n_items, user_dim=32, item_dim=64,\n",
" n_hidden=1, hidden_size=64, dropout=0, l2_reg=0):\n",
" \"\"\"Build models to train a deep triplet network\"\"\"\n",
" user_input = Input((1,), name='user_input')\n",
" positive_item_input = Input((1,), name='positive_item_input')\n",
" negative_item_input = Input((1,), name='negative_item_input')\n",
"\n",
" l2_reg = None if l2_reg == 0 else l2(l2_reg)\n",
" user_layer = Embedding(n_users, user_dim, input_length=1,\n",
" name='user_embedding', W_regularizer=l2_reg)\n",
"\n",
" # The following embedding parameters will be shared to encode both\n",
" # the positive and negative items.\n",
" item_layer = Embedding(n_items, item_dim, input_length=1,\n",
" name=\"item_embedding\", W_regularizer=l2_reg)\n",
"\n",
" user_embedding = Flatten()(user_layer(user_input))\n",
" positive_item_embedding = Flatten()(item_layer(positive_item_input))\n",
" negative_item_embedding = Flatten()(item_layer(negative_item_input))\n",
"\n",
"\n",
" # Similarity computation between embeddings using a MLP similarity\n",
" positive_embeddings_pair = merge([user_embedding, positive_item_embedding],\n",
" mode='concat',\n",
" name=\"positive_embeddings_pair\")\n",
" positive_embeddings_pair = Dropout(dropout)(positive_embeddings_pair)\n",
" negative_embeddings_pair = merge([user_embedding, negative_item_embedding],\n",
" mode='concat',\n",
" name=\"negative_embeddings_pair\")\n",
" negative_embeddings_pair = Dropout(dropout)(negative_embeddings_pair)\n",
"\n",
" # Instanciate the shared similarity architecture\n",
" interaction_layers = make_interaction_mlp(\n",
" user_dim + item_dim, n_hidden=n_hidden, hidden_size=hidden_size,\n",
" dropout=dropout, l2_reg=l2_reg)\n",
"\n",
" positive_similarity = interaction_layers(positive_embeddings_pair)\n",
" negative_similarity = interaction_layers(negative_embeddings_pair)\n",
"\n",
" # The triplet network model, only used for training\n",
" triplet_loss = merge([positive_similarity, negative_similarity],\n",
" mode=margin_comparator_loss, output_shape=(1,),\n",
" name='comparator_loss')\n",
"\n",
" deep_triplet_model = Model(input=[user_input,\n",
" positive_item_input,\n",
" negative_item_input],\n",
" output=triplet_loss)\n",
"\n",
" # The match-score model, only used at inference\n",
" deep_match_model = Model(input=[user_input, positive_item_input],\n",
" output=positive_similarity)\n",
"\n",
" return deep_match_model, deep_triplet_model\n",
"\n",
"\n",
"hyper_parameters = dict(\n",
" user_dim=32,\n",
" item_dim=64,\n",
" n_hidden=2,\n",
" hidden_size=128,\n",
" dropout=0.1,\n",
" l2_reg=0\n",
")\n",
"deep_match_model, deep_triplet_model = build_models(n_users, n_items,\n",
" **hyper_parameters)\n",
"\n",
"\n",
"deep_triplet_model.compile(loss=identity_loss, optimizer='adam')\n",
"fake_y = np.ones_like(pos_data_train['user_id'])\n",
"\n",
"n_epochs = 15\n",
"\n",
"for i in range(n_epochs):\n",
" # Sample new negatives to build different triplets at each epoch\n",
" triplet_inputs = sample_triplets(pos_data_train, max_item_id,\n",
" random_seed=i)\n",
"\n",
" # Fit the model incrementally by doing a single pass over the\n",
" # sampled triplets.\n",
" deep_triplet_model.fit(triplet_inputs, fake_y, shuffle=True,\n",
" batch_size=64, nb_epoch=1, verbose=2)\n",
"\n",
" # Monitor the convergence of the model\n",
" test_auc = average_roc_auc(deep_match_model, pos_data_train, pos_data_test)\n",
" print(\"Epoch %d/%d: test ROC AUC: %0.4f\"\n",
" % (i + 1, n_epochs, test_auc))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Exercise:\n",
"\n",
"Count the number of parameters in `deep_match_model` and `deep_triplet_model`. Which model has the largest number of parameters?"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"user_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"positive_item_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"user_embedding (Embedding) (None, 1, 32) 30208 user_input[0][0] \n",
"____________________________________________________________________________________________________\n",
"item_embedding (Embedding) (None, 1, 64) 107712 positive_item_input[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_4 (Flatten) (None, 32) 0 user_embedding[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_5 (Flatten) (None, 64) 0 item_embedding[0][0] \n",
"____________________________________________________________________________________________________\n",
"positive_embeddings_pair (Merge) (None, 96) 0 flatten_4[0][0] \n",
" flatten_5[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_1 (Dropout) (None, 96) 0 positive_embeddings_pair[0][0] \n",
"____________________________________________________________________________________________________\n",
"sequential_1 (Sequential) (None, 1) 29057 dropout_1[0][0] \n",
"====================================================================================================\n",
"Total params: 166,977\n",
"Trainable params: 166,977\n",
"Non-trainable params: 0\n",
"____________________________________________________________________________________________________\n",
"None\n"
]
}
],
"source": [
"print(deep_match_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"user_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"positive_item_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"negative_item_input (InputLayer) (None, 1) 0 \n",
"____________________________________________________________________________________________________\n",
"user_embedding (Embedding) (None, 1, 32) 30208 user_input[0][0] \n",
"____________________________________________________________________________________________________\n",
"item_embedding (Embedding) (None, 1, 64) 107712 positive_item_input[0][0] \n",
" negative_item_input[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_4 (Flatten) (None, 32) 0 user_embedding[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_5 (Flatten) (None, 64) 0 item_embedding[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_6 (Flatten) (None, 64) 0 item_embedding[1][0] \n",
"____________________________________________________________________________________________________\n",
"positive_embeddings_pair (Merge) (None, 96) 0 flatten_4[0][0] \n",
" flatten_5[0][0] \n",
"____________________________________________________________________________________________________\n",
"negative_embeddings_pair (Merge) (None, 96) 0 flatten_4[0][0] \n",
" flatten_6[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_1 (Dropout) (None, 96) 0 positive_embeddings_pair[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_2 (Dropout) (None, 96) 0 negative_embeddings_pair[0][0] \n",
"____________________________________________________________________________________________________\n",
"sequential_1 (Sequential) (None, 1) 29057 dropout_1[0][0] \n",
" dropout_2[0][0] \n",
"____________________________________________________________________________________________________\n",
"comparator_loss (Merge) (None, 1) 0 sequential_1[1][0] \n",
" sequential_1[2][0] \n",
"====================================================================================================\n",
"Total params: 166,977\n",
"Trainable params: 166,977\n",
"Non-trainable params: 0\n",
"____________________________________________________________________________________________________\n",
"None\n"
]
}
],
"source": [
"print(deep_triplet_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# %load solutions/deep_triplet_parameter_count.py\n",
"# Analysis:\n",
"#\n",
"# Both models have again exactly the same number of parameters,\n",
"# namely the parameters of the 2 embeddings:\n",
"#\n",
"# - user embedding: n_users x user_dim\n",
"# - item embedding: n_items x item_dim\n",
"#\n",
"# and the parameters of the MLP model used to compute the\n",
"# similarity score of an (user, item) pair:\n",
"#\n",
"# - first hidden layer weights: (user_dim + item_dim) * hidden_size\n",
"# - first hidden biases: hidden_size\n",
"# - extra hidden layers weights: hidden_size * hidden_size\n",
"# - extra hidden layers biases: hidden_size\n",
"# - output layer weights: hidden_size * 1\n",
"# - output layer biases: 1\n",
"#\n",
"# The triplet model uses the same item embedding layer\n",
"# twice and the same MLP instance twice:\n",
"# once to compute the positive similarity and the other\n",
"# time to compute the negative similarity. However because\n",
"# those two lanes in the computation graph share the same\n",
"# instances for the item embedding layer and for the MLP,\n",
"# their parameters are shared.\n",
"#\n",
"# Reminder: MLP stands for multi-layer perceptron, which is a\n",
"# common short-hand for Feed Forward Fully Connected Neural\n",
"# Network."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"## Possible Extensions\n",
"\n",
"You can implement any of the following ideas if you want to get a deeper understanding of recommender systems.\n",
"\n",
"\n",
"### Leverage User and Item metadata\n",
"\n",
"As we did for the Explicit Feedback model, it's also possible to extend our models to take additional user and item metadata as side information when computing the match score.\n",
"\n",
"\n",
"### Better Ranking Metrics\n",
"\n",
"In this notebook we evaluated the quality of the ranked recommendations using the ROC AUC metric. This score reflect the ability of the model to correctly rank any pair of items (sampled uniformly at random among all possible items).\n",
"\n",
"In practice recommender systems will only display a few recommendations to the user (typically 1 to 10). It is typically more informative to use an evaluatio metric that characterize the quality of the top ranked items and attribute less or no importance to items that are not good recommendations for a specific users. Popular ranking metrics therefore include the **Precision at k** and the **Mean Average Precision**.\n",
"\n",
"You can read up online about those metrics and try to implement them here.\n",
"\n",
"\n",
"### Hard Negatives Sampling\n",
"\n",
"In this experiment we sampled negative items uniformly at random. However, after training the model for a while, it is possible that the vast majority of sampled negatives have a similarity already much lower than the positive pair and that the margin comparator loss sets the majority of the gradients to zero effectively wasting a lot of computation.\n",
"\n",
"Given the current state of the recsys model we could sample harder negatives with a larger likelihood to train the model better closer to its decision boundary. This strategy is implemented in the WARP loss [1].\n",
"\n",
"The main drawback of hard negative sampling is increasing the risk of sever overfitting if a significant fraction of the labels are noisy.\n",
"\n",
"\n",
"### Factorization Machines\n",
"\n",
"A very popular recommender systems model is called Factorization Machines [2][3]. They two use low rank vector representations of the inputs but they do not use a cosine similarity or a neural network to model user/item compatibility.\n",
"\n",
"It is be possible to adapt our previous code written with Keras to replace the cosine sims / MLP with the low rank FM quadratic interactions by reading through [this gentle introduction](http://tech.adroll.com/blog/data-science/2015/08/25/factorization-machines.html).\n",
"\n",
"If you choose to do so, you can compare the quality of the predictions with those obtained by the [pywFM project](https://github.com/jfloff/pywFM) which provides a Python wrapper for the [official libFM C++ implementation](http://www.libfm.org/). Maciej Kula also maintains a [lighfm](http://www.libfm.org/) that implements an efficient and well documented variant in Cython and Python.\n",
"\n",
"\n",
"## References:\n",
"\n",
" [1] Wsabie: Scaling Up To Large Vocabulary Image Annotation\n",
" Jason Weston, Samy Bengio, Nicolas Usunier, 2011\n",
" https://research.google.com/pubs/pub37180.html\n",
"\n",
" [2] Factorization Machines, Steffen Rendle, 2010\n",
" https://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf\n",
"\n",
" [3] Factorization Machines with libFM, Steffen Rendle, 2012\n",
" in ACM Trans. Intell. Syst. Technol., 3(3), May.\n",
" http://doi.acm.org/10.1145/2168752.2168771"
]
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================================================
FILE: TensorFlow_Examples/labs/02_recommendation/solutions/classification.py
================================================
# For each sample we input the integer identifiers
# of a single user and a single item
user_id_input = Input(shape=[1], name='user')
item_id_input = Input(shape=[1], name='item')
embedding_size = 16
dense_size = 128
dropout_embedding = 0.5
dropout_hidden = 0.2
user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,
input_length=1, name='user_embedding')(user_id_input)
item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,
input_length=1, name='item_embedding')(item_id_input)
# reshape from shape: (batch_size, input_length, embedding_size)
# to shape: (batch_size, input_length * embedding_size) which is
# equal to shape: (batch_size, embedding_size)
user_vecs = Flatten()(user_embedding)
item_vecs = Flatten()(item_embedding)
input_vecs = merge([user_vecs, item_vecs], mode='concat')
input_vecs = Dropout(dropout_embedding)(input_vecs)
x = Dense(dense_size, activation='relu')(input_vecs)
x = Dropout(dropout_hidden)(x)
x = Dense(dense_size, activation='relu')(x)
y = Dense(output_dim=5, activation='softmax')(x)
model = Model(input=[user_id_input, item_id_input], output=y)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
initial_train_preds = model.predict([user_id_train, item_id_train]).argmax(axis=1) + 1
print("Random init MSE: %0.3f" % mean_squared_error(initial_train_preds, rating_train))
print("Random init MAE: %0.3f" % mean_absolute_error(initial_train_preds, rating_train))
history = model.fit([user_id_train, item_id_train], rating_train - 1,
batch_size=64, nb_epoch=6, validation_split=0.1,
shuffle=True)
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='validation')
plt.ylim(0, 2)
plt.legend(loc='best')
plt.title('loss');
test_preds = model.predict([user_id_test, item_id_test]).argmax(axis=1) + 1
print("Final test MSE: %0.3f" % mean_squared_error(test_preds, rating_test))
print("Final test MAE: %0.3f" % mean_absolute_error(test_preds, rating_test))
================================================
FILE: TensorFlow_Examples/labs/02_recommendation/solutions/compute_errors.py
================================================
squared_differences = np.square(initial_train_preds - rating_train.values)
absolute_differences = np.abs(initial_train_preds - rating_train.values)
print("Random init MSE: %0.3f" % np.mean(squared_differences))
print("Random init MAE: %0.3f" % np.mean(absolute_differences))
# You may also use sklearn metrics to do so with less numpy engineering
#from sklearn.metrics import mean_squared_error, mean_absolute_error
#
#print("Random init MSE: %0.3f" % mean_squared_error(initial_train_preds, rating_train))
#print("Random init MAE: %0.3f" % mean_absolute_error(initial_train_preds, rating_train))
================================================
FILE: TensorFlow_Examples/labs/02_recommendation/solutions/deep_explicit_feedback_recsys.py
================================================
# For each sample we input the integer identifiers
# of a single user and a single item
user_id_input = Input(shape=[1], name='user')
item_id_input = Input(shape=[1], name='item')
embedding_size = 30
user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,
input_length=1, name='user_embedding')(user_id_input)
item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,
input_length=1, name='item_embedding')(item_id_input)
# reshape from shape: (batch_size, input_length, embedding_size)
# to shape: (batch_size, input_length * embedding_size) which is
# equal to shape: (batch_size, embedding_size)
user_vecs = Flatten()(user_embedding)
item_vecs = Flatten()(item_embedding)
input_vecs = merge([user_vecs, item_vecs], mode='concat')
## Error 1: Dropout was too high, preventing any training
input_vecs = Dropout(0.5)(input_vecs)
x = Dense(64, activation='relu')(input_vecs)
## Error 2: output dimension was 2 where we predict only 1-d rating
## Error 3: tanh activation squashes the outputs between -1 and 1
## when we want to predict values between 1 and 5
y = Dense(1)(x)
model = Model(input=[user_id_input, item_id_input], output=y)
## Error 4: A binary crossentropy loss is only useful for binary
## classification, while we are in regression (use mse or mae)
model.compile(optimizer='adam', loss='mae')
initial_train_preds = model.predict([user_id_train, item_id_train])
================================================
FILE: TensorFlow_Examples/labs/02_recommendation/solutions/deep_implicit_feedback_recsys.py
================================================
from keras.models import Model, Sequential
from keras.layers import Embedding, Flatten, Input, Dense, Dropout, merge
from keras.regularizers import l2
def make_interaction_mlp(input_dim, n_hidden=1, hidden_size=64,
dropout=0, l2_reg=None):
"""Build the shared multi layer perceptron"""
mlp = Sequential()
if n_hidden == 0:
# Plug the output unit directly: this is a simple
# linear regression model. Not dropout required.
mlp.add(Dense(1, input_dim=input_dim,
activation='relu', W_regularizer=l2_reg))
else:
mlp.add(Dense(hidden_size, input_dim=input_dim,
activation='relu', W_regularizer=l2_reg))
mlp.add(Dropout(dropout))
for i in range(n_hidden - 1):
mlp.add(Dense(hidden_size, activation='relu',
W_regularizer=l2_reg))
mlp.add(Dropout(dropout))
mlp.add(Dense(1, activation='relu', W_regularizer=l2_reg))
return mlp
def build_models(n_users, n_items, user_dim=32, item_dim=64,
n_hidden=1, hidden_size=64, dropout=0, l2_reg=0):
"""Build models to train a deep triplet network"""
user_input = Input((1,), name='user_input')
positive_item_input = Input((1,), name='positive_item_input')
negative_item_input = Input((1,), name='negative_item_input')
l2_reg = None if l2_reg == 0 else l2(l2_reg)
user_layer = Embedding(n_users, user_dim, input_length=1,
name='user_embedding', W_regularizer=l2_reg)
# The following embedding parameters will be shared to encode both
# the positive and negative items.
item_layer = Embedding(n_items, item_dim, input_length=1,
name="item_embedding", W_regularizer=l2_reg)
user_embedding = Flatten()(user_layer(user_input))
positive_item_embedding = Flatten()(item_layer(positive_item_input))
negative_item_embedding = Flatten()(item_layer(negative_item_input))
# Similarity computation between embeddings using a MLP similarity
positive_embeddings_pair = merge([user_embedding, positive_item_embedding],
mode='concat',
name="positive_embeddings_pair")
positive_embeddings_pair = Dropout(dropout)(positive_embeddings_pair)
negative_embeddings_pair = merge([user_embedding, negative_item_embedding],
mode='concat',
name="negative_embeddings_pair")
negative_embeddings_pair = Dropout(dropout)(negative_embeddings_pair)
# Instanciate the shared similarity architecture
interaction_layers = make_interaction_mlp(
user_dim + item_dim, n_hidden=n_hidden, hidden_size=hidden_size,
dropout=dropout, l2_reg=l2_reg)
positive_similarity = interaction_layers(positive_embeddings_pair)
negative_similarity = interaction_layers(negative_embeddings_pair)
# The triplet network model, only used for training
triplet_loss = merge([positive_similarity, negative_similarity],
mode=margin_comparator_loss, output_shape=(1,),
name='comparator_loss')
deep_triplet_model = Model(input=[user_input,
positive_item_input,
negative_item_input],
output=triplet_loss)
# The match-score model, only used at inference
deep_match_model = Model(input=[user_input, positive_item_input],
output=positive_similarity)
return deep_match_model, deep_triplet_model
hyper_parameters = dict(
user_dim=32,
item_dim=64,
n_hidden=1,
hidden_size=128,
dropout=0.1,
l2_reg=0
)
deep_match_model, deep_triplet_model = build_models(n_users, n_items,
**hyper_parameters)
deep_triplet_model.compile(loss=identity_loss, optimizer='adam')
fake_y = np.ones_like(pos_data_train['user_id'])
n_epochs = 15
for i in range(n_epochs):
# Sample new negatives to build different triplets at each epoch
triplet_inputs = sample_triplets(pos_data_train, max_item_id,
random_seed=i)
# Fit the model incrementally by doing a single pass over the
# sampled triplets.
deep_triplet_model.fit(triplet_inputs, fake_y, shuffle=True,
batch_size=64, nb_epoch=1)
# Monitor the convergence of the model
test_auc = average_roc_auc(deep_match_model, pos_data_train, pos_data_test)
print("Epoch %d/%d: test ROC AUC: %0.4f"
% (i + 1, n_epochs, test_auc))
================================================
FILE: TensorFlow_Examples/labs/02_recommendation/solutions/deep_triplet_parameter_count.py
================================================
# Analysis:
#
# Both models have again exactly the same number of parameters,
# namely the parameters of the 2 embeddings:
#
# - user embedding: n_users x user_dim
# - item embedding: n_items x item_dim
#
# and the parameters of the MLP model used to compute the
# similarity score of an (user, item) pair:
#
# - first hidden layer weights: (user_dim + item_dim) * hidden_size
# - first hidden biases: hidden_size
# - extra hidden layers weights: hidden_size * hidden_size
# - extra hidden layers biases: hidden_size
# - output layer weights: hidden_size * 1
# - output layer biases: 1
#
# The triplet model uses the same item embedding layer
# twice and the same MLP instance twice:
# once to compute the positive similarity and the other
# time to compute the negative similarity. However because
# those two lanes in the computation graph share the same
# instances for the item embedding layer and for the MLP,
# their parameters are shared.
#
# Reminder: MLP stands for multi-layer perceptron, which is a
# common short-hand for Feed Forward Fully Connected Neural
# Network.
================================================
FILE: TensorFlow_Examples/labs/02_recommendation/solutions/ml-1m.py
================================================
import matplotlib.pyplot as plt
from pathlib import Path
from zipfile import ZipFile
from urllib.request import urlretrieve
from keras.layers import Input, Embedding, Flatten, merge, Dense, Dropout, Lambda
from keras.models import Model
import keras.backend as K
import pandas as pd
from sklearn.metrics import mean_squared_error, mean_absolute_error
ML_1M_URL = "http://files.grouplens.org/datasets/movielens/ml-1m.zip"
ML_1M_FILENAME = Path(ML_1M_URL.rsplit('/', 1)[1])
ML_1M_FOLDER = Path('ml-1m')
if not ML_1M_FILENAME.exists():
print('Downloading %s to %s...' % (ML_1M_URL, ML_1M_FILENAME))
urlretrieve(ML_1M_URL, ML_1M_FILENAME.name)
if not ML_1M_FOLDER.exists():
print('Extracting %s to %s...' % (ML_1M_FILENAME, ML_1M_FOLDER))
ZipFile(ML_1M_FILENAME.name).extractall('.')
all_ratings = pd.read_csv(ML_1M_FOLDER / 'ratings.dat', sep='::',
names=["user_id", "item_id", "rating", "timestamp"])
all_ratings.head()
max_user_id = all_ratings['user_id'].max()
max_item_id = all_ratings['item_id'].max()
from sklearn.model_selection import train_test_split
ratings_train, ratings_test = train_test_split(
all_ratings, test_size=0.2, random_state=0)
user_id_train = ratings_train['user_id']
item_id_train = ratings_train['item_id']
rating_train = ratings_train['rating']
user_id_test = ratings_test['user_id']
item_id_test = ratings_test['item_id']
rating_test = ratings_test['rating']
user_id_input = Input(shape=[1], name='user')
item_id_input = Input(shape=[1], name='item')
embedding_size = 32
user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,
input_length=1, name='user_embedding')(user_id_input)
item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,
input_length=1, name='item_embedding')(item_id_input)
# reshape from shape: (batch_size, input_length, embedding_size)
# to shape: (batch_size, input_length * embedding_size) which is
# equal to shape: (batch_size, embedding_size)
user_vecs = Flatten()(user_embedding)
item_vecs = Flatten()(item_embedding)
input_vecs = merge([user_vecs, item_vecs], mode='concat')
input_vecs = Dropout(0.5)(input_vecs)
dense_size = 64
x = Dense(dense_size, activation='relu')(input_vecs)
x = Dense(dense_size, activation='relu')(input_vecs)
y = Dense(output_dim=1)(x)
model = Model(input=[user_id_input, item_id_input], output=y)
model.compile(optimizer='adam', loss='mae')
initial_train_preds = model.predict([user_id_train, item_id_train])
history = model.fit([user_id_train, item_id_train], rating_train,
batch_size=64, nb_epoch=15, validation_split=0.1,
shuffle=True)
test_preds = model.predict([user_id_test, item_id_test])
print("Final test MSE: %0.3f" % mean_squared_error(test_preds, rating_test))
print("Final test MAE: %0.3f" % mean_absolute_error(test_preds, rating_test))
================================================
FILE: TensorFlow_Examples/labs/02_recommendation/solutions/similarity.py
================================================
EPSILON = 1e-07
def cosine(x, y):
dot_pdt = np.dot(x, y.T)
norms = np.linalg.norm(x) * np.linalg.norm(y)
return dot_pdt / (norms + EPSILON)
# Computes cosine similarities between x and all item embeddings
def cosine_similarities(x):
dot_pdts = np.dot(item_embeddings, x)
norms = np.linalg.norm(x) * np.linalg.norm(item_embeddings, axis=1)
return dot_pdts / (norms + EPSILON)
# Computes euclidean distances between x and all item embeddings
def euclidean_distances(x):
return np.linalg.norm(item_embeddings - x, axis=1)
# Computes top_n most similar items to an idx,
def most_similar(idx, top_n=10, mode='euclidean'):
sorted_indexes=0
if mode=='euclidean':
dists = euclidean_distances(item_embeddings[idx])
sorted_indexes = np.argsort(dists)
idxs = sorted_indexes[0:top_n]
return list(zip(items["name"][idxs], dists[idxs]))
else:
sims = cosine_similarities(item_embeddings[idx])
# [::-1] makes it possible to reverse the order of a numpy
# array, this is required because most similar items have
# a larger cosine similarity value
sorted_indexes = np.argsort(sims)[::-1]
idxs = sorted_indexes[0:top_n]
return list(zip(items["name"][idxs], sims[idxs]))
# sanity checks:
print("cosine of item 1 and item 1: "
+ str(cosine(item_embeddings[1], item_embeddings[1])))
euc_dists = euclidean_distances(item_embeddings[1])
print(euc_dists.shape)
print(euc_dists[1:5])
print()
# Test on movie 181: Return of the Jedi
print("Items closest to 'Return of the Jedi':")
for title, dist in most_similar(181, mode="euclidean"):
print(title, dist)
# We observe that the embedding is poor at representing similarities
# between movies, as most distance/similarities are very small/big
# One may notice a few clusters though
# it's interesting to plot the following distributions
# plt.hist(euc_dists)
# The reason for that is that the number of ratings is low and the embedding
# does not automatically capture semantic relationships in that context.
# Better representations arise with higher number of ratings, and less overfitting models
================================================
FILE: TensorFlow_Examples/labs/02_recommendation/solutions/triplet_parameter_count.py
================================================
# Analysis:
#
# Both models have exactly the same number of parameters,
# namely the parameters of the 2 embeddings:
#
# - user embedding: n_users x embedding_dim
# - item embedding: n_items x embedding_dim
#
# The triplet model uses the same item embedding twice,
# once to compute the positive similarity and the other
# time to compute the negative similarity. However because
# those two nodes in the computation graph share the same
# instance of the item embedding layer, the item embedding
# weight matrix is shared by the two branches of the
# graph and therefore the total number of parameters for
# each model is in both cases:
#
# (n_users x embedding_dim) + (n_items x embedding_dim)
================================================
FILE: TensorFlow_Examples/labs/03_LSTM_NLP/NLP_word_vectors_classification.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Text classification using Neural Networks\n",
"\n",
"The goal of this notebook is to learn to use Neural Networks for text classification.\n",
"\n",
"In this notebook, we will:\n",
"- Train a shallow model with learning embeddings\n",
"- Download pre-trained embeddings from Glove\n",
"- Use these pre-trained embeddings\n",
"\n",
"However keep in mind:\n",
"- Deep Learning can be better on text classification that simpler ML techniques, but only on very large datasets and well designed/tuned models.\n",
"- We won't be using the most efficient (in terms of computing) techniques, as Keras is good for prototyping but rather inefficient for training small embedding models on text.\n",
"- The following projects can replicate similar word embedding models much more efficiently: [word2vec](https://github.com/dav/word2vec) and [gensim's word2vec](https://radimrehurek.com/gensim/models/word2vec.html) (self-supervised learning only), [fastText](https://github.com/facebookresearch/fastText) (both supervised and self-supervised learning), [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/wiki) (supervised learning).\n",
"- Plain shallow sparse TF-IDF bigrams features without any embedding and Logistic Regression or Multinomial Naive Bayes is often competitive in small to medium datasets.\n",
"\n",
"\n",
"### 20 Newsgroups Dataset\n",
"\n",
"The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups http://qwone.com/~jason/20Newsgroups/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.datasets import fetch_20newsgroups\n",
"\n",
"newsgroups_train = fetch_20newsgroups(subset='train')\n",
"newsgroups_test = fetch_20newsgroups(subset='test')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"sample_idx = 1000\n",
"print(newsgroups_train[\"data\"][sample_idx])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"target_names = newsgroups_train[\"target_names\"]\n",
"\n",
"target_id = newsgroups_train[\"target\"][sample_idx]\n",
"print(\"Class of previous message:\", target_names[target_id])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Here are all the possible classes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"target_names"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Preprocessing text for the (supervised) CBOW model\n",
"\n",
"We will implement a simple classification model in Keras. Raw text requires (sometimes a lot of) preprocessing.\n",
"\n",
"The following cells uses Keras to preprocess text:\n",
"- using a tokenizer. You may use different tokenizers (from scikit-learn, NLTK, custom Python function etc.). This converts the texts into sequences of indices representing the `20000` most frequent words\n",
"- sequences have different lengths, so we pad them (add 0s at the end until the sequence is of length `1000`)\n",
"- we convert the output classes as 1-hot encodings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from keras.preprocessing.text import Tokenizer\n",
"\n",
"MAX_NB_WORDS = 20000\n",
"\n",
"# get the raw text data\n",
"texts_train = newsgroups_train[\"data\"]\n",
"texts_test = newsgroups_test[\"data\"]\n",
"\n",
"# finally, vectorize the text samples into a 2D integer tensor\n",
"tokenizer = Tokenizer(nb_words=MAX_NB_WORDS, char_level=False)\n",
"tokenizer.fit_on_texts(texts_train)\n",
"sequences = tokenizer.texts_to_sequences(texts_train)\n",
"sequences_test = tokenizer.texts_to_sequences(texts_test)\n",
"\n",
"word_index = tokenizer.word_index\n",
"print('Found %s unique tokens.' % len(word_index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Tokenized sequences are converted to list of token ids (with an integer code):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [],
"source": [
"sequences[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The tokenizer object stores a mapping (vocabulary) from word strings to token ids that can be inverted to reconstruct the original message (without formatting):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"type(tokenizer.word_index), len(tokenizer.word_index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"index_to_word = dict((i, w) for w, i in tokenizer.word_index.items())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"\" \".join([index_to_word[i] for i in sequences[0]])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's have a closer look at the tokenized sequences:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"seq_lens = [len(s) for s in sequences]\n",
"print(\"average length: %0.1f\" % np.mean(seq_lens))\n",
"print(\"max length: %d\" % max(seq_lens))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.hist(seq_lens, bins=50);"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's zoom on the distribution of regular sized posts. The vast majority of the posts have less than 1000 symbols:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"plt.hist([l for l in seq_lens if l < 3000], bins=50);"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's truncate and pad all the sequences to 1000 symbols to build the training set:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from keras.preprocessing.sequence import pad_sequences\n",
"\n",
"\n",
"MAX_SEQUENCE_LENGTH = 1000\n",
"\n",
"# pad sequences with 0s\n",
"x_train = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
"x_test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH)\n",
"print('Shape of data tensor:', x_train.shape)\n",
"print('Shape of data test tensor:', x_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from keras.utils.np_utils import to_categorical\n",
"y_train = newsgroups_train[\"target\"]\n",
"y_test = newsgroups_test[\"target\"]\n",
"\n",
"y_train = to_categorical(np.asarray(y_train))\n",
"print('Shape of label tensor:', y_train.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### A simple supervised CBOW model in Keras\n",
"\n",
"The following computes a very simple model, as described in [fastText](https://github.com/facebookresearch/fastText):\n",
"\n",
"![](\"images/fasttext.svg\")
\n",
"\n",
"- Build an embedding layer mapping each word to a vector representation\n",
"- Compute the vector representation of all words in each sequence and average them\n",
"- Add a dense layer to output 20 classes (+ softmax)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from keras.layers import Dense, Input, Flatten\n",
"from keras.layers import GlobalAveragePooling1D, Embedding\n",
"from keras.models import Model\n",
"\n",
"EMBEDDING_DIM = 50\n",
"N_CLASSES = len(target_names)\n",
"\n",
"# input: a sequence of MAX_SEQUENCE_LENGTH integers\n",
"sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
"\n",
"embedding_layer = Embedding(MAX_NB_WORDS, EMBEDDING_DIM,\n",
" input_length=MAX_SEQUENCE_LENGTH,\n",
" trainable=True)\n",
"embedded_sequences = embedding_layer(sequence_input)\n",
"\n",
"average = GlobalAveragePooling1D()(embedded_sequences)\n",
"predictions = Dense(N_CLASSES, activation='softmax')(average)\n",
"\n",
"model = Model(sequence_input, predictions)\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='adam', metrics=['acc'], verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"model.fit(x_train, y_train, validation_split=0.1,\n",
" nb_epoch=10, batch_size=128, verbose=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Exercice**\n",
" - compute model accuracy on test set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# %load solutions/accuracy.py\n",
"output_test = model.predict(x_test)\n",
"test_casses = np.argmax(output_test, axis=-1)\n",
"print(\"test accuracy:\", np.mean(test_casses == y_test))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Building more complex models\n",
"\n",
"\n",
"- From the previous template, build more complex models using:\n",
" - 1d convolution and 1d maxpooling. Note that you will still need a GloabalAveragePooling or Flatten after the convolutions\n",
" - Recurrent neural networks through LSTM (you will need to reduce sequence length before)\n",
" \n",
" \n",
"![](\"images/unrolled_rnn_one_output_2.svg\")
\n",
"\n",
"\n",
" - more intermediate layers, combination of dense, conv, recurrent\n",
" - different recurrent (GRU, RNN)\n",
" - bidirectional LSTMs\n",
"\n",
"Note: The goal is to build working models rather than getting better test accuracy. To achieve much better results, we'd need more computation time and data quantity. Build your model, and verify that they converge to OK results."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# %load solutions/lstm.py\n",
"from keras.layers import LSTM, Conv1D, MaxPooling1D\n",
"\n",
"# input: a sequence of MAX_SEQUENCE_LENGTH integers\n",
"sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
"embedded_sequences = embedding_layer(sequence_input)\n",
"\n",
"# 1D convolution with 64 output channels\n",
"x = Conv1D(64, 5)(embedded_sequences)\n",
"# MaxPool divides the length of the sequence by 5\n",
"x = MaxPooling1D(5)(x)\n",
"x = Conv1D(64, 5)(x)\n",
"x = MaxPooling1D(5)(x)\n",
"# LSTM layer with a hidden size of 64\n",
"x = LSTM(64)(x)\n",
"predictions = Dense(20, activation='softmax')(x)\n",
"\n",
"model = Model(sequence_input, predictions)\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['acc'])\n",
"\n",
"# You will get large speedups with these models by using a GPU\n",
"# The model might take a lot of time to converge, and even more\n",
"# if you add dropout (needed to prevent overfitting)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# %load solutions/conv1d.py\n",
"from keras.layers import Conv1D, MaxPooling1D, Flatten\n",
"\n",
"sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
"embedded_sequences = embedding_layer(sequence_input)\n",
"\n",
"# A 1D convolution with 128 output channels\n",
"x = Conv1D(128, 5, activation='relu')(embedded_sequences)\n",
"# MaxPool divides the length of the sequence by 5\n",
"x = MaxPooling1D(5)(x)\n",
"# A 1D convolution with 64 output channels\n",
"x = Conv1D(64, 5, activation='relu')(x)\n",
"# MaxPool divides the length of the sequence by 5\n",
"x = MaxPooling1D(5)(x)\n",
"x = Flatten()(x)\n",
"\n",
"predictions = Dense(20, activation='softmax')(x)\n",
"\n",
"model = Model(sequence_input, predictions)\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['acc'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"model.fit(x_train, y_train, validation_split=0.1,\n",
" nb_epoch=10, batch_size=128, verbose=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Loading pre-trained embeddings\n",
"\n",
"The file `glove100K.100d.txt` is an extract of [Glove](http://nlp.stanford.edu/projects/glove/) Vectors, that were trained on english Wikipedia 2014 + Gigaword 5 (6B tokens).\n",
"\n",
"We extracted the `100 000` most frequent words. They have a dimension of `100`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"embeddings_index = {}\n",
"embeddings_vectors = []\n",
"f = open('glove100K.100d.txt', 'rb')\n",
"\n",
"word_idx = 0\n",
"for line in f:\n",
" values = line.decode('utf-8').split()\n",
" word = values[0]\n",
" vector = np.asarray(values[1:], dtype='float32')\n",
" embeddings_index[word] = word_idx\n",
" embeddings_vectors.append(vector)\n",
" word_idx = word_idx + 1\n",
"f.close()\n",
"\n",
"inv_index = {v: k for k, v in embeddings_index.items()}\n",
"print(\"found %d different words in the file\" % word_idx)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Stack all embeddings in a large numpy array\n",
"glove_embeddings = np.vstack(embeddings_vectors)\n",
"glove_norms = np.linalg.norm(glove_embeddings, axis=-1, keepdims=True)\n",
"glove_embeddings_normed = glove_embeddings / glove_norms\n",
"print(glove_embeddings.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def get_emb(word):\n",
" idx = embeddings_index.get(word)\n",
" if idx is None:\n",
" return None\n",
" else:\n",
" return glove_embeddings[idx]\n",
"\n",
" \n",
"def get_normed_emb(word):\n",
" idx = embeddings_index.get(word)\n",
" if idx is None:\n",
" return None\n",
" else:\n",
" return glove_embeddings_normed[idx]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"get_emb(\"computer\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Finding most similar words\n",
"\n",
"**Exercice**\n",
"\n",
"Build a function to find most similar words, given a word as query:\n",
"- lookup the vector for the query word in the Glove index;\n",
"- compute the cosine similarity between a word embedding and all other words;\n",
"- display the top 10 most similar words.\n",
"\n",
"**Bonus**\n",
"\n",
"Change your function so that it takes multiple words as input (by averaging them)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# %load solutions/most_similar.py\n",
"def most_similar(words, topn=10):\n",
" query_emb = 0\n",
" # If we have a list of words instead of one word\n",
" # (bonus question)\n",
" if type(words) == list:\n",
" for word in words:\n",
" query_emb += get_emb(word) \n",
" else:\n",
" query_emb = get_emb(words)\n",
" \n",
" query_emb = query_emb / np.linalg.norm(query_emb)\n",
" \n",
" # Large numpy vector with all cosine similarities\n",
" # between emb and all other words\n",
" cosines = np.dot(glove_embeddings_normed, query_emb)\n",
" \n",
" # topn most similar indexes corresponding to cosines\n",
" idxs = np.argsort(cosines)[::-1][:topn]\n",
" \n",
" # pretty return with word and similarity\n",
" return [(inv_index[idx], cosines[idx]) for idx in idxs]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"most_similar(\"cpu\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"most_similar(\"pitt\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"most_similar(\"jolie\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Predict the future better than tarot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"np.dot(get_normed_emb('aniston'), get_normed_emb('pitt'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"np.dot(get_normed_emb('jolie'), get_normed_emb('pitt'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"most_similar(\"1\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# bonus: yangtze is a chinese river\n",
"most_similar([\"river\", \"chinese\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Displaying vectors with t-SNE\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.manifold import TSNE\n",
"\n",
"word_emb_tsne = TSNE(perplexity=30).fit_transform(glove_embeddings_normed[:1000])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(40, 40))\n",
"axis = plt.gca()\n",
"np.set_printoptions(suppress=True)\n",
"plt.scatter(word_emb_tsne[:, 0], word_emb_tsne[:, 1], marker=\".\", s=1)\n",
"\n",
"for idx in range(1000):\n",
" plt.annotate(inv_index[idx],\n",
" xy=(word_emb_tsne[idx, 0], word_emb_tsne[idx, 1]),\n",
" xytext=(0, 0), textcoords='offset points')\n",
"plt.savefig(\"tsne.png\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Using pre-trained embeddings in our model\n",
"\n",
"We want to use these pre-trained embeddings for transfer learning. This process is rather similar than transfer learning in image recognition: the features learnt on words might help us bootstrap the learning process, and increase performance if we don't have enough training data.\n",
"- We initialize embedding matrix from the model with Glove embeddings:\n",
" - take all words from our 20 Newgroup vocabulary (`MAX_NB_WORDS = 20000`), and look up their Glove embedding \n",
" - place the Glove embedding at the corresponding index in the matrix\n",
" - if the word is not in the Glove vocabulary, we only place zeros in the matrix\n",
"- We may fix these embeddings or fine-tune them"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"EMBEDDING_DIM = 100\n",
"\n",
"# prepare embedding matrix\n",
"nb_words_in_matrix = 0\n",
"nb_words = min(MAX_NB_WORDS, len(word_index))\n",
"embedding_matrix = np.zeros((nb_words, EMBEDDING_DIM))\n",
"for word, i in word_index.items():\n",
" if i >= MAX_NB_WORDS:\n",
" continue\n",
" embedding_vector = get_emb(word)\n",
" if embedding_vector is not None:\n",
" # words not found in embedding index will be all-zeros.\n",
" embedding_matrix[i] = embedding_vector\n",
" nb_words_in_matrix = nb_words_in_matrix + 1\n",
" \n",
"print(\"added %d words in the embedding matrix\" % nb_words_in_matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Build a layer with pre-trained embeddings:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"pretrained_embedding_layer = Embedding(\n",
" MAX_NB_WORDS, EMBEDDING_DIM,\n",
" weights=[embedding_matrix],\n",
" input_length=MAX_SEQUENCE_LENGTH,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### A model with pre-trained Embeddings\n",
"\n",
"Average word embeddings pre-trained with Glove / Word2Vec usually works suprisingly well. However, when averaging more than `10-15` words, the resulting vector becomes too noisy and classification performance is degraded."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
"embedded_sequences = pretrained_embedding_layer(sequence_input)\n",
"average = GlobalAveragePooling1D()(embedded_sequences)\n",
"predictions = Dense(N_CLASSES, activation='softmax')(average)\n",
"\n",
"model = Model(sequence_input, predictions)\n",
"\n",
"# We don't want to fine-tune embeddings\n",
"model.layers[1].trainable=False\n",
"\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='adam', metrics=['acc'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"model.fit(x_train, y_train, validation_split=0.1,\n",
" nb_epoch=10, batch_size=128, verbose=2)\n",
"\n",
"# Note, on this type of task, this technique will \n",
"# degrade results as we train much less parameters\n",
"# and we average a large number pre-trained embeddings.\n",
"# You will notice much less overfitting then!\n",
"# Using convolutions / LSTM will help\n",
"# It is also advisable to treat seperately pre-trained\n",
"# embeddings and words out of vocabulary."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Reality check\n",
"\n",
"On small/medium datasets, simpler classification methods usually perform better, and are much more efficient to compute. Here are two resources to go further:\n",
"- Naive Bayes approach, using scikit-learn http://scikit-learn.org/stable/datasets/twenty_newsgroups.html\n",
"- Alec Radford (OpenAI) gave a very interesting presentation, showing that you need a VERY large dataset to have real gains from GRU/LSTM in text classification https://www.slideshare.net/odsc/alec-radfordodsc-presentation\n",
"\n",
"However, when looking at features, one can see that classification using simple methods isn't very robust, and won't generalize well to slightly different domains (e.g. forum posts => emails)\n",
"\n",
"Note: Implementation in Keras for text is very slow due to python overhead and lack of hashing techniques. The fastText implementation https://github.com/facebookresearch/fasttext is much, much faster."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"## Going further\n",
"\n",
"- Compare pre-trained embeddings vs specifically trained embeddings\n",
"- Train your own wordvectors in any language using [gensim's word2vec](https://radimrehurek.com/gensim/models/word2vec.html)\n",
"- Check [Keras Examples](https://github.com/fchollet/keras/tree/master/examples) on `imdb` sentiment analysis\n",
"- Install fastText (Linux or macOS only, use the Linux VM if under Windows) and give it a try on the classification example in its repository."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
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"metadata": {
"kernelspec": {
"display_name": "Python 3",
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"name": "python3"
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"language_info": {
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"file_extension": ".py",
"mimetype": "text/x-python",
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================================================
FILE: TensorFlow_Examples/labs/03_conv_nets/ConvNets_with_TensorFlow_rendered.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
},
{
"data": {
"text/plain": [
"'/Users/ruizhang/.keras/models/resnet50_weights_tf_dim_ordering_tf_kernels.h5'"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"## Run this cell once before the lab to download\n",
"## the mnist dataset and the pre-trained ResNet50 model. \n",
"\n",
"## Mnist\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
"\n",
"## Keras pre-trained weights\n",
"from keras.utils.data_utils import get_file\n",
"get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',\n",
" 'https://github.com/fchollet/deep-learning-models/releases'+\n",
" '/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5',\n",
" cache_subdir='models',\n",
" md5_hash='a7b3fe01876f51b976af0dea6bc144eb')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Convolutional Neural Networks\n",
"\n",
"Objectives:\n",
"- TensorFlow tutorial\n",
"- Application of convolution on images\n",
"- First conv net on MNIST with TensorFlow\n",
"- Use a pre-trained ResNet with Keras for transfer learning (second notebook)\n",
"\n",
"Home assignment: fine-tuning a resnet on GPU (third notebook)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## TensorFlow 101\n",
"\n",
"TensorFlow is a symbolic graph computation engine, that allows automatic differentiation of each node\n",
"- https://www.tensorflow.org \n",
"- https://www.tensorflow.org/tutorials/mnist/tf/\n",
"\n",
"TensorFlow builds where nodes may be:\n",
"- **constant:** constants tensors, such as a learning rate\n",
"- **Variables:** any tensor, such as parameters of the models\n",
"- **Placeholders:** placeholders for inputs and outputs of your models\n",
"- many other types of nodes (functions, loss, ...)\n",
"\n",
"The graph is symbolic, no computation is performed until a `Session` is defined and the command `run` or `eval` is invoked. TensorFlow may run this computation on (multiple) CPUs or GPUs"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n",
"a = tf.constant(3)\n",
"b = tf.constant(2)\n",
"c = a + b"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"![](\"images/basic_seq2seq.png\")
\n",
"\n",
"From: [Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. \"Sequence to sequence learning with neural networks.\" NIPS 2014](https://arxiv.org/abs/1409.3215)\n",
"\n",
"\n",
"\n",
"For a given source sequence - target sequence pair, we will:\n",
"- tokenize the source and target sequences;\n",
"- reverse the order of the source sequence;\n",
"- build the input sequence by concatenating the reversed source sequence and the target sequence in original order using the `_GO` token as a delimiter, \n",
"- build the output sequence by appending the `_EOS` token to the source sequence.\n",
"\n",
"\n",
"Let's do this as a function using the original string representations for the tokens so as to make it easier to debug:"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Exercise**\n",
"- Build a function which adapts a pair of tokenized sequences to the framework above.\n",
"- The function should have a reverse_source as an option.\n",
"\n",
"*Note*: \n",
"- The function should output two sequences of string tokens: one to be fed as the input and the other as expected output for the seq2seq network. We will handle the padding later;\n",
"- Don't forget to insert the `_GO` and `_EOS` special symbols at the right locations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# %load solutions/make_input_output.py\n",
"def make_input_output(source_tokens, target_tokens, reverse_source=True):\n",
" if reverse_source:\n",
" source_tokens = source_tokens[::-1]\n",
" input_tokens = source_tokens + [GO] + target_tokens\n",
" output_tokens = target_tokens + [EOS]\n",
" return input_tokens, output_tokens\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"input_tokens, output_tokens = make_input_output(\n",
" ['cent', 'vingt', 'et', 'un'],\n",
" ['1', '2', '1'],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['un', 'et', 'vingt', 'cent', '_GO', '1', '2', '1']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input_tokens"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['1', '2', '1', '_EOS']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_tokens"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Vectorization of the parallel corpus\n",
"\n",
"Let's apply the previous transformation to each pair of (source, target) sequene and use a shared vocabulary to store the results in numpy arrays of integer token ids, with padding on the left so that all input / output sequences have the same length: "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"all_tokenized_sequences = tokenized_fr_train + tokenized_num_train\n",
"shared_vocab, rev_shared_vocab = build_vocabulary(all_tokenized_sequences)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"max_length = 20 # found by introspection of our training set\n",
"\n",
"def vectorize_corpus(source_sequences, target_sequences, shared_vocab,\n",
" word_level_source=True, word_level_target=True,\n",
" max_length=max_length):\n",
" assert len(source_sequences) == len(target_sequences)\n",
" n_sequences = len(source_sequences)\n",
" source_ids = np.empty(shape=(n_sequences, max_length), dtype=np.int32)\n",
" source_ids.fill(shared_vocab[PAD])\n",
" target_ids = np.empty(shape=(n_sequences, max_length), dtype=np.int32)\n",
" target_ids.fill(shared_vocab[PAD])\n",
" numbered_pairs = zip(range(n_sequences), source_sequences, target_sequences)\n",
" for i, source_seq, target_seq in numbered_pairs:\n",
" source_tokens = tokenize(source_seq, word_level=word_level_source)\n",
" target_tokens = tokenize(target_seq, word_level=word_level_target)\n",
" \n",
" in_tokens, out_tokens = make_input_output(source_tokens, target_tokens)\n",
" \n",
" in_token_ids = [shared_vocab.get(t, UNK) for t in in_tokens]\n",
" source_ids[i, -len(in_token_ids):] = in_token_ids\n",
" \n",
" out_token_ids = [shared_vocab.get(t, UNK) for t in out_tokens]\n",
" target_ids[i, -len(out_token_ids):] = out_token_ids\n",
" return source_ids, target_ids"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"X_train, Y_train = vectorize_corpus(fr_train, num_train, shared_vocab,\n",
" word_level_target=False)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 20)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 20)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'deux mille huit cent quatre vingt deux'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fr_train[0]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'2882'"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_train[0]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 0, 0, 0, 0, 0, 0, 0, 18, 38, 28, 14, 22, 23, 18, 1, 6,\n",
" 12, 12, 6], dtype=int32)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train[0]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 12,\n",
" 12, 6, 2], dtype=int32)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y_train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"This looks good. In particular we can note:\n",
"\n",
"- the PAD=0 symbol at the beginning of the two sequences,\n",
"- the input sequence has the GO=1 symbol to separate the source from the target,\n",
"- the output sequence is a shifted version of the target and ends with EOS=2.\n",
"\n",
"Let's vectorize the validation and test set to be able to evaluate our models:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"X_val, Y_val = vectorize_corpus(fr_val, num_val, shared_vocab,\n",
" word_level_target=False)\n",
"X_test, Y_test = vectorize_corpus(fr_test, num_test, shared_vocab,\n",
" word_level_target=False)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"((5000, 20), (5000, 20))"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_val.shape, Y_val.shape"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"((5000, 20), (5000, 20))"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.shape, Y_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### A simple homogeneous Seq2Seq architecture\n",
"\n",
"To keep the architecture simple we will use the **same RNN model and weights for both the encoder part** (before the `_GO` token) **and the decoder part** (after the `_GO` token).\n",
"\n",
"We may GRU recurrent cell instead of LSTM because it is slightly faster to compute and should give comparable results.\n",
"\n",
"**Exercise:**\n",
"- Build a Seq2Seq model:\n",
" - Start with an Embedding layer;\n",
" - Add a single GRU layer: the GRU layer should yield a sequence of output vectors, one at each timestep;\n",
" - Add a Dense layer to adapt the ouput dimension of the GRU layer to the dimension of the output vocabulary;\n",
" - Don't forget to insert some Dropout layer(s), especially after the Embedding layer.\n",
"\n",
"Note:\n",
"- The output dimension of the Embedding layer should be smaller than usual be cause we have small vocabulary size;\n",
"- The dimension of the GRU should be larger to give the Seq2Seq model enough \"working memory\" to memorize the full input sequence before decoding it;\n",
"- Your model should output a shape `[batch, sequence_length, vocab_size]`."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# %load solutions/simple_seq2seq.py\n",
"from keras.models import Sequential\n",
"from keras.layers import Embedding, Dropout, GRU, Dense\n",
"\n",
"vocab_size = len(shared_vocab)\n",
"simple_seq2seq = Sequential()\n",
"simple_seq2seq.add(Embedding(vocab_size, 32, input_length=max_length))\n",
"simple_seq2seq.add(Dropout(0.2))\n",
"simple_seq2seq.add(GRU(64, return_sequences=True))\n",
"simple_seq2seq.add(Dense(vocab_size, activation='softmax'))\n",
"\n",
"# Here we use the sparse_categorical_crossentropy loss to be able to pass\n",
"# integer-coded output for the token ids without having to convert to one-hot\n",
"# codes\n",
"simple_seq2seq.compile(optimizer='adam', loss='sparse_categorical_crossentropy')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's use a callback mechanism to automatically snapshot the best model found so far on the validation set:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from keras.callbacks import ModelCheckpoint\n",
"from keras.models import load_model\n",
"\n",
"\n",
"best_model_fname = \"simple_seq2seq_checkpoint.h5\"\n",
"best_model_cb = ModelCheckpoint(best_model_fname, monitor='val_loss',\n",
" save_best_only=True, verbose=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We need to use np.expand_dims trick on Y: this is required by Keras because of we use a sparse (integer-based) representation for the output:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ruizhang/anaconda3/anaconda/lib/python3.6/site-packages/keras/models.py:851: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
" warnings.warn('The `nb_epoch` argument in `fit` '\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 10000 samples, validate on 5000 samples\n",
"Epoch 1/5\n",
"Epoch 00000: val_loss improved from inf to 0.60855, saving model to simple_seq2seq_checkpoint.h5\n",
"23s - loss: 1.0676 - val_loss: 0.6086\n",
"Epoch 2/5\n",
"Epoch 00001: val_loss improved from 0.60855 to 0.56051, saving model to simple_seq2seq_checkpoint.h5\n",
"20s - loss: 0.5827 - val_loss: 0.5605\n",
"Epoch 3/5\n",
"Epoch 00002: val_loss improved from 0.56051 to 0.53664, saving model to simple_seq2seq_checkpoint.h5\n",
"23s - loss: 0.5548 - val_loss: 0.5366\n",
"Epoch 4/5\n",
"Epoch 00003: val_loss improved from 0.53664 to 0.48266, saving model to simple_seq2seq_checkpoint.h5\n",
"22s - loss: 0.5156 - val_loss: 0.4827\n",
"Epoch 5/5\n",
"Epoch 00004: val_loss improved from 0.48266 to 0.43141, saving model to simple_seq2seq_checkpoint.h5\n",
"20s - loss: 0.4649 - val_loss: 0.4314\n"
]
},
{
"data": {
"text/plain": [
"![](\"figs/skyarrow.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"In this exercise we will implement the algorithm in Keras and test it on some example images to see its effect."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt \n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.applications import vgg16\n",
"from keras.layers import Input\n",
"from dream import *"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We will use the same image for the example. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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M7d7P9pw51JUJ6KQVlEMI7IeViVQrV2HP//BXty04+fjIQ/7+/pc/Zq/O/o/Y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UsQUleogCfhyZ/cBjWmFRc0sNEa+emjJ18ixUJKc20MQYX+SKTSYNnNfENE2U9ULwV8+unN8/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Z4YP/zFz3n50Pnkow/57O0XlHzHIZ3ol9dOywyub1cFUZZWSS1chHTLtnp2PNQfsr8fsxjA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\nQmCC\n",
"text/plain": [
"![](\"figs/style-equation.png\")
"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"style_weight = 0.5\n",
"content_weight = 0.5\n",
"\n",
"# combine these loss functions into a single scalar\n",
"loss = K.variable(0.)\n",
"\n",
"# loss for content layers\n",
"for layer_name in content_layers:\n",
" layer_features = outputs_dict[layer_name]\n",
"\n",
" # we get the content features from the content image and the combination image\n",
" base_image_features = layer_features[0, :, :, :]\n",
" combination_features = layer_features[2, :, :, :]\n",
"\n",
" # content_loss is defined in style.py\n",
" cl = content_loss(base_image_features,combination_features)\n",
" loss += (content_weight / len(content_layers)) * cl\n",
"\n",
"# loss for style layers\n",
"for layer_name in style_layers:\n",
" layer_features = outputs_dict[layer_name]\n",
" \n",
" # get style features from style and combination images\n",
" style_reference_features = layer_features[1, :, :, :]\n",
" combination_features = layer_features[2, :, :, :]\n",
" \n",
" # style_loss is defined in style.py\n",
" sl = style_loss(style_reference_features, combination_features,img_nrows,img_ncols)\n",
" loss += (style_weight / len(style_layers)) * sl"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We will also include a third loss term, which encourages spatial smoothness in the generated image. Although this was not included in the original paper, it often improves results."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"total_variation_weight = 1.0\n",
"# total_variation_loss is defined in style.py - preserves local coherence\n",
"loss += total_variation_weight*total_variation_loss(combination_image,img_nrows,img_ncols)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Next, we get the function that computes the gradient of the combination image with respect to the total loss."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# get the gradients of the generated image wrt the loss\n",
"grads = K.gradients(loss, combination_image)\n",
"\n",
"outputs = [loss]\n",
"if isinstance(grads, (list, tuple)):\n",
" outputs += grads\n",
"else:\n",
" outputs.append(grads)\n",
"\n",
"# final function that will give the gradients of the generated image wrt the loss\n",
"f_outputs = K.function([combination_image], outputs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"And now we iterate. At each iteration, the combination image ```x``` will be updated based on the gradients."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0, 'Current loss value:', 3.4465788e+10)\n",
"(1, 'Current loss value:', 2.430669e+10)\n",
"(2, 'Current loss value:', 2.1680191e+10)\n",
"(3, 'Current loss value:', 2.0499026e+10)\n",
"(4, 'Current loss value:', 1.9802112e+10)\n",
"(5, 'Current loss value:', 1.9322927e+10)\n",
"(6, 'Current loss value:', 1.8981853e+10)\n",
"(7, 'Current loss value:', 1.872947e+10)\n",
"(8, 'Current loss value:', 1.8534095e+10)\n",
"(9, 'Current loss value:', 1.8380681e+10)\n"
]
}
],
"source": [
"# the Evaluator class is defined in style.py\n",
"evaluator = Evaluator(img_nrows,img_ncols,f_outputs)\n",
"\n",
"ims = []\n",
"iterations = 10\n",
"\n",
"# run scipy-based optimization (L-BFGS) over the pixels of the generated image\n",
"# so as to minimize the neural style loss\n",
"\n",
"# we start with random image - this will be updated to be the combination of content and style images\n",
"x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.\n",
"\n",
"for i in range(iterations): \n",
" x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),\n",
" fprime=evaluator.grads, maxfun=20)\n",
" \n",
" print(i,'Current loss value:', min_val)\n",
" # deprocess_image is defined in style.py\n",
" img = deprocess_image(x.copy(),img_nrows,img_ncols)\n",
" \n",
" # append image to display later\n",
" ims.append(img)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We will plot the images in the last 5 iterations."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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CUyS0y9i+AEO3fXz90aeRpTvZKuvhuVIBp/o7CWhLUJepsSJiYzyFKd/J8sYK1kfyeDN5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WAAAg\nAElEQVR63rnq5NSuJioKzHi3k8w8tFNc1kFoOkosk6BxTwtj/aPMT62hVKlpbbFw7t3bWORGTp0+\njEguxKDSUFmrpbLBgkiu5uHUJjOzC2QSDr7x3VdZmhhFqS/mTt9V2k5UUGwpZmV1lQf924Tyar72\nnU+y/9RJhEYJGxPzeDwJLCVy6sw6NpY2qe9owpdNEtpOIUnr2Vaq0RfKOfz4QWbXB4EoZ1+/SftT\n7WQSW7TW60mncjQUl2IpLmD/ngPI8wIuvn+HiqICmvY3cOi5HYxe8TK7tI5YnuTAYzt48+8v0bqn\nGkFxO651EV6/lKjIiMikwe5PMz90m7KmR7hxZ5TGngpuf7hKRF2Nc2gUq1LKJ7/6OfI+IQ31Oipt\nVZx7vQ+DKc/1y4PUVRWxGI+izqkI5YUUaMuQFxZStd/GwJVpXv7acyyMzrC0FKDaUkQ6nMWXjKDJ\nJvjCCx/PPrD//vr1M4PnrlFWK8O+6EChq8ZgFHLt0iAPR310NRiZefgQmUiItbEBo97Muj1B2+4O\nTpxq4fateRQKDfFoBI0iT9euevr77+CLJBgdXSAnjiOQiXjisyfJphMElvzcufkAW7EFq97AxqYD\nr9eLSZchFk6xsbbN9P1JDj/SS0VFIY1NFcxOrXDl+l2EGRlSkZKbd8fIi2KIjVrUWhHJ2Tm6Xmgg\nHFsjHsowMLbBged2sbLmZHDoCtUVMpxzTpZW1/nhm39OJO0n5fFR2tKMOxJk+N41PvvZx0ghZWbT\nRzAtobqmnAcDU4jlYNvRwd2RDaTSIM2lORLZKsgZKSpSYNSmKZDqqai34E9JSafSSJLblBbIqNth\n5mrfKt0dDRTodQTsCSYmVymu7SS8GkGcjVPaUcnU2Bz2qXUSYjntjcVcePs6BqWOZ156DJFIhkKu\nprhUTmmtBYFOy8MJN/NLK8hEW7z27dMsTs9iKq9h4Pplek42YCy24NpcZ3DIRTAj4+t/9gJtJ4+R\ns6jYXrJj34hTUqqgzqxiaWKZhgP1+LJZApsR5DkdEYUUvUHJgeOHmXTcQSxLcOFnQzScbEKnSFJd\npkGUTNPe2EB1fT07u3tRI+bqh7epqCyloaedQ5/aw4MrDubXtsnnA/Qe38H5v79Ay546MsY2trZE\nREIpvFk9Yq2arbCAheEhypqP0ndtlKauIm69P48bK+6JJYrMGk6/epr4RpLduytpaWvh7R/cQq1I\ncO36fYpLjSyFc5iNSqIJETZLBVprEW1PNHO3b4FXXnualakFVjciVKgNRP0p1rxulPkEX/7kx5PN\nP//JlTP9Z69QUqxldd6BzFCK2aLgow8GmR4P01YjZ2V6FEFWgL6+Ho2miNX1KPU72jl1qovhwVmE\nQhnZeBSVXETn3lpuXLlGMpdldGKenCCFUiHk1AvHQZjFMeNk+O4YpWVWjEValqZWiUfClJilhNxx\n7MvbTAxNsfdgN6WlJhobKrGvrnD54i1ScTEyoZwrVx6API1YLUWpyJGYn6Xr2XpEUj8eh5eRORcH\nnt3N0sI6IzcvY6tU4ljewul28y9vfJsMIaKOIMW1dbgTfu71XeFznz1JICbBHkwRTuYoq6xi8uEc\nMpmA6h1d3B1cRqqKUmtJkczWk0wYqalSI8NHsVpHWaURb0ICmSy58AbVJWoq2i3cvT1Pc283GrUe\n33qE6Rk75tIWImthpEQwt1ewMjmLe9GBNwU72kq58PYNCg1GXnztOSRCGUaLhUKjFGuFETQaRqd9\nLKysk0us8dVvvcDS9ALWphbGBvo4cnoHWoMO1+om94bsBJHxjT/9FE1H9pO3mbEvbbCyHsZqEFJh\nUDIzukD9kRYC+QypYAxhRkFOLaG0UMuBUweZXLuLWBjm2i/6qT3SiF4UpbmmAGk6Q3VpOQ0tLeze\nuQt5OselD25TVlpKbVsrJz51kL5ziyytRIAYvcd2cPm7H1LVZSOjb8K7nSPqj+DJGsGgxRUSszgy\nSFnrYfquzNDSU8qdC3O4A1oCy1uUFOp58eVnSWyk2bWrgta2Ft751yFIBLjWP4zBqmfZK6GwSEsm\nKaLIbEVjLqTj8TpmHizx4kvPYp9exuNNUybX4N7yseELIBOl+cqL/0U6h87eOnvmte98k6GZNFn7\nXppazbTv7Sb8TIj4vf9AWR8k6odvPv8thAInXocF87EaHtjFrNllfPrvdhMrlDG6uIRakuYn3xvk\n+FNPsTG1hlqbx1ScIDtaSqpzicTCCsV7qxm9FUandDHty/PS33yK5fl+dPUa9K2HGIxnKN/eoH97\nCHf+IIszD1h2JWk17aFKpUesk9J+4ADHZZXcvrtG2h/A9Eo1v39vHA0FCHVaRML7jJ1TY+u2M2P3\ns7Xq5UCbhoOPfYI7Z9/A6VpEb7JR3m3h4vQaB4/FeOsnl4nLRbR3tVBo2sPGQgaV5w5ymZ47Q0J6\nfRk6ns0wfzlKWRUEUaPM5LHmRAxNDZM3CVkemSYzvIBkr571bTXOfJRiRRi/y83KhJ7Cjk6O7K7h\nX86fQ7s/SNydYXpshGNfexyb1UCTTUtfv5s/+aNqvJNZ3j0/hbYxTel6HcFHp+n/3QS3oms4Jodo\nbAijqZEw41RzrLyMRKoFuz6OdjqBz2Inp5+ntdfIrmMvMDbko12zwYVLAxyxqvlofJJquYINczGm\nEwV88N0PqKgPg8CAYqeX4HwTgq1SrGuX+NK3H+U/Rwdo7C7g6tsrFJTKWA6HKEzo8OSXCOnN1HiN\nfO61z2MdtVBU7uDuWI6f/uE3rLqMPPJ8I46VDD//xzn2fvco/nc+QG5qoSQdJRnNsq8jzOK2hEsP\ngugbHKxs2fjMKzv41Y8uYpDtRK11UWccRF4mYeKDOYz5IOPlTzB5fhRTl4az90V87rVW9j31Sf76\nl+9jrZ/nyOEvEI9NEi1OoA0qsFm3eP2vrvDc17+KXmHA4xng0eZaVLoktYeK6K7e+7EU0p+913/m\nm1/9PF5vIURtqCUV1PZqUTZtYpwbIy9eIxRL8UevfI6QSMLUoAZlkxZB2kKVSEPHzkI27W7CajlJ\nT4Q3/+M6p156npWJSYoK5VTphcSianRFCZyzdtr3WLCv+lAooix63Zz86hPMTQ9SdciK1tRLWl1C\nfGuGS/eW8KarWVp0MbPhprF1JzJDPZlAlIbmag7t28vy+CThoA/tjlbev2JHp2tCKC5DqQoyOzSP\n2BhmYWYBvzfJ7oN1vPDyp7l29jbOaTvyvB9TaxGrMzGqjUmunb2BNySi+3gDMrkO+ypsOmfJJDR4\nnQo07jD7D6iZH4xikguRq1PkcnmsuSAzE0PoK80s37iBc3KB1q56PCtJhreWKG3RklxzsO3TUtO6\ni337C3n7wnnKd5cQDc7wYHCep7/0GXq66sjmo9x5/z5//o0nUKTivH3uEk3d1cijGeTtDs7/4iEL\nAQ9jV1ZpbPbR2KhnalZBmVmPO6ohZTThnd0mkHRhqk1h1sp54lPPsvlwg2RsnbGpm1gVYZx2O+mI\nl0QmT+eeZj787WW0pihqo4WwykPUIUUcFhJfu8ZrX3maDy/eQNEsof+dSRpKTETS24iTarKRIKTV\nWLR6nnj2GNqkDV25jYWtOJd//1ucK1J6mkuIh/K88aNBvvJXf0Tm/i0ksgTKiIsqOexsTOL3hrl3\nYYSumixbW0WcemEvF370Duayegiucay2H5JO7l8dotOSJW9p561f9mGsVnDHnmP3izuxtHYyNrdC\nKrvFseMHyG5vE1DmEWaimOQ+3vvJNU49c5TOphI8y2PsOGIhuO5k32N17Gto/Viy+dN3B85861tf\nIuJRI5OVkE4ZqetVI7c6qYrMkdxaJ5iP8qd//VlW7FGmN3TkzGKksSh1BUIauxpYtW+SlcpxOTf4\n3Y/P8+hXPs3m1ApKcYZKvRRxXoJEImJ1ZImmmmI2/NvIpDmW3C4e+dJzLDq3KOjWYy7dgUxlZGN5\nhL4HAUKpYh5MbjG17Kbt6AEyqnpcrnUad1Szc1cvm3YHsUACRXUFt/oi6C3NGAxNKJVZ7l+9ApIA\n6+MTxCICGnc0c+TZw9y9uMzy/U1UohSGqlJml6KU6GVcf7ePiD/HzkeqUAok2NfCrK45yObMuLay\nyHJx9u4tZfz2NiqlmiJNEGkmhDzrYW1+Bk2xmtH+PuwPljl4vA7v5jYPVxYoay8msbbJQjjJjiMH\neeTRSt79/Ud0n6zBMT/MaP8iz3zlVbq6aknGQ4xcHOELrz2JOJnnypWr1DRYEadCCM1O3v31Lda2\n/ayNeqhtyNLWUcj4mJjymloW/AniGTmeVS/eRBibTYRAruHZV59l4tZ9vK45NpbuY7JEifi2SIY3\nEAhSNPe2cPl31xGJoxRUlBJKB8hspBD5coTdE/xf33mKD9+8hrzZzPS9eSrMVsKBAKKwAGHIiUyq\nxKxScfiR3WiUxUisFWy441z4+RuEHEKKSrVIM0Le/cldvvbdLxKbeICOGHnHPPWWPN21YjLBAA9u\nDVAiiRHymnnidA8f/fx9tHorWfcyj9VOoA75GewbYXeznpTMzG9+cQFdiYGhVS+PfOkJRJWVrEyu\nk8uF2PdYF+pMlJQ8T9xjp9SQ5vJvr/LYI51Ul5nwrE3Ssb+YjTkHjz/Twv7mpo8lmz87O3LmC3/0\nGVIxNTJVAf6IlrouJVrLFmXpOaLrqwSSEb72F6dZWAmztC5DoBFhUcYxa3LU1Vax6VgnL1Xi8Xv5\n9Q/OcfD5Fwi4XaikaXTiLEqxlLwwz+LYDM31NnxeP0aNgjWHmz3PnWDBGUTfq8da2YlAZmJxeoTb\nI06CKQuzK2HGFz3UHNlDVFbP5soKJbUFdPTsxR+LEY4K0dSUc/dGEJGpkdKSDiRSKcM3LyOR+fHN\nzhGLCanpaGHv84/w8N4aC/e2UeeEGCoLcW4l0IhF3P7gFr5gku6DJejzGfzOFI4tF6mcnoA7Ty6Z\n4MDuMhzzAQR5OcVaF9KcH3k6xPzwOMoiGcN9fawPTHLysVpiPi8LjgUsjRoyi6tsBUPsPL6Xxx9r\n4+1ffsSuIw04F0eY6V/j5CsvsmtPB6l4gJELI7zy+dMI01muXr1JZaUZRcJPVrDBR2/dYNsdwLfi\no7IkT1uzlfEpMYYyG2uBDGF3Ao87jC+axWgQkpWr+NSXnmXy3n2cW7MEN6dQqr2EPU5SETsCcZzO\n3Ts4/+s+1CooslXg2Q4g2I6hTGUJbC3wnf/2JB+9cxexzcD04DRWvZG4O4AsAwnPFhKlnAKdjkeO\n7SKbVSMtK2PLG+Di6/9B1JFAq1OiFau59NNbfOWvXyE2NYFaHEbsnqO9XEpbmYJMOMho3wCmXJCw\nW8GzL+7n3M/eQanWIEg7OFG1gCrkZvbhPN2NxYhyEn76/TfRFauZWfNw7I+fR15ehXvFQSzgoudg\nGyaZGH8sRDiwgUkc4/a7NzhyoI1yi5agfZb2Q8WsTizwidM7Odja8LFk86dnx868+tonycTlKAwW\ntqN6alpAbXBQll8kvLFIIBjim3/zaRYWvWxvS5FJkxQrY5SZ5NS3VmDfWAWFnu2gn//8/jv0PvUJ\nPC4PCkkecSaBQSkhFI6xPLtAQ7WNmCtEYYEOn8vPgZOHWXBHUe7WY6pqRqGxMjt+n3sPN9kKqplf\njjI8a6fiyEESohpW5xZQ2VT07jmKP5khlpWjb6mmv89PSl9JWU03ybyMwSsXUSjjBJYW8Xoz1Pe0\ns/OZY4zfWWH6nhNJVk5hvQW7w4tRoWPk+j18vigtO3RoBFGSQSFr6xtkJQW4NmJkpGkO7ixnczZA\nNJSiyuRCkt5GmgowPzKDqkjJaP9dHIMTnHqmnrDLhWNjGWu9mvzaCkG/j71PHeTxR5v4wy8/Yt/x\nZrYXH7J8f5lDn/80+/d3EXVvM3Z1gs+9fBpJWshHF69iK9eS8nmQKwKcf/My8bAL/6KHkhIBHW0l\nDI9kMdVUs+SOE/UkWHP42IoKKTCKyei0nH71NGM3h3B6ZwluziERbSGJeQmH1lHoMnR2dPDhf95C\nLslSW1mDa8mP0BtFGErj2VzmO3/5NFfev4Oo0sDc8BSWwgJSwTCSSBph1IfCoMWk0XJwfzeZtBx5\ncQl+d4DLv/g18a0kco0YnUDKtV9c50t/8zmi09Oo8SMJrNBSLqelREnc42Z2cBBjOkjUI+Tkc3s5\n//qbqDQSSDk41rCOOR1iYmSWzqYihAj5+T//Dq1Ry8amm+Pfeg5dTTWu2VWSURf1HRUYpRKC8Qix\ngAN5NMbA5Xvs29lIWYEGv2Oe9gOlrEwucvr0Xg531P3XMIcGPlg6s9k0R8FGP/XyJtq//hiqDx30\nHK0hd1eKP6yj6bNxOlq+QF5sZh05e+IbhOfvcu3cHbqOHCN0bxu9uo6rIzma6oK40lCr0+CzO9Ek\nnZgPpyhMRljpayewpwmtOUN6ahXhdhC/JYTvAxmLCQMVPjPVn6kl8sEGQnMx22vnSWrj6HPr3BX+\ngXccoFBp2bD38cu5OXJLCbbjc1RF3Kx5axHXhHGMOPm7//t5HjpNOO78mlRaybHPv8Rb77zL5z91\nlKhBwt1fLFBXJmfujW3qswrW/Xl0ijRHmruZmMwyaL/Dnh1lbKdLiWUb6PxyBV0mE30BEZW2HC3J\nAnzOWcLmSiSbcoLaOfp/Z6Kye4lQ9gjuy1d49hNNrNvVOM9/QHV7OcXNMgSNNhZXJfQ2pHi/X4L5\nvJc1q4umVSH+XD/y+hAbawpUERn5+kkSU7PM3U8jOWDlxhkJp47Wsjq/QqX4UySrljmxcw9/+eqv\nKD1gpFo4x+FHlWjsaSof2cHdX2UhnEOxtMyCxsWbl1cpkJaxllnDvajkH773ZZgcZu7mBOuxEJ/+\n6su4N3SMXb1DW6GDwrocTc99m9f/3Y8ql8Y3b6TtuTKk7kJuXMvzFy8fIBBYJmk8gsd7h9WxOTZ0\nBiazQbZHemm3n0W+pxbTYpzCeiPpdJo7YguHlry4L8e52yTkxJ6jiAr2MnP+QyqiKmSiMkxNJgLT\nA3z5n5/kEy+3MP3GGmueOly+XvRVs7RKXUwETFhstWQvnscmDSKrk/GDG8sc15YgvhRl2gSa4WUE\n0ihKUyFRR5DOp08ye2eC53YfQuTNc+5nfsoOlbM5eJUTRz+esbK3Lm6dsTaGkKcDWC0y9n7mKOmg\nloOPNjH0xgbbcTk9X1OgpQ2BqoylbJTG1AKR4CJnf38L3e5mNP4cQoWRkWtDFOmjRH0aCkoUrM6u\nociBpUlPJrSJa82MXmOFIjMx5xrqdJaEKMnKPTfCdDkqSREtB7t58O4EFkMMn3eWrDxPKryCnX6W\n/RHMqjRej523bk0Qd6TYjrmRKJbJx3Ls2C9n5Not/uyfXuLOrXWG75wll9Hz8hdf5o3v/Z4/+pO9\naARxbl6apbSygtEPg1gVceQGKUl/gkPP9bK0kGFqfICa6hqkKjMCnZadT1TRVKTDk9VTUSZHk/Hh\nds7hU7Qg2pYSVia5/ssgzfuFiMW1bI7c44nPdTB1V8X6Bz+lemcJVXtriAiMrKzFiSsFjE94MQ8H\n2Agv0aRPEt68jdjoQqNV4tuMEFQtsDoyw4O7PtTVVVx8w8ejh7uwr22hM7SSVGyyd18DP/3Ld7HW\nqLCIAhw+ZibrS3Dy6EkGbgQgIyHhzmIPzXPlvUHMRgv+gJuVCSF/8YPXkESdDN25jyfpZf+eXoRY\nmB6+RXdJFpMhzb7n/5Tf/G6bhBiyKStHnmsn4Tdw71aOx06dZGpmAktdF/b1YeybCbbXMmxmnYQC\nRThvD1PSVY6BAgx6JVpDhgcrEqqkXqYmUqzGy6lq66CosoG3ft6PsamdCmM5Equabdc4f/LDVzj4\n3F5unp0n761jbaWTnGaLFu02A6tmrGU2BONLGCMh8jI592Y9tMqkRCaTzCTc6J0C0jIhJRV1eNwR\ntOYithb8NO88SCZqoO9X4+w/fpSxe2/w/KP/3ye4/3/Mjz+cOlNTo0AlTSHJKth1fA/ZVJYdvW0M\nv2MnoVRSelyNzdpANqhl1R2h0riNWJTiwrkHaCpqkccTqDRWhq4PUlImxuMRoFeoCDm2yaWyFFQY\nSUdCpH15jHo9Wb2C8HaAAqMCp9tNYCGNIa9CltbS09HL0NVpDKoQ214nOpOSqO8BnsxD1txRKnUi\nlpdmuHV7kXgkQMjlwFQSIi2NsnunjAd9V/nLf3yZSx8MsToyCzIDL3/1Jd757n/wzTNPYlAnGLgy\nhqHAzNT1dUrlWZTEkAtzHHziBEtL8KB/iPL6FlR6HfGcgCMnm2gq1pENCrAWF6IQbeL1TOGJdaPW\nGVgLx7j82w1aWyVIRBYmh8Y4dvoIK6M67Jd/RV2XnqqODpJ6G/OzIfTWCobub6CY8+FJOCnUO0g7\nhpEovBQVqghshogKFhkbHmBhehOdrZ5LZz0cOrATz5aHvLISBDF2H6jmzb/8DeWdBsR+D4cOVEEk\nzLFju3jQt0w4L8fvj+P3bnHrwghmnZpsMs3IHTd/+ndfJOINcuv+A8SyAO0dNpRiM5ND9yk35Cix\naTh6+vO8/942gYwcld7I3hMdCEUWblxb4fRnnmdpeQV1ST0LU1OkkOBYiRFMhMjkFMzfGKO4pYZi\nnQWVSInWJGV+U0mxJMHCUo71WB1VHTuwmit55zcPMFQ209HTibRQy/r6PH/2489z6sVdfHRxifh2\nNfMb1YhEPrSiTRbXpGi1pUQWtihJR1HptYxNOmnSSticC+IMp4kvBUhIFViLK3A744gkOpyzPmp2\nHkctkHHxt8M8efooN//wBi89//GMrvz0/MyZilI5KqkAkVDHvmM95ONJTp7YxZ2zy6Skair2F2Ax\nl0Faz5I7R3FxnAwBhvrnwGRBThKRXMdY331KCqU4nRmUwgyBjU1EAhFGk4pUJEImKMCiV5GQi/g/\n1L1neyOGeaV9o3eAIAmw997bVE4vmqYpqla1qi23uOdNnLard+Mkzm4Sv1nvldhxbNmW1TXSaKTp\nfchhH/ZeQQAkQaL3Drwf9gfsV+2PuK9zXec55zk2VxBDkQqPy82mxYkxIYSAlP2dBxjvmUcrD7Cx\nZkOhkpHwjeONjWC2eakqkOP0mBm8NYHX5yS6aUakc5BUR9i7Tc/D7rt8/y+f5N61PtbuPwSNkVf/\n5DUuvvkbfvDfniBXA/cv3KaoJJ+ZngWUYT+qFCjFQvacOsPykobBG73UtLUjl8uIJgQ8cria7WUa\n4vYE+tw8Il4Tzq0ZPOE25NlaTP4YV98x0dQkQ6PTMj6wwL5HTzM5qGP2zkXatxWTU9dKuqCCmWk3\n4swyxmc3SS458Yed6LRWUtYR9LogeQVi3GY3ocgqU6MPmZ2cJbewmvt3HBw4th+zxUFcXkw47WbX\n4So+/Mv3KG7MJLK5zo69NUjTIQ7sbmGkbxpPUoTTFcFtX+Ph1QF0KgUKoYTZ3k1++LNv4d3w0DPw\nEKncTku9Ho0mg6nJQfIyxGQXZXL0mRf54ryZrXgG8gwFR45tQ6jL5373PI+ce5Ql0wq6kmqmRxZI\nI8I068fpD5FCjql7lpymJnKzCpClhBjy1cyZ1RhUQqamA2wJGsnOaybfWMTF8yNkltXSvquVrKJ8\n1qwL/NXPXufIk7vo6rcR3CjGaqvD6zGhkTlYNQvJ0BbisIYpSMfRGjKZnbJTIkjhWIuzGQ7gWHQQ\nTkvIyy/CuR5GozayOrdFzd6TZKlUfPFuH2eeOsmVP7zFN17+crL5b5dn3izPVZCpUyAQaNm+uxZV\nMsjBfTvpvb1GVKigoKmA4ooqohEtc2splNmQlPi43zNLSqdDTgy5TMfIgyHyDVI8vhgKQZyA1YxC\nqUShFhKPxUgFkuRoFMQVAsyeKPpcDQ63E/eWG30iRMqb5lDnYaYeLqKW+7FZNtBlKQg5J/GHxjFt\nuGko0RIMbzJ+fZyAd4OY00pK7SQmd7GnxchEbw8/+LPH6L3bz2b3KGgNPPv6s9z6+bt8489PUZIl\no++Lu+SVZLE0OI8yEEQSEyITSNl94mnMpgwGbw1R1daOUq0mFhVztDOb3eUqYutxtMYiBIkNfP55\nvOGdyHO0LLrjXH3HRE1NEmWGmtGHy+w7/Thz87n03bpMc30lmdXtJAorWJwLoM6pZnhmk/iig2DE\ng1ZjI7kyjE4XIL9IgX3JRii4ztDQEEtzMxSVlXH76jIHTx1iZsVOWJFLRBSn83AVF/7yfbJrMwlZ\nLLRtK0MpDLF3WyVTQ1N440m2XBH8rg0muoZQiUVo5TJm+9f59k+/zqZpi8HBUQoLgjTUS9FpVUzO\nPiQzU4yhrIBjTz7OlfMLOEWF6DIU7D/agVyfz70bYxz5yhmWTYtocyqZeLiIUCjBtBLGE4qRFEsw\ndy9Q1NRIQXYB0qSYrEI9Kxv6/z1sMx9hM1WF2thIlsbIzY8foi2vY9f+DjKMuSTjQd740Vc4cm4P\nI4thfMvFrFhKCPtXkIucrK8lUcpzsa0nKBKnURsMjPRbqJKIMZsCuOJRooE03lCSvMJivFtJJCIl\nGyteavaeQK+V88U7PTz14hk++l+/4btv/J/Z/FKYQ3//ec+b9/7HdWp9Bez6L7uY++0Wba+r+Hju\nbeqMRppK93N9PMxH502IS2QcV1TxHwNGwjmFGCszmfiVl1htnPXsACJ/ktD4AgmdliFrJtlVHl54\n4zVW3hOgb6oir3CKyOrnSLojDGTmo1So2IjVc+zlVhxaH3ljsyS2aqFMRyjgJL4KWfkH0CpLCWwK\nUPZn8Q9/+xg3/mqQimIQZIQxHj3D/NwiHZ0JTorWSOUkuHx9jBqZiwW5FKNSTqVkka25EDcGwuwu\nqiYysk7V92sIlbvIKjTj6UrzxPdeQZddxH/+3IxClkHuwgpdiyNU75QiGqtibbyHyE0LNquIaakC\niUrEfG+MdUs3a7ubaRA2kHSNUoqTx/7sKZZ8CqwP18irFGO3N7EY0THxT4PMz4Aol4cAACAASURB\nVHYRe99JKtdLzkE35WelfHH3IwxZRsy/dvOTvzvAaLeXez1eOg6c4JHn5FRE15k1agnqUxTJ81kr\nMtNYnA0qP0/9zTOk7FbWJjQ8HFOgPLqN5KYbtW+Qbo2P5a12jJEVUrEkFRlrJAMBvvqnx5gdXOTT\nuTVs3nIyjq5x6bvvEFYsk99YSihhQO8t5+qFjzAlXUTnJ1jRJ2jYPIz0WIwD32jgn37ehUmwTKRv\nGbHQwOdrQsoTYnImzNAqpvTgaWI+MXK1kt07dzDdbydXW01Fbg/22hgzC9VsfXKNeXkv+fmlCEp1\nNJws42unXuBWv5tb78SwfTFIxRMCNEIR4dk0dmEchX+ZfJ+QwYU45oQQww45f/xMx56SYgqK71H1\n3Pf43RdvUbtkxKEVceqlXYSGQ0yvfY7PEef86lV61v2U7B9ldnqdPGkBJ44c/VIK6T992PXm/X+/\ngTguZd/ZJxn9qJttuxQM3r5HTW0h9fuOM3nLw5XrDjy+GDX6TMZXMvCIDFQUZDJ614xEKiISsSNR\n5bE2FQR9GOd6AI04wZEzJzEPxDm2pxGR0smGrYf0qpgVWYKYUINEm8HRc6eYXVlDLU6wNBKkqrmV\ndDpNLATprELqq2rxznixLYr53p++wLUvJsjSJBEXCdm29wimwVVq9ldilDrRicL88pe32NdSxPz6\nJiUGJSUZW2wtuZle2KCirAiBO4C8BpQls2SK3YyOhPj2D7+HWqfk1/+0jEyeydqihZn1LQqqMgjf\njKNPurh/o4uNlSARstCL/KwOeXEk5tgwqCiX7iZkn0Sjg1dfe4ahGTHTYyuUtavZXDzCwno2o593\nYZ3qQd3jIaZPEiqKkn9Syc1bnxEMJIlYhLz61QPMTyfouj/DrqP7ePYvDBQWh0lLK4njRWPUY8+M\n0NRYjkoq5Owb54iEI6QFUiaHnNTsqMcfDRD3PmTCKyIkLsSo85DyxpEKw4gFYZ577SRJu4vhgRFs\nfiURXYAbF3tZdy2QVVPEulWKUq3jk48uIVTL8K8tEPMLSSWNlLQJqduTwW8/HSQhWca3JmLDFKFn\nxEFlu5YckxexOkp10yPIYjKU4jjnHnuK61+MUpzXgCa1hUCdwOvNo6+/j7hgk46OXWQI5JRV53Hk\nVCsDlzzMj3pYutRP06FCwo40/jUhibSbwNIoiFXMWVxYkwr0RjljdhlyV5iaAg+7zj7H3fsfIVgM\nEU9IaTtajm8kSNwYYnl4jtt9Xfic68gbvPQPr6IXpXn+zJczHv+L8z1vjnx8B0EkxWMvv8bg3Zu0\nlWfQfbGb8vpiKlrrsM+pOX/RhU+gI78gD/uajDDZFGYqGb45jz5DTNBjR6CQszTjxliWwLbgRB4X\ncvzccTbNAZrqqsjIUTD8sBeBIMW6XIDLl0SfWcDOPYeYX1giFEowM+cnv74WcThJClAaS2lu7WBp\nbguHU8Yb33mWBzcmUGVKUGWKOfbESQYeLlPTXIVc5EEtS/P731xm17ZyFocnKazJoig7hW3dwuyI\nmcbGcnyuIBGNj8xcMz6HB4dTy6vf/hoB4He/XCDLkMXk7DTLrjCluSVsdS1jyIK7N/qxbMWRRoTk\naJOYxiNsRqdJKeU067bj25xFmSXl6y89xepMgol+M0X1Qpbm67FGy5noH8B5r4vg2DJRkQeMUipf\nzOXBOx8S8ouJ+n189Y3HWDL56O4eo6yhhq+/WU1WjYBIJI+Qx442R49T8r9TigpZnKPPnyIZV6PT\npliatFLc0EQolUCGk+UgJNJyCjQBwh4BiZifVMTB4y8cIWr3sjy3BCIJDv8G3d1jrG8ukl+Zjzsk\nRqoQcfXTGwRjQUIOM/GQD2E8A01BivI9efzhrbs4onPYPOByxLlzf4qG1lokVgcBj5/Chj3oVXIk\nkThPP/MVLn86QElZC97VdfIL5URiSYZHFoiFLNQ0t5Ot0VNZYuT06QrunDdhGtvActlGc30uEUcU\n/5YIodjM5uIUiZgcRyDKwgYYyjOZsiYRh6XkG2McPfsCXd03SK47ScVjtB+oJrUiJVkowLy0TPfA\nIGH7CuijTC5Z0RLm5ace/VKy+a8f9L05efk2qUiCM8+8yMPbl6kqkPPpb+9gqKumproIu0XIZ9dd\n+OJ68isrcW/FiAh15Og19N2aJkslRuAPkZDJWdmMUlwcwGP2I0jIePSx42xafTTXVVBUYqD7/gAC\nZRqXXMDmlptsQz7bdx1iedlMIiVnaMhGXnUlyXCEuFBIZkUtrS3NLM/Zsdng1W89TV/vHKilaLPl\n7Dx2iOmpNVo6ahGn3YgSQd7//QVa64swj89gqDJSV6RmdWWFxZk1Sipysdv9OCQBZLIVtmwBRJIi\nXvn6sziR8u474+TlqhmdXsAaSFGQX4apf4bcHBU990eZX4uSLUiSmylgaUqINz1DIi1gZ/5hnKsj\nBMXwrW+8zMZSmvEhC6V1QsbmClhNNzE60o3j6i0iSxYSKQ9phYi6V8sYeuddggkFPreHF795jqUV\nJ/1D02SVZfLy3zWR01xAPJ6D07mOtECKJRJhz+FKJAk/B58+C8IMikoVLEyskF9ZSyAcR6cOs+YT\nEI6nKVWGiSYV+MMekhEPx87uJubawrKwjAQB/niIOz0DmFfXySvNxh8FhVLC7cu3CHg2cNrWUaej\naCQKEooo2a0GLn14H0fUjD0gxe2Mcf/+JGWtNWjXN0n6vWTX7SZDJkMTjfHCc89w+ZMB8kpb2Fgw\nUVGmJBxNMjwwSyCwQvv2A8jFGipy1Zx+vIybH62yNmNmrtvC9pJcvAtuPK4UGZk+Nk1TJBIyAqEo\ny7Y0+UWZLLuiuG1xyvIU7D9xkvvd90na3SR8QXYcbiayLCWhT2CamaevbwivZRyRLs34rBW1KMAb\nz5/+UrL5zx/2vjl9vRshQg6dPctM/w1yDHLe+81NylvbyMmSs25PcPXmFlEMFNUV43NFQWogSy9n\n4Noo2WoxcZefhEDKuitJcW6YwEaYZETC6TOHsW36aa4vpaw0j7tXepDlSVmJ+vD7fRjzC2hoPYB5\neYNwSMbtuyaKqisQROMkBRL05fW0dzSwsmDDZU/z3DeeZKB3mliGHE22gh0nDjI/ZaGyvha1MkUs\n6OLTD6/QUKFnfXQaVVkm22qzsCyZWJxbJ7dAyabLjTkZR8oazkASIfm8+vpZHDIZH3w4jiFLweSs\nGXNIhMFYhG1+BYVCysPBOcZXAxTr0mRrk0w+lOCKj6EQCtiWfwDLfD/eaJwf/uDbWGfjjD0wUVEn\nZHBRx4a2g9GhB9guXCC0biMQ2CQthM7vd9D39u9xh+TEo35e+uY5lq1uBgcnyW3I4qWftpDflEc6\nlYNlYx1VqQ6TzU3bvhL00jBth46i1BqoqlKzadoit6oORzCKRp3AhphgMEaFIk5MqMHlcSMSxNi3\nt51UwI1ldgGlWIg3HeNe1zAWk5vsQh0CmQSxVED3lXsE3eusb6yjIUJehh5nwIWqJZdbH/TgCFjY\nSGqwbUXo7R2nuLEGjdlK2u9GUdmBWipHF0vy4gtf4cbng+QUNeFYXaOySE4sGWNq1EI4ZKKhYw/Z\nWgPlOXLOnivn8ruL+LdsDN9dpDYvE9+CG58/gl4bwmZZApQkYmnszhTZRg0WfxKvPUFpoZbDZ05x\n/34v8Q07UZuDXUe2E7FJEWoFrC2s0Nf3EN/aJCIdTM5bkAuCfPvl/zObXwpz6OLV9998/LEa3C1e\n3vnqX3DgR1XM91p5OJlJzugAg8kFpicVPP3093kwM8atS39k35+VIXhrnNwWO3WPHWJuxUahcTu5\ntV/j5TcqSAxcpNOwH8fyKA/6N2k7sc6Bc0cYuxdmNbeArUCAJ58uwTo9xuEDtWyvbObmz+yUPtWO\nM+1gfv02AksT//2j48xeeY9obj45VRsIG3V8cPP3PHf5B1REMknKCjiWVcWxP6/lD/8cx5o1h2e9\nAkG6kJNf62C5b5H6I8fpuitGmLVGS4OUX701gkenJHEpyvDwMs75InyNFQh9XsLRK+jCMhatdsr/\nfhvWy3n03RpGqwjz6N8cRtaZRUVzPnVKI+cqQlwZeh9jIIf4hAqDd47VjEyUUQERlRb7nQC7KsJ4\ntRUsORuYrRByIN+FQKngzD+fwztkIWxREFkporljL4uuDQ5+bQ/dly8QyXHjmlnkSu86hdI5Rr3Z\npINBXmpycXkkTUwtY/dcEfMuDZneURKb+8h6MkijdwsyRWxaPKw6BTRtZPDcS0dZS90nFKrixA87\nUAgaaGzMY/m2A59kkDWTiZCsiN1nG/GPmNCFBQgzfMzi4K87S5HlR1Aqt9F2WkhDyV76vGZ+9/0r\n7H/tHPI7/bjkmyiURZxsUBLtaKC9o56H790ESRhFYILBCRHHn34J6cJ7CLNbqWw7ycPxD/h/yxvI\n++EJ7v9OQnvnfupya+i9P0oy6uLsEzXEvYNUGHKZ/TxI1tYAOcxSKpNSUneQkdB5Tpa72BhwInKt\nIcjZxvfPFZOa07OVYaClQc2ObWI6VWXM6eOEzlswN0eYHkySESmmbtNBzkU9qgoVzaVT7Nz1wpdS\nSN+92vtmuSaOuMLPlf/4lKf+rJ0bF8ZZWoohDVsYsb6PeSzA6TeOQzTN5+9epflRPdn+GIVZMhp3\nG5jqXuGJI50Iy85w9Ewdwdn3Ka/awcqsi5Bvk4b9Kjr37MQxFSacqcK66uLl7x5k6tYUNfWVbGtq\n4MGFabYfrMexvop5a5hoLJdXX92NaayPREqKskJC8Z5s/vj2Hzn35y9jlOSBykhzUS0Hv7afdz63\nsD61hs2hQdPQSOcRDesrSXbub6N/1IYsW4DHleTyvSX8UTURv5TVC166+0KUHHsBYWidzMxpNlaS\nSGURdr3YyMSgjLkHXRjL9Ox5/hGad+RQUthCU3EVh3aoGJx8QGAuTKEqj4nhXhyhJGppjE2PmMi8\nk0O7N3DllTHprmEuK05GwoxG7OHEf36TzY11cuNKPNNS9h87xIY/n91nqxm630dc6cSxlGSg30yD\n2EX39RABwRaNRU7uDGnx4aA0lc+KxUOuUkI8UIu62kFaJyUlzkYd9LO8HkQeVfB8ZwM2xwQZWdns\neLGKbL2WHR113L82RirDw6otRbywlGMHGrHPLaL0htDkBtjyBXj+K80IFWFS6Tx2HTdSXFNLf9cK\n168N0dyxnUjfffxpF8bCSg7U5JFlqKBkZyUjN0fJNcjQyV2MT0bZd3Y3JYoA68t+yhsbUcZmeKxJ\nTu3hw0x2m8nOysVgzGJ9ug+NRM2BPdUsDt3FqNAycr6HBkcvdZpFqrIz6Th7EvvkB9QYttia9CCO\n+FEb1HzjlbOsLnrwx6ToSmTU6XQ0l2eyoXbR/YcJMqoVrC/ayFJlkGFeIzHmw5grZU+JjUOHv5wX\n0He/GHqzpVhGVOvl4h+u8Px39nHl/A1sZgsknaxZrzLdY+Lp75xE4A9y83IXzduykERjZBjEbN9e\nwvjQDCeeOIqkbAc7d7djefgFDdsPMrFiJehZJSdPzdGjHWysepDnZ+C2+HniqSPMDS9RVZPHrtYa\nHtxdYM/B7czOzOMTWYmHjTz/xl6G+24gkkrQGFVktmfw6TtXOfW1s9QUN+ONpDAWlNL++AGuXp1m\nccKHI5CNsqyJ2mYl3oSc+rYqpkbMSDPFuMJw+cIkQWE+vlgma11OFociGA4+hzi6iirThGVuBYMk\nweHHtjM1GMU6PkR5eQ5tp9ppKyukoKiK9qoaOlqymZocJ7jhpryoks+vDJJMR/HHXQSjalKbLlqP\nbBItL2Qp0sREIoLMt0yezMGjv3iJFdsWar8A52KYR548yWa0hs4j1QxfGiAaDmFeTGKe89EgDnDt\nihOVwk9TS5IHCxK8wgTSoAaX3U4qLSGtqkegcZLIySelyEPh3sBuceIMwPMnthFeWaCoKpu2s0Vk\nF8hobqyl+/okYm2A9RiEMoo5fqie4LIJgc1DToWUmCDBmScbEYqjiCQ6mnaXUVhVRn+Phf7LQ+zo\nbGer5xKplIjC3HyObi9HJVXStLeGudEZinUSMhRivCE9zfuqMchEbC2uUl5XgTI1TYdRTMv+3Vhn\n54nHpGQZjVjGbxOP6Diws425231EPQm2FoYo9QxRrFilqczIzicO4Vm6Q55wDc9WmLTTg1Ij4aXn\njrA6sY47HEZvVNOUoaG9toB50RaXfjOEoSWJadyESiIhZ3MDv8mFNjPN8TohBw+d+FKy+esPB99s\nKoFg0sulT7t47o3d3L1yF6vFjDzpJ+57QNe1fl7/i2dI2LwM9Q5SV6NHlEqiV4tobsjFNG7i+OOP\nIM4vp3l7MxvDdyhp2M60OULAbSI738CBfS24bR5UxUa8Zi+PP7WP+ZEVWjqqaKwoYWrcRk1TMwsr\nK/gT6ySjmbz87aP03rxIIpVEla0iZ08lF/54g2NPnaS+vp1oJEVecQ3NJw9y8+4IywNWbH494rIW\nWnblY09KqGwrp7d7HEmmBJ8vxuUr48SlBQSSOThGbWyNpMnsPINCvIlUu8HCwBhlGgFHTu5hdGgT\n19wIZUX57Dq9i4pMLTm5Rexqqqe10cjc9AKBFTNFJZV8+sUIQiKEozEiQTnhdQu7Hk0gLdeylKhi\nPiYgDyc52Hnsn1/E5vUhi4JtZY1zrzyG2VVG264CRs/3A2JMkxG25v2UCwV8+u4iepWLpl1yRlbE\noNYgDEvw2p0IpDoExlrWLdOIS0oQaAvR+LawWR044knOPdJMfM6MocRA06MFFFRoaGmvp+faJCl5\nCJdYjltr4PiRbQjMKwhtXoylctRaOZ1Hq9BlK1CIFFS1FWOoyKevd5mZa8O07u7AduciabGUzEwt\nu+oLUEulbDvQyPTAJMWZQlQiEa6kgaKmCoo0IpzL65SU5mAQrdJYpaOsvIq4awWPM44hP5uYcxrz\nUprD+7Zz/4PbONe9BGwzVMSnyc/YoKY2l4NnD2Cf7kUv2cK5GSG6YSdFmNdeOcXSw3n8sRD6bC31\nGWp2tRYz6V/m+rsjGNrFrM4vo80QYXTbsUysk5Mv50SzlqNHvpwrn7/+YPDNndVivH47N6484LU3\nDnHl40t47HZkBJHExui+8ZCX//QJAutu+q8+oKZRj4I4OnWKjuZiTNOrnHnmBPL8UioaGzANdpNX\n0cCULUgouI42Q8+Bfc0EvGH0RUU4VjZ46isHmR810dxWSnNVBYvLbioamphbWiEmdxKP6Hj+60cY\nGbhBOB5HmZlBRkc1lz7u5uTZYzS3dBJLpMk0FNJ89jA9t4ZZ6V7CKc0nmVVKZXspW2k5ZW3lDPZN\nItVLcXjC3L00Q1xdjjdRiH9+A0dvhMx9Z1ELbcg0XuYHRygVpzh0bDdjg/OEZh6Sl5XF8ZfPUKaS\nYMwpY29dOQ2VOViXzdinpqioaeDjLyaQStOkYiKCAQjaV+k8JUZRJsMqqMQalpMT3yBX5OD0373A\npt+NMq3AtLjIE689hslVRGmxiukroyQFStan3bhm3RQJ1Zz/4wIZ6gAdB3XMWtNIDbnEvWKCbhdy\nbR5JQyXL86OQX0BMmoM24sZqduCNJzm9vwnRyjr6HA01j2RS02akrqGK+1eGiMtieFVynJosjh/p\nRGheQuYKkleiQq6V07qniKwcFQqhhLLGIrIrjPQPmzDdHqV6VxPOu58RF0rIzpLTUqZDLZaw91A7\nkz0PqcgWolLIsQcLUBeXUG6UsTG1RHG+niyBlcriDMqLi5CE1wi4Exjys/GYRzEtQOfuFu6+fx3n\npp+ka5YS0RR63QZNtQXsP74Hx+IAwqgV91aYmMuHSBbjleePMts/izfqI68wlwKFkL07K5neWuPG\newNkVCQwLS6h0gnICbpYHrFizBVztCGbkycO/N9hDv3xc/ObW2tiNgJxdjce4urMFC89cpahiXoU\n4bvoOxV8+K/dDC0nEbuzeeaNw3z07kM0YRGfXJ+m90qQR59s4kymgb5xI/cv/JztjV4GI9l4U1Jk\n5Wv4RjwsbGiQ+OUoQouI14tJFStZKy5AdW+ES58PcOQfX0e8EOb9f3mfMlU528618j/+ax/ytIoH\nPjc/rDrNZ+9+wNO7ayBQwh9++Z9YK4rxDqdwJHtILSspOpvmha8/RSKsR8cWcxNLFGxrY2HcRUOr\ngtDtKKKCGDu+dpRkVjW27iQlbSFSNSqKBj1Ma4RUZj1DJLWMbCmOe/UgDUedaIxWrnwIpe5S2hRa\nMnzLTET8/MdqBWXFufzJq51I9u0gSxEmPz/I9dtpDuyv4PPAGg9MW5TZa7B0LVPV0kHslpTN0jqW\n50zoNnzk1UhJOSZocwoIL45QVVZJ97+fp/2bbRRWFlJpGeXS1hkywg9xPHyfRNnjZKjFzEyMU/Fs\nI7Pem4z0FjD2yS+RxNN8YcpkW0iNKb+Kh9IIZeE4h752ENtWK6JMF3c/eMiBPQpscS1a7To5ijhF\nnfu5/J/dnNrbiDgWYmFVyHKhkiv/MI7XYUFVtB9ZIsKF0TGqOvbhXZVi6rrA33z7IJe754hXPU/Q\n7ES6Wkz3tRHyDVrm5+fJ31dMMFxMz8+7CNg+4twPv81//DKAUZLH5Fyad2fX6dypxLMQY/JhP1n5\nBRhXh3irK4V+ezE57UUMqRYRWGZwSwJYgyJKKsuJ+So4/3Yvu1/eQToVI7roJLdUw+7HD2EensFr\nW6OouALl1goOvY5WQxNT83b2nzWwNCin6TERhvJyWv/kIHcujvGVM1/OWtk/v9P7ZnNxPi6bCKMh\nn3u947z4jSe5dVtKWhqgok3F5d+MML0WZXnKw+mvPsPAJ4NoDblcfvsLhrtX2PfEcUpbWrkz7cM8\nfJ2mnDVmNmTE9SpSyUUcKxtE0wqcqTCJVICQI4k0lUBZrmBjYYGx3ile+MbLZCol/PZ/vodeDXnF\n9Zz/43UUqjC2kIhH2js4/9Ygx3ZUUphfxUe/v4axUYllPcRY13US0QUOny7muR8/SnJtkYaGKu6f\nH2HPo830TXg4uL8M6+Qm2kw3x148ypqrksC6kz2P5yEuETL4YS9rQRGZpXsh5kFhgnWTgEee0CGX\nxrh1I43WkaKhWoPbZGFixcW7MzJ27S3n6XPb0NU3UGdUgSzK6EQGO3Y2cWNplrENPSK3jKDJxI76\nXSRNGhZTDYgWt5BvrtFRJkZsG6ZAIEBguotKpePh5xcoPqhi18FCfLeHWdQ+TdBxh7DlE1x1j1Gq\nk2Dd3KDzqQZmlkysbyj47Pc/w5hXzFCvmVxjkt51MY5UnEJdisOvb8cRLUUqSdJ7c4a8LCV6VREi\n1TpiVZjqbdu49fYXdB6qQ5YSEUoJcZZmcfU3IzgiHvSlDSiFOnp7RmjsPMr8ght7by+/+O9P8Pan\ndnKbjxDb3CSW0PDZxyMUVxnQRhcJpcQIcg/yyYUuNrt/zTd+8jU+vObDF46yvOHkxqqefL2HbGEu\nt2/OUFFuwLJsZ3DMiyjHSP6xFrZK1JhXzKT1AuasEQprd7C5GeWLT6xomjspVkeIewSsr4d46Ucv\n0H2rm1RCSmtrC/Z5C9MqDW2VNQQ3Zylu1LMaNlK/U0vZgf0c+MpRZi738OTTX85n8W9dGn2zKDub\nkCONUKKh624Pr/zkDe71R3FFM6hq0nDn427mnSLMMz4Km5sZuTVPhtFIz1uXGO1fYsexPVQ0bGNk\nxIJ17g4lRh+mrTRCuQJpcgZ/NAJiIRKJCJfTg88hRJAIUVJmYGl2ntWxOR59/HGytSq++MMFRHE3\nRQUtfPreNdKCTSzWOEeO7ODqu/0c3l5GWXMpb/37JYzVIvxeLw8fdBH2DbH3RD2vfP8QQvsYDVUV\nfHH+Oqee2Mf42Bq7D9fi33Igl4Q4dPYItmAujmUTux8vIbtQyZV37mK3KylvOIhMEse/kiK2FeHI\nISMg4F5fBF0oQGWxAteKh1mTi3e6/ew4VMOBgw3IS0tpLBWRnaFhebOYtvYmRkxmhkwKPFtighY7\nNcW1CLZETASrSFq85Ie26ChKkDIPI4/7CS12YdCp6Ov7jJZDYTo6ddi7FlhXHyLsu41//QbBslMo\nZQr8nk32nKzDZg+wvhXi9sW3ECqzGOgap6o4RfdyhJhEjk4qoO1EM7aIGkmmkvE780hkanKyslGo\nPEj1IkoqC7l34TK7D7SwueoiKhfhN+Tx6W/68fpiaCoaSAlljI7MUdW8C9NqAMv9m/zsX1/i0vtW\ncnY+gn/Whlht4KOLQxiy1OTErPiCMaKqKm58Pop94jwvvX6ajy/6iQmDxCQC+lbyCIYsVGS1MNq7\nRlZmDPdahMmZOIq8TPIP7sSWr2ZsaA1dkZaJmU3K23exZQnw4L4DsaGREqOENFo2tkL8+K9e5JP3\n7qPJ1tBQU8mSeZOukIsDOzsJW2aprM5nfi2TqnIVzSf2cerrjzP4wS2eefbMl5LN31+fejNXpyMW\nkJNMJxl4MMxXf/wy42MJ3Ml8ypoU9F3sYXwFfI4QupIKxm8uUlRdy/1fvcf04hYtB3ZQUtfGQN8c\n5pkeSoxB3N4oEoUAWWqeaCyEXCVHIBaw4fTi3EggSoQoLslicmgK6/Qi7bs6aW0s4sJvP0Iq8FJV\nvZ33371EMuVgYyvCI4/u59q7Nzl2oJLymkJ+86urFNWK8PhcPBzqJ+YboW5vCa/+5AwKzzR1lfnc\nfP8Ljjy+kxWTn537qgh4HIhIs/vQQQLpApxLi2w7W0pWkYLP3u5ja1VGafU2tCoVqzNBEi4/e7dn\nQBJ6egMYCVJUqMM6vcmK1cd7V120Hq2i81gHyopSGopAr9dj9RbSuruDwblVpublOB0qQitb5Ohz\nSXkkTAsr8K04Mfgd7ClLEpgaQhrzE1rtoTBHxb2+yzTs32Rnp4KtngWihsME/bewWa7iyz2GTJJN\nIrzJ3mN1WJbc2OxORh9cICXSMNo3SllRjH5zmHQU1AoxHUdbsIuUyHIUoNMSegAAIABJREFUjN+b\nQxiXkJOlR6D2k1WcQWGxga7PLrJjfymbZgc+qZQtXTaf/7GfVVsQXVU9KZGOicklKpt3sLTqxXbn\nFn/50+e5+8k6xr2HiZndKLMKeP/CMCq1jELBBr5AgKSulisf9xFe+JznXj/N55ddRFJuXOE05kAt\nIY+Fgqwmxkc3kSeCxEMiFhfDZORkU33iMO4sBYNDa+gMGmZnNsmrbMJlDnL/rh2hoYamukxiiQwc\n7gg/+PMXuHppGIVCTE1TJXMrm4wk4nQ0NpAKmygpL2TRqqXaKGPHY8c49fpj3PvDZV566cvJ5ts3\nZ940KDXEwlLSoiTdNwZ5+YdfZWZJgMlnIK9cxMT1QSYsEvyuOOrSKhZuzFLesY37v/iY6YUN6jva\nKaqsY2p0maXFfgqzQkQjHrSZaQThJZKxCFKJCJVGgXXDTcgNkVCInBw9k4OzmMcXaGhpYHtLOZff\n/QRZ2k9x6XYuX7hOOGRlfSPOiZN7uHf+Ovt3lVJeU8jbv7lMVkmKeCzAg+4+kr5Byg9V8NKPH0Ub\nWaG9rpj7737C4ac7Ma97aWwvIhFwIlWq2La7k7jIiGdpjtbT9WTkqLnwQT9rC0nKKnejVWswLUVI\n2h3salAilUjp7nZjTHgpKTOyOmVjec3Pu5/Y2fZoI22HO6CkiObiNFpVBpuRAlr37mBoaoOJeSnm\nDSmReTtanZGYE0ySSrxrDgw+D52lEbxjgyiTYQLmAcoL1Nx+cIWG/Rvs2KVg/cECqoIDONy3WLfe\nxCzrQCTKRRzfYt+xGjZMYdY9m8yM3MIflzL/cILS0hijGzFwRdFlKWnaVodVJEGcr2L85jQRPxgz\n1Wjyhejz1RQW6Om6eonOI4Usz5nxyuSsy7K5/fEgK1tRMqqaEci0TEwuUVrfxqrVj/PmFX70d8/S\n+5kD/fZ9SH0RNFml/OGDARQaGcXYCAQDxDWVPLw3hmvsPM++foZL1zeJJJwEhTJW/TV4NlfJzWhi\netKFMOIhHRGzsBBBr8mi47ETbGUp6R/aIDtPz/zEFkW19dhXvPR0ORDqa6kqzyAc1eDyhPjen7/A\nrSvjyJRCahsrmZ63MBEKUV9fi1Rto7wqn4UVDeWZMrafPsIzP/4K1399gddeO/t/hzn04e3P3lzd\nvEueqoQdxyrJ9UnZkI1y37pOTXOCf/n2Pb7/t//Kt55JIipfQGCXk9dwiAufdyMzqOnM7GctWsbO\nI/k8t19A19xDZGYvUnkbSVsmpbVT+B1WjEV1lB3OpsK1Rd1PjjP9L70kFqUky92knLWI5u+Tl2ND\nWJlHS2Emv736WzKjYSKqSZ59YR+//PkC9uONaJxy+u6vsp69QX04zZpugu6RBVwpC9OLxTz45Dzf\nfaqGSx9Nsakcw7A1iy4QoHdyDWmDkR9962vY/32a29NrdLwuoyyaIDEtZUJVjPsWtOw30jfZhWIz\nynfeOojorhXFUpq+kJhW5T3y2tNMKloY+mCT+KKYv34tk75uL9eXYhx02vlt7wwljWewD/yCkU8S\nbAqfRtgwwv+zr4wPBhbQHe9l7p1J6lRb7PhxMRd+cgGhQITi1GmkFSkssmJm5nqxzAVJjM1Qcq4a\nWUs1B5UjLPuE9EhP4lvcJO+RTFIbcTZHvTgFD1HJypjMqeXF04V8dP8dakr16DwbzIXMpAMeQu4g\n3oe/os+9Sp9tCf+SmpZdldjMSfqufcHff+cISwtufNuNtGXUsbnip+bIDoRkY65dwT5cyJ8/ncG/\nfefX1B7M4yt7pfz1zx/Q/NJ/8mArzFEmyNZPUPXXP6OqXoF2bhlrfy9tGbXknW6G5jq6/2cXf/uT\nFu5cukDu0UJOqPaxsHqVqvKdPHHqHOPdW4ja1tgerKL/r79ANzHEK+deJrVkRFuUxndHwb5DYl76\n+mEC3izu9SjIS25y5tsvcvuaiZ7ui4SnZvCaR5icmsXesMpmqIPckn6y1y3cmZtiq7KdR0ZXuJQj\nYvCnvRwtnmH/mTe+nEL63tU3LdYpWhraKNnfRJ1QytLSMHMRJWKhh+Ev+nj0O99jx7YytBlRViNZ\nlO3aSfeVfmqOVxKKmIiLitnRUcKJfYVcvngDmdRFlqaUyfkwJzpTbK0HyMtroLY9D8Ginf3P72fw\nygiu1QQJaTYpfwRnKEbQ7qGkLguFIpd7PZNkZQRIJR0UNrczfM2EpDafDLuFh/3XCJeqqPJtMro8\nzvKqGUOBmoFZJ1ff6eL50+3c+HUPjugwioSZLJmM/u5hCgqL+fFffJOV+xtMLlnI3FNAkdCL3QRu\n3R7WHgRRyPPw2TZIp8J86789ieX3MwSWIqy47FRHB2k8WEHYqmeiaxGnW8k3n2jmwcUV+jdVZK7M\nYN0KQPYebLMXeTgbxp86Q8A7zPcf38v1a+Oo6+04P54m4F+m7A0jX/y6F50xG93eHZTXxgjKjTx8\nMIhYkGDs/j0OvLIdSXMleWvdiANrDGc9h3NujLo9WayOB1hz2whEXOg0MQKqBvYcrOaD939Px+Hj\nZBpDDNzrJjNDyN3eCUzWWwQ3IwwvDOLzxGneW8f4kJOhqzf43nfPMbq0il0hxFhYjdnipWZnHWKd\nltWIl7XlMI8/Vc6v/uFDWpvV7GsP8C//co+W0//I8GqaqowZUr4kh7/xHUprc0hN9rMxtYxBraC6\nspbixgqu//YWL333KQavXyenqo4cVTPhyW4qcus5cvIw87PrRP02jBoN1//tE7STUzzWdhCBI5s8\nlZjlERPlhYW8/upTpMUSxgZnUIuW2XH4ScbmHUze/AB/xIZtdpL5sVGEZWFUGY1kaa1oUpuY/Bv4\nVE3sci7zcGOLoe4VWhUTnHj8y5nq+8dfXXnTsWWjoLyBps5tVBv0rEytYQ3oCMc9eJcHOfjqq9S0\ntSIQJJHp82jet4+e23ep2VdBkChSdQ6nTnSyf3cRlz+4SaExjlqbj93i4dD+LFYWfOTlNFNTk0PC\nGeDQ0/vovzWEaXEDlb6UVNSHIyTGbl2ipqYAkS6P3n4rOaU2Il4TuVU7mbi7QFZBDRlBG123b6Go\nLSE/7masdwznqp3MGg2zs+vc+WyAvTuqufa766QEFkRJE3p9DkN355AJpHzzR6/iM/uYXfFQ0lGL\nPODC40qRMuwnZQkhjChxrXkRikQ896NzzHw+gcvmx7a1QZPWzs7TbXgXRAzfHMMezeTFR5ro+WSU\nRZ8e+cwSdkcQf+FuZnrvMTBuwyl+lJR1nm8fb+NB9wwZ+RKEI1asC0NUfbWaa29fA52UzieOU10U\nQqRRcPveOAGvm/XVFU68ug1PcRFGTx9Cu5lx7Rk2TIvs2JXN4pQTT9iP1bSOJL0FxioeeXQb59/+\ngJZTj4DUycxYPxmFMu713sFkGUccCTI6NE0yEae8roD5EStdF2/zytdPMzyzSEAC5U1NDMysUdHa\nRFiaxiOEVWuS4yfq+fAf/0Db7lw69gb4Xz+7QNGpv2fRJGV7YxjLapCjr/2AigoDkeFuvLYNlAoV\nBXn5NDeX8PlbV/nJT59n6PYNVFn5yKQtuFYnKchrZtfhVrwL62xY1yjNzaL7gxtI7A84Wl5LkbgU\ndSKIc9lCjr6FZ188hNsdZGpuHr1uidqOR5ma3aLv2nnSkgSWuQUmJweR6JXk5bRhMLrIUnpYXrEg\nLm2hOrDJyIqNrq4lWqUmTj/95Zyy/y+/uPCm3+Mhr6qR1p27KMhSszjmxZmQEk17sVt7aHjiFXKL\nO4jJFKiyi2k90Mr9m3doPlpLKBYEYRaPnOhk965K7l65SU42iJT5mJft7NuTyZolgiG7nsI8HZJg\njBNPH6L75gPWLE5UxlJS8QARNKwvzVBTk4NEV8ite1YKC+0Io1aUOfXMDCxh0FaRkfAyONCFrj4f\nvc/BZP843mUrgnIJWzYPDz5/SEd9Lpd+ewOhzIlKYEMlUjJ8fx6FUMqrP3iNkDvK2LSP0tZWBKFN\n7N4owrxHEJjdKAQarCtehAoZb/z4aaZuTbNu9eBwbFFtCLLv3E4SFgG9d8axYeTMrnpGLk2y6lSg\ntm7gdwvwl2xjpH+AoUkzTvVZBK41Xj5QQ9+dCXR5aoL3x9maGaH5hW3c+ONFRDo5jccO0FoTR6aU\ncu3uBOGgnc01K6de7mQzrxhtuA+dc51p/UnslgXaGlVMD1tBKWJm3oQoGUSRU8fJc9u58Lu3KTm4\nH3lGjLmhHlQFau4M3GJhaRx5KMz05DLhoJe65lLGH0zR/cfrPPvSCcYWV0hpNahyypgyOTBUFCHV\nS/HHU2xsxth/qIFP/+53tHXm0NQZ4Q+/uUnOsZ9iWRJSU+Bm1ZLk2Ne/R1FJJqnxfiIuN3KlltzM\nDHa2F3D+rav85L++xNCt62Rm56LMbGF5doocYyO7jzbjnN7AYl0hR5dB32e3kfgG6KyooERdhTzg\nwL24Rn5eKy+++CgOp5vZ2XEkklmqm44zOemk+8ZVoqIk5sVVJscGyK0qp1BdQ2FVkvx8WBifRZ1b\nT37cxYJlg66eBTpUTs48/eWslf3V/3fpzVjYg764iqb2nRQXGJh+6CIikhFOBokGR8k/+jyG3J0I\nZBo0Wfm07m/n7rWb7DrXjj/oI5hScfLMQdpby+m6fQtjjgqBqoLluU06d+ZiXvZRUbOLDJ2SlC/M\n0TP76O1+yJbNQ0ZRGalEgIQ4A8v8EpXVeahzSrl+y0Re0SbihBVNXhUT3dPkGSvRpTxMjPWQt7MW\njc/G2L1RIksbUCnBbXbRf3WKyiIF1969iVjnRpta4/+n7j674zDPc9//p1fMYAoGvQ96JQCSIMHe\nqxrVLNmWHcdx2fvYjtOTbUcpJ47j7ZKs2NslbpItq1dKIkWKvQEg0etgUGcwfTC9t/PifIC81f4Q\nv3Wt57rvZ90aiYaJeyvIEfDpLz1HJiPm/qyf1p4dZEIOQokMqZIDiDe8yHNa3N4EQrGQL3z9aSZu\nW/F6YthsGwy2KBg43EvWBzcvTOCTlnFyRzOT78/i9MsocnqJhMWEGnoYHx5jYtGFX3ECcWiLzx5q\n4saH9yiuKiV9ZwrH+DB9zw7x0QtvIC9WYj6ymx2dOUr0Wj78aJZo0oXHs8mxZ4Zw6qtQ5++jDGzi\nMh3E7/Sxo03C1OgyCWGe2Rkr4kwSTXkP+051cvE3v6d61yBKQwHL5DDFjTpujlxhxbKAMpli2bJO\nOhOlolrH8tgSd3/2Pqee3o9l00VBVYSi3My81Y2qQoeiWEIaMTZHnO1D3Vz43u/p2FFG864Mr/76\nKkX7/wGfVUBTTZyllQwnvvwNatsqCV2/TrGogFymRSsqsK+/nrdevMo//MtnuXfhY/QlJoxlO1ie\nn0Zd1MT+Y92ELC427RbKTUbuvXcdUfQe26oq6dC3ofQG2Jxfpc48yMOPH2DD5sRhW0UsttDQuo+F\nGQ+3rt8gmo5iW19jcWacytZWqrRN1HZJqG+SsfzAiry0CWPOx9qqj0sXZ9hZneWhx/4vuVb20xev\nPt+Wz1CsSJItrmNpVkiNqJ6S1DEmFkeIS6ZoeXYv//HUG5RvP4Fdvslj7U0M7m3jcz0hmnY/xc37\nxeT872ESLLPN2EbfwWZa9+/kgcLLYGYeb3ob9wM9NFSbSRbbefvf1mn7jJjDx/u4cHGRVtUFsge+\nSnQtRyxwm5sFCbVzNTgH1vD6QRcJUNKYIJGcZqBXSF4VwZk/Qs5n5/bwJslAFUWlORoee4iDhtME\nM0vcWYji9NcjqpCTtBZR1q9lZ90e/vxvXiXU78Sz5aAte46xqWEUdULGRz/msDZIpESHKp5m2iIl\n6a5GrNexWpJGuvAxK4kZ5h3H2Tp/HVdsGHXHCRqrgqSUh9lYWuTSiJMzf3MIzy0nivY97PhyH+ER\nO2XSEhJaP/dvyJFIeymS92Po7yVhsZNp0tBwdDf2kRC9LVZacpWIdyYQhHbgFo9TQEqjWEpGX0ko\nmWctraNHv4Iiv8KKxUCqzsWhmoewSxdp3zbAr/7yHvv+1ymW3pggVVtCUFGDfmaYuwsTWDJxjrTs\nIDKxgH11gm988xxjUwoU1VJurIdY34ihELjobhugeV8TnSUp2vr7sE7OISw2kSfDttPPYHXfg/k1\nhANwpP0QOzvnyF9c4KnvfYY/fHaGZd8cDz20m+v2OApzA0PNPYi8wzgWbPz4pfNkxSVk5kZxYUOm\nMNJfXsnFV39MacUWgTt6qj8lp+PJQX77uw9Ie2aZXFxEUvDSeLIbQ9RMxDNGZWGFkyc6uXfrRe7O\n+ZHJl3n2QBkLmocw6qysrOWxXN/GseoClvk1fvwgSr/qKZ49XYdn9lUi/p2c+9ZpUpOLHHj4k7k5\n9J+v3X1ek44jyWUwGvVseVcpMu5Cp2nEv75KLDXKmdOP8+qvRjBs66K1qgid3IB5u4FjRhd7dnex\n5KxCJR6mUj1Lnb6NbYd7MVfU4Ir6aFPY2Yq1YPGaaOtsQaEJ8epv71K3o5JHHzmGa8MN+WFqzYcR\nCiVYJu6QqCohbxeT0q6RFseor9Vhbosy4/exu1PORs5LQPcMEds6q+sJ0DQRcjupePwJjm87hG/F\nxWJOTlhqorSsgoBXhKrIwK7B3fzVn/4CWZ2c+eEHVBoGmb48hsEIy9YpeirtdJtlFInU3L29gTxR\njchUxpI+g8Z1lWg+yMfOPcSXZ5nevIm+tIUyQYDKtjMsPEgyteTi4Gf3sjrqoayuir3P7mVuOkFj\njY5c1MuwQ4apuRkt0HBogM1xEZGcn6HnznD3VQ+dbSl21reRLi5QZjiNOxIgFstgUsTQ1Ddye9iG\nI1hPuTZClSzIxqIIsSZNb99ObCSp3NPDe9+9y7HPHcZ6fRWvP05xTSOe+TU8ixb87lW6umsIh+ys\nLG7w1FfOcu/qAh3d1UxZ7Xg3fOjlMnbt7aSroxmFME7vtjbs9xYxKIQYFEq6D59mangBcSpNxCDh\nU3sHOd6bIzp5gW9+a4hf/b8z5D1bnH32aW7OhhCbdOxpbybk3yQS8XD+nWvkMkHSi4vEkptU1BiQ\na4289dKr9NcFcI4n6dtXztnHBrn69usE7SMkUhGiW2sc2D9IbUcrseXzaKRuTh7bzfydS6ynIug0\nebob4vhU59CwhiNmwb5azR6jkUnXOhfuBigT9LK7y0x2+Q7htJznvnqY2PoKx05+Miegvzg/9nyt\nIgTxOLqiYjJxFyVlrWj0OqKOSdyOMQ6ee4S3f3KZ5u07aahVkxBWUl8t42Brlt07mlhz6BClrqMW\nO6mvbaNz+zZqqozMzC3SXpzDm6jGlqzB3FaLUJnm1f+6QM/OOk4+cgq3b4tCZplqQz/FBi3XLl2l\nUGoinRAgzqxRSCVpH6intiLPgitO304lkbwEv3I3SbePTUccGntJrDspP/4QPfU7yTj8zAmqKJSW\nYa6vxBkSEi+IOXv8KP/4179FoJFgGR5Fb6pjcWyZYqUAh8VKRfEa2xqLkGdTLK85IGUkKy1nXS2k\nIvYuyc0ww+G9yGI27k4No9TX0SgL0d27j/GRPNO2KA994RDjN2ZoqivnkecOsbBUoMusoBB2MOoX\nUrKtAYXTyuCRHhYcJTjXbPQ+/TDv/9bOQE8RXb3tpMQaqur34Fr14fXFUWjyCJVqZmeyuOJdGEvC\nVBS2cHpF5KQC2nq6WcuKKRvq4+Pvv8PRZ48ycnGGiNNLY2M1ltFZwhsRkrYZmreZSJJmbtzCI8+d\n4vrl+/QdaMAy58Zp3aS5sZrW5jqG9rYjSicY6jfjmZ1DWwhRqpJw6OmHGL40TZHcxFZKzBMHtnN2\nSEPQ8gpf/ep2XvrXm4TWQjz9pc9y/Y4LmUjIrr4mQp4YifAWH124Ri7owW21k4uG6W7WIUHMu796\nmb29UjatCXp7yjh6pJtb772GwLtBJB4jGbMzuKuJvoF2witXUSY2OfHQEOu3RoiknRj0cioMeST6\nVtKJDdypNZIONQPGWqbcVq7ecFEkrqazvpK0Y4ZkPsXnPn+C5OYyJ86c/ETafPXWyvMlmU1y0SA6\nrQZxIYbOWI9cKyFuH2fLbeXRz36at372EQ09vbRWakFVTbE4x6FeJX299QSCClLRe8iFPhrr6tk9\nNIjJpGZ9aZYSaZ5Mqg5bpoqKlnLEwgCvv3SBroF2Tj10nK1YHGIOynVNaPQaLn90E3l9LWFfFHlh\nhWwmQudQGxXFsOJV07NNiSecxaPoJez043Unoa6LgstNxYFHaC/tI7TqYFHWRlF9GVW1Nfgwkslp\nOX5wkB989x3ESikr1+9Q1lCHfWGDojx45u2U6TZorZaizqcgFSHilhPMGvAXSahMX0fijTIWGUCQ\ncTI2OopcVUGrOkpP5yAzUyJm5vycfe4QD27PMNhdz4nHDrG0IWBng4Sk3cZUPIeuo5aqgINd+9uY\nchfjtjnofuIhLvxuiR0DZbR1NhFPymjuPMbGQgB/OItEX0AklbE8LcAZbkVQFKJRlcQbFpORq2ju\n6WI5I0e3Yxs3/s9bHHx4LxPXVoja12jpqmf+7gIZm4/C+jR1PSXklQWW7s5x8NwQN66OMXikjRWL\nE8+qj7bGGlqaKzhydABZKseOzjJCayuoCj5Maglnv/AE186PUlLZzOpqmCcP7ODsfhPCwAd86cu7\n+MM/nCe9HuCrf/lNPvrYTswf5vDOTnKZPGGPmxsf3yG+tYl3zUU8uEVvrRqdSs7537zMji4xrsUY\n/TvM7D3UzoP330CyFcTtcCIqBNk5UMuOXb24py8Sdczz+GeOYxseJRlfo1glprRMQkGkIp3w4gut\nE3FLqVVomLXPcfUjG0UiI2115aQ9q6SFaf7oT46T96xx9MThT6TNtx/YnjclbBRiEVQqJVJxhqrq\nBkSKLH7LGD7XPA9/7hzv/PwaLYN9dDQUkxOXoVWlGGiT0dFQRiIrJOYfRoqbnnYT+/YOUaSRMDc5\nhlpWIJupwZYspbRBhwQf59+9RHt/PSfOHCKWyZKN+CnVt6Az6bjy0Q0KRiOJaBpFbpVcPEz/qT6K\nlFKW7CZ6BtS4/ALs4mYSvig+TwrMXeDyUH/0CcplZsJuL+vyNoobKjGb21hNaEgJDJwY6uY/f/QR\nkRx4705QUl3H+sIC6pyAkNWDQb5OS20RRUQoUefxb+ZJqxvxyoRU50aQ+7wsJvvJJDwM3xxBo6mk\nVuxje+8u5pbkzC4EePQLxxm+OcGBvnYOHx9k1VagvzJPweFkM5NG2VyJwbvG0O42RvwqvJubNJ86\nzpu/W2TvrjraexvYikBb+1FWJvx4t5JQIgBgbUaAK9QGyghV8gTBnIyUsJjW/h6sGTnFO7sZ/ckr\nHH3yIOMfTBN1r2HuqGXs6hgFhwNW52geLCerKLByd4aDTx/k8sU79J9oZcsewrO8RWt9NeaGMg6e\n6kMQS7JnexWeFQtacYxSjYizf/Io198fwVjRhM2e5Ml923j8RC2EzvOVr+znreffIm/18Wf/9He8\n/eYiEV+Q3TvaQJgnFnZz+cO7ZMNu7EsbxLdC9DWoMOlVfPSHt9neJmVlOsjAQCtHTm1j+IPXEXi3\niERDFPDT11PHrsEebPcvkPWv8PAT+3E8GCeRsKMpllKsE6NQKAluuUiG/bhtGUplcuZW57h1aQVN\nXkl9mR6CbvLSNM98/iDi0CZHjv1f8q3s7fdeef7Iw6cw1Kq5/l92DFI7279whsvTo6TLkuwT1fP2\nizPs+9YpPvhegKeey6AuyHBNGnhvYZx8WwUDx9upct1l3GKlfbuB771wAcEDD+nlu5Q0G3C6N/Dk\nU1QlMzS0Z1mYXCIQq0dYHCSWSFNf0UZjRQtu0RK6u1F6jDKMJ824Zu/RLtOxkVHhG/ayY+fnGb8d\n49ODedZmKtgyzFBfco7+L0do8jbRIA/ypRPtJDJbbIkyPDO0iwcXZqg5PEjeXkTA56S2Q8DwR8X8\nz2dOEM6uMpsrkJifwsxXkFQ28Mp0hq7KXXztqwYufPAmzmAAGRq+c/Qs895ygqIdbGuxsP3MEOMT\nSmyLw0jKuvGNeSkqkZC2Kyg1Bxm7JqZKYyQuCCMQSbHcX2Ggqw2bMsrf7xKxb2cD3/rVAsaQlNrF\nK5z7rBalLoV32cxgj5l29To//NE/4R6JEo3HuPbxBfoa6rlwfQRhcTU6TQjNtiPEHEuY9+5gbmqc\n6eEELf05NpbuYNbWMdSUxygsRzQopykcIFO3i9FrH5OKi+l58hzP/+3v6N2fBjs0ScIEc2J05iKM\nqTxvvXMLfVUVuZomXn/lP2kzajn7Pw5z9T9GqS7yILNm2P1UG0NGJ+N/8W9MtTQzPvcm3dmrPPf4\nMZxr4wTGoqj1AqwbC6yEckiLy2naVYZ1fpNgLop9Ksijh2u486GHx770da59fAsFArqLFTjv2KjQ\n65AJJVR0lLGjc4DshBKLfBxh6SNsbeb5/nUTZ7uySJxDROv7qJSvY378aWotU6hai9DXlhINvcXF\nGzqKYvc4+z+MvPtrC8vbKqnz56iTh1la1/LQuX2fyCB95/0Pnz91aj/SKjk/+/kdFFoBTZ2DjI7N\nkBQL0Mpb+OWvbqLdVc393y9zfJ8XrbGIWLCVZYub5YyPgcED+MfPc/n6h+w+NMCHF27iXHSRl2+h\nKy+QlhkRN8iI+zZoNvn4+PoimqJqCrIoMVEWeVEZO3oOYPOvIfHnEGaiHDi+g7mZB5SZ9Ky45FiH\nZzjyxLdYnLvHpw6VszqmIFuYpLq2HlFpit0dTdTHnZzs1lCQrpOSJnn48ADWkWXKmupQi9VkvONo\nGmq5cy3Bo1/cC1kJzoQKaSSCMfw4eVEtl+8LqOnqYNfeCpbuX2Rl1kOtycTX6uuZSVQRZxe7mzc4\ntG+QeyP1bIVW8KpMJIIphLIsblsBfWWBjc1ilLJqlJktslkJM4tWelq6mZga5dMnttHW2cbvz49T\nLQ+Ru/sC3/xaKRX6PJdfj/L4kz3UMsvf/NsfM30rSplKwtuvvcOZtxw/AAAgAElEQVSZM83cnXKT\nLTZhLI5QPzhI2LpB41Ank2PzLIwv0VaRQRjfoqy8iP42HZ7VIPWDRuQBP4LiWtbWLGRCsP/UMf7j\nn39OZ28pRWIVEoEfiTxPRU0tiWyUG5eHUZpqEMlLOP/eh2iVGT7/pbPc/nACkyyGxO7kmS/uoF81\nxYXv/yPrum68rllM3pd57tNmlucmSG5EUahkbK3Psu7Jk9DUEEgbsaxEyaWTBENl9OiKWXXlOXJw\nPxeuXKOouhPrsB9/UIFQV4Zco8RU2cu2/mbWbQI2t5Loa3dg3ZDx5riC7R0KfPEB1Po+TLVL7D34\nWUhtklXI6OzZxbzzBnZLEkkqTl+vltUVFxtFClI+FQaVnMC8hEeeHPxE2nz5wrXnd/T0ItIo+PDi\nLClRnLq2QW5cnSCeLUZk6ua1n16jbk8z199dY0/nErpKMWDGsuphLe2jpXs/Xst1rly9yOGzh7ny\nzkW8dje+tAdTgxphQYW+V0siaUeHm+GRRQRFJoTKBClxDFFcQ8fOA4SDXnLxPIVkjCPHdjE2Nktx\nbQXLNtiYXODIF/+I0YnXOHm8nJmxCPgnqW0qJ18uorlXQWkuxLMPN5LJr6CozHOit5qZe5uUmFsw\nISa1cQtjUy13r9l54k/PEA/I8EeM6GSQ3tpJsbqV96/Hadizh7ZuPZuX77K5toVJIeHZhg6WQk2s\n5nrY1Whl3/Z2Rqar2Qh48aclOLx51AYj1hkXmhIN6xsKKqrbCTs8hOI5Jq0u2nt2Mv7xA049vJ/K\n+nbefesWVQonSuclvv8XZir1WV79+SzPPNpInXyFv/vuZ7Hec2Ay5Hj3rUucPNnE2ISVtFxMuUlJ\n4+B24rZVWoZqmJ924lxaplmfh0wQiTDLzoEWHkw62HOslazTh7q4jtVVG6GtOIdPD/KLf/glu490\noECBf2uT8voiyipNxLMJLn44TEFTgaZIwgfvzyJUy3j6C6e59tYMelWOwuYKX/jTbQyVzPLOv/6I\nrVQV+aQF+caLfPtvOpgdXiTsCGE0FRH0WlmwZklqa7BFDVg2IJxQ4PToqS7X4l7N09Ldy/zcLCJl\nA7P3l0kmpaRk1cgVRehbB+ns6WPLLWVmI0NtXSO2LSUvXMrQ1V2JM99OhA7Ka1Ps3X+OVDqJ0KSk\npqmfBfcaKyseiKbp7Kxged2Pq5AnFdcjLkiJOAs88cQn0+aL50ee39behLioiOv3Vsjn4tR09nP5\nwgOSBR1SbQ8v//JjmgcrGP14jl3Ny5RUaUgWKphfdOHNhmjqOIRv+Q73H4yy/cgJRm5eZ3V6ibwm\nR0mDBqlSgbReiEAZokSe4NrVceSmSvLiCMlCnFiwwMCB42TiMTKJNGHfFucePc6tu9Po6utZWs3j\nXV9iz+dOMDH3MgePVjI74UXoWaLKbCJplFDXDkZhhKceaUGicKCtTDPUYmBh0klpXTOKuA9ZaJTS\nmgruXrPy1N8+QsQtwb4moVSjYyvYQoWpnQ/fj9Cw5wClVToWLlwl4NyiQpXjscHtzK2WMp/pYmfD\nGgeHGrl7v461QIRAMsXiJshKNKyuBJFpdawv5aht7MQ172TTF2XWFaC2swvL5Sm2n9qPsbmT82/f\nwCSwoA5d4Yd/00KdVshrv3nAs+caqFa7+PN/fAbrPSvVpfD++Uv07a5gccEOCiEGvZrWXf0krVbM\n2/VsrAawz8/SZhAhUqaIRcJs629h4p6FvtP9JLeCFBmqWFmxE/Yn2Hd8F3/4519z8LFBRBIFscQW\nFbUyDHo9YoWA994fJpCRUVys5NJbS2QVEp74/GE+eH2GEqWIjGeFL32tiz3Vy7z1v39EPG8kH1tD\n5XqTf/zLVsY+niMU8FFiLCYfdXP7XpC0vpHNqI4NrxxPQIs3UkKVqYjYlgpTSQPWpTnkhlbmbs8T\nzyrJy2rJC0VU9Ryio78XX1SG1SlmW383a14Rr1yJ09ZRSUDQgyPSgUYv4+y5RynkC0gNGmpaerG4\nXWzYtiCVp8FcwqYvz1YuQyKsJRbNErAnePrJoU+kzV+/e/f5ztZG4gIpw/dtpOJhKlt6uPLRODFh\nKdKiLt7+7WVaBww8uHSfztJFajuNuN1aFqw+BEYxFTWDBDZmWFrZpHXvo9y7dYeVyXlQZWloN6BR\nyinuUpCV+SnR5rl0aQKZqYlsLk44FSMWzbN97xFy0RjxUJBkJMYjjx7lzt05VHUNzCyEiTpWOPaF\nXYxMvsDQwTJmxzZJbyxS1WgkWSqjqjOLURbjqafaUardFJenOdBZjmXGRU1jA0L3Gsr4JBU1ZYzf\nneLpvz1J0C1lbVWMyVCOy19Le+s2Lry1Rc3+I6i1Kqbfv0rI4aBCkmRfUwuTM1oWhYMMmlc5fLCe\nmxN1TG4EiabSjNtzKEqKWbG4kKr1WKZCtHRvY23chjOcZMIZwtDZydpVK/2n91HR3cvF1y9TJVnF\nkLnJz7/VS0OxmJd/PcqTD1VSqfHyd9/9NKujC9SZ8lz84BqtvVWsL3tBlENXZqR11w5S1ll695aw\nanHjssxjLlMhkCdwOLZobKthecJG3+ndxOMJxOoS1pdshP1xhk4O8sa3fsHpzx9FJFHg8nuorFOg\nL9GiKBLx2qt3yEo16JRKrr2zTkYj5PHP7OHD1+cwqkVknWt84astHGpw8vJ3fkAWPYWEDfnGK3z3\nb1u4e3GeWNSFUa9GlI3w7iUb8toe1lxa1hwKtjLluGMVlJfJIVILwnKcjiUk2gam78wQT4pIy2sQ\nCqQYm4bYPrSdNAamVuNsH+hh3SvixYthOrrrSMi68UaaMZToOf7oWQpiMTKNhtqmblb8IRybAdLx\nNKU1Jdj8YqKFDAGvlEy2gHs5yLPP/PfvzU9EObRwZ+n5m9NWZm6+CxottWfaePGvx2is6meoREnp\n9gMsJi0o1kdo0LtA+AHJfC3LmjLmz7/PTkMOUVGauWs38WiljLx9i1DBQ4m2Ahc2urY1MzLlo17g\nQBAep8WtJfesEfub99DU2nlyr5n/+e/vs8NQQHNAgLzhDMv3VtnxZ4NUeYMYWtSsWP1I99gY9U6z\ns9/Hgj/IA4+Jmqlxbl1yc+ThQUbfmkJRKyBYXmDzroB44D6e4CwdlSLO7C7FsXqTkk4Vo1fWEfae\nZuzl19i6dpNkvANjs5Y5ASjNIaojWpSlMs5/7wY13S7K1SXMTHgYnpnBosmys6KR4soQS9cMWIJW\nemXVOBx1ePwhiups9Oof5p5ohoxni2tvj/DIY19kYd7Gn/7yKWROC4VchvGpEJvONAMPD9Fcl2Q+\nGWTpZoHlDTedh7VoUiJGV8Nc+MUwy9lJGj71eVbXPCzPrnFylwx1fRf6ZRHLjiTCaTHp6jS2V1Is\nJWN84XCI4GoDir6dtJoklEQrWAknKaq/ySu/LPDEU/s53Wvi5ua7nN67g4TfgdVWwulz+9ixx0x9\nk4Tz43fImhT4clEm3t4kqCmg9wqRbiowmO6i6BVgKBVw88JdpCY10WSUnGwvBlsJp/7oAFsLRVi9\nS4SK4lT2qimpSeG6WYJIaWDjhovP/fExzM0DpCP9KINeDj/dxos/+D9oCtXU7C3ntV9dQJkLYT7V\nQquqme//532aWg2suO5jKM8SnS3C3G6jQhBBUDnDjWEV+byQpz4v5eLPrbxiUzM5vsqhbbU0dDWS\nkZVQ01DC7I0lGgceQr82yHO9RVSfaWcKJQ/1t3wig/TlG+vPL06t8uDWHGqdFHOtkVf/MEVlUxvN\n5ToaDm0nEnAS9C1i0Gip1H+AL2tAVVnCS9/+K554djtZkZDF6/dJFAtxT67j2lxDKBey5nMy9Ohh\nxt4fJ5dZQxRfpyqapPSxXj54+SZV5WIOPdfN975zkbn5BZoOliCRNuJzpzn+RDf1NQ3EMhFsDhvt\n3QWuLd1h2/YgVkuWC6sKVDkfs6NBeh4e4soHI+SkCQrGchyrCVK+GJvLU2iL1LR3NOK0LyMQ25le\njKGu7mbxynVsV+8gyhppKBWz6rMj0PspUigpllfw0W9v0VGmoLpMhXV2levD9/Bp62ku76TJuMbs\ntSDzgQi1ahkhb4GINwIJN8cPnsRmdzNuseKadPLQZ04wcneBr337WUSeacoqFIxfmyGdUzJ0eohk\nzENZg5GXfjOFP5Kg56AGpVrG5fdmuH3lAavRBAOPHGLTGWdlZZn2pmKUdf2IUnJWLAHkwjyxnJix\na1ay624GH6sk6hCja+1HI1CikutQCFKUVbm5fSvJsWM99NfJmJy5z8HD7QjzbpIhI6efOo65rZbi\nKinjE3cpNkgIRaUsjo6TEEqpFGsJ+xJU68JUVkVobCrhrQ+uolaoSKVFqMqPk5sRcPZEB8lwM8N3\nV4mJc1TUaejqTzM6VYzdFiThDvGnf/FlSorqKCo3os4sUNds5uM3z2PuUFCc17K5Po1C6WR3ZyeG\n4iJ+9uu3aa6qYGHtPo3NJoKeEDJpmNJi0BZmsa5IsfiTPPz5Mn773UkmZ7ewL/nobW2kc1cvq/MB\nygw6Vu0e6mu7aJCb2d1VR4W5iHyphqMDjZ9Imz97d+758KaTmQfjxPJxOluqufjuIiZzPW0d1dR1\nN7O1ZWN9eQ4lCtpb5pm2xijWVPHOd77FwU/vQCAQMnn3GkKTlPtXJgm61xCJwROGHceOMXdrFK97\nEkk+giKdoP7oAO9+MEyJUcHxc9v56Y8uc2vEQsdgNWmRGm9SxNHT3VRVl5DPpXD7LFTXppmyrdI5\nIGdpKs6o3UCRLMrCgy2azh5n4rXLhMMCEJdid+UIO3wsPJhGWaShs91M3OnE655jfimKxtzByIUb\nbF4bp5AppVInwuZZRpeNYDDoUYuLOP/SFXabG6mr17JsmWdmegqPohtTRQ9tBisjl5ZZDWgp1+VI\nuZLkY1m8tlUePXMSx7oTy6KT5UU7h87t4cHYGp/9k4eRuucpLhZw5eItSqoa6Nizk3xwk4GhTn70\nb+8QE0rZ/fA28vkYl25scu3GFBvuHIPnDrDijLG2bqW+tYaS2h7EAinTU06kgjwxsYj5C7Nk1zZo\n21sPBSXG1p2oEFFRWU4hGcFUnWH8boD+7S00maXMjkxz/FO7SUccJNwKHv+Tc9S1lKHW65mbHENS\nLMMbSBBeXiXqzVOqU5DfilJbWqBMt0H/YD1vvHwRUTKNVGBAZ95HwpvlxO4GMikVb7w5S15eTFt7\nA62Dxdy35PE6QqT9br7+7a+gVZXT3VFCidKLSa/j5sfXMJvFpGKQyTjIp4Js7++kvEzFT37+IrX6\nYryxZXRlRWR8XtKFKFX1KhTBFZyeHF5fgr2Hy3jtxXvYljdYn3LR29JO775unBYHKlURkUiUBnMT\nVVItuzo7MBgkyPQKTu79ZObm939363lRKsHc1DSxRJgWcwVXri5R11xPS1sNNV1mvE4bDtsy4qyE\n9sYNJhc30VU0cel//xP7nupHKpTz4MZ7CI0F7l1bwrMxT5GmiJnVMHvOnmb+2jCByDLibAKtWE7r\nnu28/N4IxnINxx7ZxU9+cIGbN5do7a+nIJSylRFz4tQ26hsryRUSOOzLVFeCI+2nrr2ImfE4Fn81\npbo8CzN+2s+cYPbdDwiE5WRyJpZXgiTCflZnLUiUBtob60gGN1lbmmPeEkXd2MDox/dxfHifnKSc\nKq0Ip30JZTBEXVsVkrSCC+/eYl9LGy0tJYQDG8zcG8deaMFYP0h/0SzDH81jdZWgVEZJu6NI0xJC\n9k1OHTuMY9XHktXG6pKHY8/sZ3hsjWefO4PUPoNKL+DOxSv0Dm5D39iMMOlm1752fvrD90lL1XQf\n7UdeLOOd9xa4cWsW22aCgdMH2AjGWF9fo6a5iTJzO3KxlJkxOxKhkIJWw/QbI7C+RuNgE6msgNLG\nLnSqYgw15eTjEcoa1czftdO2o5WaCgnTw9MceGaQVNRB1i/h6OMnqe2oRCRWMDV8n7xWRiATI+W0\nE3KJqa5QIgiEqDWKMKns7DvYzu9//QYBlw9jcQ0qQz8Oq59HT3Qik2p59dVFIqk8AwPb6dxRyrgz\ni33FiSjh50t/+Xmk6ho6zSraGnLIUHD72g26ehTkckpS0XUKqQA7ejopLxXy81//AYNUSF4cQVWk\nwbFwH4EoR1WdFmnKxvpmiFgcDp9s4g+/v4xzfYPVmXVa6uvp7G/GMr2KVqsin89TW9VAiVJCW30j\nxjIVhjINJ/Y0fyJt/uClO8/L0ilWFmaJJrdobatkdHgFY2kpvX3NVDSU4/dtsmlbJpcS0dUWZHJ+\nDWNVN7f+419pPdiOSCBh4s4HoC9w5aIVj30SjUbHvD3K7kMHsNydwu23IBcU0IjkmHt6eevdYUw1\nxZx8eAc//cFFrl1fpq3PTF4qxRuTcPrhASoq9QjFWTZty9RXKUjrxZjqVCxb0ljcpVSUilmadlN3\n5DDLf3gFT0xJMmFiYtqFIBHEOrWAUKGjrbaGWMjFkmWR+cUkopoKJu/N4HzjAcir0CnyhDyrqAMR\nqjvryUbg6odjHOzupqleQ3JrHevCIuGifgRl2xjQzjH84SwLm1UUaxNk3WHEWQVxxybnzpxg3epk\nbc3F4pyN4+f2cWdigyc+cwqZZx69tMCNi5fo7OuitLOTqGudI2d38e///DYyfSkdh3ciVsB7Fxe5\ndHUc+1qEXefO4kylWFpYo67JjLGpBblIwsjtJZQqOWmlhrnX/n+bZX1NZAt56rsGqCk3UVRuIB1J\nUFquYH3KT+NAO6VGCdP3Zhh6ehAZAUJLaQ49epiG3mpyWQnTww9AryJQiJJ129laF1FbJUYcCVCu\nFlKl8XD4aCe/+enLbFrXKCltRF3SyYbFxalDXWh0Ol56aYlgIMHBUydp7qzFGi5gGV9EK47y1//y\nFXLiEupKRAy0iCgSFXH75hXa2kQUChoyiXUy8SCDAx1UlIr4+QsvI0tnUWvzCCViNuceUBBmqG4s\npliwxcKSi5xQwvYdlbz+2mW8Dgcr88u0mOvo3N7K8uwSxjItuVSWxvp69AoBnS1NaDQSSisMnDzw\n3+fmJ6Ic+vpP3n6+Ve+kaVsJl164jCnr4OSnT+LyjDIssuO8mmF67HcEV0u5F88Qsleh1zTyu9tT\n1Nds0WxSUZHwE10SoRRI0YcrmZNCiyzORKgcaaGHQwNCQnIJK9pWnIECnloVBnE5v/z2KEee6WHx\nXgWGaA2GzQVcwUW8CimF928jrt2JX95Os1tCg0HD7Js+Lv/BRUK4H8dmAmF1iPKnD/PgZzfRbFcT\nXlYzekmK+cRBOjqF/PgfbqJuM6JPdXHVa0R93oq1FBbXlHyuz8CId5me/T10ZcS0iG0I5wuUxDfx\nbXlRNMRQx9tYTizzqZP9qC0JLNoVVH4JSmUa+1qY7LZyBKseHv3zVnwzS3Tu60CiVvHBL29hn3ay\n90wnYn0eZU07Uzf9uHMrrN+eYs1ppbKjCmVOSnjsNvP+CdaV1cz4G5Aplnj9hQK/jtvx2JWkC1pa\nitfZGtNS3d+OLaLgYHc5Sykdwjop6+cvU3nwEL5JL2V9QV55r5rGIRmyiBNzfT2G/Uosb45hy5jp\nbdRysj+KJdDAWy85aFAdQ9uqwRhfJqwVE40WyFl9XD0/Rp2ki2Wvmyer4cHyKtUqJ0e/egr7g1V2\nVA3wYN1JS10ts9csPPZHj/DkCSXRtAWVuQNn9A5ba1EWXBJydjmG1SjRUg0P79tAZVDxm3+/z1CZ\nDbF2jtHRURob95BTriLyJvjOj77MhMXN2i0nKlUzku4Ity9vsKevkcn8LGZhG45olFt3r1PeeIr1\nW7MIYm56a1S8aVlD6MrSd2o/g2o3o9d+z61oGzU6EWc7jpFTGChSOWj64y2k2Sw/++ElmrzLHHz4\nk7ke/2fffuH5xuoAHQ1ZFu5bKFcLMO/ZRia+hmV2mTvjVkJzM4hRshkSEd6KEc4c4L3ffkTvw22U\narLEbRakBQ3ylJA6eT0xgwE5eeKFapbn9Dx8rIhA1oc9VYzDBsE6Hbo6M+985yPKzRVMT4rZVdxJ\nTXANW8BCTJhn5uNJqjt6EOkNKEJpKiqEWG6lufLzBTxUUEiakEoClPTVsnprEk2pCoWihfnlFH2P\nPEVdeYwX/+rHlAw10dJSx9x8huDUOjGFmc0HCQYGNQRCa1S3baNGq0KejtOSkVAZtSFJh6g0lyDJ\nKoiGXRzaXkk2ncKpLlApaySccrLwIEPHY8dRxhIMHd/GxriTHaf7ERfreOWlOyQCIQYPNxLyRglo\ny3Atu9CmrUzNerBuWiktVVBRHEXi8XNrdpz5dB8RWSe+qJ0rV3S87VSyENCS8iWRarewLmvRd/YT\nD2TZPVjNzFQIldHEyLCFqp5e3K4QihoJU1cKbNvdCOkATToD7UMG5h4sMLspQ2MycKAhhV9Wzshk\nhuJqM+1tZuJJFyOTq+g0eUTZICvDG/jiRmbsFrZXiCik48gFbg49dZjVRRvVxmoWbT4Guxr56KNR\nTp45yplDcmLCFNIdJ1hyhlDJSrhn8VCqqGJp1IlnK8c3nhIwUJXhtf94h6EBFcVqL5uLK7Sbm9GK\nYuRQ8s1vPM2tm+t4/TEKChMpjx9XPElVtZ6A20OVQUnI5mZtY5mCSsaSfYtcxIWuGO7en0eQknPi\n8TPoS0Vcufw7NqMSWrvKaGntJRkqEM7bOPIlFZngFm+8PIrR6eT043s/kTb/7l9eel6n2aKmLINz\nwY5CnkXfVI1EGGVlYoJbI0skPS50ejFet5DQVhq3fDe3X7/DnnO7UIpFpEdn0KqLyEWyNKtqiKpV\n5DOg0JqY3xRyereOdCKAL6slGEqR0YGouJwbP3wXfYuJ1TUB7epKygUhbO4lBNkEK2ML6MqqqGmu\nJBHy0dxZxvCVVUb/a4yCopxAogqJwI++pRTLtetIy9RUVDUzPRVm96cfoUJi48MfvETF3kZ6miu5\neXWVreUYMm07mzc8NG4vJxVYp7G3n3qDDnVui7JwDGN+gyJRnFZzFVFPmph3mdMH6ol4bLiVoM3W\nEg1sYl2O07zvDOpEiGOnDjC36KBvfx8ag4Y//OxDMjkJJ4/vIOTyYQ1GECVCaPMrON1Rop4limUZ\nKoxxyuQFbt2dYjJ3iDXqWbMscXVCziWHmsmAGlFKSKpCic2lQ1vRTjKZZ3tfL3PTLtQ6HQurQVT6\nWjyBBKKWGtbub9G2vRFRPkZHjZbaRg0r89MsO/IUm8vY06EiWRDj8peiLKmhrb2OWM7D/dFlFIUI\nFSYF4XkbYXkZc/YF2qRpZKICeqGX7ceasds81JfrWFzyMHigj/evjLP/0F621eRJK0vQNJ9hbUUK\n+m2sbHgopMXMDC8Tsgn5+hOl1ClcfPibK/T3GSgp2sL6YIwDQ7vIZ/wYZEK+9s1nmRp2EI6mKQi1\nOFY3SSWy1FYr8blsVJSmiTnTTEwsIRBLicVcxKObSLR5XIvzJLN6Tj1xEI1Bwo07l4jkczTXm2ht\naSMZjSMizt6HSpFIkly5/QDh2ipPPPXJvIj09z965XmpcItyfQT3ihuluoBUX4xaKWTi+i3ujS2R\nCHgwGWR45wOs+DIETfu5/9JNdjzah1CUZ2tkAo1JTS4Brcoy8koxubSUIoOOuc0ch3aWkM8FCMcV\nxCJxCto0EYWU+z95HXWVEas1Q31JOXWqFHaHlXQ6yMrsPKbSMqqqTWyF7AweauODlyaZ/q8HCIpK\nCKfLkQsjyEulrNwZRlKiwqCrY9ESZdenH6M0v8LH33+dmv2tdDbquXV1lYCtgETVjveyj5rtVYTC\nNmpbBihRFaFXh6lMxCjCjSy2RVtLBXF3lOjmIkcPNRBx2YgqC2gUTaQdm8zOhmg9cA5FxM2ps0eZ\nm3Kz7UAbCp2G82/eJpcV8tCx/Tg3rHj9MVSZAEahg3gihXd1AlE6jLlWRpVaxtSkg5HoQRZyVSwv\nzHN9TMANv5b5gAJBqoC404zDXYxUW4UIaGk0MzXpQms0MbsRJS8yEEwKwFzJ5lSQgYOdCPIpumsM\nlJercVrnsXkj6Nqr2NWlR0ieRL6c4spammrLice8PHiwjEKYoLG2mNSGg6yumjnrJFWZKCZJDkHW\nxrYDNXi9Phrqlcwv2Dl57gTvXhrh0PEDNOrzKA1NSHUHWF0SU6g7gtPhJZ0sMD/jwr+e4v95qh6V\ne4J7H06za2cFVWVJFkYmOHHoMIVMAL0ozVNPPcritJdkIUsiIyfgDJBIJqmv1uJdXaO8LEU+EGB6\nYh65pBhRPk0iYCNLDOfaNFmhhkMn9lJsVHPnwTBp0rR119DS3kHY4yeVCHLiXBPqogw3bj0gMW/l\nmU9/Mm3+rx+++rwo68GgCeJc3kQlLpCXKykqljJz5zZjk1Yivi0qSmRsObdY2CwQ0u1m6jcf03Nu\nJ8UaGf7JcUzlWrKxJL26IkSyPJmYGF1ZMWPWINtbtajkKcIJCYlQCIk2Q0gh5sELr2Ay65ibiVNh\nMFClTbK+YqGQCbE2P4fRVEJ9fTmJrIs9J3t54T9vMPfbCUTqEkIpE0WqFAUtOIcnwKimxNTM4kyK\nfX/8CBVSO1e//RKNp/tpq1Fz5fI8EacItaaTwPt+ijtqSAa9VLa2Y9SoMMi2KI3GkGRdKCJ2ujrK\nidlixG2zHDpmJmrbIJCOYNSbyTtsTN930XnkaeQJD6fPHGZ8zE7/3haUGhUfvHSTfEbMo2eP4Hfb\ncDlc6LMBdNkNkqkYbts4okyUSoOEhgoDwyMbjKf3MZ4yMDs1zsi0mJs+HUsBJelQDtlAF+sOGTJl\nKUVqKY31TUxNuDBWlLPoShJOK0kIVdBehXfCy86jvSQCAdrq9ZSWafCtzuPeimLqqaO3WYlUkKQg\nqcRYW0tNrYlIxMfwiAW5IMqOvgYym5tktCYWVubQhT3USSGXW6drbwM+r58ms5CxUQuP//HTXLx2\nn4Mn99NULKK2fhfOcCdBvwFh/Vk2N7bw+zNMTdpwb6T5k4FD3Z0AACAASURBVMeawHqdkcuz9G0z\nUFmVZXFimiNDe4mHXVTrxDz22ClmxxwUxCJiCQGuFQ+FnICejnI2rAuUGfMIoxFmJ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438\nvBaWRy4TvJeLVbsXd9co3oUpROIALSVK/PYgE8YSVobf5vrteh6qlpCTWmM9ImRneY3808X0/nIQ\nb0LKsdYG8uZd+PxStH4l+W1FHD6bj+VGD/ldB5FnFnDzYhRlUxU+8yoSp521XDEh+yRjITfFZSVs\n7Oylelc5N380gGhaRuwlEUcbXuPlhnYGbv8GZ0pHY0EV0aSErKSCvsWbrFsTlBw/Se+FGxjqo4yn\n0qnIrUWdaSKZ5cMSW2BzaotUSEQylWL/Ew/SFFRy4EgDznUVax16ivVFbF7pZ3bAx2f+JpffXZxH\n4WqlJj+f3f+k4+eDwxzfrcAkKyZtdZ6TTz71iRTpn94ePafNq0Ct0qGNyYkkxBTWHWHLtkFGQwKp\nQkjAnyBDZYDcbG58YOHBr3VQZ1ombN8gKZEhTJYjSi8lP6SmsD2HmVkbLv8CxAUYykupyoqgzy9G\nrClgV4UGSYcDV6gEsVrI1t0wWUVq3NcioN1hM2bFuQNa7xK7qqWszq2AdQ1sWuyhAu7cjlFYkM4H\ndwPcujaCQh2k0JDGqjNBaXkmUqmeK+942fVsPY6VNL7wkAZ1ZII5Wybrzg0mx7b58O1p1HoZa6OL\nCPwqtPodqktNTP7hIomRdYLjDrwliyQHR2nKyMNUXUnf6CppZWaWBp0889hZMgvSsXl2aHz0MFNv\n38MtWkdXHqMht4yi0gL887OY28FfXIJgbpGmijSOH6rGNTOISOKnqlWG2XuIyz2ThIQpsouf5K+e\nqWTgrbcZ0LcTrdvL9EaY7WQaDYfPoE6TcefOfbJlIg4e6eT2tbvoMsKIcxqwrwTZ3VlF1Z5d3Lra\ng206gkBRQnrBBgO3fk9OdjpdZUECGzHM8UwG7/2F/ltZVBSn0ZljZsQtRxG2U3GwjOsXbjE57aO9\nth6h007UkSQtliC/IJ8DRzOY7humtL0UVb6RoTthEuUGHFsOYoEA5hw5MWs/t4bn8AobiKXaKXug\nhL/8eAr9UhTlM0keP/F5jncWMznUj80poKWlGG1MTIZMykTfBGsLsxQ07WHgziwt+yVMrcaoaW8h\nOyOFMM3NpsvCimUSeVEWqVCM5jOttOQaKauqYmbZTVp2Ph3tjQx392Aet3D8i13cPr9FXKCiPCeT\n08828+cPf0bT0UqyFUbiG1M8/eyjn0g2f/vWvXN1dS0opAqWF+bRZeioaNrPxrIFSaYHqVxFKO5D\nr0vHEU5juNvB2W/uIT9mRRAI4fMmEQlyycgqJjspJK86m/UVGy67mUgggbygnIK0OJm5akoqKnhg\nt4KKoyKcsTxUGVJsCxGMBhnmex7Ss734gss4k0nSBBu0VWjZWtrCtzAPVgmWlJ57fetkpGXy4V0v\n9/qGiQpCGJVxLMtB2vZWEYmkcf13dlrP1hFaDfLSaTUuxyrOqI7wWoh7NwP85HtDCKUa7HOLJOxR\nsrLkdHYWc+c//4R4w8X2kJOA7D5a231q1UoKG7vYsLlR6ixM9W7x9PNPU1hXzMKCi9qWehbHZvHG\nZpAphFS3dJDfWMLGxDj+KhnO3DyEmwu0lBs5dbiWyNY6BnWArAYli+5a7tybIZjYocj0CK891cq1\n89eZ1FchaTnAxJITgVhG3elPkRBIuXO+h2Kdgb37WrhzdQiDXoDIUIvVFqWtsYyWQ0cZG5oh5Awh\nkpvQ61cYuvYm5RUmiqUePGtBEnojQ1MXufdujMJKObtLvUzY5bita9QczObuvUGm57xUVNcTczrw\nbwRQhELkqBSceaiE0Z5+6jrK0RYbuPOxjWRWLpYVHxaPh9ksJc7NOSbWvaz4ixGKc8ltr+TNP6wg\nWYxT+ZiAh57+Mm2NOUyMzpCKx2koz0etlaAQpejtnsbrMqPKzWdpwUPLbiHOrRANnR0YjZDCwcrC\nAqHYEgW1haS2Q7Tsq6KusRSdKY1oLIIhN4/i4jzu3h1ldsnOmZf2cfmdccLxFA01tTx+9hB/+Oif\n6DxcSGlREe75GV567pM5Fv+7d/rO5RjzkEukrMwvY8w2kFfagj/gI6TaQiJUEEtuk23IIJAQM3XD\nygOf340x6USaTBBwx9GqS1AZM1FLhWRkaHFbLHgD2wS2I8QNRipNAvQaAdnZ+Zzap6NylwR3WI3a\nIMBji2IyKDBP2DDlWQj4V3CFdzCmeWipT2d5cRnb6DwyhxqnKI3enkl0ukLeveliZHiYSDxEvknK\n0oKTg4e6cHml3PvlFrs+1YXQus1zj+hwbK3i2BYTXArTc83Pj/99jFRaLq7ZRUJWDzkmKSd3lXH3\n56+TZvOyeXuBpHYN5eYI1Wk6sgpbCG37MWaFWR3a5ulnHqWwLpe5pW2qKmpZmVrFG59BJhVQv2cP\nebvKmB8eJVCiwJpTStw8R0V5Nif3NZKy28nUeKnea2LFXcnoxDwub4Qaw2leeKKWq1e7Wc6tRLF7\nH1OrTsQyCbXHHwehgp4P+6kvzKW1uY5r79+jrCwD9LWsLgVp7Wqi/vBhBm7dJ+HzI92RId6ZYuTe\ne9RUG0kP2/CshFBkmRicuUr320EqqrV0VYSYdYtwuMyUtmnpGRlkajRAQU0tIY8L76abtEgAXTLJ\nkw9WMXO3j5ZdZSjztdy+uQppJjw2WLT5WU5XsbK1weSig2VfGSKFCUNzNRdf30Qxu032Awk+9em/\noqw4nbmJJWKBBDUVeWRlyFHKxYyNrrO5toykMJP5WTf79qlYX7LTcmgfel0YaXKLtY01vP41iiqL\nifjc7DleTVtjBWqDiMiOF21GDtn5BfT09jG3aObRzxzlwl9uEBULaWhs45mHT/LGxe/QdriI4twS\nHGvTvPLsJzMO/fqN7nM5mUakEhGzk3OUlueSU9aM0+kmqrIiEspJJrcpSDfhDgtY6XVw8tOt6AUe\nBIIkyWASta4QXaYclQSysrW41yyEglE83iBxYwaFhh0MGjE6jY6zp4wUN4nwhiXo0nfw+VJkpYux\nzJoxlVjx+5fwRgXkq7bp7MxhamqG+Z77KBxa3CIpt273oVQXcbnXz+joGLFUkMwcBeYlN1179+Jw\nSBn65Tp7ntuL3GHnuWdNrM7NsR2UkrAluXvZx399Z4SovAjL/Rncqx4KsrUc7Srmzu/fI8MTZvXG\nNAmtFb11nCKxlOzSVvD7KclMMnczyBNPnKS6OZuVxSCFRYWsrVjwx6ZRSAU0HthPxbEGpofG2M5O\nw2ysIrg5RVlZDkePtZOyejGqvXSeLsMbb6Knd5Yte4CWoqO8/GwDNy/dw1xUi3TXbhZWbUjUcqqP\nPUxSpGDkg0G6Gqpoaq6i+80rlNVmg66JreUoVe3N1B87Qc+1IVI+NzJBjHhgnJXpK1TVpaPwrhEx\nJxFnZTI4fYe+d3w0NmeyqzTEiifBpsdCWbue3vuDzE8EMNZV4XfY8K950MUT6GIxnjxUyvq9e7S2\nFaAu1NE3sIlEk49tE8ZX/ZjTdSzYtri/4GLOk09QqEfVVMfF36+gn7WSfSjK4y99iRx9BouTiyQj\nKYqz9ZSUGVApxAwPrrC8vEZacQbr61G69mtZX7HRcfgA6elx5Ekzc3PzBENmapsq2XZbOfZgMy1N\nZcjUO0RSLgQyCaaiQvp7hxifWODsK0e48MY14gIJTe1dPP/ICV6/9B32niwmP6MAh3OZzz793+Ra\n2az7P88t2E20l6Yx75CSv6cLbUCEriULmcDL1X+/iCPTSH/f29Smp7P7obP0fGWA2NoYNjvEo+vs\nevAF3rk5S7Y2DUnHDqMXw9TWdODzBRh0+3ioy4iqrJy67EyMyShTU3f44J83KO7Uo9mXh8o7glyz\ngWa7h7ylBMGMfZSODLK+z0Sy/QB5JTHu/LSX/CeV+ELDSHYEeHamOPHg03x0fQ1jl5r8pnJSc8MI\n92moU+az7vkj7vfWUJjWqSwswJpeyKJzHv98B8rMHHo/jFH2cAqTMkVIusR7P9zgSM4asYJcdh8p\nY/HiJbaScvzjA5QbvdzsDrM3T0aerIl3u4Ns5kXYGHPx+b//Grbf/BhBcyGrvQpixXKiOgV9d+0s\nWfUUhB4mJ/ceBSopiXU3ttgG98NtsGojJB/FrVWzbzLK7q9+lgs/cDCpUfDUN/4GV0Ya951/Qb6l\nJEO8xLYrxZ6uJv7rD5d4+mg7vaMztFTLCW1mQXUtnp0NfvlvU5Q9exC9fp22khBjI8vUdwi49JNh\nqg+XYp8U893zC5Sr3Uz5i/nSI1qCzh5sWSUIthfZe6yYof4hanfXkfJFsBtlbCoE1Fdl4xxdYDux\nTVZGE00nSombu8lpijN5U8vH54cId7QSLZQTnNphSZWLdXgE+ewsR9oVeFN6youaaD3uZurqXRTy\nNfovb/Hc1z7FWP9l7twKUNwuo6W+jmXBBNPLJjqbivAtd1NepCdDWkzE76LMYEQT0RE1b2DeFpNO\nAKsniGMtgUMeI0MWx7PkQiDVoM6DO2Nm5m0L/M2JLnT7Q5SWVvLGlz7mxX/oYOnPf0AiqWEofJHP\nPv6FT6RIL156/VwglENhxQ6hVJzMrAyMpmL8ihgqWZQL37tAyqTjzsW7ZFVkU7P7AZxvrGIetjA8\n6SQta5uDpx9lcdGMIBajoCGNwdvLGPQyhDIZI0sWju+vQJaRRXlxEdqwi+7Rbq7+1yxltVmUtOcT\nDs0R8XuQb1+n2m9DndtOcHGYaVmcwpMPIFGHGX7vPtqDIkTVVjQRIeseL0cOdeLa3EaYGcaYlQvb\nDipaq9g2hzF7PmCpf4K0+CgPnD1Dv0XB1J1efP425JoiBt+/R8OhCpSKNHzbAq7/aZzmkgiZJQUY\nKkxcfOcWkViKpaERinJzOf/ODKcPaKitOMFHvW5CsgTuOQ9PPN3Jmz/4NTt5UWxLSbxhL54sCUMf\nLZEMKCkWPs2OaJrifDk6oQazc4kFj4Zt7xpBHEQkMjK2gpz67DP84V97CRsVtH/py5ClYnRxGKEb\n/MoQpWkyinLkXLx4m8KSbNZcdvbsL2R+3UBWdT3haJDXv32F1if2II2Y6diXj2VmlNoWNTcuTtHc\nacIRLOenb/ZRV+1jaaORb/6PNBLTbzFmrENmXaGmNR+PY5OmtkqIp+GPxTAUZ1JUoMK1EUCUspGV\nkUvbwSbkvjXCshiDgzGu/vEmssY9OLQFOJfCTCUMONxuogvLtJ1OQyouoyOtkYquRSaujJAZlXL5\nnW6+9f1z3Hr/KoPDLjTGdKoacrHLV5lZTKdpXy3LCzfJzdRTXVPFxvwgBw83UlRQyfrEJHPTG+jT\nsnAJvdjnvEjypGQV5OBa8+IKizCVaDH7PKw6l9hXloepQ0NdfQO/OHeR5763h9GrwxSmV7Lk6Oaz\nz3wyw+3P/nLxnFhiQJ0ZR21QIUwq0RgKSelECFQubv/yDl6hiLGecUyVxRQc3oP16gy2cTtrK17S\na+TsP3SUTYsZ/5aL0q4q+u+OolWJEEvEzNlcnN5bhtKgp7qqHEFwle6hMS58r5+8qnSqa0vxedYJ\nBCMkHHfJj25TUNTB1soEIaWAquN7EId93L+wgKpVg6ZWhDwSZzMcpKuzHPuWC6UacgtyELFDVmkB\nAbEUu/UyjukJdgIDnHryJNNLcu59cJ0dbQtJSTart4bZ82AjGq0euyfGtbe7OdKqISNDj77GyPl3\nrhGK+XHMb5BhMPD2n67w4JFMikr3cfHqKv5UjLnZNZ56fg9/+ME7SAtCeD0JLFshFgJW1vonkLqk\nlKkeJh6cRC7ZoTRfy/L6GhsWB87tDSRpGsIxiFmCvPSlp3jjR/dwpcHBb7yK3GBgbXkJguBSKKhR\nC8nUxei72YuxWI814qOzq54Vt4qc5r1Yrdt89L1rVJ2sJ2FbpXVXJRtLo+zelc3N84Mc2leKS5jD\n7968R2W9kKWRXD731TzE1vdZUdWgTkUwZSnYdjqorqpGFFcgE8XILjRRXGlk2+xjO7JFSUERuw43\nEA1u4XIFWVyM8t5vr5PWdAyXoZiQI8GdrSRbPjG+cf0SSwAAIABJREFUtSXqD+UgSuXRKakgq3GS\n/kv9mJImbn54i7/97t9z8/2b9PYskRKL2d/ZgDu5xvSclIMnDzA2dpU0oYzKlnqsqwu0t2VT11jP\nQH8vo/c3UGdkE4g6mZxcobKulNzcHDyBHYIuIVnZCsKJGK6AhX0NheQ255FfUsHP/+kin/7RLmZ7\nZ8lU5mN2j/LqU5/McPv9X188pzFkIDck0OqziISTZBa3kVCAyGCj7xf3cApF3O+eJDM/l4wTh3GM\nzmJZ3GRuzUZhYz7NHbuxuJbx2T207u9gtH8ImSiJWilixevhYHsxBoOWltZ6kuFV+kcmufyLPnIq\njVQWF+GxbRBM7OBdv02eKE5BYSObK5NERNB8eBdhr5vZ92eQd2jI6dQTcgZxiwR0dRbjtbtQaHYw\n5hgRCMTo8/Jwp6nZ2rxOcGsGn+UOjz55iql5CYPv3kKkbUNiKGPt+jB7TtdRVFCA2x3h5puXONiR\nhU6Vhro8gwsXbxKK+PCu2MnKzuLy+x+zf382uaYmLt9cwxbbxrxm5bFnWvnpD99HqHXjDUTYtMQw\nx92s9t5HZk9QoznFTngeuXSHwkwl6wuzmB0WnNtbpJJpRMJRov4Ir37tMX77X9fwatTsevlpVDk6\nVu/PEg5AQJNBoUqIMUfM1J0BDNkqAoRpaahiwZpGbtMuNm0R7vz7earONBPZWqX9YAPB7WX278/n\n5oURdnfkERFm88b5AfKqBKyPqHnpy1WIfT2syXLZCYVQp0HA4qPMmI9KqSJNDmVlheQWaBHEk4Sj\nZqoqC2nZXUVox4/Ht41tM8HrP/oIaf0JNsRGgs4UvZspzNsZ2FdWKNldTCpo4mBaAWl10wxeuos+\noqf7ejf/+P1vcf29Xu7cnkQiTqO5vpSY2MnsnJ99h/awMN2HRJSkuLIW6/IMezrzqO1sYHhogNGh\nJYqqK1izrjI9u0ZtWxW52gxCITFeewqTUUA8GScWc1NTnkFxXSW5BcX86vvv8/x/dDI7ukS2ohB3\nZJaXz34ypxJ+8NvL5wrLixBngFKVjccVILOkFYFGyo7WzuBv+3EhZvT2OKr0bMoePYpjaoiNNQcL\ny07KygupaGzGYl8mGozS2t7G4OAQKlmULIOKJaeD9kojWToNh47uIexfon90gas/7yavIZ8CYzYe\nlxV/HHyb98iRpMjJqMJtm8fuDdF14gABq5u5DxZRtugp2mUksh3HTYq21jxsW1ZUOiHZhTlEkxJy\nK0rYUqixO3sImefYnL/KU8+dZWkRut+6ikrfiTyzko2P+9n3UD3N9fV4XQH6PrrM3vZc0sRS0iu0\nvPn+deIRD55VG+mZerpv99DaZiI/v43Lt5dYdTgxr2/x+PNt/Oxf30ascxMMxllc3WYlbGez/z4S\nW4ymrEMk/Eso5FCQIWRrYo5g3MnixiIBhwaPz088HuW1bz7Gz//9PfwyPa0vPorWpGFjeomQX0hE\nm0O+SoJat8Pq7SE0eilRTYq6snJW7CoyGjtZXPcz+pNLlB+tI2lZ59CpXezELTTUZ9Hz8Tidjfns\nyLP43es3qGrVsDok5Oxn6hBEJ9gSqEkSQyXaIeYMkKvMRKVUkKmWUlNWRlaehrQ0BdG4mcbGMup2\nlREQxFgzO7CYI7z9o4somk8yH9FitccYM4M5aGJraQVTWwUSCtkt0RHOGmKu/z5ia4zhm6P828/+\nlhvvD/PxR8OQVFDTUIxI7uf+uIWOjmZmJvuQS1IUldZgXphmd6uJ5n3NDA8NMTq8QHFdBevmZQaG\n5una14FRpUQsTyfoFaBNCyOWJGEnRGVpOjWtdeQVFfPLH7zHC//RyezIAhmyfOyBOV59/OR/jzg0\ncm/93OU/unGn+rnw67co2vZTc6yCW7053L7zHn93rp0R2yaFdguKss/w05E/ofN9iEXupeJsLb5l\nO4N9fUgzKlG9/CTbPzLT+r+kBK7OUHg8j8KgnRsfrmEwGjHoNrkSEfP279Z4+Ml29pXoiWw14Jy3\ncNmTy2IknYP1bbhCTgTCECsWEcI+B+alfkpPnkGytULnMx1MflzAoOca3fdH0KpDaJJddHQU4XIl\n8ah0RCzjvHNvCMt9C7VPVWOofZXzP/kzp3NSnHmqiNrqbE4dlJDrULFlyUaQ2CCoSPDQ07sYi5Yy\nMHmdwhI122s+lAUaFNJcCh7P4PZ/XsFd0sR8eRmtQxG2i638/NU/UvZyE3qxmB1ZEvPFVdqPRTGf\nn+bFz27QMGUh2LDATPcKp778LOPDFiZ9CWr0sDwiYHJ7F59/oZ55zTY/T46RtW5Hrd9i8++ukO4X\nEM8SsFLaQc5MDs9/qoU/z77B7Xv3kSqewZjVQjwwisIf4ea8h0ZVIUbhGoKYCptARuuGgCm/hOLM\nk2Dw4F6ZQ7Xj5eyDVaz7VES2lontqHDZ1rF43Si2d2h9oJDZXifukIlCrYQSVQ7LvQHqmlREd0Tc\nCASYvr7G8ZYOvvv7q7j8dt74zll87n2s/CnIK6e68Ns3GfdZkJan4xCHKW86xfC2AfulXxAoktOz\n1M/zjx3iH/9lgs88uofVzW00T6rYGUkR8n7EPlM6IrkIW5oaz537vOkSUfh4G757d7khifDwq7VI\nM+XIqiVkurxcU65T0i4jXy2mZ2kF59YSUykF6qpaJsMuvD1brLo3uBNv4FBLMZf+7Q1qqk6zrVQS\nz97ghYPPfSJFetfiOff670YIutz0/GmU6oYwtQ1FjI9ruHn+fZ7+5lGs4ytkmGwoJV1cWrxMjn6M\npeQyxS1VCCJ+Lnw0xE5OBbvOPsDIBxPserKDren77Gh1pIkl3O5ZoqKonOz0BO9MhLl2y0nXoSYq\ni/Qs+coIbdlZ2FDhc0DboYNYgwmyEOJ2pSEJ7TB45QqnPvsECpWfqoIWLrwZYT28xszkCIlUlBxt\nPWpDOo4lFyFZiuz0NW797i7eeSE1B3ajLH2RD370S17dN0b53oc4uc/Ap54ASSqTqF2OLRoChYTX\nfniAieFC7vYOUmMS4HJHUeYVItnZYe9rZ7jzr934C08wrEmjOOVhJ7XMt//hXVo7K1Er09hJaFm4\nNcGLZ/U4bkzw/MkUTSkb1vB1ZiYW2Hf6ENPzPqbccrrytFh75MwKSnjwK3tZDAuY3nHi8Voo1cON\nX36XSrUXrcnAajyXtBUxzZ0V3Bka5O7tMYTpR6ku62J1+D3SIjDaO05uUT4C3wbSHSUTKxIiGyac\nFilUNuD3hLHMr2AqENPRWMByMIe1yT5kWUoSUQmLE/cpKTXQWKhnccpKOBol0yRFHJUwNhxm1+Fi\n3P4wd2cFzE+6KGst4+0rHyCKJ/in/3iEhL+FoQurPP1wM0tbZtZ9IUoaJKw5vCiydrOwY2Di+hu0\nP5jB+d73+dLnX+Gr/+sN9u+rZ2Jog+JTBYh9IaZnP6SjPUVwfp1EbhmC+VUuDIUoOPEQW1NjTAf8\nPHvaiAwx3rQwEruZoXUnNcdrUYqhf7UXu8CG2x0lJs9g0pOGaCfEwPIyd2bEHNpVwMD5ccq0Jcz7\nbMTUm3zuocc/kWwOuPzn3vp9P5FgkHu/H6D5qI68gnzGBxx0dw/w8Oefw7c5ilLtYEdcy4x9ghz5\nGKthD1l1ZeyE7fTeMRMQZvLA02cY6pll77FSFhfnkBgyUGXpuHNpkNr2duQqJW/ejHLjYx+Nx/ej\nz9CwvZNDwO5kw64i6kxQ09DGml1EXpqKoFeIRqvhxocf8ejfPkHUEEGny6b3mof1kIeZ8TFUSjlS\nfRXGskKmp5eJi6KkS5YZ/U0frskwbSdPos95iF9///u8+mQvHUc/zZHWDB5/LokiacJrk2DfFiBB\nw8v/+zEmVwx8fOsemfI4DpsAaa6BaFzEoZde5dKP7+HTHmVBZcIg9yOPWfju/7lASWU+SqGSRELO\nVvcAX3i+kNkPunlpdykVWhtWex+rZjPtx/Yzbw4ya5NTV5DL1HkBm5J0jv3dCcbscmYCTrwuB9m6\nHW7+/ofkyCwYK/UsrCVROAV0HO/i4+sTjA4Ms51eSWt1B2O3zhPbXMK8Moc0M53glhlDpoqxBQ2R\neBF2r4BESR1OSwyXdR2TQUBdey4hZQFLfbcordDijiaYmZqjpFRDRZEO85qPSDiAQhtHKlUzOuak\ndV89zmiS4VUhk7N+yirLuTJwi1jUwj9+//NsewoYvTrNE480Y16x4jFbqKqP4BaGSemaiOhMLE7e\n5tGnjIz0vcH/+PKn+fI336Flbz1T91doPlUHbDIy/TH1LRKs84tI8zMQeZx0DwbI3b0Pn8/C4oqH\nB47mIY0kcLo2kSYsjN5foWb/bhKiIB/fuY5N6iQiiCJUZzO2ESCUDDAwu8nYyiblRfksfrRAtqyI\nDW8IqdLDyw//vz+5/3+8Pl/k3Ds/vUswHGX48gydx42UFeTTe3WWgYERHvrCc3jWJ1DnBIkKK1i0\nzFChX2F500FBbS0J9zb3BzZBkcejnzpOf/csnYfKsK4sgUKFwpRFz+UBGro6EKabePvjIDc+tFB1\n4iB6nZaoLA+v14ttQQaeGPWtu1m3yciQihAJNch0Oj5+9yLPfecs5vQQOo2O8R4X69te5oeHUClU\nkF6OqaKUqdlFUjvb5KrXmfxVL7aRAAcfehi1/gi///WP+cpXZth78kWqc5M88WkZaokJ84qAFY8Y\n0Y6WF7/+PONL6dzsHidbGsPjkaHIUpJISNl19lnO/7yfiOIQa1kmslQhNHEb3/2XixSUlyCOgVyt\nwnqtl2+8VMfSlT5e7CihQL2BxTbO6tI6ux5ox+zYZnxNSV5hLsNvg10lZM/XT9FnV7Hsc+N1+Mgy\nwK1f/YRCU5CconRmVxMItkI07m3i42v3mbjVR7iskPryBvqvfYTfPIF7dY6dbCNBlwVlmpQ5s4Ht\nSAE2W4p4QS1uaxyv24ZUniC/OgtBeh7ro7dpbFITJMXk0By11SYqqzJYN7uIJdyojCJ2xAom59zU\nt1RhTcHwppqxyW2yC3O51nuDcMLG3333mzi9ekZv3efRx1vZ2rIT3Fijud2DT+Iloa8hptCxuDbA\n02czWR14iy/+zQt8468/oLatmqmJTVqO15IQzzIwcZmymnRcy+ukDBCzO7k/ESWzay++nSAL41ZO\nH8sjFYsSCnlIbZuZWLNTu+8Qwaib62M9+EReEtIQaHIYXfCSVMe4N7TGzOomxtxMVj9aJEtdzKLD\ng1y2zacfOfyJZLPfmzz3l++8T1woZbJ3ka5D2ZQWFdB3ZYLh8REe+vQTOMyTKI0hYpSy4LCQI13B\nYg+SX15JyGZjY9lDSlXIkUcOMDsyT8veMsyriySVKgRZesavDNF6aC8JpYm3rwa5884GlQ+dQaNU\nklDl4Q8Fcc5KwJuksrYDq1uLUhhFQBqZeTlcfOtdPvu9MyxlR1HptUzctmH2+5gbGUWjVaPMb0af\nXcCadY1I2EqudoO53/Zj6/Vz4oWnkSt28ctf/Yj/+VdLHD7zEoWZQR57SYtKYmJlQciCXYqYNJ79\n3CsMraXT3T+LSRzH65YjVMtRqXOo6jzJ6z/pIaQ8gMWQQ05GBBU+vvftOxgrChAn46jS03FdH+Jb\nn6ln6dIAL7SUU5S+ic0xyeL8MrtPtLNlC9E3naC8tZTBN4N4tTHav36Gm9NiPJEoDnsUU46Qu7/+\nFSbdNsZCOfMrEXBHqNvTxJ07C0ze7sHfUEx5cQVD3bfwbAwh9K0S16Sz7feQplMyu5FBJJnDli1G\nwFSHzyHA6bGyI4SSRiMilR7zTDc1tQqiggRTo3PUVuRQUKLF6vASjLqRGYWIFAomZzeprK1iSyxk\nyJfBwFSUdKOJW703iSccnPuPf2TNJWH+1jAPP3OIjQ0zMfsKuzs8BAVuYppCwiIxTvckTz+tx3b/\nA77yv1/ia19+k+rOSuYWbbQeriWmnmZg+Cp5BWp2IkGiyggRh4epqTD6tiZcCiEz9xY480AZqUgM\nn91C0LHCgj1IRedBIikfH9wbJCBOEpP7kOYUMTZrQ5iepH9onYnZRdJz0ll8b5EcfSkWbxiRxMcr\nj/432Rx6dzN6rkC3zNt/+BNffe7LmA5ruTgdw7FSyl99S8w/fstGS5EKjW6Ox1/7JqHR96nPzuOJ\n177M2rsTJCIuGvcY0FhdbFucJCuj3LulRD7qY9wwQiKRy4v/8hh3fvZrJLkCJn+zxNNHXkJ3rABb\nxEVQ6SAlHEa0omVXfRmh1Cgpfz0HGrxIVRrGp69SWlrPuHcQTX0hH/7mDhsqK6HFJhLpmZQZF7H5\nJHzqW3V45gP0vjVBTUaCSuVDGAusNLXkU7gtJBq+zqa1ho/encXuKMI/P83vLySxHkqREa2mLaxF\nVO4gvqLgYGkFRcZcKqpLmbgv4uyu3cRX/BjKtEzNThFf7ebhg/voTiUwqTzYrgooqajGUNnL67+2\nYcjVMDdfzaJqnQNPKrnz5ghlWY9QWRMnFJFw/vw9nnypEYnAgbximWVvNYGb4ySXCjhdqONKLIM+\n0TbLhV6eO/sCxg0zZ75cx9D2BCN3KmnWrxDI3c3eiiYyXfPMjblJz1kile1hZcFF2QEDSnExqiIr\nZ8/E+P63ryHLb0fm7icZF+PKThIMlPL+LQfN/18n773yHaiFiGobrSiT/ksLVJ/QENtKsbq6yGOf\nPk376QL+/INxYpoqsjMTvGmuxnpnhKFpN1JRDKFxgVOPyPjL0HkOtaYxfW+HNPdNWrUPozAscP3a\nbyjO0rB4oY8GhRDnXCaajABXQjOEtldQJPwspMb4+EI96Y3jfP7LT/CTD2Lote/TntHO9BWQSSfQ\nltSQeHMCgVOK0CFDqQnwXvcQBetBBNlH2FYo8cT8NHalkLr9/PKzz9O3sUZ5PEHdoUoWp99h3r+D\nMhjmxK5mzMsOnjh9+hMp0t4J97niwgibG1d56OE2FKVa+vtDjI77eOT5bP74nW5KG7Q4PGZeeOWv\nsK6OYJApefjh11icnSddFKZlTzZCxyaeoJh4uooJl4TomJmUfpuET8A3v/Eg7//pA2QqOXN98xw6\neQCZIYBak0E47ECjnEewI6KrMxOneYZ4spjOdiMqg5xbH16kobOS29fHURXn0n13jKBxgQ1zIaQi\nZBeEGF4yc/rl3YRjcnovLZKf76Jiz5OktPPsqVUjk+SwFbmB12Hi6lgM1zos3rBzfzmdHWMm2dpc\nKqUpjBEj3jUXpdUaaopyOLyrkeU1H48dOcZ6j42SCgv9S3dxLPSxv6qZVVsaWpkf23KYU22thEQR\npm/dR5ZUEhSWccE7zYHPqXnrj+N01D9DZbMMrx+6rw+Ttz8TlcaHU+5GaZWyPDiBecLHkd1tzHmS\nLK/7sUWVHHrmGdavX+PU/6XuPdvbMAxz7RsbIDZIcIF77yGRoiRqb0u2bNmW7Hg7sbPbNEnTpFun\np2mbvp3nZDjOsOPYcbyHbEvWpDYlintPDGIQJECA2Bvvh/cH9KvfH3Ffz3U91zMeziMg8+B2mcjL\ncZOQ5rK5cTdyeYqVoTlUbQpW/GFWHS6UNfnUVZRg6pyn93CQG6/eQF3UQGT9DtPDKyjyZITXvQzf\njJP/UDtnvv2/qT5ajd28QWwjye0hG6Wt9SRdUfwrXp544Qg7m2v59a+voy3OxWhQMzCs5Pasnfm7\nZkKhBAmRk57dOj797EOqGnIJL2/gGO9nx6Z9NG2S8/5vfkfN1nzuvvwBmxvKuXTZBlIBy74VsqyR\nUDiZitxlZKGCSM4cf/MvX+XC+wLUmjOodQ2YJ72oAzfQlbUxeuUy/oyAsF+BUSHkhm8CTThNfmkR\nq9lCkiktpooMakWWEyd78XnXSTnT7N/fiN12HWdklYw/xonnHsAzbOOph/d/IdnsX9o4XVsqxmK+\nxpbeKmQ6BXNj64y7hBz7ahsf/et7FJUpWfMHOfzoi1gmh9BLs+zZ/zDz/bPUGOSY6rTo9OD1iEjI\nkjgsIeLuCHJdmsDKOn//j4/z5s/fQimD2ZFZevY2oMZFZXU1tgUHeTkeYojo3STF75wkm9GwbWch\nEomOD377AY1b6rh0dYiiqgZufHaHsG4Vz7KYlC6NOi+LdcrKgcfacK9mGL04TKXeRdMDD5EQL9DV\nqkGdW4VrdR6fRcLNYSErzhSTV+zcnVEj16soNeioUEapVJSytjBFZbGA7ppydu5tJeLY4OnDe7GN\nW6grXmBo+QYbziF6mhoJRQ3Eow7CZhf7ensIbsDS0BCpsBBVXRfnbLPs+E6S9/8wytYdx8gz5SBL\nQd+lfgo75WgMayQFCXLWYH6on8Saj117u1mwB3GH02wElVTuPoLl0hC9h3WIcsJY7EkEWh8pKmiq\n3EYqHiYUXEFVpsPjzhJxOsivK6NjSxm1pQ5274hx7+w10qkcoolZbl2cQZqnImWzMjQaRtXewkff\n+Gc6n+5gdnQNaUbMxxfmaN7ZTcITIxrY4Ikn9lFhzOO9P/ZRVK5HpZJxbyTNrfFVbHft+AMJVleW\n2LW9kLPvfkpViREhMaYGJ2nv3MqWbdX89t9+TevmXD766e+paKrmzMcW9MXFWC2zZDMhospVRqKj\nDNoKyYgCfPt7z/Dhey5kqRkKSotZdiyQWBtGU1TG+I3zBONCIgIFwkyMBW8IlVCJsS3D1GoGsa6E\nguI4YlmE44/uZ9mxjDyu4EB3BXbzGJ7gOjJNhvsfO4B5YIZnT+75QrJ5e37jdFWtAt/qJG0ducjk\nUmZG1pnfyLDz2U7O/evb6HLF2CY3OPTk85gtU+QkE+zdf4KJOwu0lWnIM2lQqOQ4V9LEMgmsNg/i\nbAppToqAzcbf/MNzvPHK+0hFYRyzs3TsLkMed9DS1sjspAOVYJ2ULMHuHhnLc2PIpAq27SxGKtbw\n7q/fp6O3mQtnBskvq2Hw0ggByQYbqwlShhQKnRjHkIV9X9qK2xti7Pw98lRWuk8cJiJYoLlWQl5x\nAxa7jfWFKJduCQgERQyfW2JiLg+pDBqL8yiUxCiVGfHbxyjPDbGjuYo9+zcRcq/zzEMHcM8s0lxq\nZ2SmD49rhE3NLfh8YmJBO6x5OdzbhWXKjdfqJBmMU7R5B28OjrH3R0I+ebOfrZv2kmPUIEdGf/8g\neW0y0tIVVAoB2kCS0VtDqFIxdu/qYM62xnogyYZXTv6OQ7iv3GXHESNybQrHcoSoKkYyUEZ57XbE\nEgkhnwedSUsoLCZhtVLWVkdjo4la0yKH9kqZ6r9DMppBKF9h8JMRpPkGFKs27t1aJ6e5nrde/DHt\nj3YwObKELjeHTz+do2lvF0F7HEEoySOPbKVYp+PV352lrKYIeY6U0dEwgwsBlm/YWA2mcc1PcWRf\nGRfeOU95voFUIsL0DTMNm7vp2tzIq//5GpvbDLzz8hsUtNfw9vvz5BZVMjc+RUoUwS9a5WZ8mGlP\nLslolme/9TBn3ppBKfCQW6xmY91MzD1BYX4Zw/0XCWUkBOMiMjEvcxYPOoWagp4Ad2YiKMvryK+I\nI1WnOXbqMHarFaVcwa4tNdgXRvFHwogkUR58ejezt0d44bEvpm5eN4dOV7cZsM7cob1FTZYMtvkg\nMytxep/ewuf//TFalQinJcW+k19i2j6POpWgc8tBZocW6WrOpbBAiVgiYjUKgWgCr3udHHGCDEmE\nAQ9//b+e4vVX3kQmj2KdHqZjXwHyiIv6xjrmptxIk16EqgRb28FtmSKbybLnQCkikY63f/shPXta\n+eTdfjSljQyeHyCrzrLuCpMpzKDIkWG/bWH/s9txWT1MnbmJWr5Mz6M7CWQWaKuVkG9qwLXmxj0e\n4tJNKV5/hnsXFphdzEchTVNXrEUpiFOj1hOxj1CTF2BfSzU793cgimU5dbQXv2OZ1jIXk2Pn8a5M\nsaWtHa87i8+3jCbkZldHE9c+HSQVDiMQCsjftJN3xmfY/udCLr8zQGdzLyKjCq1My+jwNJK8IDFp\nAL0shSGYZWFoBl02yvbeFhZsHtbjCUIrUnK3H2a9b4SuvRqMxUIWJ8wkdRLSC0ryanqQCNREIkEU\nWiHRpIL0gg1TSyWV9UU0lDrYt03I4sA9YtEE8cwy82/dRlpZhNzlZHDAg66hkbe+/c80H2tjdsaC\nulhM34VZGnd24V2KoBWpOfn4LgwaFa+9+gm5+fmIkDA66mZ6OYGjz4I9lGVxYpxHHmrk3GtnqSnL\nI5tIMXllgeaOZrZ2tfLm//mArrZCXv3l76nY3sIf35ymoKae4RujiKRCQhkfA+kxZhNGooEsj33t\nKGf+OIEoGqS4VkMsZMa3OEFNaQWj/VfwZSGWlJDIeLCshFCqZBi6vdwcdpNTWEtZPUhVcN/9+zAv\nLyOXidm1tZrlhRmSsQRZaYRjT/cy1z/CC4//z2x+McyhvhunTVIFf3n6Cf5wfYnZvpsIpxTs6q3E\nNXyX/G4ProEUqs5q3rt5h74rg2SkSlyLF0kfiLI2vIsPQ/1ohSUUOPWoG7UInKNkHwyim7MhtjpZ\nvLWG0adjya1jY9M4UYGXtFTMzrrncd6YIq/jFF1/cYIndkcwDW9Q8fhmLI5Vkr4tfOWfHyAyIiY8\n5GHaF2Lg1Z/y6hu/pmBdQe9fl+P1DhH/pJSfv/Qu6twIadcA5ackyIMKTj5aiGc+n7sjZrIFaeQb\nvUhbpBytj/MH90W+2X6YGquPwgIRh/5CxU/+y8AWhZxoGpp7xIy9OUnY8znSyBLxwn3k5nrwx3R4\n5mrpW1mgPqcSZTCH0I4tXD4/zPKslp3iHNRd40TVSfZWapjui7D3Wyfom1zh2vW3UVliHNnRSdqe\ny624BHXcieczIdLjQvZ96QD977zNyNr7mMZWaSssZvKjKTZ971HO/NUnjL8/zKNfl3JvYISuyBXs\n02v8Jt3LmOV1CjdquXHzFnX1JyiNqsjODNE/WMMbL0f52x8VkhHb2Nhw8dYrEvY9/VeUB39JUW8T\nH37rE378T9/ElVbxpbZ63jg/yv1HO1HelfPFG2mRAAAgAElEQVRSv4Rte5WkZr3ceS1E/n1JvlNj\noD0+y4f3vERzBikyNOOUCDj3EzOTljhSfwVxmZYTx8pYzZZw9kIfaeE6T7aa+PXNm2wqKEVzcg8f\nTB1CqZhll2QLsaUwIxeylB9UEVFI2VUjJpvIokq3Y3fMMupWYzwgYv92E1MTIc4E3yNUp6F9Sw6+\nPBlZpx+JqJTW7ToWnNfwBFfQ2qopKtzErTd+xbw8hKeyE8eVVzBan0AgVJPKJEnmZihPuNh9+Iu5\nnfDpbefpXL2O5tZGrvdbWZ4Voo/B5qNbiEY8mKqDTAxmqO/q5dylj3A47rE662Z15EMMe7JYPq/i\nI+tNTIX5aAIS5MV6susrZFtspJYnyBX5WRh3IBVKmFhconqXB+/kKutuDQ1tBzFP3sNoauGhP32O\n/XVylGtrdD/UzdTNEUyyRp7/zqOsun14lr0Eo1J+9/Ov8vavPkYcq+TIs+WsxTYITWjp//y3KHOS\n+L1TtNT6EUVEPPzgZtJhBSPjQ+jiWWQ5h1AalOwsyDIWtVBf04rWvUSNLsyDz9Ty21fHyFHFiAfT\ndHa2cOWjz0nYF9jwT6HYvos8UQiVQIckWc2NAQvGygLkK36k9T1cuHqZ6YENaisLyN82QSguYHdr\nJTNvzPLID7/M4Kifj898ip40Hc1dKIT5XJtMkoebBYuG8v11PPTsMa5ePMPy3BXkKTm5eh13P73D\nE995jLNvDTJ7fYn77qskZBkhLz7G8I0VzPnNLNw+j3fMQ3LJR+XeJuQJPTKbi9V5I0uWHLZ3w8b8\nIopSGcNv2TEc+xoFyUtkqoq4+8oMP/qvo0xOmjnaU8AHH9yidVs3udkc3joXpXt3CWKvn3uDPlQl\nGV7cV4ooOMTQnJV1vxN9cRvRkIqrvx7CMh0mV9+E3ybmhWc3EUykuP6pBWXYwhN7VXz2+mc076lD\n07ONOcmzZLwBujrqcFvS2EekqKvBH85yoE1DgUqNzlDJxNIoCXEuBeUCejrKmJ+Nci80gESrZtPO\namSFApKWDIqYFmNbLgND90hn1vGMJVCVNTB59S4rSQHRXD3m8XuIYtXIxHo8gQ0SWTX66DQPHz/8\nhWTzvYuTp4tzlDS21TI25sbtkVBfqqNuUyOBFS9GTYSZ2SSbDh7hZt95PP4JQuZVZscuoOtUMnld\nzMDqHAXqLNqMjIBcSyC9QSbXgTA2Sa46ydKgE1FWiNm1RkFtgNBylLVVGQptI9G4l/ziIp7801P0\n1ufidK6y8/59zN4dplRXwp/8/XO4Vv1EVgK4HF5+/vNvcv7Du/gyTezfl0cwlmRjRY65/zUUyQDh\niJOWDhDFRRy9vxtJbJ3Ju7fRR8VoSnbhQ8Wujnwcaw50+hoMCTcl4izHT3bzxjuXIRth1RZj2/Zd\nXP3kEomwD5/FiW7vDsRBD3KZHnG8nP5rVgwmPVqBEEVpI59/fpalex7yTQbaD8bJRsV0NtQzfWaY\nR778Aj6Lio8++QBhLE1DaQuaonIuDqygEkZZcMpp6G7m6KOHOffxWcKuMTJhAbrcPOb67rDz/k5m\nx21c/uAazz26B9GyFbFljKELo4jqu7Ce62fDEocFP7X3NaGS6lgatuIxy5kZSVPfIUHoCSLLT7H0\njhXTka+hiQ2Qbmll4LU5vv/SDq5fGGDn3lbOvHuNA4d2IcoK+OSqg9aeGoQbYYan1ymuVHJyWzVh\n7zxmu5WV5RUKWrYR8EqYvDmHc8mLIa8JryPKIyerSSbFDH80ThE2nnnUSN8bH7Pr0DaMzftZLXgW\n9+wCXV21uNwSFoaiFFWJEfkz9LRqKSqIYTTVsOBYQJOrQSyI01ZfwNJCDGdkGpRq2na3oqlIk15T\nkvapkBpDOJf9JEIJ1kd85OurmBtaZs2TIio1YJ6fQ5ItJCIWkIrEiEYkiCLLPPHwF9McevPS+Oka\npYbyinzm5rz4gzIKTQZqO+vJrqVQSEIsLUdpvu8At65dIRKfJW5dZ27oLAWtWgbuhJlcd1NsiKCQ\ny3EE0iBLE8wukZNjQ62Ks7LgRi6Q4XCso9Zn8FqDBMI5iNTV+LxOqhqKeO7bD9BWk8+yfYP7Hj/O\n4uAEWrmOH/74RSy2NcKBIC6vj1/86vuce6ufDfkmdvXmEU/E8HllWIZeJ7a6QjSyQnu7BpBy5OgW\nCNmYHr6DLi2j1LQJD8Vsai5mY30FmbEZgcdGlVrO4Qe6OfNZHxJRimVzjN7eHVw9ewOH3UHGHyG/\npRORMEAyLkeUKeHyp2YMegN6qRRFXgWfnnmfdesaytxieu4XEfQL6Gxv5t6Z2zxx6kWi63rOXL4A\nyTQt5W1odAaGbjvICjPMO0Vs6m3jyAN7+PSTs/hdCwgzSuQaFfaBYTq21zK9uMiNs308e7wXsXWZ\nrH2M6b5RJGXVrFyeJrQUg0k3lQ+0kKPQMzdsIbIiYWwogL5YRE44SVLixX7RgXrnk6jSE2Tbehh4\nbZFvv9zDxK0Jth/exTsvn2PP3h0kENPXt0pzVyNs+BlfDFDbVMQDneVEfVYsFgtrc6soWrYR3ZBi\nGZnHOrWMqqABz2qSBx6pQJBVMPj+bRr0Xp45VUDf6++xfd821GV7CRc+gWPKTndvNZ51EYsDASrL\ndOAK09kko7goja6wFk/Eh7G4iIQvTGNLIRZLlLXwJBK5kpbudjRVcQQbeXitEkSGKPGQmFhAgPO2\nE43EgHV6lRVPCn9Ay6rNjiRHQ0okIpFIEfZlESe9PP3wri8mm58Nnq7TGCgt1WBdDhKOaCkq09C4\nvYWoLYUws47DF6b9/kPcvXIFAYvElhzMDH5GUa2GO3f8zK9vUGzMoM/RYLb6kWiybCQtCCU2lMoU\nHnMAlVSLzRJGJhXhsERJpjWkxCWE417qWvJ54qtH2L6pmvl5D/c9eT/2KTNaRQ5/+ZMXmZtZJhuP\nsuj2839//X3ef+0yMc1WtnWryaZSrLsl2KZeJ7LuIBr20t5ViEgg4oET2/Ba77FqXUSLnCpTC2sC\nE11VRWQSEbKaBjK+JapUcnbdv51zZy4hFiSZnw/Su2svV8/1sbiwjDwlxGhqJhJzkRXoSWdLOf+h\nGZFES0FBDgp9Phc+/pBMLIE6t5LeY2o2vEk21TVw88MLPPvQCxDL4+zFC8QTWRpLKygwGhjtXyUt\nErCwImDTzi52Hujmk8/O4nfZESWkKPN1OAeGqNxchNVu5urH53nmod3IbS7ivnks18eQFpWzfsNM\n1JKCBT9FhxrJ0xlYnHQStImYGguSY5SiBYTyKPYLZmKtR9DLzSSbdzD8yjjffGkbEzdn6Nm7k3df\nvsjW1m6SSiX9N9yUVlWS3fDRP+KmrrmUh7tqia/bsFos+OaCiLcfJBWUsGp24phZQmaowO4Msedo\nGSJJHuNn+6nXezl5PJdLr/+RXQcOkpO/m2jxEyyMLrNzfzOekIjFwTVK87TgiNNRDYW5MYzFjaRk\nWdSGXCKuEC2txZitYbzrU8hkalrb2xGW+pEE8vDPikjrAug1+WSzWuy35hHFZTgsfhyuBIGolrVl\nF3K1hogoSzKZILyeIhvZ4LmT//MD7xfCHPqz50+fvtAfYPB8iJaTL7K81o/UNMaNWIrvfO0xvJYB\nZBUVfPbTfnbW1GFdnkBTuYZyvojFsAPXbRG7DylpKe9h194aSmuULH04zp/9w1MsfDBFbk8BQxte\n8vKyGEViMvsK2Ca7n7CliMk5M1aHk7deGiAwPMeFl/6brESK5vDjWO5dZX5iGc/GLNPjV1hz5lNb\nqiYvJ0qs50vY5xYJLscoLDMyYLWib3Lgml+lvLEIo1mLrkDBh++Oo4wr2P6ohuU/ziP6RiVuzyKa\nm6sE8pp49807lDeXsef403zy739k1ZtgPTyKX1bK7KUPadhSjiRbTl53LcLKVZx9OfwxtZNvnzyJ\nUeXj3TPv8fLf/Tk//c8lZMJP0enTuFZkNN+fx9qcgBM7jnDfQ4d57+O3kMzHCFbex6njzdwYstF6\nrIFrH66i70gh39XD2rwYuT+DbT5Ec3cTj31zH1UdudjmBvj9K9PMjqZRbsri758kf+MA3/9hB8bi\nMLcH1WS0EQqEy2xubmNiop/qymo2PVnFrdsrJORZYtppFJJWMtgQdVaSzCnAueRkdiWBw+NDpA1S\n/fhmli6dx55bi8csIRGdperhU2gCWUZXVCwJa6EqgeiWgUBxmjV/GvfyIrWKPAyFBvSiZdoaFBS1\n5VMl0VFdsJX3X/4tJx5uY2ttKZ4VNfcd7ebotm0Mzc5hDpRR0pLH2bO/If6olDWRFpkwQc3BzYQ/\nvMi24zsYbxJTZ2xlY1lK0upG6g+ymidGrFnnuK6W+akkkhhECjNcGwpzrLuBmwNrtO0vxXHPh06R\nwqqUMzZfTCYs4N++/y3Ou2bY12hg8aKbaEWcKe8wzz70xXwrO/rwD073X7uHzSmicd8JpEoLmopJ\nzt2e4cWnv4xl7B6mylouvXORfd3FjF+/Qm2zhkqTkMWQn/kzag58bwvVxWp6TE0UlRTy8Qef8o3v\nP8n0jTlUeiEr9hXyjSJKihuIVFbQWLiLiaUEy+40fp+TvlevMb3iZOTjs/h8Uip7DuCyjLBo9eFy\nrbKydhfvhgqZMk1zhR6P7giBjAJ1agaBVMbcjT6qt0iYHlmjdW870kAhuoIazrw2gyArpHuPhNTY\nJIIjJUzfvod2epTVtJK+q27yjCUcfOJL/OJffs+ceY1E0Ich18TEtTuYKhSkpF0oyyqQtOQwdFnJ\nQnAzOx7YjyJHy6cffMQP/vZHvP/WCJnAEHkFAgIeCT2H0ijWtRQbSjj24AGu3rmC+foyDXtPUL25\niqkBL7lNxUwuipBqI+R3H2R12E486MdhW6W7axO7Hj1CaVkat22OKy9dxLOwSl5NlrXBW2xMSPi7\nv6iivCLCveEcIlIVxhwfpq5a5i+epbC2kIOnSrl9fR2XsgpHZo7G4l2EUlOkqlsQa0rJFQhwZ9P4\nJs1kcjbY9dQxBi7fZVlYSFpcT2jFRePBE0RCGZZdIpY2ilCbxAi9MrzROEvLG+SrVIhDQSprVChE\nfjbtyqfCVENpcSkVZUpGBpY4eryWnZ0FeFxSnnruQWpbNjHtEOGJdpBvlDB68VPsJRn86RzqSvMo\nbdSSnR6nY085AmkJUnUlVlsIv8VOKp1mOOtHpjVzqqWThX4rUqMUTzTCjD1Bz5HtjF9doqKrDL81\nglyrIhQWI5AX47X4+fJjR1laCWMqFrAx70AqEjFrH+FbT38xa2WPfu0fT1/9vJ+ZpQD1O/dgKElj\nKndy89IEDx49jtM9jaqwhjvvX2ZTuwrLyB1qCmU0VWZZ8q7isBjZ8kgP1QYJJUUtFNZWc+mj8zz+\nxG7WZ20gSOFwuMgzqpBqalG1b6G0YgfjZh+eQIJIJMa11y4yuxph4P3z+P0SWnp247HNY7assTy/\njNkxhD0gINckZ0t3Da5oNSlBDkb/KHJFEtuNW5S2iVmYSVO9Yy9Bm5zcvHI+en0UFWo27y7F5x6i\naF8eywMLJK/fIJMTY2hagVyQxyPfPMG//sMfMTtTEPCSV1nJrcs3qa41Ekw2IzbVY9zXRt91MSvx\nLqo370CpL+XsB59x8pmvcOnSPKLkIhKpCI1MTuvOCPjT6Axqjh7cze1rU1y9tMT2h++jor6T4bsB\nJOV6ltwaZAYNJVv2sT5nI+IJkBQkyTUYOfWVE1RWypkeu8Pi76+wbrZT0SVnfeAe3rEA3/+7Alp2\nphkbD7Ce0SITx9HWFOK4cQ1tYQ77jlcxeDeApKabmZVBWgqbSSvcBI11xA15VOTmM7YWJ7nsJiGc\n59Q3XuDiVQsBQS5eQRGrTgftx44RimRxromZ9BsoqjCSsMRY8gWwWlbIUYoRh/1UV2rIRp1s7snH\nYKiiua6dsiIZ01Mr3H+0i3295TinNnjk8WMUNXYwsiBlIVBJRbGWG59+gF0iIy7XUqE1UF2lI+me\no7u5iFxdFSl5Oc7FAL6AnY14BE+uiqxsggOdTUTG1hAWp5mze1iNCth+aCuD52bJa60kvJZArs/B\n5YmQoy/C7fZxYEcH4Q0/VcVCNhxuUoI087Zp/uy5419MNl/4x9PXzt/B5grRtm0LxZUaJPlmhm6P\ncfTQYazLs+iKyxm/OkRniw7H6E0q9QrKyxOY7Q48Hj3dD+ylIl+MTFFAbVMlVz64wpNP92K9N4FE\nLWLFvoIqJ4eQsBxV+w6KW7cxMe/E408hEsON311iyh1l7PN7bATk1De1E/TZmV9wsDRjxrIyhTOU\nRl+qZUtXA7YVDUmxCu3KACptluVrtzBUpFlxKKnbcwTvQhqttpTP3p5AL1bRs7cFm2uQhq1yzEMO\nMveGUUgD3J2SEw+LeOa7J/nXH3/C9LKH1EYIU2MrV85ep6RCRVaxiay6nbJ9jfz+Az8JyU4qerYj\nVZm4eu4WRx5/nL7rHoQpB8kIqHNz2NKVIuUJo5bD4YP76L/m4LPPJ9lz8gAlLb1cv7KBpDwP24YB\nqb6Q8q27WJm2smZfIaOQIMlkePZbj1JaCWOXL7Dy8QihJSd1u/T4RqcIL/h57q/zqOoOMzPhZiOj\nA0kWea0Jz5XryAs07DpaxcBwHKq2MOmYpESqQ2eKEc5vJCLW0VSqZWApBu4YGeEYj33lWT6/6SEg\n0LCazsEbCFJ56Ai+aIJgIMmwVYGxoojkUgBnMM78rAV1oYyI005jlQphcp3WDiMlRQ001DRhysti\nWVrlxENb2L/dxMrkBl964WFMzT1MLeWw6C/CVKhl6Mp5zKEMaLVU5ObSXJtLJmBhx+Zqyorq8Sc0\nOO0hwvFVXFEP6/lqkgzS217PxoQbeWcW88I6oaSMnSf3cOfCNKb2SkLuKLkmHRabD5WhEN9ahL27\nmghveCnXC4j4vIjlAhYXpvmz5+//QrL5+Nf/1+lr5+6xthagtqOF8oZy0moLw3fGOHR4Pwszw+iK\nKpi8NEFjsxLH1E0q1DIqygSYLRaC6QLa9/XQUKYjFhLTvLWZSx9d4uvfOMDCwCgiuZBli52MKAdP\n1oR68z4MDZ3MLTkJhDIggFsvn2d8Lc7YpWHWVgSU1zcRWLfidHmZuDfJkn0KeyBEfr2J9tZqoikD\n4ZQEvWsAtTaD7fZt5MYw634DZduPMXN7lYryWs68M45eKGXzzmYWXUM0dgmxjK6QHBwim17D7Csl\ntJrmGz88yU9+cgGn3UvUFyK/ZRNXL9ympEIFmk3EaMHUU8HbVxKk1NsxtXQjlOYxcm2Ubccf5MYt\nP4mMG2Ji9Hk6OuqiZIMRlLIER/Yf4MZVD384O8RDT9+HqekQF6/5EBWpsYcKEOQWUtqzD8voPEGP\nF3IkSNNCnnrxPkqqhIxfuoz/7CjhJSe1e0z4R+Zwj1t49n8XULklwey4jVBcCXIRwmoTwb6bpPUK\nDh5r5fadIMKazcyt2JCueyiskJKu3kRcoadOl2ZyTUh2NUwsPcbTX3uKSzcChERaXCFwBwI0Hr+P\nsDBLbCPBsFOCqbyU9HIIx3oA86KVnHIdYauFxioZBN1U1xgozKuge1MnZcVpzLMuHr5vC/u2V+Bb\n8vP8nz5JYe02ps1KbPEKig0yhi9dwBzMkNWqKM3VUluRiyBgpndzNWWlbayFxayuhogkQ7iTAfw6\nJUnxCG0t5STMIXK2ZFicWiOakHHgqcOc+3CE0tZy0nHQFGiYn7Gjys0nEIiwc1s9qdAGxaoMiYCP\nWCaO2Wbme1/5/8lb2XuXJk4nZJ8gNq1gH17nb7/6XcrzKxn+o5/A4gAKE1z91efUbCnlG797FN9g\nhHhOgtyaHoYH13G1VvP08VOkNu5x6Z0/0HdjFkML/D/fcmKQrWB3hDnVUQPpXESxekqi32PCNc5Y\nZJ3JWyOIcs6T2xol6/yAsryTFD23hfnfBQloPyR/t5b5c04SwXwmu3I53irGUChj/soEqYCX6tIQ\nZ3+aQJwqp1JczOOHO3Gi5xsvfhOMdjSKIDv2VGDTlGGcEOAIXGb19xmKtjtJO3WMT0dory8i6BpF\nVZNHuSrBt443M/iz3xHLiXLLP8HC0EPUlxeyIXibwFwHO7+zCYnLw/rtz0lHVFzNmnmwdyvCWD/+\nuTh3FKCWrXAksZkP65YIj6xy9/YUJXoLatMReg6rsdwrwm+ew9gkIje1i7q1Kr7y4lau/d9/oetb\n27DfmGDDruU/vnoWd8iLyChBe78Bjedt5KEsj7/0ff7zrQnO/OIdvvf3NcT+Y4TxWCnxxgeRToxg\n1WhpqVLjGAzSvnMTY9fvcKhBAqZSLveXEE6M0Vuph+QsHUdUnDy0iZG3rqLsaMedhHJbFaV1RkSZ\nfCTxCP71flxz12nxjLDly09w06uhUiUl64tQqkozf1OJmACGRJKubW3kSNaorZOxu6sTm3ORgmIh\n4sOPUK+VU7/jMB6LgY3cJkrcAyRnxNjTB5EG5WjWzxO7pSfn6U5Enm0U3Z7m9j0vDQ12CnTDXF32\nMctJDOIYgam3iDRYKTE+REw0yJNffop/fOdndFVV4jwn4juv/BXDd8/y3a8/i8gvRImHM/9xBeEO\nL+NnFrA3aagpllDgEHPs0S9mcuiabfp0TngUicDBvQE3X378L5GgZHImhDJmRqlc4vpVO/rGUr7x\nvU4cs7kU1ZgIKIpZdOWSajGxff8OItN3mbNf5erNcapyPbz742mq9CnGh2/w+F90ISnIRS5uJbJ4\nlHHrLBJxDIfVSsIyibEyjGdxjlJDFTUHdjFzexlB0SS61gLOvH2NPImGMYmcTY0ySgszLJtFLM7f\nRC5McfUPs1BQh1JUwrdfOMryyCInHz+OmCEqS0Js36zFq84lPiuib+JjoveMmPKj+KV1rFoSVNRk\nmB2+jbE0l642A09ubuDV//oN8eQyllAEv+soPa165MF3CFtqefCve8iJRrl36QLSxDozCyl6925C\nHJhidcaHy1BJzAPlil4sxizutSDLi8sojRtk0dLU2UrQHCUb8mMo11CuLKa8sIgTx+q4dPYjunfW\nsDxmxbYa47O/eYtg0gdGHYZ9VSSsAxRkohz9259zdyDEmd/9nkee3IPrnhOrJ022sI24BwyaGorL\ntGRnVti5r5W5Kx+yt0bNii6fgTEhLpuLo0e6uDdppbW3lP3bVDjGZ4iL9ORpS4itiVHWFaBKp8iK\nnQT9NiZHLlMqd9C88wDTLiX5Yj3hVQsdHSasi//fKGfY5uHE89tRRJbY3lHO1vYaXNYZ0oIUPQ99\nnwwSalsMWJcbyORWkVq1knA48BZ1oQinWZ68QMxeSLZzM2rlIVILZvrNdhrbDMgEIzjnp1gqeIQa\nVQrn2BUyRTFC6krySuI89dyX+Plrv2RLdSHOJfjB3z/C4JXLPPCl/UgyEVJ+Mws3Z0CVg3XUTlIt\nofNAB5p1Bycf+mImhz4fHj8tDU+gEK8zNrXGzm0vEgylcQfBa18gz+Dl2lUPknIjzz3bwexiBn11\nExJ9GSFBERFdLs2bW8mu2DEv93P73Fk2F8R57WfjNOgl3Lzax1P/tB9DXSFSSRn+4G4mrOsIMkE8\ntjWkzglyZG4iqxZMBg0duzqwzCwT0Fkx1JTw2buX0ZRKWEwW0tSUi1EnZmrCycziEtJsiLtnpkFT\nR4pCnnl6O4H563z9qQcwCAapqhXS1VVFJCUn4k1ze+om1iEDhhwdAW0pM5NZNrdFuXP5cxobDXS1\nmNhWUslrv3gToXSRpXkrRtl9NDTKSFk/JDRfwounG5EnYeLaBeSCOZxOBR2bmggt9RMOxXEnCgl7\npBQYulgIJ4hnRMxPepBrQyQDItq2tuBcXkQpSaDL02FSGKgqUrKlWcrMzA2qTMX4vSvMLXr4/Aev\nkJJGIU9L9cEdmEfGUYklfOVv/w83byd446WP2LPrCGFXFE8wjbKsnkhUjLaigQK9AoPfR7lRScoz\nTr0xjV1awHi/DJ/fxalTOxiZ9KMok3OyV0bQHCQQzSFXoWXZFaO4TEdeOkBgZYFgeB3z8BXK80IU\n1LRgC0qpMRaScDrp3tOI3bKOVCLGb1vl1GPb0MvstFWXsGN7J7bpMSRyFV0Hv0smraKqTYfbU4C8\npA6f1QlBG+u5baQSSVbHr7Dm0yIp70Gu34J7cYZ5v5/6bhOJ6Di+0QX8DYfRS/w4hiZIh0UsK3RU\nb8nj5KNHefuVM7RvKiXgz/DUt+/nzqU+Tj66i2wyhM82hdNiQZ5fhNVsISPQ0X2gBaXAyeP3fTGr\nK30Tw6ezgTkkrDM04aSj6ylWN6IIlGrsiwvodRvc6Q8gMuh58skuJibDaCpbUVWVEldWkpDm0t7d\nwIbNgts8wfTVazTqkrz56nUq82RcvXSdU/+6l4KmCrT5DTiDtcza3SiTcXzLfkT2cURpP+mgG1Ui\nSe/uemzWJVblTvJKy7l49ho5FVlskTwaKyvQKcRMzplZ9AWQhFYYfm8CtPXEhIV8+elu/Et9PPno\nNookE7RtVtHV0UQqliEYydA3McHkZD75uiKWkykWbVqa6wIM9H1GTamKB3e30mws4dc/fRV17jy2\ncSvFOb1UVMUI264R99Ty1R9VkyOE0csXUIlm8IeVFJq0ZFbvEAgmiEjqWFuC+s17WPQFScRF2Oxr\niHJipAIS6lurWV0cQpyOoi3Lx5Qjx2SU09UiYGFmiHKTERlpbg+bufHDX4FaDkUGmvZtZ3pgFllK\nwSPf+nMG7kn48OVbbN35MOGgjFA4jrGyllBSjspUglEpRLXhpqZYTk7cRluJALfEyFgfxKXw5EMN\njCxGiVfncajCTzIMjjUJ+ap8Fpd81ORr0EXdpH0WxMkQs2MDlBVmMJZVMW4L0lCcD8tu9j20CeuS\nByFZ1qxOnn5yGzqWaa0oYXvvJhbGxhEL8+jY/6ckhXnkl8lZC+YhL6hmbXmVbMxKPLeeqDDB8lgf\nnlgeqbxGlIZNLE4Ms5ZNUtFSQjo0Q9oqm9UAACAASURBVHjGhrxxD2plCPP4AqJYEeOJOM07Kzj2\n4G7e+s27bO6uYn09zjPPHeHO9esc2N9NNOAnHVnEbltArjUR9Pvw+6H3aA/S5ApfOrb7C8nmjamJ\n0wG/BblwFcuik6KGB/DFBQgkOazYFyg0ZhgaipLSajn1eBt3r60gr+lEXVWIwtROMKygu6sVn8uJ\nxzXHZF8fTUYRv/vFBUr1Mm7cvMnJf9tPcWsp+RV1uNaLmJ13olcI2XAGUK3PkY6tQ9yDNpNky9ZS\nVl12EvkxlNoCrlzoR1YJ65RQqDNh0qgYm5zDGkggDa4y/MYwUEIsp4RTDzYiXO/nmVObKZZP0dCq\nZ9eObpKRBOuxNP1TC9ybMlBd34jZu87EjIqq8hBXz71DXWkuzx/vplKn59WfvY5CMY51aJEieQeN\nbVE2LAPEApX8+elypDEYvHwFuXqWZESDUZ9FEhxiI5AhLK3C40rTvPsgo7Y1smkJdm8IgShO1J2i\nrrEUl3kEUlE0pgJMGjkFOSI6m9NYl0aoLMkjm0xwY2SOO3/zW5DJQael8/5DTN6cJBUT8cwPfsi9\nu3I+fmWIbbsfxhdWEosn0OYXE0sryamoQCOOoE6sotNn0WQ3aCzKEFeZGPw0TSwe44WT9UyZIwQr\nKzjUGCIRybDoyUGXV4p1bI3ywjzUmTWitlkMqRAz4+OUFCmR5Rcy4fLTXlEIVgf7jtXhcqwiFgpx\nmx185bkdqFIW6opK2Lu3l6XZaQSCPNr3fJ1YRofKKCCWyUWkLmLdvUEq6iBT0EIoHcM2eZX1dB7y\nyhYk+iZGbw+DQUFxXSEba5OEbBa0ddtQqv3YpuzEvLksiuM099ay//g23nz5LXZsa8ZlWefYg9sZ\nvzXAgUPbECZSRL2zeNwukpk8whE/oZiQHUe3Ioy5ePr4/8zmF8Ic+mRg8HST2ID1wjjJ0g6uTFxn\n884M1d3rHD+4C39EwLS3jfmFOPmeBAMrPRi707z3X+9QW9LNiecbmLkaJDR/l9AhFys3uthanuCu\n0ojP5KJL/SXWnUGOf6+TcJuby6//G7F6A4H3lul9chMznxqRBxII1dUYmvJR6pPISjx4R2Qk73Tx\n9L9sxn7vKpWrraiTemLZBn55boi/+afTTA15ueG+jFE5S9X2XvJVM5QkbzIX3qDkfA6r7Q9QWRjm\nzoUblOWvM1KgQjCsp49KQnMxTuyRkULF+44byBclrG0kGEeC9hu5JK27+M7zhWjC08gEEOqWEbZs\noylyAWGsgHmlmPD0CpPWGLfOrTOj0NBRpSI/oaFp39fRVz7E7V++zKQjy4FtP6Ioz8eYfYHf/WcC\n1c5ZHO4MFeWbOP6DHmQm+MnP/oqLbw4jNJRysHAfr937gOrNzYTVQUrMfkJTGWpSBTz+zFE+/b9L\ndLBEY+f9vP7PQYxfaeWBXV3IzIsYjoTZVZvLX33rBid+9AKdJWfZcbQUeVTJeG4rNz938u//fh+f\nfzRB0ZHjDJ6bQWasRSsYRFtUi2h6BvlBGS/djTDR7yXsHaBreyVRUzWrjmL+cPFznn+4nRKtgfzH\nN/P5L62U7dWyLspj157trIcWuXLxHf7w25v4BZDQhElfKcZlWcJ1uxGXfBSpXkhoKYBZM4lSlsCX\nEyU6toDpH76OyVHMh24LF+8OUt4sw13bxec3N5BXdNMrW8BYFkC2eo6VjJQiwxOgrCYamKfHV86z\ne77KZ7eTdD1VzI+fvULq6G5e/trPiejMXJ0YxG1zg6GJ442nmCrXoY5IyWeUg8dOfiGF9BcfD5yu\n0ZUxdneJ4m31fHx9mNbNGxQV6mkqqyGnoJ57ZiXOSTe1JRrGPAq8GjFDH9+jpKWL7h1NeEbiKNyT\nOORW/Gt15EfWiehMKFtrqJcdZmHJx7FnOvH5Rxm+e5WgSoV7cJWe+9qZvuIgnhRiyNVS1VGHTCxF\narAwPaxFaMnhOz8+zpUz59GJyhApKliLlPPpm1f5y1/8kNlZN5bxEYzFUnILK8mXWjFmpkhr1Sjv\nyFBu3kFbbS63/zCNtDhEOOpHsLGDm8YiwgsOdjQbISPEYVsjnxgz035sYTFND26hztTO0aNS8jNC\nyGyQ3q3G6+4gvTSOZCNNMJkm4JljbMrJ5FwYsUyGQJtFkxPk2MHnUTQ1MnHjIo5pDyVND6LQBbEN\nLnDprItQgY+g14uxaAtP/ckhpDIZv/zVz5l96y4RhZ6tO3r49I1zqLZ3kFiLIhaHCE/YKM2toHFP\nO/Mjo6hEGRQ5lZw9H0RaWMyTz/finrFR3KJlxxYFP/3hMA/93XGC01fpPdDEhkzPxFwa56yAP/mT\nBxl55wPK9z1A3/k77D7YSSbqorTtAKvzNpQNIqam0oxO2SDkoXV3C1mxhumRBFPDoxw4XkltrZbi\nPTv55b/fpLCjguFlJc8/1s3Y+BRjM3f51c/OcXHMR2FrBRvLFoYmfcxOKoiL3ARDUcyjQVaETvJi\n66xm9awur/P0n+9DkVvFHccKd+9NUlTnJaiScfdWlCJTGe0lfjT1GqJTfyAeDZCjOIa4dhsrk5NU\nxTUcP3AApyOHTY918+2v/ZTeR47wm//4DJFAxrTNzILDi1hUyI7tJuSiBI6VJMrwJKce/mKmE156\nr+90V1sLNy4NU7OtnItjKxSbhBhVuXS0liKW5zHuzhBZtmOUw7xHhrDMxL3PhlCUNNB9/wHCtjiR\n6UGE+jBSsZ6gy0ckt4aiqioq9JuZGA1z5OF2/J5xxvsuIFIK8MwH2Lq3leWri8QFMqKCDDUtLYhy\ntBjyI4zdCiANKvnW3z7G+Y9uohDJUSvLCIZ0fP7RXb7/99/FuebENjyNpFaDsdiIEhumHDsbYTWh\n2Th1e3ZRU6/g7uUpogIZfqeFTHAnlpIGzGNT7N9cTjIRJxXNIA+luXN3noQoxOYTe+nqqaO7rZxs\nOo+ExkdOS5ZguBHXkBWlUMBGJIrXNsqiA+aX1tHppWRFckKJZR57+usYa1sZHxlhZnCJ0ratGMvS\nrI1Pc+XGKhmdlFW3jYamXr787R1IFTpee+lXzF6fYy2u4PCJI5x95TyCHZvAFsVYDu47g5TkG+k5\n2MHcyG3kAil5uVV83hdDV1DI/gM1RFY30Jcr2dKl4vVvXuTQC/uw2hZo6KxCVVrA0lwW+6qW737/\nIfp+9d8oW3eyNLnEjm4NnugGFT17WbL/v8y9d3sb9nmvfxMbIACCAAgQJMC9NylKIiVRey/LsuUV\nz8TOsp0cN0kzmuZS2p6etE3bNM20EydecTy1bMmy9hb3HuCeIAmCAIi9cf74vYD+/jt+3sN9fa7n\n8/1e92NHliFhYjTMzOgi/lAQc3keKrWO3js2/KsuGpvyycmTY2jayR/+z6coS/IYmZHwwld2M7+w\nRNdgK+/+6QKto060VRqWFvppH/IxshBDlOLCH4lxt2OFYMxPZnCB2YAJz6KfJ18sR1do5saMm8td\nQ+SVBwkF4lzvmKewJIM8TYz0hnxm2z9Ek+ZCZ95EtLQa5/AAWVItTds3YV90sfXwVv7mm7/k6JeP\n8MdfXCQu1uAOrbHqDBGJq2mszCJFpCAUA9dQB8898cV8VHn19I2TtWUV9N7uJb8ug0/vr6BXKRF6\nxayrMJGqMtAxFyA6PYVUEsK6moIqP4fuz7uIp+ZTvW8fSXeUhYFO0jIEJFLULC8tkjCXk6UtpEy/\ngbbeVfbv38DKTB/WjtsIRBEcS3HWtTQwc62fuEBFWBAiu7QEdbaFVJWfe1cWkCfEvPyjL3Hu/Xuo\nlApSRFoCQSG3L/XyxDNPkkgJM9VlhWItqWo16tRl0sSLhPwZrM0u0bBzN+Wlcm5d7CaaVLBsm4bo\nFhYVJiZHZthYnY8qKUaSVKBMhrhxw0ow7mDL8d1s21xDXW0Za7FiQgonqvwwsZQ6prpmEcaihEIR\nZqytTM1KWHKuodeJEQu0eIOzPPq1L6PQ5dLTP8RI/wy5FQ2k68Is9g5xr8NFXKcmEPRQUd/CE0+s\nR6018rv/+D2L/XMs+cUcf+Y4F//rPOJdG0lYvegrE8x2t5Gh0bB1bwOLU70IhQYydTlcvrOKQJrK\nlm0WPIsO5EYx6+szOfP9Cxz+6jYWJ2yYc3LR5hvp6PDgtGfw5JMbsJ59E3FJLbZ2K7u36LG51qjc\n08TI+BSidC3z00EctjUC/hCpmRmkpmvovTmBMBijrC4PY46arM3bef1fTyEtL2FqQcrLL+5nzr7C\n4PQEl0+30Tq0jLE2lfmVProHAkwvhZDJPXh9QdpH3ax5fahcE4x6Mog6Q3z1O40k0+S0On1c75qg\nqiKMwx6kbcROrkWJXh1GXl/MZOdZ0qUuMlILSWtYx/zAMFlaE5u3b2RufIXNB7fzna/9CwcfP8R7\n79wDoRKHawVfWIjPp6CuykAokkAoFzHb285Xv/TF9Gi+furyyfqKSobvDJBTbORC2yrKVB1Cn4Cy\nfD1ytZ72aRdMTyAUhXAK00jNzmbo1hCk51G29yB4w0wMdKDMkJBMKLHb7UQN+RTqislJraa108m+\nretYHhtirL8NUTLA8nIKtc31jJxvI4kUb9CLsagYdVY2UnWETz/sRiOT8NKPnuajt26gVskRKfQE\nwwnuXRvkyGMnCMe9LPSMw/o8JOkadGmrSEMLOB1KXAtjbDuwn9J8IXevdROJq5memYV4C6tBNYvT\nTmqry0hPxDCq9AijKVy9eBNfeJWtjxzg6MGN1FZV4oyXEFLZ0WWGCUZr6Lm1iFYtx7XmYnG8g9l5\nMQ73GmkqITK5AZ93loe+/Cxp6ZkMDE7S3zNFYXk1aqUfm3WIu11upAY9saiXiupmHjmxHnO+hf/6\n+e9w9s6y6BPy8Jcf4dp/XEC8vYnEuIfszXKmOtrQatUcPNFE/517pKcWU2gp4vzlBcSpWpq25uK2\nORAbpdTV53PpR6fZ//x2HPMOMjIMFFfmce2aHa8jna+8tJGJK79D3LiNuUv9bN2YztLCKpXbmrjf\nOoDUqGJpwc/K3BqxYApCpY60MgPd14dQCUXklGSRWZCJYV0Lb//8PILCUmbmYrz03WPMO1zMOJzc\nPNtHa98Uxlo5Cyt9DFpTmFqOoFB6WHUGaJ3y4PYESV2zMhY0EveGeParVQhVarrcPq7en6CyNMLs\nxCpdc6uUFshIE8WR1hVj7TiDOc1PgaYIeXUNM0PD5GRnUbtxHVNDSxx8dB/f/9bPOfjYEd5+6w5J\niQyXe4lAREgoLqOiVE0kKEatT2W8q52vP/k/XxL8QpRDba/9+mQnflpeqGD1owjS+jHKp+tJny9m\n5zELYkEUf9okPQsfcfxREJvg9D9dJyu3GF+qltZwhPSEH6l5kY8/CpJ4+AVSfSMcWzJS4tYi3pnO\nVf9dXFd9fHy6le9+twzXpSm0tUm6W5cpW1/KAjYMq7l8/d2nGf61gvuX3iR7nYTnntMjWMsjsqKn\nW9zKoiWIKGWYreuruHG1ldm5ebY3ZyAIb6ZhvZBbrUnO/dzDGVWc3VuSCEK3CQy4qM4wcGlyhF+8\n9DT32zy4MzdiXh2g5uVCJkfSGe2XU6aHQOp1ZCMS5haHKBV4qX7wCS7NRFjpmcKUUUYgtYj7V84z\nH9RgW1ohvyQFuz+bhm3LFOarKS1+itIcH0G1m5pSDfHJXiYFCdZ97xjWT1qJL84yZBJj8Jew+E6c\nfV+TMnZzip88fhez7yqGjN0kcnNIM3koLDNTMC9AXVVIUV4m6+o6GO+ZpGprDc81xSl78gB+RvEY\nR1nsO4o4Z4Yup5+ishPke5dYDtoorfLTeucGd+5N4pPnE7WNoUxpRq20UG64wL//6GOefGA9i3Ny\ncsr02LucVGbruP96N2ltq/h2GFCJ9OgSy7gEGRSqptm8Q89b9++zeE6NPS7koLaQgsY1VDNxHI5f\n8umHMTbt28nRY1W4B+cJp4iYMHZz9fwM3SIV4/cH2H1kGxc+vMW0vZwTLTWkhmUk9Zkkbs5hqDPg\n67jN3oMVrPSLeW5XDvWHQ9zouI3fbuXScgNe6RiBuTlq6szsKJ/DYHqKAb2NofASSvkMnRc+YffR\njdy7+i5NFTu4uOzDvJKPzBFl43MnENet8IDWRPvHN7CVanh6++4vZJD+5ve/Prk4O8a+Z/QsXxjA\nnXBRJKtCG8njyBNZSBVROiJr2P2DNKZPoiyu4Na5TjJKjMSSaXRNrCLTKRCzwLWrPoIVJyiwCCgI\nSVEEQ8gb9ARjYTo+mKKtc5QjzxayNjBHZo2I1vYRNKYiFi0qZDYBj//nN/jsbT8xfy9GXZzNW7WI\nInrCTgXj8RkMaQl87lma9lXQcWuQ6a4BshtUiASpNGwt5EZHnIuv93M9KWPnAyJm5i+S8DhIk+to\nHZvlz//9DPduudBm5MDyEuUtm5mLqbnX5qCyNoc1yX0KguB1j5OljNKw/0t80g9e5yrqeAGZBjWf\n3b6HPa5kLpAkJS7BE0hQvjkXpSaHfYefQqux4XZ6yC00ElwYYyXgoOrLjzJ9f4Xoch9zFbvRyHKR\nzElIy3bjXbTzDz88jckUI6YvIkVvISNTSEFFNazMUFZkoKxATVWxjZ57Vh4+vIXnN8rZtiGb7Bw/\nTt0q80uHSRMskpQYERjLUXuG6J10k90EruAcAxMBAhEjel0psaw6ivUJahT/yqs/O8/2r32J+Tkb\n8qSG8a5RSsuy6b/RzvTEHMn6ClK9CVL9KfiiAoqqJFTvsfCr/7jLjD2dmaEgJzYUUVEQJ9UTJ2J7\nh88+E1BU2cJjxzOx24OkRFdQ6ny033dyZ1LAvR4b+w/v4bOLZ1hLMbN7XSYDjhWM9XVM3UmgrUtn\n8P5NduzfhH3GyZH9O6k5msW16Ru4ese5Hk0nqnexNDDCwa0aytalYtQ0Mydf4dKAnVxLNh/84Q+0\nbKnn6sc3KFjfgHVkHKE3SVSS5KHndiDPcJJjMnLx4/PIa408t/t/vuzw/2J+//ZrJ4c6RzjwaCmz\nn/cTErvJM5qwSFTsf7COWCyFTmcEz9o8VbketEUltF3qIatYj0Cho3vWhlYuRpBY4lpniPm8nZhN\nCkoUMiRJSBglyIRJOj+3MjI4y47HSlietGE0yJkaHEeeZWZGngUCAY/98Ot8dtqLwNeJQiuiabOF\nFH+csF/CdMhJpiKV+cERNuwrp+N2D4uTPWTVZJCuTaektohVRxqf/tttWpGz7eks2i5dQhNaRpWa\nRntvH+/94XnuXF9BkaYmYltg4+4tDK3KuHXfSXmNhbhkBrMwgGPeisob4JFHnuFSVxBX2EumsAK9\nUcfnN64REitYDAhJhlPxRwWUNpag1Zay67HDyIXLeFftyNNTkSUXEKa42fzYCfo6/fhcVhYMOxGl\nylHKU1Cn+HDY3fzsm38iZ6OFkMaMUGVCKUmhqK6KqHuakjo1aSQpMScYbB/n4Qc28pVt6WyoTCcr\n08+a3s+CowaFyktUqkGuMiNYGmbcLyO7MciSf5pFn4Gp/jhCWSHCdDOVyiXq09/nnX+7imHbXoRC\nP3JJFkOd46RrJUzcG8O9uETEXIxQqCRlTUJYrqW0wUJ5bS6v/eU6w6tSJiZjbGu2UJ0LZSoVofmP\n+fizAJklLRw/mo4nLsE9s4Qu1cPgYJhOp55L16ep27SFc5+fJylJY2ODjmV/AkVFPsPWBMo8PcN3\n7rLzxG68UzNs37Od+geraB+4iWtinov+IDHVGqGRcZqqVJjXF2DSFjERCNI94aakyMCfX32fLdsr\nuHz2NvnVFYyPWEl6wsQTEfY8WI886SC/Qsu7b55CZknjxeN7vpBsvvremyf77nSx9cESrDfuI9FC\naY6BfJOKI49vxReIMxqXs+aao9ocx1RSRte1bvKrMhGlZTA468QkExP2jXFv1IPdtAmdQYhFKkKB\nEHGeCmHcT9e5QWan59n+RDn2xWWMahULE1PIMnXYRPlEE0GO//23+OgDJ8roALosFRvW5SIIhIk7\nhEz51zDptSyPTrFhRzE9rb2sTLeRW2tAJhWyrrkKj9/A1X++TrdUx7ZHi2j79DYq3yRyaSptQ12c\nevV5bp6bQ2NW4J2eZ+POFiY8Im7cn6OwupJQdIGa7BC20WGkThuPPf0kV9tDzK4uU5O3hWBIwN22\nVsQ6I2PLSVTKTFx+EQX1pWiM5Ww5tofMjDVmhidIM2iIrU0iF3vYeuIY3f1hYpExVpS7EeqVqBRh\nZHEX3jUv//bSf1G2tQr0FiQqE3HfKuWbqvA6rRTVaxC4vZTnShi5N8rxI3U8sE7O5vVazGYfbvUa\n/mA5EnGYpMKIRGdC4p5mOq5GWyEiGA+w4NUxZvWR1FQh1OpoyVmjRHORd35yDmobMKWnEA5qGOle\nwWjOZKhnlJBtjlBBCcGoCo9fQURnpHpDOXXr8vnjq58xFUtjeDFEY0sBjUYRZSlyomvXee/TZXRl\n69nRAB5xKq7xFTJVXkasce7ZlHx+e4bKxmbOXr6MKJGgeb2OcbsfQVku9zq85DWUMHb2Blu+fIz4\nyhTbdu+j+lAN3f3XcE2Nc9XrIaLxEh4bpnGdFkllMWmqbMZmHIzNesjOMvLBu6dpaqng9LuXKagu\nZHZ8lJRIDJ/PzaFH1iEMz1Faa+avb55GblTw0okvKJsfvHmy/WYb6w/kMdbbi1ARpdCcQZ5WwUNP\n7ycUFmANS/EujVOVJyajqJi+693kVujxxZVMzvvIVguJucboHl7Bld2AVBslQ5SCEhmSfDVSgZ+e\nc3dZXlxg86PFLNqXMGuULE/NIDenY0/kk1SEePTktzl92oMiNIQhK50N9fkIggHwiZlyu7GYs3FY\nFyhuyKWvvRf/Qid5dQYUSiEbGkvxRXK48X9uMqIys+N4BZc+aEcWXkAhkdE90Mr7rz7F7TPzpOem\n4piYoXLTeqZcSW50L2AqKSYWXqEqK4RzeoLkyiQnHnuKG91CplYdrK/YyaIzxvBYHylpanqnnKRr\nLNi9MnJrK9EaKtlydAfpGhcz1mFECiWCqA2xwM/WYwfoGY4SDI3iT99LTJtOmipIiseGN+Tj31/6\nFTW7qxFmFZAi1SOPeiluKiXknKCwMZ3Iyipl5lSs98Y5uKeKw9tUNKzTkW3x4xQukwzngzhJUm1A\nnW5A5p1lVmlGa5IScK/hjWsY6l8jbqgjqUpja44DY/IS73znL5BTRqpaSFxsYHDAjSHbyNzEMtH5\nRaJlefgTGpZII5GZRX1lGUXl2bz3xhUWBUb67B4qNuVRa5RSlaom7u7htU+siEsr2VwSIyDR4Jpc\nxaheo7Vzjb61bC5cmqJkXRMXb95A6g+xeb2e0TkXsdwcuka95KyvZPS9K9Q89QBpIRst+/aSu7uK\nUWsra7OTXHMt41X6CI4OU9OQSSA/l3SZhbEJN/MrfjKzlLz/l7M0b6vm7F8uUlZfwMzIGIKEEI/b\nzbHHNyCMLWIpTuOtP32E0qDipf8fsvgvRDl06ux/nfz0LxKqWzZhXxhFu9HM8v0lXLlSgrMfoS8y\n87u2KGK/jHtvTDEWVWGQWjA3J8kztJC7ewOht+/Q0XudTQ8/y+b6vViTHoYmhLT6Jnh2fwN3b/VT\nJjYTy0iSnpvAevkmKYI1XEjQJnKp2ZqDQZDJL7p+idIeZvODaUSsDYhNa/T2JJm7PYpbI+C7Tz7L\n8vgAN64V02G7jNLop3HTAbSmanCNkhJyUFcYxrBpD3f//Bb2JRH313oZT7j58svb+d+P/zMLpY3o\nvR7+9oVHmJvWMTWzgkIpIqyf4MkftHD3dQ+aHDNVlatYR4U4BrRo07yceLGCt77/KgGRgVK5mmX7\nMF2TYzQcrqdIlErnHTdBfz0hdRLnyl2uvHeNOUeYVbGWyxM6VCkJ9uyU0tx4gNmuXjRVE7w91UZg\n1M+GhhCsC1G65SGkfiUiewc6Sx2tnQt4COKaUTAzscj6E3sxpubTefFTPhs/hlyUQdv1f2B+cB8b\nDkYpLy9HZili+Z03KDm+E3ljEXc6UsgRqBFaslAqTHx87j5F6y0o5j7jRk8aTl0VeTl23B4/qd4l\nAlE49N06WvZsY0A6wOzNbiqRMNxmQ5CpIWu5mfhwFquZo4gTUW7df4uE0UdyOIfBlYtUH/o+TUf3\nEQ5OoVNmMr00iyuhJ32sDmtiFlePmqk5BzOibh547AXi4UHGh/SMR5OU7yrhZvcg6Qo7GQW1VBXm\n8q9/vYLB4KBzqJd4D0zkGPnnf3yR3KUob/7T+xg2bMK2kEmn4xazI+m4lj1U11dyrXsYTXoCSXYR\njzy2B3WWBstDBbzxlbfZ9tg6/uaJn7Pn+SMsdYt47uHGL2SQvn7+jZM950fZunc7dzqmMFRV4Z5J\nw6fMICTpIE1n4nJXCilrapZuj3DPJUIvyUKQHkQprUe3vRrre5eZH2mn5cQh9jxxiM5bQziTQZZi\nbhp3lnLmzEWyM3IJisRklIXpvdNGMrZKxKXEGDNTviUXTSTA51fb0AlllDU6mRnWINPHCU2GsE0s\nEDMYeP7px7CvLtA34ON++yBpRWJycjdQUNOEaqELoWiWdNMyOmkm1z59m4gjhTt37ayEfXz/50/x\nk+PfwKVrIOQJ8JVnD7HoSDIxNIM5Q0zK4hjP/+Ao916dIbWmDJXWhX0kQv9glAyljc07q3njv99n\n3hFFhpSob42ZBTubDu0jIYgyah0nnJrHyrILBAHuXxhiybWCHxVXR5R4Jnv5yb+YKKjcj294gES8\nj8GFRWbaJijbl0+Kb4ac8moy0tJxTnZjqdrMxN1R0o1G+tvELMzNsuX4CfIy87h9qpNBHmVkbJUb\nl15ndaKIqk2pmAwGcotyWbhzlcMPPoJcp8QxLkXi1SBUiEmGM7h3oZOSjXlMtb+O1ZZBVFeLRmNj\n1WZDo0kSDXnYt6uZHYe2cP9eH5FQCIHDy+z4/ydi1Mhy8IW0rIXs5FjUXLr8PlHxMqJgDm2tH5C/\n4ymaWjajjw0QFmcz642wEojiODZVVgAAIABJREFUHy1h3rXCmiuFkfvTuKUOduw8TDK4itObii+5\nRk6Fhk8uXKTUnIneVE1+uYrzlzuJEaGv7yKucQfOvEIePHwQ9UqCTz56H0FZBfMjQjrb+1gJy/HN\nximoyqB/3EFVpRmZPMm2g9soLc6jbn0er/3t2zQ27+A/vvsntj60FecifP3BjV9INv/jzd+enLg9\nzdYjO2jvHCWtoJqoXUNUrMObWEaWKufuZAoxnxhnbw/DYTUK1AhVEZS6eizriuj50wXmxqysP7yV\nwyf2MXytg5RklEmvg5aH6vn43U/IN+aiNitZjMzTNziKXi3CvxxGLc7EvCELo2CVG5+2k6VWsa4x\nzshoChKpFJlfymjrLOLsLJ554Rghn42OnmlGBifR5hrJMFWTX96Iwj1CzDdBTDBJWYWOU6//npRI\nkt4bK0zbffz0T9/mh0+9wFK8kqQAvvXNR1mYjzI/N09OlhipbZhnX3yASx+NoanZRLZBgdMm4P7g\nEupUDxsqKnjvzXex2ex41rxEk2GW5pzU7dqFRCVnenaCuKQA28ICqjQR58/0EQpJ8CTDXLsvIRro\n55V/KaG8YRdx+xihuU5m3TDYM0jd3kqi9knKK0rIUmewODNIXlkL010TmDIzsA5LmZpepGHvXgoy\nsmi962RGsIMeq5+zlz7H48qmukGNUaunojIf20A3LXsPYtEpiHpERJc1pMaSBCUWhm/dY9f2Mi5+\n+Hvm/KmU1a8nER/G4QURPnzOFR481ELToWYGhkdBKCTm87IwPISIBMFggrhYxdLSFOk6Na03zpOq\nDBENZDJ45zqmLQcpry5B6R3B5lUSDMpw2GMsrmYzPW7HH4wzMTiGSOmjyFKONlPMajCJJ7BCmiWd\ntr+ew1hlQSHLpmG9njv3hggGfQx0Xye06MBVnM2BzTvRL0e4cuU8vpJSFqwe7t/rY2UhQEpCSHZB\nAWNDy2QVaklVyNi9fytFVaVUtJTwxnf+TFbtBt74p3fYfmILTmcK3zrR/IVk82ev/eLkbN88zftb\n6O0eQ51RhG9JQESmxgMIFWKu9axCVIprpJOxgIZUSToCWRSZsQFLYxEdv3of+9gojYd3sffINmY7\nRohGnNhCPpofqubCqYsU6E3k1mTTtzJM35CVDJmc4IIXmVhJVmM2OvEK7Ze6KTapaayNYp1MEvYL\nSYsrmBh2Ec/K5itfO45/aZbW9hFmrItoLNnoDRUUVzYj9Y0SWZvAFR6iqFTOuTd/TdSTZLzLycC0\nk1++8VO++cSXWAoUkpSK+f4rzzI5toZjcRpDlgDV/AAvv/I4Z093oinbQq4pC6dLQXvXHJm6JCWm\nbE6/9yGr7iVWl+0giGOfc1K/eydJqZjxkRHkukKG+gZJN6k5e24YYUJJRBSktVdCKDrCiz8upbau\nmcBsD+6pVhZcEkYHR1l3ZCOB+Wlyco3oVGpWVxYwZG9gumuGLI2JyWklU7MLrDt6mDJzDn29AsY9\nlUzO+vnokzbW7FrqG02kydSUlZmIrs6w+9A+NJIo/hU3AW8K0qSYoLSYufv32L41h9d//l9gzKZx\nXRVu1wh+QToxRCzOTHFg90Zq9zQyNTqFTC4hFAzhHbMS9UeIk8AjSMM5Pku6WcPAhUuIRWGEKRn0\n3riLsWU/hWWFpIWtLAZV+DxyHMsxpr1ZLFjdBMIx5qyTJKVuzKZCTGYNYakaZ8SNOlfH4Fvvoq7L\nQ6WxUFMr5/79UTyBNYbbrxJ3egnVFHB4y1a0jjDXrl1nNT+f+XEHXW1WbHMuxEk5BksWkyM26pqL\nScbi7H1oD+U1VRTV5PLWT98hp66BN/79I5qPbCAYFvPiFzQ3//HX/3ly2epi6+EtdHVaSdfnE3ZL\niEqUrCElKUzhdu88RGK4rMNYvSpSZXpShEEUxjrKN6/j7n/+geXZSSq2N3Pg4R2M3R0gVRpkwbvG\nxkdqOf/hpxTqM6hqKqbfPUZXTz+5Ci2hJTcCiZzsdbnoUx1c/6idQqOK5nVJJufiuJdjGGRapkZ8\nJM0FPPWVB1idHKOtx8rq5DJp2SbU2jLyy5tJWbUSXLPiTY5jNgW49N5vEUdiWDvcDE2t8t9v/hPf\n+NLTeCMVxAQi/vZ7zzE+4CYYsGHMiKJbneCV7z7Jpxc6URZsJdtgwB/Sc+eOFUNaCKNKxY1LF1mY\nsrLiWEaiFLI86aJx+2aQyxkfHiHNlM9Q3yg6SzoXTg0jSCqJCv10DMiJMMwr/9BIRXkdvoUuVsfa\nWHZLmB6dYvvj+3CMWTGbMtAqU/GvraDSrWOsfRKTxsj0nJwJ6xwbHzpGudnC1LiSmUghE/Mezpzu\nxWlXUV6fgzBFRGVFJonVRQ48sAWVIkBwzY7XmyAlmkpEbsHedZ9t2/L595/8kpJ1lTSU5eGML+GV\nm3GFJYR9DrZsqqXyQDPzkxMoxWKC3gDB0XFcSx7EcgFBkQZ7zyjKggwmz19DLQVRVE7PjTZMu/dQ\nVFaI2DGAM6nD51QyN+5nmTzmexwE40lmR6wo0gLolbko1QIEGXrcwhianGyGPzyFZmMBIqGGxnol\n1270s+r3M9l/l7jDSUpVAds3NJHhi3Hj9n3cuWZWp110tg2xNGlDLlNhKspjfGiW2pYygt4gB07s\no7SykpLGQl7/2z9iqq3j3V9dpOVYE8GQkG8e/5/Z/EKUQ6+evnJy75N52O2DOAWVuPq0ROLvkiqd\nJuBKMm8NEWkuJK9/mh37xBz5npRPX2vkmW/kEfH/hnd/vIThhWwqC8tZvG3lKw1eHo4lGb07TGiL\nl7O/G+TAATO2UJjyrXv40a8vs/lrxwh5whSyhLJqFyMuO3fP3iE3PYjIpUO23k3vazcxPLSRrjN3\nsRXdw7ywzHIoSkxtots6QkFBOlJXBsE+EdLsNSLFCmZPrVL6tWziH7ho2ZtK78ceVBlmxL1CPln8\nK/JoGZN3JTQ3F/H+5+10L1nJNamZnVnh19/Zys++M4+6LkzJ1iYCYglLd/opM1m4K53i3rtiRmcm\naWguQlyuZCEspjmviC3VR+ntPcPTf7+D9v7f0Hu7l6ismVvz2TQdlrNP2o1iYprHN4eZ8CrYWvwo\n705dRZKiR3I+j3BxDglrCtv0uwjPR6gtHENUW8Yv3pTglAfZIfEizFSTbs5C6EhSojew8xETZ85b\nufxGK7kHNHT0z9I6NcojtRtpvzKBbIeNz3tm0H56g4c3VVNzKA35uIMOeybb927h9Q97SNv3Nayv\n3WTVl8uT6rcJyrO57nPhSKukNr8Bod5F5OoQZXUmcg81Yak0sCXVguFLCfL0ayhMi8ze8bOcHsI1\n72ZWXYNeHOHxdWo6P/wjc2M2NFsep/3j27iHkvSVZ5O6bpLt5RU0vdyM3ari0//9O/SPlJDq0mPX\nz/HJ//oT5gNh5saKaPzZY1z71a8xHqvEe62b+S4fT728lQYWKEoW4phzcfRf9vKrc3dZb5oi01kA\n4Qksm2J4J2qR59aSIS9hKeTg5lt3WOzrZP7MNKU/O4HHPc6OTDWilmr8rcN86dH/2R7//2LevmQ9\nuXn7fgZ7p3Gl5SDxaVDwJxIMIElo8Vt9RBRevHc+Y3eTkL0/3svNKyk8fTCHpOQqrafsFO8tQ6/M\nwjY6zzdbJNQpFAzfm2RZ5aLtMzu7WvRYLBJMhVn88VQ3ResqSQuoiTsmSd+2i2mbn5nWm6glIRQp\nWvIqFrj78RBbHt/ORx9/jDR7Ft/UJNaFGGKRgK62XkqqS4m6IRkVI0tLoFbJGOqM0vBANf5e2NSo\nY/ZuGHGGlMiclVfvvoUqWU17q5wjBw7QcbcD2+wqFflFzE5N8cs/v8DP/+4sDrmAvG2NSA0mbL3L\nCJVylhNw5fISM7YxNuzaiFAiY8m7SuOWLOoL8hiz9/PA0Rp6e9ppvzmIQ29ixiOgYl0mNWmLTPd2\n8sgLG5joWCKR2M6cfRKDPo2laSNCtYy1pQTrS2twLISpskTRm9L5068mcPvjGEVe0ipMlNRkkUgK\nMauilO028Zfrq9z5sA19nYKFgTh9zmEKy/OwzcfwZ88wMDNF+M44GxrqsdRHUYm8LAYzWbc5n7Ov\nf0ae+WFGz9zFK5Tz4q7LrCVL6HdoWA6WIyospiDbzPTlGxQXFbPhoWZqq3KIekRs3r2OzLiTckuA\njnv9+ExyZCINo2E9BRYZ6wq1DPW2E3cEUOfspPXjIWKrGqwGNar6JCrkbD+xg4VFN7c/uUrWkVqk\nXjlB5yitb1/DvFGMyy3j6He3ceHsH8jNUTA1a2VlYpaXXjlOumiJ9eYNiFbCHPn77fz7L3po2imi\n3KBBovCgK/Qyt6xHnqdg1S1GrEnl5t1pRmbnGJyaY99LD7EkXKSyromsWgszoxN889gXcwE9fX/x\nZHl9M0s2O5N2LyqthcT8BwQEc0SFmfjsS0T9a8Q67rB3n5hdL3yJ3vtRHj6Wh3Oth66PreQd2ES6\nycB4/xivPKqhojCbob4pgqowd8+PsGNvAWV5MlItGXx2b4CcEiN6n5TosgtL0zam5ldZ6byBLBEH\ngYzCah9XPm5l/0M7OfXeX5FmzONxTDJkFyKTC2i/M0rB1hqC4RhCiZCEQo7ZLGRq1EvNtgrWpiXs\nbLQwMugmo6SEuZUe/nrtz6gF5Qy2mth7cBftN3qYWrKTY7Lgda3wb2++yE9+8CcE6SaKKgoQWczc\nap1Ea1az5gtw8dIkU7Z5dhzdTiAeZdm1zPr1egqLLSyuDfHwl4o59e6nzIzZcWtUeGQZFJfoUUjG\nWVqwcXBfOZ4pN46FKmaXR1FJBSx7cpDIJKzYImzcWMPCdIhckxyzQcybv+3GE4yjx4ssR0thgxmJ\nXEFepgxdeSFnuwPcOn2TzHwRrgEZ1vEeMnKzmJpYBVUI2+oyS51TZFsKUChAJXERUReQWy7n4qnP\nKWs+zsgn3bjdXh55dIkVXwPtSzrC6lxk5nos5iy6LnVRXVbK1uZy6spzkcYUHNjfiNLrYU+FgeEr\n9xFkphKRpjEZkFBQrMSsluIY6kfqi6PXVnHlAytEJQzFBGTvLSJqd7LzwU2s+AL0tg2gLy/CvwKx\nkIOxC3cxrFfhckZ47AdHuf3Z+0hSQ0y7x5kdHeWrf/MkycQi1dmlCH0xdn1vM3/8YxvrN2jJT0+i\nyZQQkytY8QvJLLYw64qSJlPQYZ1ibN6JdXqMnV8/hEvmYsP29dSvr2Zi1sY3DjV8Idn8sNN+sra2\nicVFF/NLIVRZOURsl4gnXfgFOmJrdqIuJ/7udg4f1NP45MMMdAZ48EA2a94Jus/1k7+ridTsLCZG\nFnj+iJqyqiz6BidAI+De+T6a1+ewpSmXkADujU+SnalE7ZUSXw6Tt6GZ+RU/c3eukCqAaEJCQZmP\nM+/e4Ngzezl/+iNIHWfZ0c/QShytRkTn1QGKdzcQjIeJCJMk0rRYzAkWFp2U1+cSmBWzfWsO0xNO\ntBWF2IJ9/O6jV8lO3chwZx47drbQ3dbDUtCDRm7E7Qjy83f+hh9877ckNRZyywpQGvO5176MKiOV\nFW+A9091suJ1suvBfYRSEkzML7GpSYEmQ4Iiw8vDj5Vy5v2b2EaWCWdm4BPJKM1LR212MtQzz5YN\nOYg9MDNVwOLKKEa1DHskF7k0zvy4l5Z9dYwPuikuMZKjE/PX37cTTEjIFAUQ6ZRUNFsQKGRYNBKE\nuhquDHm4+NGnZBfq8AzLGB3sID3XwMxKlLDHz9KCi8UpGypVHtr0VKTJZSSiDMz5Yq6f+pyWg8fp\n+P1nJCJ+tj8JrrUq7s1oSEnXkl1YQVVFFZfO3GVDVSUbq4soNmlRCdTs2t+A2B2kpUrN8KW7qMw6\nIgoVY/4QxTVm0hMCvH0jKONC5JJczr3dj1KuZXQtSPa+UhKLqzRvryNEnP7uWdKLsnEuJ9GqRcxc\na0dflobH5+PEi0e4eu5jpIogw8uTzC/M8NjXH8DtmqU0y4JgLcGOb+/mr2/fZ/u2HLLT/UhkSULC\nLKISCVK9lkVXCgqVDOuMjYGxVcbmbez/+mFsiSUaW1po2d5Ar3WKlw5/Mdk8N2g/WVxYyeK8gyV7\nAIUxk/DUTeIJN16JjrB7DlHEhat7kAMHM6l95mEmOkOc2G/BE52m/UwfxXuaEWmMTE04eOKAivq6\nHHr7J0Ar4s6ZNhobzOzcXMFKJE6XbQGVIkZGSIXfFqWsaROzC24WWm+iEgrwhRKU18Q4/cYVHn35\nGBfOnILEMPP2AXpWPeTlKei7MUjR9kqikgQRkRBhholsS4BFh5OCsgJ8diHbtlmYmlklo7KQ6bVW\nfvnOryk0bGHoThZ79m+js6MbRyyMXKzF40zyn3/+Nv/r2/+NzFhMSUUxUrWBG50rqPQqHB4/Zz/p\nYmUtwO5jewiLhQyMLrGpXkxJbS6CNCcPPpzLR29fwj7uJG7JwidUYclSoTAvMWH10ViZQcQWZGa8\nAId7EI1chCtZijZdwmDHPNv3VTM97iGv2EhmmoAP3rxPKCkhLSWAIkNOaUsOIoUUgzJOSmoNN4f9\nXPrgIwz52fimlUz1dpCWpWXWKcC37GN62sG81Y5MnoMu04ha4CAWSJKfL+Xa6c849tBj3PrVeSSx\nVWqfVZL0N9I9LiGulFBcUEVxUSG3z7XTUFXFluoy9GIRqQoNO7bXI/JH2LZej/XCbZQmDRG1jMlY\ngMKybNTeGMnBeTRJIeK4nk/fGUStTmfSGcC4q4SobZHmLWWE43EG5gIYS7JwrYZIk0iZO38DY2kq\nLrubR195kCunTyNShJn021ictvLQ8w8wPzuGOVNH1B9l17ePcPqtCzQ2ZZFljqCQx/EHCwkkE+iy\ncpmcC6NLV9A3McvorJ/xuXkOvnwYe3iBxi2baGmpoWtkgpeP/M+fEb4Q5ZAtGj4ZW2zi8boKwvFU\nrveMc/SEHMfkEquhJC+faGJkwoltcpKqnVnM/J2PR279hH9s/gYmlZCB1Uzqwz7kegcb99fT22Nn\nZt9+VKVlBAenGLlzD5tynHCkFnVtMc/uqyApGCc/COXZEt66BfNDE+Slr6FWPcqF8ThBXxeh4Xvs\nfPoAlug8U4txBH4VNstmJJ0SimoWMObt5sxrb6P6YZDlS8MofA4eOLqRU299zkh4iIAmlUde3sbV\n1DO4bo+jDktoKN9OcWMeZS1GhmwmFhKTKNrHeP6Vp3jn4kU8JUXsxs56LSwNzeCoLmQ1ZR6xw0N6\n0MjxX22l+1yQ9TWFxOMJ7n9yihvtZ9n1nw/xl7/tIy2ZQ9goY8dTFYzfvcVuVQqCh4+jzHQxfUdC\n50o/Go+VZ+vqyMowcOywkoyqANXVDXQpLtM9u4xHESItVIDAN8yEI8JjxWnM6eZZH3RQvl/KwL0A\nn7dFkGZe5gd16Xw4FUA+aeBbP/klVy9c4mBlgjMfLtLXtkBRU5Q1dQ7W/igpRJBmpLCmCDD06Qzf\nPFHLvNOFPRjk2799kU+v3yQg8iImDKsJ2lrXUJZF0UUViGaXcC4EITuD3g9nOVy/kbbVJFX6NRZH\nIqSlOsjM9vH0t4/zx7/coXjvYVLWJGjikxxYV4tE66BALWXstfWk1YtZGxDQsKmGz0dfJc1WydN/\n38LgmJUjr3yZmVkr5RsPced37yK3Swl5PVx2zrJrfwWBeSHrntrJT995DUG+l/nuEcwZVViUM8QL\nvNwaUSMM76a/6zIOsYPNFUcQukYpLTIQ2dxAXU0GaWEVkcxsxpdV3Lk+yZTzLj985ovpHOqctZ5E\nXkRpZR6+VTG9HSs01E6TEowyOqLlb37ayMjoPG5bmJoWDXP3RXz1v5/j7x49iTCSYHVAilQZJcPg\n5fhje/n8wj3Em9aRnpvJzOQo8/PDhBILDC+ZEDZt5aln9qISDiMKBNmx0cCNNlgYmEdjkqIp3U5b\n+wqS6AixaD9lFXry8tUMzSwRiYqYTSllddCLqURFdtkmOt59jYJH1XTdnEeTvkbVoc18+PtLzKWs\nIMhJ8PCPdtA5f4uwY5aEV8CGhgYsZbVUbajGOuKhw2kntjLJl79xiPc/uIws24h+rZ8nKwKMnJ9k\nzZJKvsnE1OAY+bnF7Hq6iSvnJznyzA7Wgj4un/2YzqE2HvrxXs797K8YC1RI1Rp2PLafia5eKjVr\nFO1fR2VDLvbRAEOjQfSKRarNckwGLRn6KI2lSSrqihhfu4tHEmFgwU2+Voxt1YMvHGB/nZak2kWh\nYJmGBjUdt1fwDAuxR37DC4cVfHLNT0icz5GnvsSp926QX5TD2DUbE30rlObLsDkEDExHkKyukZVf\niWvezcC9m5TvKWdOFCXa6+DH/7ybroEFYsEoOlMSz5KL6fZZ6vYWIBcFUUaiOG0JLHk5XL14h20P\n7MM66aZxg5HVQRcZCTcVRXKOP3KAO1d62LR/Jza7h5RgF8cO5lFStoAgRUXv5xaMRWLCSRHmoiLG\nOy8QsYd58stHGLWuUnliC2Ozy+w4doC7Pa3EO0UMrESZSSyxriifBaePxqYSfvXnD1C0hBi1jtFc\nvQ1zbIC0LCEfX5GizN/BwpKN8dZbbN9WR7ohhsA5RIo8TlZJNom4GJkkDcGynVuXbjEx3MPJrz/0\nhWSzbWbwpD6rFIk0RjJVz3BniG2bFklNgbG+CN/6/hYGekbwRoJU1acw0SXlK//7BD994hViUhH+\nfj8BTRSt2M5z33iEa3/9FEvzFgQyOdOjw0yPDbLqtrEsysRqqebQU3vQMIFC5Gfvg430DwvouDGI\nWCNHV7iNwekEUfttfIFh1tXqsFSJ6J0cJRwVM+7MxzO7SHFdAUpTLqOv/ZXSB0sYau1Bm+aisHEr\nb//2MxbEctJ1qxz71m7ax7sIzY+g8QYwV9SSoWuivrmS9pEZpgJhwnYbz37lAT5//zNk6UrEK+18\npclLz7leogoxFSYzo0NTGIvNHHr8IJ9fs/LcK8eZnHBz68IpJj0TPPzMXs7+9HcUVmsQS2Vsffwo\nfXdvU1kcpqo6i9q6OpyLCfr6FyjIz8SgsJOToULui7GxREx1Qz5LjhE8UT/WZTtZuRGGpl0kfR6a\nNuhRqvxkS/1UV6cx1jqDY3CSxegHtGxO0tcZIpReyPqtzdz9pBtV1QasAw5m2ofQyaS4QyqWQjHS\nU0NI9LkEPEGGL12koC6XZY8P/+gsP/7XI3T3LZMaj2FMS8G5ssrawBx16zPxOWaRK5U47AGq1lXw\n+cUrPPjUIVq75qjfmoe1ZwqTUk6+wseDx/czNzLG0Yf3Mmq1E4pNseeAhfqdKcjT0uk4JyC/2oBQ\nqkarNjB9v5UUQZInn32QydFlCg7WMzKxwtbdO7hy7z7+lVQmF2PMhxaprSzGHwpSVWTgj3+6RLIC\nemyzbKrdRrp3gOxCEe98HkRXfJjFuV6GLl/j8J716LROkslp5II4ogwpilQtqcok3pFFLn16k4mZ\nQU5++YvpHGqbHj5pyipCLAkhkKsY6IvRtM5NRlqSwW4nL31/P6MDw/jWXNRUipifSeXrf/cg//js\nD/ELI4T7QriVMsxaN888v5e2c5+RWVqJVCGiv62L5cVJ/PiZEqm4Kcti7zNHyTfakOFjz+HtDE1A\n560RRBoRWWW7GJmIE3bcwusbp6Y6FUu9mJ6ZNsJ+CXPuIiIuN1lVhWiMGYz85m0KT2xgeqgXjdxJ\nVsUuPvr9HZZiKtLMPg4+v4fOrm4SUwPkiEOoiiuRpqynsaWOrpEJhr0JQs5FvvaNB7l95jOkyhQk\njj6e27hG36VuEnIxZXnZDLYPk99Qzqad+7h508qJrx9gcmmN1vPv4Yx5ePiRLZz76W8wVUoRKZVU\n7dzO2EAv1VUeiixqmpo3412D7tZBzAX56OR2cnVKAjYvW6u1VNZn43bMEkyG6BmeJLMgxNCME/we\nqpvUSJVOTJIQleUGRjuG8C2MMek7S9NmOQN3vcTNRdSvr6XzQj/6TdtYGJ1n7G47crWeUEiBfc2D\nQRZGkVtG2Bdk4MIFjJXZ+FOCLFmn+d4/72diIYokKMCoSmFhfhH7yDglJel4XNPo9GmIxSkUlFg4\n8/FnHH1iF6PWOYo3FDDeZiXPqCVPEuD4sX24JiY5+sA2xmacpMgdbNmWyaZdMqQKA52XI2RXmpEo\nVKj+L3Xv2R6HQaZtn9OLpquNeu+yqpvk3lvs9B5CDWGXzrI8LCyLs7AssCxL2UA2QEIILSHVjp24\nO7YsW1bv0oz6SDMaTe995vnw/oDn45u9/8N5XMd9frguXT7LE2ZUSgEfe+oYi1MrlO6oYnbVR8eu\nLgZHZgiGtEyZvfikXmrLyxEKwJir4o9/vUa0Wslti52OLZ2I1xcprpTzfn+Cgpp7sC/dxNR7i1On\n2jCoHaSCcySFMRSVCuRCCdl5KRKLTt57830WV8yc/tRHs3Ooxzx+uqKyAaEwRkamYnI6RntngLw8\nIcO9Tr7yrSPMDk4Q2vDS0iZlfUPH3331JM997BsEJAniE0mCBiW5ejcf/9RBJq9+QEFNDUJhhts3\nevEHraQVSSaiEq7Ljex9/CQNJXYEGT9HH9jHwEiUiTsriDVCjA27WHTLSG9cw+2ZprVRQfnmDGOr\n14j4hWx4qon4/ZS11SNVGzA//zplj+5mbvIu+YoghXV7Ofvfl3FlslEY0xz8zClGh4YRW6epVMSR\nlDWhkO6grbuJnuFx5mNCwvZ1Pv/3D3Pj3PuIJWmEjlGe6vQxfGkImUFFY0U+s+PzFNXX0rRtLwOT\ni9zz8QNYN+Is3H2HVd8GDz7Qyfv/9t8UNciJpKFsx3ZWx+6yZVuUUqOSjtZ2oqEEY4MmKhqayVWs\nU6gQ4JkP092kpmNbCVHvGpFYhIHBabLLfJjnbBAO07gtF7XaT540TX2tEdOYGY99kiXvVTq3qZnt\nd5EuqaFuUynT101UHzqObWGWlUkTYoGaWFLNutNNntSHurwKUkImLp0hqc4iKzvDzN1JPvf9o8yv\np1EGNOiUSWwbHhwL81TN4x/kAAAgAElEQVQUykgmHKg0MuTKDFWtJbz31gVOPbaX5cU1SmoKmBkx\nU5GfTbVMyP2nDuJdXebUQ7tZWPQhVHrYfaCYtn0y5LI8xm4EKWqvQpmVjSG3mIXxabRKAU89fR9z\nE8tU7KvCvO6nsq6O8Yk5AmiYN/uQ6NNUVBSQTiTIL8zl7Os9yDYV0zOySseObURXPRhL1HwwlKCy\n6gRBWw/jN69x30OdaGQWFHIbKaKQn0AmFmMoSRAw2znzxges2C2c/uT/ks6hxx/+zuk56QI37s7S\nH+ghO+LG6wkj7cyngiRLNguhYTtTPWlMq5N8/KutpN0m1LWVuK1KBI+rUbttvPvWGZaHZOR07Kdy\nIoHfGcQpGmdlzIUhR4pSnUJebqAzq57YxgDmkAVl+TbOvrbMkW0BbAVVPP3kw/z76U0MvPhrUgX7\nuTh8jrXLyzzw7G6GBu4SS5ah1Qyh0u3gRt8Un3z2KLe/5mbfMyfZs7OGp3/0HvXd3djG77JbfxSr\nZZW7LyxSsk3E5z75WWb9Ka73LGIzScloxlGOx8k/sZlLgyO89ovvIrUt0BeJ8ZsX+1AVtuCZDZMV\nLEeDDFGdDGd/kHgKGqrjVDXrielKyRZvRiAuQHPrHRYLdNR0qnjlN2pyTlRi9P6NWP8mckoMpFb7\naN31KH96fRmtYI03B+J4pJsQDdxmeU5Ncn6W9/+0zOT0KDcv9vL1Z0soqTzG1cUpYhY1g/1W/nRz\nA6VqDdE1K+3f+Q6m2Wou985z+J8O8d4Pf0Agq58PB+/iscn4/MkWVGYn+eV7yArnopIvsKpq57XP\nv8xn/vlhfvDEN4iUa0kJ5Fx47nm6Ht/O0mIdQ1lbKLLZKZYX4jfU8sYZH6rCA8xFz7E+KUTRpOX3\nvSPsPbSbjSUrad0GOdmFVKsLmB5z0tl1Esv4NLt21zK9nOS5s9t4v3eEfCIYn6nmQ9sVNIp81kdn\nWYhKUKYlbKh8lCu6ML93nvq9HUw75SSvXkDQlSY/x0Zb416mbl2mx+tju8TLiapurtzyEZQssrVa\nRZZUgNUfRK+oYGj8JpuNGxQ5R1i8fpfgQ3UMXYihSPRjka5x+MF7CcWCFCv7iK4sI1pd4Iuf/cRH\nMkj/4Zs/O222C3CFUticJnIK4jhcAkq2b0NWGsLc38vanJ+BW0HuXh/nn56sY2xiDZFORSqrhMx+\nI96xNVaGzmJakaNvvQ+/R45YqiGSXmWxz4nfv0G+QUVhWxcV4mJ8lkmcqggudQ2X/zDNgS1S1Cot\nO7uO8eCz1ZhGLhBLFTA0PsvArUE++WQXztkpfJkiCvTLlNS3MWvaYP8T27jwvQnu/YcvUVdezc+/\n9w7d3Vsxj4xwqmE/pqshBv4aIFsZ4Vvf+TSjrg3evRAm7LMTiA2TH1FRvLWM+ZEJfvjDL7O+5GIs\nVM3v31vDoz3EzKgdq8uFsbEUmy+Na8VHYbmWiuIom1qK0GaVUFK4hbC9BtOtQRI1YvJq8rjak0BT\nkIfU8QF+Xx0VhjIkNjObWtp59dVL5OcYuD6cIiWvYLFvFE8sD2UsyNUX+vAsORh660OOfbyb7hP3\nc+3aMnen3EwNZzjT44b8CL03A3zie79lYqKCu5NR9ny2ib5X3qGwfB3H+ixSb4pDBzexOjSDvr2N\ngoyIYp2bZW0H7/74vygsETHw+8skpMvIq7J569kzbPr8UQbMRpYN2/DNr6DxyRjRl3H192NoyjYx\ntTaE1eFBZyzkvbMDHPzYPdx94w7JuJOsbCUVpQVM3l6kaUsLwUSAwtJq1h1ZfPdMJxfe8bFijVO8\nxYjVbkKjV+B0x3H5wFBcSCgWxlhVyM2L79O5ZTvmSQfm8UGEpesgsHP8vv0M/vUGJo+XWoOETa2b\nuX52AL1UQE46ik7lYSk0wy2zh7U7ZnYVjNKVvYx/cR2nNkXAJiKYFBLQpNi/cwsBQYJQZgXznVmy\ntWG+/vRHUw596Qcvnb7dEyGe1uIIOxDKxFhXPdTt3o6xRo7XM8f4kIWpqxZGh10881gda6tuNA0V\nOJ1Skns2E11ewz18jaGZMNrN92GbF6PM02Kx23FOzCHOxPH5orTt20cDRXhnpwlrU8yFY7z/3zNs\naZRRoZfTdfA4Bx+oxL3RgzimZ3JiniuXJrj//g4c07PEJDpKszcoLC1nbT1G11NdXH7uLEf/8UsU\nl9bwPz+5zraddawN3mVfRyPDPT4G3l1DH4/xT//6DFfXJrnxYZxw3I5YtIxBrKK8IYvRviG+/f1n\nmVkOMZep5Hevr+LWHWJo2oM/GCK7rpr1jQQbizZ0qgh1xQp2H2ygoMCAWrsNu6OA4ZsjRJtKKGqs\n5cOLfgrLC4hYe/BG6jEos4lal9mxrYk33riOXKHj1kSSmKECk2mUlDyHuNdDzx9u4Z2wMP7OHQ4+\nvJe99x9jYHqNO70rmJaTXOu1E9bIuX45yNPfeJ75sUKmZuUceLKLkbfPU9yYImlZIVsqprkym/XV\nCOqCBnKVMRRyDx5lKR/+xysU1HgY/uO7xNJr5JSU8IfPXaLo6d2MuvLxqbdjXV8huhbAb6zh9sUF\nErIi3B4Hs3MLKDV5nD8/Tet9DzJw7gbylB+ZXEJDSS4r0ytUlFfjjccpqGsjkCrkP97Tcvl3Vkxj\nIXK3G7HbllDoFLidMdwBAdrSQvxeH6oyDbfO36B5azvLCxv4/MtINT7kghD7jnUwdHaE5XUXeoOK\nbbvbGBuZRB9QkOMNkVccZS66jiWcz/yFQY4V9XC4MMD81AQL+FhYiuBYihIRZTh08hResZBo2IF1\nwkyOOslXn/xozmV/4V9fPN17O0QkkY0nESIlFWFftrJp33bya3OxWsYYH1xkvneFuzctnDjSgHPd\njbGrgVlzlHT3VgRhO47ea/TPBNG0n8CyoUabU8Cyw4V/bIlQxI/DHqDr3sPUJooITEwR1MYxhZxc\n+tUMjc1aqmVidu4/zM5T1fj9PchSSqYmrFw4N8Xhe7bjMVlICTUY1W4KinJZsUfoenwPN557g/1f\n+Qz55S38+fmbbN1WzercANuqtQz1Opm44UQTTfGprz7GLfsMw3eSOP1mxEoreUo91XVKhu7c5Qv/\n8GnG1yKYIrW89q4Nr+YQwyY3G851aro6WbaE2VizkKP109KUxe7D9eRmZZHfcJyJUQUzAxPYynOo\nbe9m5HaAwqoCVpdGiMZr0SsNJGyrHNjVwTtvXkeq0tA34UdauInZqVFEilxcbge33uwlOGpj6mwv\nex7cy4FHjjIwZGP09jKmhRQ9NzeIafX0XPPy+Bf/C9tSEXOTEvY+eICZWx+SW57ENjxJoUFMfo6O\ntcUouvxqcrLCSMQ+vNJKbjz/e4xGC+NvvE5aHaVUVMZvvtpD7ufupX9JRCy7EZfPi2fJh7CkkcEr\nC0RkZdjsUYbnF8krrOPaJTMtx49x/d2b5CiTKFRSSoy5rE7Y0OXl4EvFUBQ04Y7n85uzUi7/ah7z\nZIicllI2NpaQ6ZS414J4Qykkhhw2Nvxk5Ru4de02jS3NjA7NI5QGSKYcqCRRDpzoYPDdCSxOD3kF\nOTR01rFiWSI3qCDH5aekWMhswMG6vISld2/xQPUN9hb4Gbk9iEseZH4xjmcpSEqXRXfXYcIyBWGP\nh+WJWXTaDF9/4v/9gP7/cV/9wUunr1/1EyefsDhFXCjBtmSheedWjBXZrFknuNOzgG10jcHeJfbt\nrycQ9KJpqWLaHEd9sJuYawX3QA/9Zj+K+mNYPDmoikuwhuIEb0zgT0ZwuH3sefAwDcJS3MPjRHMz\nWIlw+VcmKpp1VEqTdO/aze5TjXg8PcjTEqbG7Xzw3izbjnXjmlyHLCM5CgcajZJ1T5TNj+3k9uk/\nsv8fP42huJk//+wDWrsqsK8M01UBpiEnY9cXkXtSPP0PjzESnWfoSgqHfwmZzk62zEBllZhbVz7k\nc199ht7JDayCev781grR4vsZmvFjXVuiefcuLMtBnPZV8rQbtNRp2H+0Hr1cgaToACMzMizTc0wa\nNTS07Gf8VojyxnKWlqfweIopKSgj43axa2sdH5z/kLQwi0lLiqyabqYnhxHrcrCvu7nx9h3CYy7M\nF4Y48OgRdj98gLEJCyN9c0xPB+kb+P/WeG9f9fDQ3/8Qy5yepRUl248cwDJyB50xzcLdMXJVMrKU\ncryOCAZDPjlZATKCBOjqufofr5CbM83SlYuE1A7aqOC337qB+DNPMWuBLFU+K8EA9mU3mrJG+q4t\n4heV4fRLmJhaQqUrYmwyQPWuPVx/+0OyVWKy1AqK8vRsTLvI0mvxZ+II85rwpvN56Szc/M0KC5NR\n1PUFeD0OZBoVbrsPjyuGIt/I7NQK6kIDtz7oo2VLJ9PTC2jUCVICHzJRjG07Wrhz0cya001hsZGm\nnS2YZ+fQpsUoXCFKChXM+R045UUE+m5wIv8CO0oC3L59h4A0yMTwBhumCJlsHd17juOKSUm4w2yY\nF9Dp4WuPHf7fIYfef2HgdPGxatr0naSFKhQiOab5JXzzW4kwSvnOPMq1enon5gjHzTz1SDeaJQF3\nRLuIyw4SGbvD4kt+Nh8pZcu+Skzrb+JghpJ93fiu3mRspYZ9T2+hcXmIxspc3h+6SXm2mhuLy7y1\nmI1j62No7tzAaXOx1FdBXk2IfKONnGQMCS46dtUzNerigF7PxHqEjp1Wqso/ibFFAoIAK1dXqP2a\nhvd+5eX+vV1sLE5w8N5yCjxJ+iQjdMnyKD9ZQWEsB//WMLNn5VinEySyzNz30AnevH6TrGYrI3cj\nPPnQKX73n4Pkifx4HFJ0mipM8zOsLAd58oH7WBr5gA3HHfru2PDJtnFoZzWbauOcnVpl+zE51qiV\nTY6btLYdZPqyhSb6WUoGMBZV8k///ASEIlwaniZPlWJudIMZ8xibT0h5/m9jPP1YHhpFNkpNN+rN\nrbz1P7fJz+SQjCZZiC6wvyzB1tp8ZjMB9rVt5z+++wqu9TVEsgKu/36U1qPdSEbFrPhqkRotVBQ3\nsXnrAcyeJMUNcqqUy3z7U7OgLGfi9jkOPryXXeWbMfUPUKXN5eDP/w4TdtbmRKQr1uiqiZASVFO7\ndQ+u98zkl+mRxPNYsjuoTpfgdAgQ5ENOuo7RK0sYd7RRlC0ikZBgtY3iEpVRW+DmfChONCNhfrEC\n5vzk+nUEndtpeqqUbI+JRW+Acd8Gg/0zCIMRAoEE3tF5kuJFHKEMWaF5ZCsraC1mRqsKua+1CG13\nIyHLLPKQHUNSxoLByHJtmrXZccqFE/z5609w/U4vwcgy77x0BWc8TUdhAbLCUwzdmuKAVYR3uIen\nPvcguaVC9nV8NGc/X/7bB6cbDm/FvhSiuaEEhELm56K4N/IxJy0YjQaa62QMTJuoqM/n77+8gzVb\nGn/5PsYWYmQ8PgJX5iio09N26gDXr14mMHuHqk3N3Dl/m9CdWZ789klUyz10tSl48beXaN2l59W3\nrzAarSBV0ErgRh9pNrg1LaNleyGdzfNEl9wEwk7q2lpZm7bRUFrH8GKU4nwPm7ffg0IrQG+UsrDm\npaZLxODVBba2tbK+PMixp7eiEfhwyk2kI0Ee+Ph2RJksnKo0y3NFbEzYSKpdHH7wAV578zwCRYC5\npST7j+/j4h8uo5a5sLhDtDeWsjpvoW9ggUP3HCRgmcPjGubcmTGydA001gXRlWczMDHEA0/WEo66\nEFjNlFSXsTA1QlnuEkurXtRqFd/70QMkxQWMz8+Ql+3nxo0RPOszbDuYw6Vzk2yqh9y6AjbSOhoP\nt3P1J38jFo2RTFnxBFbpqBfS1lHM4uwse/Z18It/fZW55SkS0iTL10yUNrahCGVhnoiT0ijRl6g5\nfO/9xDBSnHYhy07yP5+4CKoVDIoFdmhlaMq9BG9P0aLbza4XvsjNSSfuBTX1ZW7yJBukNZU0bd7E\nzJm7qLTVlNTXc3NqlIL6As6/20dVRTE1bU3MTjhQG4sRyZSkklKkcRdpaSWxgJVkjop1sYJkSAAZ\nSDiTyDJ6tp3cSswXYs6yjtvl4+6tIYoaSrDMrEMiTto3h0imRBCJ4Jm6RbYshCUapLizgr1Hmon4\nVxE6bbRU1bMUzTCdI8YtTPCYJsKf/utj/OWbPyMeSHDu5hjr8zHqFXXkV9Zw84NJMIeR2GM88cQ+\nlOIoD+/f8ZFk8/nX3ju99+QBIrEUuUXZqJRCHLYE3qCScesKOToJrTVKVrxe1Ho5//iVbvonwkhb\ndrJsESGOBgn29FFQYmDLyb1c7x3DuzBGUW0DH14egqkpHvvKISKrs3S3qXn7pcts3lPIq+/0sCCt\nJKGowD83TNi3zh1nFrWdxbS1rRIyL7NstVNU3cra/DoNDY2MWdLkZSdpa99GWCFHnxvDhYSSMjUD\n/St0N1dgHrjEgc88iDi1QlruR5ZIcOrxLYgFSvyyJC5vKx6bF59/keOnHuHM2UvEM0EmzHHue2o3\n19+8hCTpIBxLU1VRyMaiif6hefYfOoY0ZmNleZCrl4eJS3NorIyjzdFiNt3l5P31xFMeUpYlSovy\nWJsbRa9dY9UWoUSv5Zvf3o9Als+QaQiRwMvkxAReh5nmNh23L0/S2pJDdmUOjriOqp2d3H3xPYKZ\nNE6LmajfTmNlLvUtxSwvz7Lr5Db+8NyrLFnMZCQCFu+aUZWUk6U0MD9oRagzkF+Uw9YTR1FqjYg3\nzOjzdbzx7Lvgn8KgnqQiEaCiOI2jd4q6/BMceemrDMyt4p2WUmpIY1T68Mb0NNcVstAzQopcSlvr\nmF+aw5Croe/uDIU5Mjp3tGMyu9EZsgm40kSiCbQFCpasQpzzo9Tv7mZGaEQgTBByuUh5BCSEWo6c\n2Me8cwNXSIB9fY3JwVFatrRjnllEo1GSCtgRiQvwO33YV3pRilK44kEqOqrYs6MGm3kchT9OdWkl\npoCKmXI9a5EQTxTG+POPTvLi33+PREzOh1PzhBeibClso6iojhsX+lFvgMoV594TW0ingjxxuPsj\nyeav3njv9J57dhOMJSipLiQaDOFyJwmHJIytONBrRXQ2qFh0u5CIU3z7+8cZGvYgb9uFw6NDGI4R\nvnEWbaGOXQ/fw4fXpnBbZiioqqH/0hAsTPHQl/YTXLawqzmPS69coetQKb954w6L4mrS6kYco1dJ\nEuCOVUl1dxlNdfOELWvYrG60eUW4Nlapqapjek2AMTdGU0M9PkU2eflp1oUZykpU9PQusqumgMWJ\nK+z4xAPI4zZQi0hsOLjvgS5U+XlYg1ZSye1EA1FsGzPc+/jjvP2nM8STAWZMER7+zG5unbuMIGEj\nnMhg0KtxW030j8xT09hGpVHKykIf59+9jVxvpK5EgCZPy+xYLyeOVZBSxfFPmCgv0uFYH0UudeJy\nx1CJJHz1GzsRZ+mZXzMTCK4xM23GtjxMS0seA7emaNqUi65EixM1tbu3MvDieTYiMbwr88TCDmpr\nDDRsK2VxdpI9j2zlr9/7K0uL0yDLsHRrDFFhIXn5RqwDywi02egKDOw6eIycnFxEgWlyi4t459k/\ngHuKEt0AxZkM2YoEnjE7On0rR1/4MgvLVmIrIM8IyVfF8ESEtLSUMj8wRzySomZTJWbTOAqtnIFB\nE6V5WTS21zG/4EAiVWK3uInHU+QVGLC6FPhnxyndtI0FQxlSJQQcFtK2KJGsXI6fOIB51Yrbn8Tr\n2GCmf4zGzS2sLayAVIpCksEvKCSw4WPVOo7WoMKTDlDWWMLuzdU456ZQhqGqtIUFn4qpoixcqxE+\n1pTkpR/s4sXP/Qh5djkXJmaQ2CK0KOsRJw30fjiB1BkAW4BH7t1OJhnmySPbP5Jsvvj2+dN7ju0g\nEE9RVFlILBLB50niC8iZ2whgUKXobs1lzrZKhij/8ty93B1wodt6gEimhJTfT/Dme6gK1Ox88Ci3\neuawW0yUVDYzeH0C1pc4+ZltBC12tjfkc/HlS2w/WMXvXu/HHC8ho2jEO3GVYMLDhENHaXsJTU1W\nQstzOB1+svLzcXpsVJeVYbaAXhunvr6CoDSXokIBa4I05YUybvWtcbCpGPPkNbY++TBZsXVSKjkC\nl5vjJ7egLi5geWORRKybUDiEw2Pm6EMP8+6fzpIRRpieCfF3376Hi6+fRZB2EkkJUChFeCzjDA8v\nkV9aS0mOlFXzHa5fHiAl0lJbKMVYXcCK6S7HD5WSUYtJzq9RW5GNyzaOIGUnFEmiFEr5+y9uQZeb\nzfj0JMGYB9PMLCvmPirKZIwOLtLZUYQqW4wjo6R4azvDL7+HI5HAsWgimfJRU2OkZnMx60szdN23\njTee+yurK3MgSrB6ewJhUR4qtRbP2AoZVR5yg56DB45gzM3BYx0ju7SK9//ut+AdoEh8l+wkKKVR\n1k3rqEp3c+8v/xnz2ioJexiROIvCLDHOkIjq+mLm+01Egn6aqvKwry/iDycZHZ2juTaHhuYqTHPr\nZAQZNlZcuLwBCsqNWJ1qItMzFNW3sCTMRqAVEgs6SK+F8UnU7NvXzdSSFa8nTiToY25gkrZtHawu\nWUmQQSIV4xIUE/VHWVqdQKGTEk0EqKouorOzFtf8HDkCIWXGTaxENcwWyQlNenm8OcoLP97GC8/8\nlCxNDTdm55FtRGlVNRALqRnsm0FotaF0BXns4S7CoSAfO/b/ZvMjIYdu3Rg7PeDZoFRSSbxQgE57\niCe/dhhFYivqWjn2S0NYFqcoNcqIK8RM99vZ/bGH+PC9s2SV5+LO2JhN9yEV5iKofhTXoI7OmIl+\nq5Rdpx5ga8NdRt40o9zbwDtX+pmdDyL0TDJtzeW+40dQ2uXUBMU48ocZjudw7dYEj+/UoouaaPnU\nfVy6aOaersO8IzMRMx5n4eIc+pP5/PfXLyCRxzhw8j5URQFc14e59x4F27YUc/b7EeYP+fl82UHm\nfTKURWo2R4K8tThAlqoYfXeKfYf3cemCg5KwjMkxOycquzEH32XFN45CJyCyIqH1i424bC4SiSXu\npuZZlHZSoVXy2lknGl2CkgIHL7x8garWWUy9bg7tOsbymJUz61d4uKmEpfsbePtNC5trg5w/M8vw\nsJm9ZWkmbnvZ9Ywa2/klqlobaNeJ2ZAaKGzdgkq0yEL/DD6JgLI9VgqSUr52ai+HP1mDTVhBSVzH\nZPwM0uR2FPoUkgI15SW5ZIlD5O7Us/nRNh6t7mTdLeMV8Vmkuja01Sn8ZwLM7riP6mgPh3dK+NqX\njhD1mpE5JincpWbuVTPlUQlvfPrzjP5mCXO6nbhliu4tQmqbLCzm1FO0r5Kxi4PoSHOpRo26d4HC\nzmwqOrLJpCX8y0OnuTx2h7qyIspP/Qi9Y5jAjeeRj/v5bs9xWuXVeDIunvneKTRrc9iXFdR1NfHJ\nvGz0JbnUloMmL4v+m7cp7IhgGmui+kQj+jYjIQ+IzMMcbN/PB7/5G/a4if7FBZrqdqKeC5EJNFGr\nEvOdpx5nfmqcgqZq1P4k3qiMH31pD597upaF/j521oeoafVjaNuCN5SF0H2Xrs3HP5JB+qfrPaed\nk0k2dXURJAu5vokjz5zC7s5FlZtBuriKyD5IaV4SmcfOhdvTNO1+hD/+YY7KomIKRFaW7WaCeTVI\nC5rxDcZ4YKudQVOcE/d3UXQgxOR1OyK5jhfPjBBMbGAbHiWeKaazYx8d9UV4fTYs/jV8FZ0M3Ozh\nSEOa+oSDts9209PvZfveBm7NTuMzHMK6YUddpudv/3qFuCJC9/atFGrEzN2e4OknDGzZqua1X6dZ\njIbZW1FB6aZatDkiqhDy/uB1NIJ6pGIZR791kOt/dJMjCWC1zbO5+yh21zD6lAmVyIll1EXXwx0E\n0l48Phd31vqJGSsIpVOYe204lIVUq+Ocf2sAUb2Z5UUrezc9iWVonJmxXrZWGRHWb+dO3xqK1Bp9\nt1z0j5vQ48U+tkzbnmyW+mdpb6zGWKJHmSenqLqEigojkwOLBKJx8huTGHPzefrgHr7xjQ7SaQUG\nQznj7htIhEZy9GDMFpOM6jDW5uOMBTlysJX6yiLifjUX7w6zuaGdTHSamLwUW6UR71Qfp5/V8g9f\nv4cCdRnxWIrsqlz+/Pptju3fx+NdW1kfnmIgZkDuirKzRsm+e3WEyqUoVQqGriwR3QgSUKoJTM7R\nUpFDcaEIhVLBb370End/+leaT7STKXmSUsk8I6/9hI3lAI9+7TByUQ4LoSAnPvUgcr+fqNlMW2ku\nLWUllBfnsm720NaWx7J1HrkhhNNmoPHILmoLBLisHgrkYTrqK/jLj/+MOhWhd36VvMZcFMk4CkUj\nXRkZz3/iHnyxFZ783DFcczZMyRjfebyUxz9pJLw2Snuuj/YdNko1WUTDSQyxEAd2fzSLNf926+5p\n97KA8oYKMjIjUm0dXQ8cYGhNTUFDLoHZKUTLPZRlhcC1wcDCLDV7HudnL8yhV4rJE61iW/EQFMoJ\n5dcTnHJyaEuEkXErJ+7fTk6Ll2vvDVG6qZD3ri/gCKwyPbqAV95IsbGdAx2l2E3DeFJxgh37mbpz\niT3lTqqjKxz80h6Gl9bY80ATE3OzrLKXjDSESivlwr8PERT7aaipoTRXyXj/JB972MB9j+Txy98F\nCPoFtFc1UN1opLBCRa5Ww5mr7yEIVhCPwdGvH+TquQCijI+1tUna9xwi4R1HuzZEXsiDfz5My6FW\nksIYNqeNvpVhQvl6Umo181fnWNPW0F0vZ2hwCH/WHKGQg+aCLkz9o1jnTJSqJRS0dzFw04TVNIrJ\nlmJk2ktOZoXAnIOyCgWWO2N076qnwGCgrCmL9p3ltDdXc/n6OPFEhNxGBTm6HO47sYOnn96KSiSl\nJL+SkbGrCNQq8vRpjEYFPr+YqqYKrM4g9z11hKxMhg1Pkt5BE90dlSSdcyQNNTgrSgkMneMX/1bN\n9/7PlykpzMUZ85E0Cnnlb1d55ORO9jbVYVtaxBL1oAkk6SzRcOS+asp3lSJLK7h9c5pIKIQ/ISRg\nm6dQJyGRiCIzZj3J42cAACAASURBVPPuXy4w9sc3qD3WSqr8cTYXRDjzi5/h7Vvk4FP3kiELr0hO\n16Ed5EniROZnKZQqaWkooqO+ijXTIsYcMevz4+Qb0ywvCuk40E5+jhvnoo2sTJht1RX89oevIpcK\nuLU0S/aeImJxP3pFOQWzGf56+jDSyDgPf/ZBNsyLrCVc/J+PlfDgI0VkUtPokyM0l06TUyAkFYui\nTac4smfrR5LNVz+8c3p9IUH79mbkWdVoihqo3bGZUauB/OoC/LOjaNy3qdIEUPrdmJbGydv1WX79\n32PkKAXkp1dZn1wgptYRzCkhNLbItvYk49Mr7DrUSmF7hA/f6mPTzgbOXZrB6bUxMmMmJKqjtLKL\nrgYdgbVBnJEk0dZdTPd/wJ5KB4WxZQ5+ejsWn4c999ewZLGwnNkD8hiZTJzbL1gJyBMUG3VUl+Uw\nPTLPo/dn8dQz5fzgP1dIhBVUl1XQ1lJI/SYDKoOGnrvXEHlr8QcyPPG9x/jgXR8RvwPriomOYwcR\nuYcoWp1EtbyCLCaiff9WIgEXa3Y7k7Nj2Av0hNVirDdMzERr2NqiZGF0lIhknWgiQUl+G4u372Jd\nnCUrkaK8qZnh2xO4lxaYWYgwtehH6jcTsmxQlK/FNjPJjj215Oh01LZoaOk00tXWwoWrQ8SicXJr\n1Gg1Su5/eAcf+8RBFEBtTQ1DAz3EFCKKjUrycqVEUnIqGptZnPdx/ydPkfYFWN+IYrF62LypiIR3\nGZ+wClthDrHhd3jpZ1v56Y+fI1crZmZlgtJdWv70l2s8emw/LfnVOIMOAnE3hhR0Vho4fqSaqlYD\norSYsdvTCFJxXK4EoXULRXkqIv4wMkM2H757E9Of36byQBvypkfYVhjljV//mtCNRdofvY94WklY\noqVp92ayIg585mHK1Fo2baqga0sz80MminNEuNdnydXGCHri1LQW0lIsxj1jQqMMU6st4J0X30Yl\nhV7TFMI91YSEQeSpLIzX3bz1/W6kges88pVPsn7XjCO2ztNHNNz3gA6lahJdYpQtjSvk6hKkQiGk\n4SQnD3805dDLV26f9q0laN7agFBahqawicrOZu4uydBWZhOenUDp7KGxOI0m6GdmYQjDjk/x2xeG\nUAsjyIOLeCyrxLOy8WdlE5lfpqMpyfKyjc7uGoqaAvR+0EdbVycXr85gc28wYp4jThX6kg52txtx\nLN3GHxcRbd/N7PA5Drf5kDhGOPGJHXhSIXY+WI3damU5sRNNoZhoJMbIC2t45BGKjDqqy/MZ7zfz\n8AMGnvh4Bf/5/AapqITC0kraWgto6zSiyDFwu/cGsmA13oCQT//bE5x5004k6MG1tsDWU/vQBmbJ\nMQ+QZVkky5embmsb8lSc6bkFFhYXWCnUIzCosNyYYl5UT+cWNc7FaQTCMC67h1JjM6beO6xbzEij\nISprqpgamcG6YGLGFGFyMYDEP0/EvoFaJSJsNdG9qw6jVklNi4rG1jy6OzZx4/Y40XCE7HItMomQ\nI8e388xn70UUjFNVXMHAnR4SKiHF+VJy8pVERXKKKtqwr4Z5/POPEHPbcTnD2KwOaqsMEPWSkjbg\nKC8iOvgmf3t5Lz/91Q/JjnixB1ap6MriLy9c4Jmn7seoyCcYdhMOOtFmkjSWaTl5sp7yehk5ulzu\n9g6hzpLgskdw2NbIVssJBCJkJCr6b46w/JczVB3dTFbjw7RkB3j9Vy8Tv71M+5MP4otKSGbUNB/c\nithnwTV1l5ocI01Vxezd1cFU/yT5egHOpVFyNWGS/iBt2wtpNkqwj05gUKTIV2m49Np5xCS4u7SE\nbHMz6/owUkkWxktuLv10J6yd45FvfxZb7zjO6BJPPGjknkMicnNniXp62Nm4iEYRIB72kPYkePDE\nzv8dcsgdlpzeCIhJxb2EInpC8iDeFTHbH29joC/AstbAmr6AgNXBdO869c/+kmVHLl9/6BM477wG\n65sYueNCGZinRjONolDDV775JN+6poWFCArvMm+9ucZEWEFDy162h2Lc9gixaA5gnnHwd4V9rBpW\n+IL+fgTVJjbNXyWmW0MfLeH3/c+T3ahjqKcfeWwbrqsX6DjYys2+STKrbtyqEex3g5S0tuGPR/nd\n+1FGbLfQKrToAwtMTEbQPFGAUGBDZa3gZnQC+VotS3cE2CLXcVlirGy/jjyj487iZYpbmoldtKGo\n2oH28WdY+o93uTWaoLb7IbqjQQLiarZva+XQ4xFkAhfqXBlZmWLOvvAWjR3tqPMV6OsOkLV2G/2G\nlYHxR/nC0S2c/cY/I8xWU1gv4fKtMHf9NfTM9nH/l08iEsrpu27DIbPTe+V9jHo9ax4vnRXFeClC\nZVEz47YzLJPi6ncSDExTkJ3NhuQwjWWbaMsVIW3fjG/mAxzTQuyDYZo3Gznz2vuoh3PoSG+mOubg\n539yYs26wNeeaOKHz/0nt8/cxT0U5Vc//wLf/Zd3WbFaSZ7cjbK5nGv9v+amMsm3ju/ml78b4+X+\nCOGLV3j//DIP7q/A4L3J9Z7tdHuHkO0tIWwO47Os0PnIFoKr0zx5Mo+eQBU3L6wyIoiwGPsE567N\nIveUM6RxMHnJw/LMKLVb0tQkYrx54w4ZqYiEH1bdHlSV2Qz1O2htWaU+7YSQhsGrIxiNtbw5uk4s\nEufOipKmnGIM2gTlO5W8+pOf4b+hJxoqI1v0IdYJBa2HqlFmmwiEwzz1mV+y4/G9TLw6wXt3zAy9\nNEP3Xj3ji3GO7977kQzSYbfodDJZwLzTQXaOjoArgSadZMvRFuYcMJ7UM+RTsTonY2Y9TunJ7+DJ\n6Lj/8d2kHItIw0mmB11IogJ0cgd5lTL23v8V/vjGKtGohmJfgss/72dRXk35vgfJcVhZdqXwq7cT\nCPg5mjdDQiKlO1+LxG9iv8qMQODEp8jm0oU/UFykZNzsQKuux+dxU9FZzPzkLJ45H0nlMsO9ixQ0\n7sMvF/Prn84yavcTSCSo099EMuVGfqSWcGgJlVXLnZgDTaKdZdMqVy6fJemPMi89T1SqYmTeijw3\nRf9lL+n2B+l6+JOc/9M1PF4BjXVddOSWkUoVcOLwIfY/mkVpYgFNkZCsgjze+cWb7NreTUoMeZu2\nkt4woc0oGHacoKuuiJX3f4nHZUUiBa8zjDkoYMI2z32PdxNO1zA3a2bcbGWsx8qG248j4KW6poSU\nyMD8dAZ1jpye6SQWjwqbaZztWxpJ0kU6KaXY2Ez5rkpuvnkFmVDE9ISbzvY6lp12fMsiknoxUmWS\nX/37HIJmJw/fv4kff+FHXO4LM3LuEt947uP85Z0rlFTWMRuTU3eykd+9cIZQPMa9D+3lrTOTvPmd\nK8z09zEx56CqrRFZxoLHV05Bcp6iqkJM63EcsQ32HN+BWyJn3/F23llJMNEXQ5NThiXUyaRXgMyg\nIiJL4pv3EF4aRcEycoGUq9dvIlBkkKtBkLCgkWmZW7CiFa5iiI0SjcYY6p2jpjCXobtOcvPzuDUY\n5Pj9mxGuOWksEfG3776PdGwrQyPzlKhvklpeoaxKiU42i306wde/8wrlu+sxX55j5OYMnv51sguL\nEMilHOhu/0iyeW7YczoQ1+EJhJFk6RHIxeSJErR1NLEWhiGXlCm7jumJOGsxDdp9jxCTl3N862bk\naQsaZMzMOyChJis7i/KqHDraT/HBHQl+exblYiUjv1lneaOMWOMONCkXDl+KjLCOkH+dzYVWhGIh\nnXVG4lN32aczYcwV4lPmcfbcb9Gpsxk3Z1AqC5FLZKgMEWzrU7hH3OjKowxfnyWrejtWsZy3fjxO\n70QIRyhNg+oD5OF1spuqsVsm0AdymY9nMOh2sTQ1zt1LF3F6fHgil8gY9QzbhGTlwqXrLnzbH2PH\ns5/lld9cJBEXklvQREtVJXGfhoNbDnH0KRUV6QXylWIwxHnzRzfp3HeUgE9EblMTTucaxblFDLs7\n2dxQjq33f/C6LThXN0jHXKyIshi3LLH3gW4iyXoW5laZmHXwzu/6mV8KEA0HyK/MJZSQYl+OIpDK\nWLWGEAnzGBkdYv+9B/FHqiCdg0FZRueBeq6/fZm0SMzc5Br15Tkk0yJsViHSVBpDcQGv/HEVVYWf\nxz7exr98+t+5cNPJ6IcDfPNnH+O9n7/Fzs6jTEfFbD3Yxav/dZGAWM3hI7t4++oo5378Pn0XbjM6\nOIGhtQqv10VaUIE8tohKoyGcSaPTqdlxpItAOk5tbQVXFnT099rJLlRg9RSykNYi1UmJJhIk/VLC\n5ruEnCPEbR5mxoeJp2KoZCJEmTT5lVrcoSi66Apq7wD+kJvFYQtb6iuYGF9DpZQy0mvlkWd3kRpa\n4EhTFn/5yqvkj5YwffU8dZo+ktYVjCUyNOFVLNMhvvncH9BX6nBN21npdWIdDqAx6BDIpBzd/dFc\n+bwwHTwtEuUxbrKANAuJSoFRmmBrdyuLgSjTTiETMwJmF8RYY2r0u+/BlSnmwLZ25FErYrmIRZML\npNWksxTUlGTTsfkUH07msLKQpEqVzezLXhZtSuJtzYjjfvzWMOR04rNa2V7hQCJNUV9rJG7qYY/W\nTG2xCo/QwLsf/BFddhFT0yLk8kLEMjlaQ4igfwpnrxVVaQpT7zQZYyvr6Dj/X/3cGkvgjUioVVxA\nL7JS2trAwvQQqkQ2a1E9Yk03C3PD3H7zr7jsLiKBW1ClZdKlRa4WcO62C8f2R9j25U/xi3/7AIVK\njsHYQFlFJSm3hIcPnmLPPj0t2g0K9UBWgj8/P8Cm/fsI+cRoy5qw+QKUFFcx4W2nraUaV/8f8DhM\nrCwtkiUK4pMpGB1ZZtf9u/HHSxkeX2Vh2ccHr01hng/gC4UxlOuJyOQErFHi8STWeT8i9Ny5Nczx\nxw7jCVYAOox5VTR3beLmy2+TUUmZGLHQ2laCVqdl1RojnAGBoIBzvQ7y89188tkOvvz0D7nYs87o\njQG+/+tnuPqLl9necJTJgICtx/by+k/fxSfUcejwDs68P8R7//YmfVcHmZyyoG1sxOn2gKYMkc+E\nWi4hLZNhLDVw8MGD2OMxOrtaOTsmZ2HWS5ZGyHqwAKtEh1wvISKUkomLEa9NkPEuEbFsYBqegUyc\nTDSMTCmmrqEIjy9IERsYQiukkk5sE4s0GHMxzawiEUoYu7bIx//xAKLxKXaWx3nv9OvUmcqYu/VH\nOrZYiUxOUF0qR2lbxh/V8MV/eRm1VkR4LcTk+TXWJjzojTlIs+Qc3/fRFLfvjgVOKxU5DI7OIVXr\nQSrDKIqxe99mbNEU0/YU07MpxpckuIJqDNuOYBVWsmdrK1lxK1qNjIVZB0gqiOmyqS0y0tBymJuj\nKladCvIyKhZfDbNojhKrKUShThG2xKGki4jbyeYCF1mKGBW1OXjHP+CwcYmaHBkBUQ5nL79BQUEF\ng4MgkuSiztGjVFshMc76LTuqCjHzN8cJ6+rYkBVw6T9vcmMsTsCfoUJyjhyZjbr2JoZv96IR67DF\n9Ejyj2Ae7+HO63/BZ98g4boDZXom4wbEEilv3LBh7XqIPd9+lhd+eBm5Ro6ucBM6Yxk4I9yzawc7\nOzUcaZJQoImRFqb4n5/303DgKEGPDF1NNQvrYSqaWlmINFFbUYKz/zX8HhM2ywLKTIBIlpLJWyts\nvnc3cWkVgyN25i0BLv5lnJmFKL5oFFW5mqhaSWAtSDwYZnnCSra2kJu3Rzj+8eP4o8UkRbnkGqtp\n627m1ivnEKjEDPWZqa0pxVhYxuJigKRIgDuix+wSoxVbeOrRBj7/qZ/Qd2ONodvzfP0Xn+biT17l\naPMeLq562Hb0MO+8cBmfUM6pe/Zw/mwfF//9DMO9FoaHF9A11GG3eUGZTcI6jVatRKbRUFFRwPaj\nu7FGU3S2N3FlRIxlwYNMkcSZLGBdqCYrW05MqcHvSCFbHUOWcoHXy8KcmWgkTCoaRCYR0rGllmAw\nSrEmAeurJGPrOOYWaK4upH9oFn22gekP5rjvE51kJoY42hzgg//zEjvXCzDfeJGqzS7EkxNUVf1f\n6t66PQ7zTN8+BzQzGmlGGg1pBCNmtsBsmWS2YycONk6aBppymjaFLcSF7fbddrebUpqmEGrAYSd2\nzCCDLItsyWKm0Yw0zDzvR9g/f9n7O1zHfRzn81znnYLMsoDFncpzR99Glh4mYYsz9K4dhyVKmiYd\nuUzG3h3/+2/4zwUc6pgVHx33uNHmBen92I6mYomaSTOa4hDuJDsTLU+wLlfDiRteVh3ZjV2gpnLs\n57j0tdQ9sIVnvvkrKv5YRZ1YR6ArSGpkhb//LYmmOjHf32jhtU+DZO6vJ8ntwJCTjaZUzcXOMIHl\nDPbvqKYy28GvX1UgzTyBZPwmKdIkzqaXcGmhlZycOUrHjORv/x2ygjQq1jp5850eZuflFLQWYp62\nUbK5kLrKnaS5Frk2fRvpfC7z3d3UtmgIuHM4ZzXxePUODAtznLgxQU6VjtGZRaJZWmKnw1QIFrg5\nuBbXeDoK4duwXM1//O1nvPzaR8Rk75Jd4iW4RYO2zMf8bBfDHWbqNxZgHdVwemaKshof0Xk1xaWb\naGnVMtUVwxOx8/Xf3s2CMULw5iesrE3DFN9PxOHm7m3lDE4cIxSbYnOKhk6Lkm9+pY0Vi4okHawM\nRtBJbYT12VTnafE4lhBmdiBrz2Vu4R0Kt6uY/ouWxMNC2ge6McxF+Mfpc2zMzmBjVYTJDB03j39A\nXuZqah7IRam3896ZSZIf3sQj5suoFqT85L+fxhY38uwX1+OcmufEYAY/+9mjXDtWzutjfejydhO5\nOcjwhSmG5V3ExgJseSQTta6ZjsQUN64HEaR7SEn5iK4eI7HcNeRp3SgC1SRtbEQynExxoZExh5CS\nCjW1Jbk43J1k1snpfeF9Mnxz+BtUXPhdH9f7+zDPxJgbmCRqzWDu2hvcn53O9w5Uk2Xx45/yMN0v\nxZkWILlmLUefuZtRaTr6A0+RMjpO+dIi5156h5T8YtLqnqI3pZoNsQtIKtzkL89RWFTD+bdOULW9\nghM/fZfinWpGL3soLDNw6sNOTF0hnvzmwc/nIh0KHhXGEwiIMDUUJKCyk+YaQJ0fxhbRoM3WIs9Q\nsnjTgmbbJjThRQpM54kKq8irLuPN373Ctmf3o5RImDvRjiIXrry4zMPfKGV9TZC3/nIH7de2ECdO\neZ6ftFQYvxMFXxrrD21HnXDz2odz5BsmKJV3Elue4I6yjrPdDRQVW3BMiFCv+SZ6bQYbN8Q4077I\nQocHzf512G6OIsrWsmr9JqLhGUw2E4mAC2v/KMb8PGwWCe+uuNhf2Ao9fXx8p5dkUT6ekA1xhQH7\nJStZNXpcXflEVtQkBOcolNbz1ee/wKk3PyChnCetOIS0pIg0ZYDBlQt0fjJI5c4qprwK+scjyHQT\nBD11ZOtbuOdIOTfPjyKSmHnqa+tZTHKxcOU6iRw/celqFKoKmguzWJzuAds8NZpsrLYEP/zeYVy+\nGCGpg+iSH1lASCJVxurt5WQuz+LRjBCeyuDy+ZNsPZBL7ztOWJ9OX8ctDHERp85dp6WqnOpyLUGB\nmDMnbpKQpLKqtRJd0MPMqJf8DZXsXLpKo8vHt3/xFZSWNH7zu2dp/6iDO0spZO/bx5yolMun52lp\nriEpnszF0+04QyaQRCjYVoE2q5BwKMTspBP8ERRZKyzGs6nfvBvR7CJluetIKtKiVwhw2A04xAni\nYjduTQolSTGkaUks/vocwro0RBEzN/80xcj4HP7gEqGpGOqkJO5c7qEhL4eNq6rJTRIzfMKHJ6Em\nPduIorGN3Q8/RP9gGsXbHsMwNoH349e58PvL6EvEOPK34DW2kBcbQqcNU7I6n5jPwqXROXSpGzn2\n11HyGpIZu5VAqU+hva+T4d5ZvvXV+z+X2XxzyHVUSgxvwMPSrBdHaI7Ycg95lXFsUQ26PB1yvRzb\nmSFS19RgSFiQzV1DJi8gv7qUf/7nmxz49r1ExX4Wrl8gW+vn7OvTfP3HqynX23jnjVtoHmomluZi\nTYEPvVLI7AkPKDJZvaMWQ7KAD17pRqi0sKvsNkrHCD3OCj6+VUZxlQv8KYhyH0adLGDNWgUXrzpY\nGghR8vS9TJ3vR5iTQ+3mjcix4XDNIxXO47k8iKGqHuuMnHOLTrZWrcZ65Trt4z1EfXq8AQ+hnHTo\n8KDdVI+3VwgjHoKuHoxpRr77y0c48fv3MeZ4SNUK0JXkodB4mVw8z/mOedZ+oYkpm5jOpTjO4CIx\nQSNqbRn795fQf30ctdLFg19sxC520n/5JnJJkCRlBcaqNmqKDcxPDpJktVGtziQcDvLTXxzAavEQ\nYYVYLITAIyFZnUzNtlIKPXb8qmV8MiWfHj9P66ZCOj8ZJ6XUQG9HNxnSCBdvDJJfnE9FvhFfOMSt\nwXkWF3xs3LWGoN2KLSDDIXbxUHo/efOTfO+7T5MiSvDNrzxA99mb2AQ5RJt3sqCu5ezpYYwNStKT\nUujtHsAV9IPHQXFbM9ryMmQBIbaRBUiVEmee5LRMGlY3Yx10oM9cRUqmAn2qCnssC49MhDcgwpVW\niCYjlVyjEtPP3kG5p4FkuZv+FwYIp4vwOBYJ2GLk5BsYaO9iTV4G5VojamkmVy9YURSsp6C2Gnl+\nA1v2foGJ5UrWHPg6GYM3kLe/w4lfnaOkUIGz5m6CmhKyFAF0hhAlbWXELMP0jHjISFvH+U8WKCrW\nMjoTQpkup6PjBp2dw/zg2c+nq++l7sWjkmCQcCLK0qIDp3eBoK2D8hoZ3ogGY6meULYU19lBVKsa\nkXvGSTL3odQZKWuo4JWf/ZXD3/0SiZQgS+2nUasdXP3MxJe/v56G0gDv/e482i+uJqyy02L0oRRE\nsVwUgCiZ+r11aJISfPrKTeJiH3sq+8jwDHJjqZxPhouoWuMh4IwizL4XeXKC9etUXOuOMdfrpfRb\njzNzoQt0Wio3b0YUX8Efs5MUHsN75Q6qqjo8dgWnbvnYurqJqYtnuHHnBsFAIdGwh0hOGonLflhV\nADMJ6J4i4JnAmJrNN370KCf+831qyuIolHJUeTrS9SEW565yZmiJ1Y9u4NaSh0uTAaw+O8K0aqRy\nDQf2FHD94jUKs2UceaAJV3iF7kvXiYdMKDLLyS7dQ2VpPkM9Q0jjUSrUGSTFPfzbvx9ixWojHjcj\nk8QI2aIkawQ0bqnAaDfjS3Ph0ag48ek1Nm8s4/rx22gK87l9sweVOMS1rnGyCvIpzTUglSXTP7TA\n5KCZisYmps0eohl5WCcv84XCRSocI/z8uSdII8E9h9fReXoAl6IIa90OZnM2c+HTPvLq0snSGeju\n7cU+PQ1+OwV3taLMz0QUBM/kCkiVSEVu0rKM1K1ezcj1SXT6clJVSqLeCOZYMZ5EjEAkhUBaASQi\nNFVrmf3Vm2i3V+DxLjPx2gRJujTEXitCTxiNXs2tK51sbsklO1VFNKSm94adJH0VxS3NZBbXU9q8\nl35zE80PfofUm6eQnXmZ7v++wCq9CGfxFnz5RcjiVqorNGi21hEeukzPcJAM1TrOn/KTW6xn2RQj\nVZ3Crdv9tF/t40fPPfG5zOYfr80cFQf8RKRiVsx2lswT+C1XqF+bhi9iQFugY1kcIXp9DlVdNQpG\nEVhHUOvyKKwp4PVf/pP7nn2SsMqH7eYl1NIVOs6aeeqH26jOcvHJn86T93gLoSwvNYVhgss2/DcV\nIBRTtbUEXbKYk3+9gjNh49G142j9Q3RMlXBmuY6GVj82kx2hfj+pGUKqGo309kuZuOHC+PUnWDxz\nA/JyKVq3kaSYjbDAgsQ/gq9zmLTKKjxOPRe6/ezctoa+M8cZHBnAu5KNIB4gqJXAtRBUGWFRANcm\n8DrHKM8w8Nwvv8R7z79LU62UjHQFmkIN+vw4K5PXOD9pY/3XD3F5ZIFLs1HMZisiuQFZipK9e3Lp\nuXCZ/HwRhw83E0t46e7oRBRZQZVdRmb+LkpLixjoG0QkFlCsTkXss/L9Xx5iZclMSuoK8YSX8HKE\nlLQELa0VGF02QpoIkYJ8PjvZwcaWUq5/3Is+v5je672kCr309M2gyS0kL0ONUq3GbFpmoHOC7OJy\nLB4xqIpZvPURh3K7aWKCX337EfJ0KWzbXEnn8RsENDoca/YwbtxN15VecvKl6AxF9HTdwj49AW4L\nWW2ryKjJJe5O4BtdhnQdBFZQ5+XSsLaZW5fGURvKSFOnEA1HmYrnE4sLCaLFrSiEgIMNawqZ/uXr\nlO2vIxq1MfbaEgKtHrF1kZDJTU6JgeHr3bStL0GfLMfuUdDX7UJXXEnJ6jXojEXUtBzi9mwFTfd+\nHdGl99B0/JOrv75AkzqJpIK1mHNzSZF6WFWjQ7mpiuDtC/T0x0jXb+DsSS95FVk4vGEUWTounbtC\nT99t/u17X/m/AYe+9r3vH11lKKKirQSVYIAN1Rv41/VLRFQxvIMB5m52k1M2RPmaOhy9C9RL+igL\nwvOfDvL+P9/mmVW7eP8PHyF3LlBVIyWqtlO5pYEntmTzt/ePk3bkPp5++H6GX34HkdaBOFSHrecm\nDx6+C4XYxXsX3yMj+wj5WS5W7iyiV5YiCAfJmzWjHbOwnAOvnBhkq8rKjENEX1hIY0s5Xtco96x/\ngA9emeL08ln8Ti9rmhXkFOSTs6aFAXsF1ptdpKfEmXnFS+WvtNiEGTQE8xHOLhBeXUc0bYmDdVpK\nvrmV4iw93qlrzNgbyM/XUu2c5ESnl65pBQr5Rq7/7C1i1Qb2V9Txh//5A5EKG52femitaqX8vsep\nKs2kLmOOX6yMcuVvnWRcmebXvRZip6ZwZZQycyWZx3+9gbLcdCaGwWQrYtv+ItY2yvBYLdxo/5iK\n5CCGbD0F+Qq6h2RYUk1URzLJfbiOD/wjbGmoxTztZzpYQf+Ai8NPNjLdOcChb9/NaHeczAI5iS4n\n4ntz2VG/D7JniF5Roq2XkF94k5Kghxs9gywtLaJTixma/ow933mE1174PRkbUxi2lqC3zVEST8bY\nJqTrhh3fFjSePAAAIABJREFUym2KN25EFBxlbsaMOkPN5gMHSBGWke2bxGu9QlNVmDxDnHfbuyjL\nzyUhEDB7S4ElOxWRpRP7jA1/MBuPKEbIrSKWmcb0zAg7//1fDJiGePCZ7fzo+82EE7MUb6rkvU8c\nRBEwXWSlO7WQG7dcbHhyL0s9Hi7dkSMobkXqqeCRnAXSGuSIA3Lm5BsYuniFsmYjlZY+wgUVdA4O\n4o5dZUtFMbMJKQXlq/DNStnWloc12YpxcyOGTAH7dv3vgrD/F/PFn/7zqCEjjTXbixCa7rBlo4Hj\nf/6YoAwsjgBRzyQlOQmUOcVMdC+yrmCO4vg0P3/dxLnTnexdreGj/3qL7HQLdU0GYrIkqlvyKM4W\n0NnRyebvfZO2TQ20v/gemekRBnt8+Odd7P7BYXLS1bzy4r+o3vw4kuQY3rGrFBTlMDuTRLo8TmTh\nEuFUCafPWinQJpidHWRsWY2yvJTCPDHrdt/L0NAKt94+T1KBkqz0GGX5GtTNGxicSma5ZxZvqhJx\ne5AtXyskXpmKdM6A0GalZP0qglE/NTUKtj79CIJkKVLnKDZzNnWNTaQF3VyZmMJmViMIyDj90odQ\nEODgXQf4/X+8SHKhnN5rK6xqbKRh5z5KMyXo0ib442dj9J5rZ6VjjI97nURGbMTRYxMWc+iZg+zb\noqX93DBSeQnltQ1suzsTkX2Fjz/8EGXSHJU1RoylJdy57cOdbmWrtpq0nVV8ZLpGfWkuklkBtmQ9\nUyYTTzx2PyNdXWx6aAeT82Hyy9NxzbnJ31ZLdXkZYrkT860A5oiHvU/62ZBwMGjzMbUUolA4gl3q\nZv8Xd/DaXy/jqtEwOStALwpiH7hJQBXFveiBFTtptVkU6EDkCaDQZCDMKkWd14o+OgduJ+kpQTRC\nOwN3JtAaU/CNO7k1V0EkM5OUUIAwEpanZpFKogjqS7FPr2AddVH+3WexEmbDnnradpQTEdjIaS3j\n3LE+cpsy6MweY6mhAmt/kNJ7tzE746drJJXku9rQ6lQ0WM6xvkmBI03MqKmS6XEDlaUS0pcvEs0v\nZeTybdSRJeoa1pHitlLVUoLNPE1VbQaeWJDWHVuJhJw8cv+hz2U2j/z8raMNGiU19ZkIXHNs3FrA\n+39+BZRJzDsjZKdZ0Ur95NZXsbTkoy53mQxJgpdeXqC9e4QdGzP55Kd/IavQSVN+OuJkBcW19Wh0\ndib7hzj85S+zZ38NV//2Psa0OBfPTIJXwpbvHiYzRcXrv/wndQ9/DX2OirnOd8kyaBi1KZAnuXGZ\nrrAciNHzmQNjhYKp8VuMO7Ro1q7DqBRSumYrU5M2Rt+7SDg9lVxFGEN6jPz772VgMIF11I2TKOGB\nMI/+YCfRnCBhWwFKsZTCpmos0QgV5Vm0felBArJUlDETPmcaZXWr0YnjdPTdYcUpJRAUcvHl90nK\nDdN23z5e/unLJGVr6T23QMuaSqpXN5MiiJCVFeKNT7rpudbOyq0xTl9dQuT3IlAaWQ7kcd/X2ti0\nLofO0yOkKLOoritk292VCOyLnD5xkhTlLGk5yTTUtjA4MItQEWS9KhPp9jLOzQ1QX5tPbCSAJF3H\nqNnG3od2YOrvZ/XOLSwt+8mryYeYj6yybNraNuF1mRgbcjHhsPPj51LZn3KHhQEHo2Yv5alWSquS\n2XXPHn74nx/hra5iekWE0usmYpnCtrKCT5ICCxaymo3kaOUIInGkSJBVFOFNNZKXJcA/byc7W4NU\n4GNybAy1LEDYEWIyVI5UmYNcriZJKiM4P4Q84sFdUIrdYmfp7DDyx3+IVyqiZmcRubosrNZZNh1Y\nw+2TlxDKRHysuE1kqxHrVRfaLRtZsYi40BEhdc8a9FUy6maOs3OVAXPUx5Kvlq6lHOqb5CSWrhIv\nzGPiYjeFySbqWppQJLkoW53DUO8dqit82AQCth/aQTwa4LGHPp+y+Md+c+xonUpOZYUagXeRpvU5\nnH3/U9yxIE6vgNI8L5GlCQobWliccLC2MYJYnMKbf5viSscwO/cUcfwnfyUn30tdTjqazGz0JauI\nC2dYHh/nwa8+Qeu2Gq79/RilWhHXzs6AQMeOHx4gU57KsX/7JzWPfx1jUQa2vrfRGtIZs2aQJHTh\nXryK2RXk9hk3BU2ZdF+5yPxUMsqte9DIhdRs2cLUpIPpU33EMmSIvWZytQIKv/QwQ7cjuCfC2CLg\nG/Hw9Z8cRGSI4TQXkAgnKK0uZUEooLy2mMPfOEIwJQNFxI7LJqJm42r0UhE3OvpYWkkQCMe48sYn\npOZA06GtvPH8XwkpVUz3+dmwPofKygrEMREqrY/Lpy4xcruT2dtDtPc7CDuWkaXl4JeVcejRNloa\njfRemUIiU1LfoKd1Tw1ip4VLFy8iTJ5BkICGhrVM9k8gFsVZnaFHtruGC7PjVFdkkxjzkqzJYGjR\nybZ9W1gan6R29SoCESGF5dkE/T60xjTWtDYTjnqYscQxm5b54bdVPKIdZnl8jtvzK2yqkVFVpmLb\nnm389M/nWcyqZGFWhTwcQBYex7xowhkVgd1P3qZqlMlS1LIkwoEEstJ8Iro8snURPEsucgsMJAvD\nWC1LpAjcJIWlDLtKyNDlIlPqSJLJSQmYUHiXWMnMxDppxzViR/b0T3E5xGhq5SSSpDjdLtoOr6P3\n+GUSkgRn5BMEm1NZvuYlUFKD2QbnL0ZRPNSMvkjATtd59lUp8WS4MS3m0u4opLZVQ9LKbQIZOmY7\nOmgtElJSW06GMkBhvZE7vR2savBgE8rZunM3Pq+DLz/2wOcym19+4cOjlSlJVFZoELnnqGnQcvXk\nGfzxGFangMY6P+6JYcqbW1iaNdNYF0Uoz+K9Fwbp6J9h9/4SPv72n8jK97FKpyJFoSKrtgW3dwLP\n/DSHvnCYltZaOl57i4JUMcPXLCDJYtMzreQpUnn3h6/S/K1nqajOZOrcX8k2qpjy6IlEzNimL2L1\nR+k/6yenQU37mQusDElQ7rkXnTqZ8tVrmB0ys3hznKAEJOFl9OlxCr/8KJPDcZb6vTiDCawjLr7/\n88MocxJYFguJR2KU1ReziIS8VRU8+q0vkFCmoYi7sJljlG9YRZZSxtWLN1l2inB7Qlx57X0UBXJq\n7t7NsX9/HatMhnkwzJoWLatqS4lGxagNfi5+8hnTY7cY6xvkWvcSMfcyUlUWHkER9zzeRktTHreu\nziKXq6itV7BzRxMCt4VrV28QikwgS5JRXtLA4sw8IjGs02eSsquRs2NjlBdriY65Sc/M5M6Mi80H\nWrGMT1DZVIPHL6GkopCg14ssNU55Uz3+oJ8FtxTn3BLPfiuNZ6vnWZmYontomQ0lSVTUqNlxcCdH\n/3yCaV0Tlol8EgEPKeFZFidm8ISj4A2Qv24VqvQ0UsQSvK4wKXWlBFQ68nMluJccFJZkkxSNEPA4\nkUcdSEhh1F+LKlmPIjMTjUqFNGQhxWTColJjnlrEOuNB/fQvsHoUpJTFUBo0LC9b2L6rha5PLxAT\nRrmWvkhwrZyFUzO48yowLUu4dAmUX2hElS9id+gcBxvSkWicmC1GLvjzqNqaTZL5NksICJlmWFOd\nijHfiEzuobTByGD3VVZXe1gOJ7PzwH78fgdPPfbw/w049O7ps0enwwJ8V1PYu2mR114XkbwqypW3\nRsjetZbyxj5eP9pDbryQiTuDVFUs4zUWc+pmLXq5jgsfvkT1OjnGoWS27l5H2/0vIaxt4K3v/Yy0\ngJzQ8X4GnCLc01eY6B+jc+U+7v+ZGEt7F2HTFQx5GaxXjTHdJ6X13ioe+Np69pdW8unYNH3+GQzp\nWXi9BvJLgwhVGsTCbDKEJezaVYdLG6CrY4ydOQL8o0Wcv2YjTeMiN7mRweVbtOhPkpHVijQpztC5\nEwzOjTExWMbOH9/D6Nud1LZkYerzErx8i533KBmfX8ArTia6FMdRNktWcjYPPVyEqyvAxqYS+hNC\nfPMeojt0RG+LeeBbhXg8k6QkTXHH8inuG/DGyTjqfBfjAjFOdwr799bizYiwq7WVglojZs8fuPn2\nIt6gnNRtdprmhZy/7SSzUsD6Zgm9biO2GVD4hpk0m1mtKKSwop5PFv9B/1EPazc38L6/gy9qH6e5\nPpuODhdei5nCBxuJpy8huPUBC8G1ZBUqUWqSeflGChdG3qFU0sl2wwGyjGoESeUcWF/DtePneefY\nB9y4PYtWsgZd8QS6DfWcvD1I31CIkHmM9HIZpv4lHJNjrN+xj1XZ5USGz2Of76ViQzp7NlRRkpNN\n3O8lOTzC5rgAoj4W1T6MS2/jENyPRFmAW5zgsaQizDV25oQqDoR7GD3fTiUzNCfBnk0VdF+9ypjF\nhrHAicjuYv7pb9O0IZPHvvM4S5d6Gb8xQ9GuejYfAL3gMhWXzvHmm/1IMjaxlKTCqEvnw1dPUZTR\nj71oI3H9PTiv3SEpI5es1BT8Ui+6zDK8Sj8l1RrO/qaP0ooGdrR9PvvZL3x29WiZJpWFcRM5+gWO\nfxYhY42B9rMuMjdsoazMy3vP/Y1kuZqoL0C53kL2tu9w9q1pdFVBRt99k4b1qYwfv05tSwOPf+9X\nhDMqOP/iLZh303djnO4PJlCmLNPfa0VQcTd7ni5j9pOPmOu+TGNtCirvJP6FBe5+eC1bd67i4AMt\nzCxOMzS3glRTQTC9hkx5AkVuBj5JLknJWnLTU3HEhcye7qa0Tc/snSgLV80IDMlE4kqc9i72lV5n\nQ14x4UUfowvXMI2bWbBUsOqR7TgHZiko1jE/ZuHO1R6OPFGDdc7JfCAFT8BEQmJHIFJSXpqNShim\nvjybEVcKblsA9apUbDfGeOwr5diXpiBqY2T8ArNDcS6O+IglwkwKjSSUOaxrlOOSKWjeuYZ969SM\nz/2W7qsWbs0myFy/SNKSiH99MIS+Wc7GVVqWvM0sj5rRJvtYCDtRO7TkHmjmzMdvM//mLNWthZw2\nDXHXpgOkJqtwBT2YnT4oNBKIuok7ZohIc0j3hsmqzOVYuwRPuoiM+XdIs2XhEeSxItjPQaOVN9/9\nJ/94+X389ghjM26amtaTSKtk5HofXlkCHAnIihHqHGKha5ai+mqKV1Ujjy6TL7KSJFih1CiiNleC\nVB5HHJymRBnA77XTOd1HU2gGe2QVTqEGJBIKDDpczkWE0ShtmW78fVcJCpzcuymP1noB9oEZuq/f\nZkdtKh5bHOGOdbTuq2ft+sO4euYI3VlGua2RZKONrJXLZJ89xrHXrmKJrSZU3ET+9m2Mnf8vjJpe\nzvv3ECp5guDUDELlPNmGBEtuE9k5LWTqU6naWMG/Xr1BSVU9h3f97/3s/xfz+wt3jhZpxJjmFzGk\nL3Px+jIFrXW0t89TsXE/BoODD7793wgEKkJuKdr0CGVbv0T7iXF0xUEG//Z31u9Qc+fYJ9RVbOLB\nf/8xzkABnZ9O0HOin2u9C1x8dwS33cy0J5VEyX1s/3Ih9kvvM9vbwdZdObA8RWhsnIcermfT5rXc\n/UATTusYVpMXkbyQlawmUqQxNPoEPnkBAWEaOSoIJolYePcSefsKWZzyY7k0DzojKwINtulT3F07\nzDq9CiZdTFl6mZteYnyumuK71uMYGKc0P535iRHm7ozxxOMbMa14mFoJE4k5kCaFsHkFFFVVIE8S\nUFhZhcmrJxSMYCySYhrs5slna3HOjqJMXWZubpoVT4S+5QQRhx+zvpaYporNFTKcTiFr2lo4uDGD\na/0/pbNrnjGXguKNAmwLMU5+vEh6mZ660mIkomZGR5eRpgSxe0Jk+LWoD2+g/e1jzL9yg/rVhXy6\nNM3m+s1kZmlx2Z0E7A6yqwpwht3YzCukKrOY67uFUKHATAPpuXqWj/2UpsxSxmjBm9nG/U0C/vCj\n/+HPL3xEQBxkcMBEUU0rrmAa1lkzkZRsiAhBHsQzO8981zR5ZdnkNGWTmp6M1ucjYJ6iqSKd1dVG\nYmEzCuESeekhHGYL00OTZFinWfBn44uFqchJI1Ucxet2kCzys782hHy4A78vyu41mTQVJSOzznL1\nzDXuqy5GgwLl4Vpq1leya/1ewqPLLHXNktqQgzzHRpnzNspPX+SjV8/jSqrHqatFt24bc2d+y7qC\nLs4v7sGTeZDYihORyESWUsDgwALVm3eQowtRt20j7/79BiXltRze9/msY//mXP/R+kwVVtscCsky\nF26sULKmgevXFqndfgid2sHxb/0eoUBJKJqKXi+keMNjdLw7jLpewNSbr1K/OYVb75ykPK+Be3/9\nA1zhHC69N0nv+9e53rPI6XcHCPsCzISziZceZNU9Zbg73mDg9Cl23l9NdHEM4cwE993TyIZNm9h7\nbw1e1yCiiBRPUM+Ksh65XICxOMGKLI+YRIlB5iGYkGH64Dqa7fnY5r0EriwgLixj0iNDstTBxsIR\ntmYn4e0yY/LMMzE3xfjSKgp3r8EzNU1+jhLz2DCTXYN88amdzM67WXAkCEfdJOJBLB4BudWlpMpF\nZFWXYfEbiPijpOn9OGf6ePTLVQQWB9BpI5jMbpacMgZXwngscRY0pcRTy1hbrcS7LGbLznW0taTR\nO/ILevsnmHYIMK6VMzMj5OIZC5mV+TRUVaJIqub2pANpcpAVWwC1WI368Hquv/wG5mMdNK4v58T8\nDFvXt5Kt0hAJebFaHCgMGqx+F4uLdtJVOgY7O0jXq5gL5aPVi/Ede55VBQZui3bi1O5ha6aV//nZ\ni/zjT2fxRe2MjFnJqm0mKFZinrARTDVASAZpYVzTk1gG50jLTCe/qRSdLpV0bxC/bY5qo5KaUjWy\nmJO400xRthCLZYX5/hEM7jlcMQ3+aIy8tFTSCGNzOpGL/RyocGKc6SIYhvq6ZPINYmTmYUbae7m7\nIh8jmaTfm0/l1mJ2teyBJRu2W0sU76lEkTRBje8iKaf+xqXjVzBF61lIbcCw9yDzZ/9CW34HZ5e3\nsJL9IKEVK4L4NLUaGd03Rijds5VcZZymLS28+fIpyuoauO/A5/PB84Xzt46WZ6Zjc5hIYZnOUQ9l\na5ppv2Gmsu0g+gw7J7/7AqGwjGhcjT4rmaL1R7j5zh209VJGX3+Vhp3J3Hr7HLnKQh750/Msx7I4\n+cEo4+9fonPIycXjw0TcYZYkxcTqHqBgs5Fw9+t0nviULQ/VYx3oImVhlvu/0ELL5lb2HajH776F\nOCxl2ZGBI6URqSxEaXUci0RLcpoSXcKCPSLC+mkXqu3FeOY9BC/NIS2qZdiWimShg62F82zPl2K9\nPos5sML4wjQjy7XktzXjnlkgTSVh8VY3c31DPPrVg4xOrGDypuAJRIgFAyy5E2QWFaBMlpLZWMOy\nT4MkLCZd7cI30s1XvlWPb2EAvVbIyLyPpXgyQ4sBPHPJLGdWEpMWUZwjxb8soe3AWravTaOr+7d0\n9Q4zH5BQ0KhidELElcs2DLV5rK6rRSYsY8DsIZ7kxGwLI48oSbtrNV2vv43lgxvUrSvj1MwkbZs3\no05VEYoEcNlcZGSqcXqdLC3aSEtJYfD2IIYsHWaBHl2xEt9Lz7C2XMct0UHmlbvYV2rmf47+lX+8\nfBlXyMbokB9FWQ0hsQDHpIOoMhOQAW4Cfi+mkVkUahmljeXkGlQk+4N4LZPUleZRVqJBgRORz0Kh\nQcb8whL2/kn0nllMbiHJchmZyVFkQS8+jw+lPM7hWi+5Y53gclBZJyczQ4xo6jbT13o5VJVPXlxP\n1oNlFKzRcmDdASSLTlZumijdryOVSZpjl0g/908ufHAdb7AYm6wSzZ6DzFx5jbtLeriwspWB2H6E\nixZSpLM0Zsq4dLaHpsN7Ucv8bN6zlrf/eZrCihruO/i/O24/F3Cor73/aEIfZuDq23jFk6ykFXHj\nk7fZ3JRP3uFv8pcfD1Opm8AVnOS5Q2o8VjUei4LNG/Zx/ZNLlG9KxX5Ly4/O/Zo1B1O5MJyg/dc3\ncU32E6yNc3kKsvIyuT1sIlJVQVZ+E9vr7NQZU3FYTLQ99g3mrr1BcV0Qy0c23n51mUtmLwnLENn6\nfZydteBSZJG1JoLXrSeYGkPemIdqzsSx796mYmszU+3dfOm5dM7ckJElF3Gz9yyhVAlp81vZ2Sgi\noTNzfmiG2kwpRXoHxzvPU5eSgkhfhzMli6QDNfz9V3fQpuylZG2YL7auZ+6TJeI+GcdJ4eDvGrkz\n1MGirA/NTRmpWTGKjNXcWvLQ8UGEkb4RPk3ouLOoZO8GF6HLQR75j98xvDJFhVxL2+59dPbZ6bvm\nJOXaAqcnu/jxF9ZzeIuU7/9hki1tVUzeHGS538TIxTSU+Z1klxjYWv8kqfeo+eyXfyEx20BeupuE\nvJwZeSXGmQ9QlC4yNrXEU/+9jthL0yxdTCHpRwcYOXGV6e4u5G4l0+YxnnzUwMGSbJKTDehrUsg1\n7iKvcCPTrknyYyL2bk9gqCjj5IuTXDg1S3XjaoIaMSqJFOvwAIT7iOuFlGhSidlHkBhk1G1wklMu\nxbMQYWXRQUIZIN3lY0lmwWFyMHpjnIqAk3+88EdiBSWIDHdQ5qqYXTnLDhU880QGt//0F9ZrLHT2\n9pFbokA+X0X9xSTCmiCJyRC+gU3UvPQ+0Y9GENt8xOthVck6fLtepLLrBMHaEiLpRcybVZw4l4w/\nEKZ+k4qTx8bpt0r46L+usvrJIyTcb1AV9VAiFDLaISWuzEDTrSDtbg1pMgWb1n4+pbevnLl1NBJx\nc+faGVS5IqYCRYzc6aBifSl1d23jrV8dJ0m5gsNl56m7S1Crk3j/pJ/tR47Q/W4PG1obab+h4Nt/\n/gVr7i9mYdFIx4c9BObfxZu2wu0OG+llVZh8MQoaVmNSNWMUXuKu9UYCC/N84YEjTHf/i8pCN9Pn\nI/z1j06u9XgYHl1CJtuEeQUskSQKS5SExSnMe8Fg0KH0THLuH32U7Ghj/NIJWg7WsTDgRSwLMXtz\niIQ4k6TQPVRuVGGTm1jwJIi4Pai1Ms6cPY0hkIw8U8OSP4v81rUcf7ubdPUG0tVBDrZtxjvjx2MV\nMBVboO0XbdwxjRKOWYl4gqSotBjzVDgFCq5fXOL24AgTKTqWvXLqMy2YTH7+7T9+gMdjIwsdjz17\nL93dJkauuZg83UvX+Rl++LVt7N8i5B8vDLKlrYGOjkuI4i5uvukhVtGNJiOL1dXbEOTFufLxGeIz\nIjLEEsbFGiKqLOi+jSR3lptnZ3ngyTUkehdITHnQbNrIlUvdpApEtLfPIlEXUJXu4EhWBdtKG0kK\nDqOvq6JJp6NynYZgWpCcAhl16zfQ8fYc0wuzxFQliAqMyN0uIm9fhkwB1Geh1WTiDPqIeIPUlixS\nv6qUGfMEIaTEk+JI/C4mPCtMLU9RIrJSyhQf/NsvCORnk5ySSWmeioxYO1tLV3j12Wban3mONbJF\n+t44TltVJqIOLcX2InI8QyTflFFgP8L0/r+T1jOJbNpJwBGjOTkVzU//P2quf0hxRQO2aDGq5ic4\nc9xBDB+NGTYunB5jVN3KuRe62f3cE9D3DpqACaOqio/Px0mOliO4M0VeWw5ScQp7Nn0+r5X98eTN\no565URaWbqFUqRgnl6GxMUrWrWLVhhZe+4+PyM71Y500sXtzGo0t2Rx7P87eZ57g+p9OcvjpfXx2\nzMYTv/kFBfsa8IYM3DzTR1L4NELFMqZrLgy71+OIJdBVNuJLrSXZdZ1t65WIvQH2H9rLcs9rFGt8\nDHVLefXNEOdveujpsyJJbWViOZmAQIZBGSeckDPnSUarzkbhnOXany6Rt6+V2ZMnqdvchGV2GbFe\nwPKlXojnkUjdTsv2PBYFYWZtPmw2H4bsdHpOn6AyWQZJEjwRLWmlDVw7e5WkjHrEvgBH7mtlcXIZ\nnzOIxRfg/h/u5rZ9kKSMAEmpERxWL9q8TJzBNG4PrtDRO8mwIA/zipnCDC8rFgc/ef7LLM2OkJOp\n5rFvHOR21xLXz5uIDg9z6+wi33xsLZuaJXz0z05WrVIzOHGdjBQvp//Vi0c1Q5FOw6rmdXiEdm5c\n7cc1YCNfl8kdFASTVcTGB5GmmBjtm2XvobVMDyzjmg6grqpm+OpVDDkyTMsiLDYxajH8oKGSbetq\nkATmKS/MoVoho35bCeiEKORBdt59kA/eHEVRpMYvLIGCTJQuE6H2TlAooDgNozEfp1eCwC2lptjG\n9kNbGOvrRwpEfFGkARMOApjMM+j84+wqDfCvb3wXR1ouAkU+mhwtKdZT3FPv4Y9PbuDyQ1+nRTXN\n7Fvd7FUlkbUoxBCooMQ7huOGkLzZvUw+8B72f1wnw+ojPhmgNTmA6rcvUHblX6xraWDcUYG++asc\nH0gmGPGzq8ZB/6kOehOtnHjNxaFfPI5y/D1K002sbtzEn35rxxdaTdK4ieq95fjjUu7a+vkEt388\n13s0uDDM5NhNsvJymYzrmFgwU9RcS2VLJX//yTHUei+OaRN79xpYu7mc1/8VZN+PvsSNF0+y/d6t\nXDof4qtHv49hewsuUSYTN2YRC0+REFpx9XvJ3rUJjx8UZY2EFLUkHB00NYnJkCjYsWEt/qkPKc8T\n0n1TwNsfhjhzPcztATuBeDWLfgOBmBSDIkEoloo5qkUuz0Zin+L2X65RdP8WFj/+gILVNTgXzERU\nUgKdk4Qi6Qi062jeVMpcSM6c247J6qYw18DAmY8pTE5ATEAiSY22rInLJ9sRqyuRRqMcuX8zpjET\n1nkrnkiIh3+wl7GlMdwSD+mZcnwWGzklBuwBLXdGbNy8M0W/T4vTP0lOuoOVYJznf/4UttkhcjKU\nHHn6ADNjDtrbx/FP3qHv3Bxfe2IbLXXJXH6vnYYaOeNTnUgTFk6/dw1/ioWqgkzqWmqxex30D85h\n6ZkjW2NgJCElqM3AeaMPtdTHeP8UrXvXYbd6cC050RZXMDnUQ26+Fut8EG9UjUqezndbS9m/tgyJ\nbY7yXAMNijj122pBKyMaW+CRJ77Iu6/3IzemERQXQoEaic1O7Px1MKQhNmooKi4lIEgn6pNQWRxj\n/4FDgeH2AAAgAElEQVQmei/3okkREPZ6kESWsUeszC0NU5g0z6ZMH69/9dssi1SEFAbEmlSypLfY\nVenixScPcO6hL1IVHyB08haPFSswuoNkheopjcxhPh9F2t+E+SunWX7pKoYFD+HzZjaI7Ih/+RPK\nej9mXYORxVARujVf5d1RLf6Am7U5A4x9NEh7ZDOnXoXt39pDzuIpChWzbNy0kx8/P0sguA7NvIX1\nh6oISJTsbd30uc1meHEM03QvOqOeaYGWySUHVetqWLWukj9953WyC8LYJ2bZuVdP85YG3npfwKHn\nj3D97xfYdnAd7WcCfOmn30G/Yy0RdRZTVxykK7vwRE0ER/3o2tbhsycQ5jcQV1UgCPSyqkVEenIW\nuzasRuk+SaEBbvRI+Pi0gOPtXu4MOPGGK1gJZRGMJqFVJBMKyDDH8xBIConNjzL5ej+6/WuwnfqU\nnPoa3PNmfOlSEjfGCMdTEeU0Ub0qj0lfKiafjyWLk3x9PoOnPsQoDSOMCBCLVegrW7h8phOBugRh\nOMLD9zQyPzyNbdGOJxjgqR/vY2ZhhJAygFQjJuKwYKzUM72SytC4nSsDw0yEswh4JlBLbETkcn78\no4dYHLtNvkHOE1+7m6UZJ+cvjBIw9TN4fZFH7llNfbWMrlMXqC+NM784QCy8wLkPzxEQmWgo11Nd\nX4bb7eX26AK2/lmy1UZGwhAu1GNuv4lOFmZ2bJrWnS0szlnwu+yosvMYHbxNWWM23pU4XkE6EomM\nZ+8u4a7VZcRtZtaX5VMkcFO7cRUJdQoR/wJPPXOE998dQpGjIpRSCvkG8Hqhp49YhpKMvALSNNmI\nZVoIJ6golvLAA+u5fPw8WQoxwoCLhGeJoMDBrGmUnNg0uwuD/PWrz2ITpRJKMSLQZJCnNbG11MYf\njhziwlP3k+/pIflaPw9pxRT6Q+gEdeQG5vB3CfC063D9qIeFX10ic9aJ9bPb3JUWQvLvvyT/+sds\nrS1iMZpJxvZv8Mq0Hn9ExNrMESY+6OECWzh7TM7qJxspsZ6lKHOZtrb9/OA7d1hxt6BbnqZhezaR\nZB17tmz7vwGHfvhG39HZC1fYVi7k/EoTqRYrlqwcWo4cQbE8Rcdn56jbHkduVaESQiApjkL3BX7/\n8gW+88QO5pZSqaqUozD1cfx5Kx/81y+pvWucOftFJPMKNm79EhMDdzCsq2fmzHmyhFaC6SpUXhNK\ng5i5gW6K8woZHuigKzWMt1qG+cMxxMVu5iK7KdUmsWpxmY/fGKbr1ZPMTwyxp/AJ8tV3yN1YS2O1\ngtU1ufz9pzPseHwd8ekBjAotVm8aE3IvzbkyskOZuFwC0gvuxjmbR2YkwuxSEg2NTmJjYTpvfMbm\n9TK6xckIZi3cXDzHPUdaEW5VkPTBKWaP/QvHxUK8ugCqZiWtbZs4d+UWEskK99+lZFKQRMSs4aDI\nQXFVKVt/9jyvvfRn1Bk13JgNELrVTc/QVbSyXvonhqnfk8zK7CDDy3WkCUdxH+vCeASmhEPofR4W\njWtIzOdi885TnyVDX1lP3G5lKj9Bf1RNan46oukZPpvzEY6D+rwVxRNlxFpHuPaohdZHJAiSVLw6\nvohd+ggDNwY5uN2PIL2MxQkb8dlbnDp/nHThbQ7tfpDffTpAdnITqZvVDN66wmqjm/W6LH71qIbS\nrDgp9iCChA5dvhdjZgrqZB9Jzgx+/ZMPyM8uQSxaQhQJolEXM39qntzUdB7/zdd4//hp+s78gsnX\njjN15zS3/vIqj+qk1CYt8NEf3Xzvleew2sbZEFbyyakrVKxREagLkuhfIXlBi0WtQ5zwMZU8z/hA\nlL/uuIdXn3qQF7t/Q+Mzm5ifFODpE/HajIi2HzzI7vt8vHq/AtGTj/LIfTJmo1c59qsgz331O4w0\n2jDhRl7fzFzaPPnNTQRXtMwumtnf9vlcpL98+9zRpStdbFinYXzOTzCRQCwxsKFtE56xYUYHZljT\nnE6xKBPL+HkMZVrKyr/Bi0+/9P9T995/bdjn3v4lIbSFhBAIAWLvvQw23tuOE2c7TdLs0Znu9bSn\n37ppT8dpe7qTpu1JmjTbiZM43nvbgFlmbwRIILQH2uP7J5wfnzz3/3C97tfr+rzv94cv/+yrrNgD\ntDSUI0vYsJ8K8urv/0hBZTcLSxfRCAwUV+3EFRewobmE+e4ekuEl8irkpK6KyNHJMHX7aFhroGd5\nlp5UB6vlKkZODpKdo8InKUeQGkSacDHwcRcTJ6bwBYRUFa2htixERFVMa4mQlmwBR/4yxgPfexih\nbYxEOJWIMIE3ZmdTrpZyuRH3kg5Fzi7cNjc5Gg0T0z7WP1hJZDlK1/Vu8vLiLARSSIvPc/Xsp+w7\nsJGyjjxWTl1j4MR5gv1xpAo32c0FbGyvYG5qkjmTnYZdtUwtOgnHs6hS+qkoK+TZb32Df/3xNZTq\nBKNWH8v9JhZGe9HkBbk1NEzzPpDK5+nsz4LIFKunO1n7RCEx4SSygJuV1Hqy1RmszA+RbShEV9RA\nUBzBFvdhy85FlpvG6s1+5rwikulpZM65qdpeji9lkq735tneriZF7mNRrMYVq8YyNsf6dSLKm6tQ\nuOeIht/GOmWlq8dNQ/l23DcWmXCpkOQXYnGMopSJqczW8Y1n1pHW7kWhCpGnUlCS201DuRqZ3EY2\nmbzxl48oq65BNtqHWi8nu0jC3PFZHt5YTENTM8ffPs/ywi8Z/O9/Mm06h+fQr/lRdRLZlRH+ddjO\njm/tQBfzsKWuiJuXDqPemoZNnkmGx8OVOQvB1GzI7McuTXLlrJWXv/8Ffvftx/ntH56g5s46VvyZ\neNwZnHfoWPPdh3ng8xpe/JKSzPIONrZlMLB8kYsffsL9D25jfvk2JXIfpQ/Xs1yfRkHVTjxhCR57\nlLu3fTZTfT/99ORB7+Ag5ZU6LC4PCaEUYVjCnt0buXXmPNZlF3WlarKEEsLxETRaJWVtX+Fvz77C\n0z/7CnOjNtbsqSclFCMytMS7v/oLJRV2RgZOky7NJ6t4FxMWB43GDBydXaiVAco7DMRWYijiqSwN\nJGjfqWbIPcGMwEUgXUPflSEyNBlE0nIQSldJEXqYPTuC+fQc0aiEMqOB5koRAVUBxQYV6VIrfS+N\ncs+PniJimsLmioMiSiK8TG2OGm1qNo75GOVNd+KYXyFLpWJkbpm7n9nFsi1EZ/8IaoMSqytJnnyR\nT946zH3P3k9ZWxljJ04xcPYc7kkLMlEYmVzB9i2VLFpmmLWHqFpTyOSteVBmUZAWwZibzyPPPMM/\n//xvso1JFubCjPbMMjkxgkafSW//bSrXy1FqXEwOp+B0zxDtnaDt7hoEySlSgh5iGWVkiMRYzIvk\nlpeiycgGsQBPIIRdqYPcLPzj00w7g+SUVRCYWKG6tYRUsYvRK/3UlCoRylIIRwpwJCtwL81y98Na\nRDEJRtES0aW/MzSaoG9kjLVr1jF5xYLFqUdiNDLVc5PUiBRDYTb37Kogq3GV6gLI06nRqgapKdai\nEtjIFil46cevcuf9GwjPdSNPVxOSq1i8PcfDdzZTWJTDkb8dYujWz7Ee+RNdHx/Ffe4N/rbNgPmt\n07z16RL3//BeFEkTu9eoGO57F8XaXBYD+cjFEQ5fs+BXalkV9qHI1NPZOcPXH9vHkb/+mm997y5a\n99axGMoiKsrjrK2I5m/fw71P6PnNATFZJRmUV1cz7R3j3T+8zKN76rH7ZvGab7P5+VqCu5sIhutR\n61TYnCHu3vrZZPPHHxw5aOu6RXV9LiazA1QKCAnZvq2ZvnPnsfqCNJXoyUGGN9SFIl1HWdtT/PPp\nv7L7uTtYMnvZeudWvC4/qa4g7/zovygu9tN14wTZ6WVklm1letpGRZURc9dN0jOjVG7OQhQAsUON\nayxESWucQecUFoEDn0LC4MVhROlZJNMNIPUikgYwHR3FcmGJWFREiVHL+iY1DnkWulSQi13MvDnH\nzm8/iXtsitUVPwJFkLjPSp0xE21aJp5FPzU1G/DafWgkYsbnvNz95F6s9gg3B0aRG9Iwe1LITV3k\n5DuHOPDFe6lorWPwzBX6r3bhsZjRSIOIhUl2ri/Hal1gxgNFrflM9C6AJAOdNIo+M4eHHn+SN//8\nBgYjzJsi3O6dZnhsDHF6MYPjo1RXyjCoQ0xM+HHZFrAPmWjf34gwsYgoEiCiMqASJplfspNdVopc\nqiIqSsHni+NWZYBURsTvZ2xplfzKKtxzy+SWFRHy27HPWcmUxwhEBSQEekLCTHzWFZ74Siky9zJN\nGgvqyHFujcHVgWH23dHBdJeD7mHIqS5j5PIlUsV6UuQytm82krs2woZaDVqxCmXqKBpVgoyMBHqN\nlNd++W8eeHgzy/3dyPRqggIR1tuzPP7oFnQqNSf+9g7TEy8x/PZvmL50mfljb/LvbbmY/+c4py9Y\n2f3cTnLzwqxdG2F04GOyG6twBaoI+x28e20YRVY2Hs9NpLlZXOyd4ttfe5JD//PffO8722jfWcti\nwIBDXsTRuUrWffs+7n8um18e0FJcHkGW30SfbZKr/3qZe7bmEUhacA3e4NHvrcG2fQ0mXw3qdCXL\nzjD7t342H1X+44PDBwNDgxhypSw7PIRECmKrSbZtbeb6JydYTQgp0WkoEcuICnrJzs8hs/wArz3/\nBk13NBDwxajfsYlgJILItcp73/kVebkurl04SmFuAdqSLcxP2Mku1+MdHkCp8dG0M4+EK4bWk0tw\nxIa+zs+Ef4mFqI2AKMHQ5TFEmUbiaUVERW7E6gDmswtYT8yRiKeQZ5CxZUMW9lQNenkcmcTP/JtT\nbPnuY7jHpglZnaSkC1h1WakuyUCXmc3qsouW6kY8rjgaiZhZk5t7n7sfizNEb/8IkmI9FpeQnFQr\nFz/+iEe/coDy5nqGz1/j5tmbhNwOJKl+JCliNrUUMr84w1RQTGlLNjPd8yDNIiM1gFadx5679nP0\n3Y/JMCRYnIsxODDN7eFpkqmFDI+PUF2upjBLxPDkIh67E9/sCs27G0nGbYiJEk3RQSDI3LKHvJoq\n5FIp0UiScFSEKzUdUqSEvH5mXAkKSqtYHDGRV2okHgnhcAQQhLysrsaQSPTEU9PwmB189yctZC9N\nUpZuId19iqHFFHqHR/ncgS2MX1/hYl8YjcHAzMgAYkkOSY2KdW06Sjck2NmkQeoXIk1bQJMGubkS\nDOpU/vr9v/PMF3ZiGuxFVahnyR7CPmbiwQPrydYqOPqrV7COvUzfa79g/Fg31mPv8a/NWuZeep+T\n50c48Pid6LLDtG70MDZykqLaUlzeYhJBJ2+fG0KjN7Iw2oU4K5PLU+M8/+gTfPjBv3jumVbWbi/B\nLc7FK6/i0FA56776CPc8mcNP70+hvjmMJ7WBAaeFW++8xu71GuSSEObOM3zx4FZsuzYyFeogS6PG\n7Q1xx9b/PXH7mZBDp7qsB7/xxQSWpJAZZy6JyCxb1teyU9iA/m4ZH03lsLNxGdnCTS4NZROQmlGq\n7kXgNTE0OoJ55t9UF0cw+S3849AwXzi6gfNPn6PlvqewhVfovJ5CrGGaDaqdOGR5BPfm0NndycZC\nH/qYgoBjibDTh6aiEdvFYlpFEyxY/GRn3091aYJ3XjlD21fvJ8UvQrnZSPPGEmK280xYQ7gz7mPo\n9TeRqpL0xtSM+3tpCFrxidNpLr2fTb4/oC6oZePX76PMHye/0EMseoOHv/IskSoNYxM55G9KofuQ\nkAJjE/FQiMqFFd7r6iJ/JIU+XybyAJyZD+PZrOH7W7Yx0+2l62IvjR2VDF0MYHy8hSZ5HfNXj/DN\nn+zi1EoHUesMocVzqMMFNKx6Kd5vYNO6B6hId6NCi7JAR/jKBMurcu7aIcckq+XEDQ3N+g6a61KI\nLEbJqVOQ0yzizR9e5+bSLI6FZcSLfiRJJdqBZQQZ1wlQiN4wRSDPSHrcwZUzMwjlHqypIcTRDETl\nYiT5YhrVEVLPgnMkhcFxDwMzXSxOCDj0+gWCiUUicgtKZwpjHi8jK2Hq6loYFBhp2HInDduqiZlB\n17CXJbOXmnV7qd+ynV+9MMZXnyumoPU+5t0R3MWP8dtLbhKqfC4sRjl8RceV0bXcnJmifYuE1NRs\ntEorQ1Yr834rtW3lRDzj3LG7jY9TExzpy+aDlywcvumkMT2Ltmf2kAgVU6k28cfXYjx+8Kv8/Od/\nZc8dG+jvjvGL18c4eu4q2zdtZKnRQNaMg9KaQdqrGpgbLOf3/3kDT0SKVC5ifGyOewxilv/yCeI1\n6WyvfIyPXutnzm1htd7PQ017PpOL9KPR2MHf/OceekaW6VuswBpxs+uu/bSUV6MqjdHpKEKlvI3I\nPYl5VIjN6scR7SCp9rC0MIOUM6jiFgbnIrz/rxM8/OcK3vnWEIYNDxB32bHaPGhLc4iHMnFL05Ds\na6XnwlW2lgooM0qxTA6QSCyRnr2elW4xdSnjxOMKMrPKyavK4+o/jtL64F4EqTlo67Oo35hJvnCO\n20NC5CU7uPn6J2TliRh16lhe7EEjmkaYXMOuDe0YQx9RUpvNU089hUG9RFWlgFhwiPueeQx5RTmL\nNx2s2V/L6WP96LOKSEuuIBufZsQ6jn3YRteyj6Q2hZ6bJmIFGWzb1cil08NYZ2bRG/V0XXKwbV8h\nbSWlLPYO8+BzOzAt56BSKbl6/iJaeTnapJbm9dUYissoFpopkCWprFAzeWmE2fkEHbtkpBSs55Uj\nOqoqO8hNiSNIM4JIjyLfyPmPzzK6OId3yY7SISSwHEAwNIcqy0W6sRplrZJIQE4iIGZ42smCz0NI\nrmG2f4683HxccREbypVUOjs5MxHB5NVx8cYVrg8kmfCMUJgnwuoax+FKw6NKYcoyRknzY0xMRvjy\nYxU8trYDR2cXwcmdpCpVxM05SFOreO1309z7UCtF+gbcIRmORBvHTgZJyyrnzJUkH57Npv9MIxcG\nZ1DpljDKQxTrFJydWSSjRsvGTcUY0tNoXKfgzWtWpuLbOHUYRs+aKNmQQ1RmJB7NQ+BepedmhLt3\nVfE/b77NtvUVRB0Rppd1vPveSbY8dSddci1XTl/jW3e7yEwTsqB8gJff6UcpzCHhXWXBm8raOglD\nh6+hEgZRxkq43pPk1q0BxNVpHGhf99lkczxy8Gcv7GZqIsKIJw+L1crezz9Ea20Fkuwk4wI9qWEn\nqStWlvuXWbLJcCbXIEqLY5oaQ5p6k3BwkUWniCMvvc+d3y/lvZ/3Urp5H44FOxGBB0OZEaE4A4Fe\nw2rHNoY6u9leBkVKH2bLMK6YHa18J6GeFAoUU/gIU5xXSa4xn+73TlC3fQthSRVZTQbad+RQkxPm\n+Hk36qLN3P7kFLmFaczbNdiCg2RqnQgUDdzVUYzM209rXQ7feuZJaop9pGeI8K6YePTLj+EVZ2Ma\nW2LTXeu4euQ6WrmEfHmQ2PAwFtsCC+OL9E3PEzfI6eu3IMww0H5HMydO9hBYmiM/L4+e0yu0rpPR\n0VqMY3KafXdvx+pMwVhRzMXLt8hILURODu0b2igryKZVa0Ynd1HbrGKmZ4S+Pjv129LRlLbxpzcF\ntLdvIRBKIyYvRpCmJ6nScOvsNfpHZ3Gt2JFFUvAFQkjNi6iVESSKAjQVauJxEUSEmM12xoaWSMiz\nWVpaRJllJCLLZGOxA2Pn24z5ZZiCOs6cH6fbHGZydJqsFCFz9iEmXJkEhGFcqzY0JftYWlXy318t\n4HMN5dx6+zgL19aRk12Nz6JFJC3iTz87yp33t5IpNzAylcQRK2G+T0qqWUtPp4iPevO4fbmeWyMT\nVOaEyE+PYZSl0D0+RH5ROvvu7SBdEKelQcAfPlpmPu1uLlzU0H2mH10WpGWVYB5IkCaXMD/poH1N\nNjeu3qJjbQkBs4cRcyoTU3YyGtrpEecwZbrNQ5scVMqzmBY+wJmuVcwuCRkJD/aQnLxMOaLRWVyW\ncXzzSXzBco6fvoG2Mot71635TLJ5ZDpy8OffvY+JqRhjsQKW5qw8+IVnqC4vRZoOZlEGwlgIid+O\nubefxcU0ZqKNaPO1mMdMGDKG8NsniCZUvP+r37Hzi6Uc/m0fDXfei2naSiDuJ6soF1GqgvTibDyt\nG5m+NMS23CglChuh0AwJvQRxdDO+rgga4TQrCTdFhmJKigrpffsIFZta8Uib0LYUseXOAqrz4NNT\nTtKLOhi/fI0MnRJbQM1qZJL8ggRBsZ7drRUoAia2d+Txjc8/TFWph+zcbMyzszz6lUfxJbTc7plg\n3Y4Geq70IRMmKNEk8PX1YbWbGe6foHdiEUG+gsGbNkRyLa07ajh5coiQ04QhJ5ve4xYaWhVsaSvG\nMTvD+i3rcK2qKC01cuFcD3JJPpLUUmrrWqjI1bIhZ4YMkYeG1gwsC9N0dTspbFWTV9HGq+9HaNm4\nDptNRVjbjESuIiLSMdgzwMDwLD6Xn1RhklA4AEsWpLEQihwjiVw1UUcMuVyJxxNh5NYIMWUmXq+T\n9MxcYnINbYYI4fP/IJJSzMnhVE72zjHiTMEyP44m6Gd5xcZ1RwZWlweBPIncsI+kIIOXv1HCM41V\nfPLH95npLqE0rwrbtACxsJz/+dGn3LmvFZ0kjcnRGCH1GszdYdJnMxg4F+PjXgND1/O5NT5MS1ac\nnGSYimIZt672oDYoaNhZSUlGKtW5cX736jzTmv2cuqSi8/Qlaiq0uJNahrvdVJYWMjW4Qk2+mt5b\nPaxryCPhsjO2ImXR7kdQsIa+RC7LS4t88Y4Y+cgxGe7l0jUPkagYvdBGUplJBDHKOSsu1whLo24E\nig2cuthDikrGQzs/m3LoyFzg4I++sp/JeRnD0SLsJguPfPOLFOQZkKdLWRSrkZEkZF1k7sYNRga1\nLCcayanMZnl6EX36KCHbBKkCNYd/+RKbP2/k+N/7qb7jbubH3KxG/BgqcpCptGRWFuE0tjF9bYQt\n+hi5IhP+8ATi8jzwrMd/I4g8ZQG71I5enkGp0cjw4SOUbqzGJW5B0VbJnofLaSoX8+Eny2gK1zF8\n7joanQq3W0YouUC2LkE4XcPOhgrUASe71xfwo0c+R219GF2mnsmxGR79ygG8SS3dt4ZYt7Ga0dsT\nCAOrFGqFuPv7cDktXLvSy8iclUS2jIluLyi0NG8r5dSJMYJOE7n5OQweWaKmXsy2jkoWJ8fYuG0D\ngagUY0kxV850k64qRSQoxZBTSX2+nh0ViyglAZrWGLAszDI8EiCnWoOxqpF/fZRkw+bNmOcl+PVt\naDLSQJlH9/U+ZqaW8Fr9CEUQiq6C0wZuO9qiTBJZOaw6I+gy03E5woxeH0KclUEkEkKq1hIUKFib\nF8V77J8o5Xl81Bfjo24zQ6YAS0vziK02LM4g16xpBMN2IvEoKbp9RIIKjny3nPurc3jtJx8wciuN\nCkMFpoEoyUQx/3rxCHt21ZLqF7AwISSY3oHjhhP9TBpj50Ic789i0FxK/0g/FeoINZp0KvOT9Hde\nJSNfQfOGekq0cgp0MX7+dxMzsns4ez2NkaudFGQpcEY19A2ZaWmpYH5ynrr8dEYGe2mrzkQddLIQ\nUrDiXsWb0ci4rBrL0hwv3JFAT4RFxT5u3lwlGkqQK1hBpFbjjwhInV3Cs3wb94IZedUdHDvXRyQs\n5HP7tv6/IYf+9FL/wfW6ISyrc9h8ZhIL6dyQ6Jm8ssiEsA/pcSfGMiuv/vZT7rjzbh7fneSnf7xN\nW20G99+znsZCL/YrcrzRZg5sayE+4qLjkd1cHmoktHoJaaqShyqysCRKMal3E7dcpcWfQFsUZfLa\nMDX3tzJ59Tzd5zOxLehJl/nY/sLdvGK20pqQsG//Zm5OLyCYzGTAKGO2y4/UOcra0gMIb9/kh9/Z\niq+kkixnIfXSRZzzZlIaS/GFxey8S8yhi+WcWDTz8DPZvPT744j8URaXZAw4pglfEGCVxJEnpIzN\n9SKRlBBPXOb5v/0np0+8SUZpmOw9RRjWtFBb4sLhHcMRqaBE7aN1dzqrws3MDt0gOTHJ3V/dwOCb\nS0wt2uiZeh1teoDVpJr6jRJOvH+F4koJX39gLSmhRc4OjpF+S4LiKSPq+Ap4ohQUxdEARUYdEnmA\nLI2Wl/46QH52mNWEmorGOryaGHlZCSoyM9n5BQN7Nu2l59hVplCgri9n5pqP/Q+1MT/toXZjnKGB\neszJap7YUoEoJcbp22beeuslXvvHdzj8zhyi1BA/+Nrn8TidqLRhXMthHnnxAPaRJJI1Gv52599R\nxnKwm5SMxa0sXPUwNxLkwqUl6r68i4Y2BeOWFE6dvkF+0514FrLpfPskqcZyzB4V8bABrdxHgTKG\nLLrKtd5hPBUqFJoQ5QoFG9t2oxO5qFqbzcGvvE5Sa+W3v7qDQkk9H779Ji5vChFngKe++2v+47tv\nslPjxV+ZRaS0nNGeIHPOo6jDKk5qi+j663UevqufhlIlXUtyjgmlTH50lM898RSNrXD5jaPs2VVK\nd98ZRk/1kF53P22772K8e4CHtu/8TC7SF/507WBR8DxKxQxD/T4y5DBhF3P5cDehnABz1xcwJmfp\nfPk1mtbW8fw3qnjrdT8Z2amsbTKgT13G3G9lbDrEmnu3Eh82sfeRL9Lb7aY40wspQhSqIuRFxfjy\nGjBdvs2m7ABGQ5Trxy+w+fEmzh49SfepKmSCWqQpfloeeppLIyuEkxHu+PwexkZMBD1aTAkF1gU/\nlrF+cuvvxTU3zHPPbqWgMBep3ECm0IVrYYryO3bjtnho2m7kgyNyrpp83PlQOX/8/WEiy2FCiVy6\nbs8QWFpl0uPC4Vgh4fVDXIwrHOGBF7/FxQvnKaiOkN1QRFG1gZaNxQTsi4RFSgoNMnbe1UBEVoRl\neAjbsokdd2xgtteDMxLmvXfeJiVtBVckndq6NDqPnGFzcyY/eaGceHyZI9cs2Ib8VK6vxFCSjiTo\nIUMXI1cQZt2uCkISL4m4go8O95BdIECRkkXLxrW4Q34qqgso1Cn48q82os8q5PLhaTyiKAqdjD4U\nT/0AACAASURBVL4xJ5vv6GDUGaKxOoWhPgkBQRmtFWKeWl/Jp8cu8vIf/8aXvvEIo0PTJAU+vvzA\nHVw+1UmYQkZNHor3NuL3ZqPSJHjz8z9l6vxFilXlWJRNTNzs4tIndmbnlWQV5ZOa7qVzeJ4Pj05R\nt24/vtsObl8ZZeLSEt5FF9TWkKMLU6mMkR0Wcm14FMOWItyDC6hWLcg8avKKCsndsYVvP/13YgIx\nD2zPIztdw9mPB3GFkghEXrZs28flsxepWVMEUjVJuYqrfV4Ges4iVCg4ZXJAZJkvPZJHjURL5yx8\n+P514iEnD+1vRiWaZdU6QkWVFvO8Det1H7H8Su55eh/H3jrDFw7s+0yy+cVfnDtoTFzE5urn9tgS\n1ZVlDMwKGei8DaoIEzdnyIgvMvjWy6ztyGPbfes4cyZJmjbGmkYDqtUFlgdGWTTFaNzdhm95kXu/\n+AM+euMS9WUyUiQSIjEtmbWtYKxj9paFLZU+KrQ2+ru62fFCOx9cO8vt9wrRhStJqpNsePQJLozb\niYsz2HDveiYmnawGVazY4iws+RntuU1Z231MT47yxWe3UlNejK6gGlnKPKbJOUo33E3A4qBxbTNX\nb0TpGbOzcV8DL79yCq/HS1isYnJsCWEswfSyHdvyCmGfj0hEiC8U456ffJPTpy9SUCFF35xNaWU2\n61tLWHXOIVVrqCpWs2l7KwlNEYvDYyxMutiybxuLswEC0QRvvfY+MZEfiSqdysoMrv77UzbVp/F/\nvllCJOnk1PlZJsbClLauobY8nYTfSlO7Fnl4gYZNBYhFXrz+EAODNmIpMSTqTDZt3ITLE6KhpZSy\nfA0PP7GZDIWca9dn8UvTyM5Lo+/WJFvv3smkPUFhgYilZS1uQS6bjEK+tMPI+4eP8+tf/5anDzzO\njGmBNJmQZz+/naEbAwx5FUyPp7JxbxsWWybCqImPnvwJve8eI02oQ7Z+F32fnuXSp6NMT9nIUGej\nzc5hYHKJC8em2NregNpj5eR7Z5gwefFOB6GjjaxUD4XCVTLCYnrGh5FVKtAIVhE47Yg8QnRpMnQ7\nH+b7Xz/EgkPKo7tzkMYiXL46Q4pGjyZNQOOGZk6cvk5tcwUBv4RYmoYZu4q3jn6AOiudMws+/CN9\nPP/5fKpkCo4OyfjHPz5Ap4A72nIoUizgXZwmS64gKnFh7wnjk+Zw3/95ln/++WO++dhnk80Xfnv6\noH71It7VSQY6J2hdW8ONW36GBydAGGPofD/KuJXBt16mtUnN5rvu5OawhnRVhIaqDES2ERb7h5ma\nctK8dwPLTht3Pv09jr5zhsZ6BRKRhIhIT259E85UPTariLrcBarT5xgfucHmLzTx2vmLTBw2oIuV\noMiVs/HJxxlaCOCOKum4dwuL8z7CgnRcM8tMzq4w1DNGxfr7GZ+a4tlnttBYXoLGWII01cro0BiV\nHTvxmvw0b2rnVs8qV3osdOxdz+9+/z5ebwCBVMHw0DzKNAlzrgDmhWWiXjeRkJBgLIUHX3yBs2eu\nUVavp2CtjtIaHWsbjLhdZtKMeor1YnbsXk8isxj33CITg1Y23LEViyVERJDg/dc/JlXiI02mpq4p\nn6vvHmLvFh0/eKEAv9/J1b4FRmaiZFVWsam9hJhjhjVrDejUS6zdWIIo6mY16GNwyEogEUWjz2PT\n5g34fREqagpoKM/h/s9tRCEW0jlsITVNizJDxvjYAm37tjE666W8Uod5KkBcmUeNBn66v5DXX3md\n/3r576zftJVl8xJxxwr3b29ieHCCXo+eRXMq7TvXY3FkEglPcvi573Hx5X/h9qSQuWErY2duMtS5\nzNC1BdJ1WkRiDV1jU1y/MEtHSwspZhPH/30M00wYu0UI29dRIrSTGJ7EmK5nyj5EVBZlTWMuQdsM\nQrsXVWoKGTu/wI+/d4JFu4r7NqnB46O7145ElwNEKSoq4tbYAHUtpcRWpYTUKm77NLx+6C30xjLO\nTbtxWsf48oNqilLSOTok59U/vY9aLuaBDj3a4AhRr5c04gQFKwTH48x5Mnjwp1/jjZdO8c0nd38m\n2fz6fx0+mOG7zop1nImRWZo3NnL2/DzmRTMSlZy+Dy8gji0zcfifNDdr2X7fATpH5KjSgqypzSIy\n041zcBzLvI+6HR2Y3E72Pv8dzr1xnPpGBcKkkJjIQE5DO46kDqdfSHmhhSr1MEsrA7Q/W8vfTl5h\n4kM12nAe0nw16544wOxKEk9ST8uudmzuMD6BiuDwNONzVm7fmqB0zQGmZ+d59uktNFUVk2YsRqV0\nc7tvgso1W/FbQlS2rmFwyMeV7lnWbNnI7377HqvhMAKplKHBJTIN6TiCIiZHFoituonERESEIh79\n2Ve5dGOQ4no9lR3ZGKrUNNWkE/CYyS42kpctYeuu9SR1ebjnlxgfdrDtvn1YTKsEVsMc++QEqbIw\nBnU69c0F3Dh5lK0dKr7zfCVev43huRVGZ7yIs4zsXl9FyDZJY3seavE8m7cWkpJYJRJyMT5qxROO\nkKbLY+OODrz+IBWVRspKtDz63C6kqXCzd5YsYwlihYSe7ina793LZN8M+ZW5OC0hBFIdxUohv36k\ngt+/+Ad++cr7dGzfictuQ2C3sX97OdPjZm44NMxNhdh89y4WnAZSYzMcfuH7nPnDOwRDKWhbmpm7\neIOpXisTt01IxSJUqlwWLD7OHRqmdU0tcssEpw+dYN4SwrKQCm1tFItsCGcXUYhUmJaHsUZdrN9a\nStxpRR4LkBrxoN7yPD978Swmt47tjWIEgVWGZr2o1WW4XA6Mubn0zE7T0FxGik/KqlJCp0/Fu28d\nJi23mJMLQWzTvXz7cTV5ZHBiMI1XXnoHpSjO/joVhZJF4qt+MgVJREo3zq5lukYU3P8f3+LtVy/z\nzSf/9z6wz4Qcev3IWwcvztuJWMv5/762jrI06E9m45tPR7ZyiTUyM2devUn7HiN5RSkEjZupz7iH\n9bUlXD90ktSmQvY9p6Oj4BKvvv4BgdQl2jdpKTI2EXINM2fPYdf/+Q/eOPxjGgxiHt1XhPXcRRzK\nm9zfmssrb0eZWmMiR7yB+/+cj2tHFm88f4itd7XTkl/K2LwZS99VomnZaAts3LPkxFkg44z+Ya5N\nnGTafxvZxxHyM66gy/bz1It/RWbKInN+ku6+Fe7Ns9PmHuLTN+34Pc04crLJqhFx9YwElX6AZFBG\nS42U5dvTdFo9PH/PFk7MGyncvoH5Vy7Sn4Sm3FIalzZSX9qAUHub+eluDJklFOum8V8zU3nvNt79\nUyqUWpgUHKN0TIPElMnY6E2mnTfZXAh6cw/RimysIQlTZwNUf83I2AU9xlg6ZWsUKFxqXGYrY5JW\nAsE8JifMbK7cgTPuIljQRkVzBQEL3La5COrXI7maR1aeGZ1Ui7q0hqlrh1m3NRvr6gK10R30+sdJ\nTcZRVuk4/9Yhlgcu4Qmk8Js/PEOJqIzrSzbKG4PccV8DerGW0RQhK8rneXc1gOBMksXOi9x3l4r+\n91/hQJ2b8aEZ1j3Whig0gTJ1GvPpBW7Nt/O+L4FKKuXg4w2sXnwdT9yD6+wU7ek9NLinabTOkZ7w\n0L6jltKGJp7KKqXOV8K8wsXtgSHKY0J++N4lHvzcy5wdL8E3tELEGOXmTS9tuVu4qongHTrFxo4M\n+t2LlJfvxzc9jNU+QeXaImr1q5xNOIjLDWypKsRYvUy+38+Jf3aSjoncor1Uxt5BUBrmtLWbFKce\nTYaNJe8UP37xGMWPFPNo/f9+A/p/Y377wQcH41PjpKRU8txzHaTHFphObSEuEGH+9H9oCU/jd/dS\npdFQUZfLUqSDrLy7qM6NYB5fQqJRcedz5VSmzXHz2PuIYx7W72+n3JjNcN91Zh3VrH/+cY689gEV\nRRls3y3Gu3SWVXsPtfo8fvPmbQaLvJTm1NP+3XLiW2S899+d1DY007HxDham7QxfHkOsUJBRJKDA\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f5PIVF15JIzGJBGFEw4nzPTjD8wRFMQzrGnAOdpEjnmNl6DyKLB2e8QQL7gzE6WVMHpMT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UlLK+8fG0IqSLCvUUBufha9wVZe+eMRPvroGBq9ioufdvGj7345vfnie+cOVxYbCfhlmAw6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\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FSTJRPzWVNlYmx0nFTSxHGggvxum3pjj+pSyJ87fYYW6jUFGLreBm/e4cEyohUeKIfAkk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tfPObTzAyMoChuky+CIt3JlAaLEwtLKGRJmmoF7C8XEYvWqGrLk4x5WJ2zsW90SCbt+9lfCKP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\nEfzuCE++eAb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hWJkgmZvgP547gnEqzq/fGqcu1NH+6lO4u6ZQxG10rl+j+wtd3F4poDE/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\nt2AoJdl74jjn3x2i3mHAXOvEbJSy79A25icjvHrvZ5PNP/3nH58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Ank7yRQXZUo5oysvG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8d/YypkYbNmsDobCCoQYLjto2VrsSi0pP3l/kwedP8KWnDtFmUXPxdx/Sd/QgV67OoxSL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\nhBBCCHGG/R/kYCYx+23TiwAAAABJRU5ErkJggg==\n",
"text/plain": [
"![](\"../figs/ssd_overview.PNG\")
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['figure.figsize'] = (8, 8)\n",
"plt.rcParams['image.interpolation'] = 'nearest'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pickle\n",
"from ssd import SSD300\n",
"from ssd_utils import BBoxUtility, Generator\n",
"\n",
"SEED = 4242\n",
"np.random.seed(SEED)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Load the anchor bounding boxes and initialize the bounding box utilities:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"NUM_CLASSES = 21\n",
"input_shape = (300, 300, 3)\n",
"nms_thresh = 0.4\n",
"priors = pickle.load(open('../data/files/prior_boxes_ssd300.pkl', 'rb'))\n",
"bbox_util = BBoxUtility(NUM_CLASSES, priors, nms_thresh = nms_thresh)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's look at the anchor boxes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"print(np.shape(priors))\n",
"print (priors[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"priors[i] = [xmin, ymin, xmax, ymax, varxc, varyc, varw, varh]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Create training file based on PASCAL VOC data. This will put all the information (i.e. box coordinates, categories and image paths) into a single file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import os\n",
"pascal_gt = '../data/files/VOC2007.pkl'\n",
"if not os.path.isfile(pascal_gt):\n",
" from PASCAL_VOC.get_data_from_XML import XML_preprocessor\n",
" data = XML_preprocessor('../../../../data/VOCdevkit/VOC2007/Annotations/').data\n",
" pickle.dump(data,open(pascal_gt,'wb'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We load the annotations file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"gt = pickle.load(open('../data/files/VOC2007.pkl', 'rb'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's take a peek at this file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"key1 = gt.keys()[0]\n",
"print (key1)\n",
"print (gt[key1][0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The first 4 positions are the coordinates of the bounding box, and the remaining ones compose the one-hot vector representing the category. Let's now split the dataset between training and validation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"train_perc = 0.8\n",
"keys = sorted(gt.keys())\n",
"\n",
"num_train = int(round(train_perc * len(keys)))\n",
"train_keys = keys[:num_train]\n",
"val_keys = keys[num_train:]\n",
"num_val = len(val_keys)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Initialize the generator. Here we must indicate the path where the raw jpeg files are stored and the batch size we want to use:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"path_prefix = '../../../../data/VOCdevkit/VOC2007/JPEGImages/'\n",
"batch_size = 64\n",
"gen = Generator(gt, bbox_util, batch_size, path_prefix,\n",
" train_keys, val_keys,\n",
" (input_shape[0], input_shape[1]), do_crop=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"model = SSD300(input_shape, num_classes=NUM_CLASSES)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Callbacks in keras are functions that are applied at some points during the training procedure. We will use the one called ```ModelCheckpoint``` which will save the model after every epoch only if it improves validation performance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from keras.callbacks import ModelCheckpoint\n",
"mc = ModelCheckpoint('../data/checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5', \n",
" verbose=2,save_weights_only=True,monitor='val_loss')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We define the optimizer and compile the model. Notice we use a custom loss here ```MultiboxLoss```. We will take a look at this loss in depth later, but for now let's move on until we have our model training. Since it will take a while, it will give us some time to review technical details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from keras.optimizers import Adam\n",
"from ssd_training import MultiboxLoss\n",
"\n",
"base_lr = 1e-3\n",
"optim = Adam(lr=base_lr)\n",
"model.compile(optimizer=optim,\n",
" loss=MultiboxLoss(NUM_CLASSES, neg_pos_ratio=2.0).compute_loss)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We start training the model here. We will only do 2 epochs to be able to see some intermediate outputs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"nb_epoch = 2\n",
"# 1 epoch - 500s running on titanX or K80 GPU (batch size 64)\n",
"history = model.fit_generator(gen.generate(True), gen.train_batches,\n",
" nb_epoch, verbose=2,\n",
" callbacks=[mc],\n",
" validation_data=gen.generate(False),\n",
" nb_val_samples=gen.val_batches)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Once it finishes the 2 epochs, run it again for longer. We will see the performance of the model in the next lab session."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### While we are waiting...\n",
"\n",
"#### 1. Loss function\n",
"As a reminder, the loss used to train this model is defined with the following equations. First, we have that the loss is a combination of the detection loss ($L_{loc}$) and classification loss ($L_{conf}$)."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"![](\"../figs/total_loss.PNG\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's see the two loss components:"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"![](\"../figs/loc_loss.PNG\")
\n",
"\n",
"![](\"../figs/smoothl1.PNG\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"$L_{loc}$ is defined as the smooth L1 distance between parameterized box coordinates. Notice that $L_{loc}$ is only computed for positive classes. Boxes that are labeled as background do not contribute to the loss at all. $g$ and $l$ are the ground truth and predicted bounding box coordinates. See how box coordinates are regressed to offsets wrt anchor boxes $d$ in the equations above. $x$ gives the matching pairs between ground truth and predicted boxes, which is obtained by selecting the pairs with an IoU over a certain threshold. You can check the implementation of $L_{loc}$ in ```ssd_training.py```.\n",
"\n",
"$L_{conf}$ is the multiclass softmax loss. Check the implementation in ```ssd_training.py```."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"![](\"../figs/class_loss.PNG\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Finally, let's check the implementation of total loss computation in ```ssd_training.py``` to see how these two are combined."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"#### 2. Model architecture\n",
"\n",
"In the paper, the authors give the following figure for the model architecture:\n",
"\n",
"![](\"../figs/ssd_arch.PNG\")
\n",
"\n",
"Now let's look at ```ssd.py``` to see how this is implemented in keras."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Once training has finished, we can take a look at some of the predictions that the model is able to make for some of our images. First we define a function to display boxes above a certain threshold."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"voc_classes = ['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle',\n",
" 'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable',\n",
" 'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant',\n",
" 'Sheep', 'Sofa', 'Train', 'Tvmonitor']\n",
"\n",
"def display_boxes(img,preds,th):\n",
" # Parse the outputs.\n",
" det_label = preds[:, 0]\n",
" det_conf = preds[:, 1]\n",
" det_xmin = preds[:, 2]\n",
" det_ymin = preds[:, 3]\n",
" det_xmax = preds[:, 4]\n",
" det_ymax = preds[:, 5]\n",
"\n",
" # Get detections with confidence higher than 0.6.\n",
" top_indices = [i for i, conf in enumerate(det_conf) if conf >= score_thresh]\n",
"\n",
" top_conf = det_conf[top_indices]\n",
" top_label_indices = det_label[top_indices].tolist()\n",
" top_xmin = det_xmin[top_indices]\n",
" top_ymin = det_ymin[top_indices]\n",
" top_xmax = det_xmax[top_indices]\n",
" top_ymax = det_ymax[top_indices]\n",
"\n",
" colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()\n",
"\n",
" plt.imshow(img / 255.)\n",
" plt.axis('off')\n",
" currentAxis = plt.gca()\n",
"\n",
" for i in range(top_conf.shape[0]):\n",
" xmin = int(round(top_xmin[i] * img.shape[1]))\n",
" ymin = int(round(top_ymin[i] * img.shape[0]))\n",
" xmax = int(round(top_xmax[i] * img.shape[1]))\n",
" ymax = int(round(top_ymax[i] * img.shape[0]))\n",
" score = top_conf[i]\n",
" label = int(top_label_indices[i])\n",
" label_name = voc_classes[label - 1]\n",
" display_txt = '{:0.2f}, {}'.format(score, label_name)\n",
" coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1\n",
" color = colors[label]\n",
" currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))\n",
" currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})\n",
" \n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now let's take a few validation images and test how it works"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from scipy.misc import imread\n",
"from keras.applications.imagenet_utils import preprocess_input\n",
"from keras.preprocessing import image\n",
"\n",
"inputs = []\n",
"images = []\n",
"\n",
"idxs = [0,2,10,15]\n",
"for idx in idxs:\n",
" img_path = path_prefix + sorted(val_keys)[idx]\n",
" img = image.load_img(img_path, target_size=(300, 300))\n",
" img = image.img_to_array(img)\n",
" images.append(imread(img_path))\n",
" inputs.append(img.copy())\n",
"inputs = preprocess_input(np.array(inputs))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"preds = model.predict(inputs, batch_size=1, verbose=0)\n",
"results = bbox_util.detection_out(preds)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"score_thresh = 0.7\n",
"for i, img in enumerate(images):\n",
" display_boxes(img,results[i],score_thresh)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Results don't look very good at the moment, but we should probably train for a lot longer ! In the next lab session we will use a pretrained model and test it on some images."
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
================================================
FILE: Workshop/sessions/dream.py
================================================
from keras.preprocessing.image import load_img, img_to_array
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
from keras.applications import vgg16
from keras import backend as K
from keras.layers import Input
def preprocess_image(image_path,img_height,img_width):
img = load_img(image_path, target_size=(img_height, img_width))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = vgg16.preprocess_input(img)
return img
# util function to convert a tensor into a valid image
def deprocess_image(x,img_height,img_width):
if K.image_dim_ordering() == 'th':
x = x.reshape((3, img_height, img_width))
x = x.transpose((1, 2, 0))
else:
x = x.reshape((img_height, img_width, 3))
# Remove zero-center by mean pixel
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
# 'BGR'->'RGB'
x = x[:, :, ::-1]
x = np.clip(x, 0, 255).astype('uint8')
return x
def continuity_loss(x,img_height,img_width):
a = K.square(x[:, :img_height - 1, :img_width - 1, :] -
x[:, 1:, :img_width - 1, :])
b = K.square(x[:, :img_height - 1, :img_width - 1, :] -
x[:, :img_height - 1, 1:, :])
return K.sum(K.pow(a + b, 1.25))
def eval_loss_and_grads(x,img_size,f_outputs):
x = x.reshape((1,) + img_size)
outs = f_outputs([x])
loss_value = outs[0]
if len(outs[1:]) == 1:
grad_values = outs[1].flatten().astype('float64')
else:
grad_values = np.array(outs[1:]).flatten().astype('float64')
return loss_value, grad_values
# this Evaluator class makes it possible
# to compute loss and gradients in one pass
# while retrieving them via two separate functions,
# "loss" and "grads". This is done because scipy.optimize
# requires separate functions for loss and gradients,
# but computing them separately would be inefficient.
class Evaluator(object):
def __init__(self,img_size,f_outputs):
self.loss_value = None
self.grad_values = None
self.img_size = img_size
self.f_outputs = f_outputs
def loss(self, x):
assert self.loss_value is None
loss_value, grad_values = eval_loss_and_grads(x,self.img_size,
self.f_outputs)
self.loss_value = loss_value
self.grad_values = grad_values
return self.loss_value
def grads(self, x):
assert self.loss_value is not None
grad_values = np.copy(self.grad_values)
self.loss_value = None
self.grad_values = None
return grad_values
================================================
FILE: Workshop/sessions/glove_download.sh
================================================
#!/bin/bash
mkdir -p ../../data/glove
pushd ../../data/glove
#wget http://nlp.stanford.edu/data/glove.6B.zip
#tar -xvf glove.6B.zip
unzip glove.6B.zip
popd
================================================
FILE: Workshop/sessions/style.py
================================================
'''Neural style transfer with Keras.
# References
- [A Neural Algorithm of Artistic Style](http://arxiv.org/abs/1508.06576)
'''
from keras.preprocessing.image import load_img, img_to_array
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
from keras.applications import vgg16
from keras import backend as K
# util function to open, resize and format pictures into appropriate tensors
def preprocess_image(image_path,img_nrows,img_ncols):
img = load_img(image_path, target_size=(img_nrows, img_ncols))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = vgg16.preprocess_input(img)
return img
# util function to convert a tensor into a valid image
def deprocess_image(x,img_nrows,img_ncols):
x = x.reshape((img_nrows, img_ncols, 3))
# Remove zero-center by mean pixel
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
# 'BGR'->'RGB'
x = x[:, :, ::-1]
x = np.clip(x, 0, 255).astype('uint8')
return x
def gram_matrix(x):
assert K.ndim(x) == 3
if K.image_dim_ordering() == 'th':
features = K.batch_flatten(x)
else:
features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
gram = K.dot(features, K.transpose(features))
return gram
# the "style loss" is designed to maintain
# the style of the reference image in the generated image.
# It is based on the gram matrices (which capture style) of
# feature maps from the style reference image
# and from the generated image
def style_loss(style, combination,img_nrows,img_ncols):
assert K.ndim(style) == 3
assert K.ndim(combination) == 3
S = gram_matrix(style)
C = gram_matrix(combination)
channels = 3
size = img_nrows * img_ncols
return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))
# an auxiliary loss function
# designed to maintain the "content" of the
# base image in the generated image
def content_loss(base, combination):
return K.sum(K.square(combination - base))
# the 3rd loss function, total variation loss,
# designed to keep the generated image locally coherent
def total_variation_loss(x,img_nrows,img_ncols):
assert K.ndim(x) == 4
a = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, 1:, :img_ncols - 1, :])
b = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, :img_nrows - 1, 1:, :])
return K.sum(K.pow(a + b, 1.25))
def eval_loss_and_grads(x,img_nrows,img_ncols,f_outputs):
if K.image_dim_ordering() == 'th':
x = x.reshape((1, 3, img_nrows, img_ncols))
else:
x = x.reshape((1, img_nrows, img_ncols, 3))
outs = f_outputs([x])
loss_value = outs[0]
if len(outs[1:]) == 1:
grad_values = outs[1].flatten().astype('float64')
else:
grad_values = np.array(outs[1:]).flatten().astype('float64')
return loss_value, grad_values
# this Evaluator class makes it possible
# to compute loss and gradients in one pass
# while retrieving them via two separate functions,
# "loss" and "grads". This is done because scipy.optimize
# requires separate functions for loss and gradients,
# but computing them separately would be inefficient.
class Evaluator(object):
def __init__(self,img_nrows,img_ncols,f_outputs):
self.loss_value = None
self.grads_values = None
self.img_nrows = img_nrows
self.img_ncols = img_ncols
self.f_outputs = f_outputs
def loss(self, x):
assert self.loss_value is None
loss_value, grad_values = eval_loss_and_grads(x,self.img_nrows,
self.img_ncols,
self.f_outputs)
self.loss_value = loss_value
self.grad_values = grad_values
return self.loss_value
def grads(self, x):
assert self.loss_value is not None
grad_values = np.copy(self.grad_values)
self.loss_value = None
self.grad_values = None
return grad_values
================================================
FILE: Workshop/sessions/tracking/README.md
================================================
**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*
- [SORT](#sort)
- [Introduction](#introduction)
- [License](#license)
- [Citing SORT](#citing-sort)
- [Usage:](#usage)
SORT
=====
A simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences.
See an example [video here](https://motchallenge.net/movies/ETH-Linthescher-SORT.mp4).
By Alex Bewley
[DynamicDetection.com](http://www.dynamicdetection.com)
*Note: This fork has been modified for educational purposes*
### Introduction
SORT is a barebones implementation of a visual multiple object tracking framework based on rudimentary data association and state estimation techniques. It is designed for online tracking applications where only past and current frames are available and the method produces object identities on the fly. While this minimalistic tracker doesn't handle occlusion or re-entering objects its purpose is to serve as a baseline and testbed for the development of future trackers.
SORT was initially described in an [arXiv tech report](http://arxiv.org/abs/1602.00763). At the time of the initial publication, SORT was ranked the best *open source* multiple object tracker on the [MOT benchmark](https://motchallenge.net/results/2D_MOT_2015/).
This code has been tested on Mac OSX 10.10, and Ubuntu 14.04, with Python 2.7 (anaconda).
**Note:** A significant proportion of SORT's accuracy is attributed to the detections.
For your convenience, this repo also contains *Faster* RCNN detections for the MOT benchmark sequences in the benchmark format. To run the detector yourself please see the original [*Faster* RCNN project](https://github.com/ShaoqingRen/faster_rcnn) or the python reimplementation of [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn) by Ross Girshick.
### License
SORT is released under the GPL License (refer to the LICENSE file for details) to promote the open use of the tracker and future improvements. If you require a permissive license contact Alex (alex@dynamicdetection.com).
### Citing SORT
If you find this repo useful in your research, please consider citing:
@inproceedings{Bewley2016_sort,
author={Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben},
booktitle={2016 IEEE International Conference on Image Processing (ICIP)},
title={Simple online and realtime tracking},
year={2016},
pages={3464-3468},
keywords={Benchmark testing;Complexity theory;Detectors;Kalman filters;Target tracking;Visualization;Computer Vision;Data Association;Detection;Multiple Object Tracking},
doi={10.1109/ICIP.2016.7533003}
}
### Usage:
To run the tracker with the provided detections:
- Download the [2D MOT 2015 benchmark dataset](https://motchallenge.net/data/2D_MOT_2015/#download)
```
wget https://motchallenge.net/data/2DMOT2015.zip
unzip 2DMOT2015.zip
```
================================================
FILE: Workshop/sessions/tracking/data/ADL-Rundle-6/det.txt
================================================
1,-1,1691.97,381.048,152.23,352.617,0.995616,-1,-1,-1
1,-1,1233.55,467.507,133.65,218.985,0.980069,-1,-1,-1
1,-1,108.484,461.531,97.759,297.453,0.942438,-1,-1,-1
1,-1,256.996,420.694,101.497,296.434,0.938051,-1,-1,-1
1,-1,19.5685,469.707,87.4595,343.434,0.872169,-1,-1,-1
1,-1,1869.09,376.414,49.91,222.562,0.754415,-1,-1,-1
1,-1,1252.28,509.199,59.49,133.608,0.65418,-1,-1,-1
1,-1,0,265.374,109.767,381.325,0.629887,-1,-1,-1
2,-1,1691.29,382.432,187.78,363.197,0.99714,-1,-1,-1
2,-1,249.136,473.997,108.874,235.769,0.986837,-1,-1,-1
2,-1,1272.37,447.903,72.79,217.543,0.985943,-1,-1,-1
2,-1,88.626,445.029,104.591,328.988,0.967904,-1,-1,-1
2,-1,10.6657,486.485,106.665,375.935,0.812186,-1,-1,-1
2,-1,1875.74,375.006,43.26,203.969,0.749853,-1,-1,-1
2,-1,3.37989,285.912,96.0394,365.456,0.579795,-1,-1,-1
3,-1,1703.29,380.319,180.14,344.491,0.997301,-1,-1,-1
3,-1,1275.12,449.542,77.48,212.878,0.984799,-1,-1,-1
3,-1,260.157,441.321,103.242,275.996,0.981736,-1,-1,-1
3,-1,91.6913,480.534,105.259,284.27,0.973085,-1,-1,-1
3,-1,0,274.243,111.097,669.417,0.925986,-1,-1,-1
3,-1,1252.19,475.714,49.63,185.759,0.786207,-1,-1,-1
3,-1,1874.27,375.553,44.73,199.789,0.75428,-1,-1,-1
4,-1,1698.06,390.287,158.29,320.432,0.996181,-1,-1,-1
4,-1,0,284.033,109.849,628.917,0.990643,-1,-1,-1
4,-1,1291.85,440.04,74.5,255.35,0.98008,-1,-1,-1
4,-1,254.554,426.438,110.3,300.785,0.979648,-1,-1,-1
4,-1,92.9269,473.023,108.595,292.996,0.978604,-1,-1,-1
4,-1,1261.51,492.931,66.1,162.723,0.842982,-1,-1,-1
4,-1,1871.56,373.3,47.44,203.455,0.818952,-1,-1,-1
5,-1,1696.65,386.656,155.3,347.506,0.993925,-1,-1,-1
5,-1,0,246.68,116.7,677.913,0.990555,-1,-1,-1
5,-1,1273.26,442.289,92.31,235.936,0.983911,-1,-1,-1
5,-1,97.7056,475.972,94.0824,276.654,0.948322,-1,-1,-1
5,-1,1868.96,362.823,50.04,206.49,0.880095,-1,-1,-1
5,-1,264.041,434.493,115.622,285.328,0.750853,-1,-1,-1
6,-1,1705.35,397.595,153.22,335.607,0.996048,-1,-1,-1
6,-1,0,245.031,123.924,693.271,0.988873,-1,-1,-1
6,-1,95.77,479.716,107.673,293.688,0.986938,-1,-1,-1
6,-1,1276.15,442.949,92.07,234.107,0.982222,-1,-1,-1
6,-1,1866.39,349.775,52.61,218.644,0.89568,-1,-1,-1
6,-1,251.272,446.831,91.44,256.325,0.870085,-1,-1,-1
6,-1,1262.9,480.995,53.99,172.091,0.776214,-1,-1,-1
7,-1,1686.41,392.86,183.13,335.486,0.996342,-1,-1,-1
7,-1,1274.76,438.168,89.09,234.233,0.972008,-1,-1,-1
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FILE: Workshop/sessions/tracking/data/ADL-Rundle-8/det.txt
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FILE: Workshop/sessions/tracking/data/ETH-Bahnhof/det.txt
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================================================
FILE: Workshop/sessions/tracking/data/ETH-Pedcross2/det.txt
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FILE: Workshop/sessions/tracking/data/ETH-Sunnyday/det.txt
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FILE: Workshop/sessions/tracking/data/KITTI-13/det.txt
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FILE: Workshop/sessions/tracking/data/KITTI-17/det.txt
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FILE: Workshop/sessions/tracking/data/PETS09-S2L1/det.txt
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================================================
FILE: Workshop/sessions/tracking/data/TUD-Campus/det.txt
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================================================
FILE: Workshop/sessions/tracking/data/TUD-Stadtmitte/det.txt
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FILE: Workshop/sessions/tracking/data/Venice-2/det.txt
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================================================
FILE: Workshop/sessions/tracking/sort.py
================================================
"""
SORT: A Simple, Online and Realtime Tracker
Copyright (C) 2016 Alex Bewley alex@dynamicdetection.com
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see # 1. Group A proceed w/o pre-processing datasets. # 2. Group B proceed w/ normalizing datasets into the range of [0, 1]. # 3. Group C proceed w/ standardizing datasets by z-scoring (de-mean, uni-variance). # # [See results](#Observe-Training-Process) # In[ ]: def PreprocessDataset(): from sklearn import preprocessing ## Load dataset (x_train, y_train), (x_test, y_test) = mnist.load_data() ## Transform labels to one-hot ## i.e., from '7' to [0,0,0,0,0,0,0,1,0,0] y_train = np_utils.to_categorical(y_train, 10) y_test = np_utils.to_categorical(y_test, 10) ## Process features. Set numeric type x_train = x_train.astype('float32') x_test = x_test.astype('float32') ## Reshape from a matrix of 28 x 28 pixels to 1-D vector of 784 dimensions x_train = np.reshape(x_train, (60000, 784)) x_test = np.reshape(x_test, (10000, 784)) ################################################################ # Activity 1 (Pre-processing): # Group A: w/o pre-processing datasets. # # Group B: Min-Max Normalize value to [0, 1] # x_train /= 255 # x_test /= 255 # # Group C: proceed w/ standardizing datasets by z-scoring (de-mean, uni-variance). # x_train = preprocessing.scale(x_train) # x_test = preprocessing.scale(x_test) ################################################################ ## YOUR TURN: CHANGE HERE x_train /= 255 x_test /= 255 return x_train, x_test, y_train, y_test x_train, x_test, y_train, y_test = PreprocessDataset() # In[ ]: ## Show part of training data: features and labels ## Each row is a sample, and each column represents a feature. print("{:^43}".format("x"), "|", "{:^4}".format("y")) print("="*50) for sample_id in range(10): print("{:.2f} {:.2f} ... {:.2f} {:.2f} {:.2f} ... {:.2f} {:.2f}".format( x_train[sample_id][0], x_train[sample_id][1], x_train[sample_id][156], x_train[sample_id][157], x_train[sample_id][158], x_train[sample_id][-2], x_train[sample_id][-1]), "| ", "{:.0f}".format(y_train[sample_id][0])) # ## Recipes for Neural Nets # # In this section you will play with useful recipes for designing a neural net. # ### Tutorial/workshop activity 2: Network Structure # # Each group uses different types of ingredients:
# 1. Group A uses **1** layer. # 2. Group B uses **2** layers of a **tower-shaped** (same width) network. # 3. Group C uses **2** layers of a **pyramid-shaped** (shrink width) network. # # [See results](#Observe-Training-Process) # # ### Tutorial/workshop activity 3: Activation Function # # Each group uses different types of ingredients: # 1. Group A uses Relu. # 2. Group B uses Sigmoid. # 3. Group C uses Tanh. # # [See results](#Observe-Training-Process) # # ### Tutorial/workshop activity 4: Loss Function # # Each group uses different types of ingredients: # 1. Group A uses cross entropy. # 2. Group B uses cross entropy. # 3. Group C uses squared error. # # [See results](#Observe-Training-Process) # # ### Tutorial/workshop activity 5: Dropout # # Each group uses different types of ingredients: # 1. Group A uses 0% dropout. # 2. Group B uses 50% dropout. # 3. Group C uses 90% dropout. # # [See results](#Observe-Training-Process) # # ### Tutorial/workshop activity 6: Regularization # # Each group uses different types of ingredients: # 1. Group A uses L1-norm. # 2. Group B uses L2-norm. # 3. Group C uses no regularization. # # [See results](#Observe-Training-Process) # In[ ]: def DefineModel(): ################################################################ # Activity 2 (Network Structure): # Group A: uses only 1 layer # second_layer_width = 0 # # Group B: uses 2 layers of a tower-shaped (same width) network. # second_layer_width = 128 # # Group C: uses 2 layers of a pyramid-shaped (shrink width) network. # second_layer_width = 64 ################################################################ first_layer_width = 128 second_layer_width = 64 ################################################################ # Activity 3 (Activation Function): # Group A uses ReLU. # activation_func = 'relu' # # Group B uses Sigmoid. # activation_func = 'sigmoid' # # Group C uses Tanh. # activation_func = 'tanh' ################################################################ activation_func = 'relu' ################################################################ # Activity 4 (Loss Function): # Group A uses cross entropy. # loss_function = 'categorical_crossentropy' # # Group B uses cross entropy. # loss_function = 'categorical_crossentropy' # # Group C uses squared error. # loss_function = 'mean_squared_error' ################################################################ loss_function = 'categorical_crossentropy' ################################################################# # Activity 5 (Dropout): # Group A uses 0% dropout. # # Group B uses 50% dropout. # dropout_rate = 0.5 # # Group C uses 90% dropout. # dropout_rate = 0.9 ################################################################# dropout_rate = 0.0 ################################################################ # Activity 6 (Regularization): # Group A uses L1 regularizer # weight_regularizer = l1(0.01) # # Group B uses L2 regularizer # weight_regularizer = l2(0.01) # # Group C uses no regularizer # weight_regularizer = None ################################################################ weight_regularizer = None ################################################################ # Activity 8 (Learning Rate): # Group A uses learning rate of 0.1. # learning_rate = 0.1 # # Group B uses learning rate of 0.01. # learning_rate = 0.01 # # Group C uses learning rate of 0.5. # learning_rate = 0.5 ################################################################ learning_rate = 0.1 ## Initialize model. model = Sequential() ## First hidden layer with 'first_layer_width' neurons. ## Also need to specify input dimension. ## 'Dense' means fully-connected. model.add(Dense(first_layer_width, input_dim=784, W_regularizer=weight_regularizer)) model.add(Activation(activation_func)) if dropout_rate > 0: model.add(Dropout(0.5)) ## Second hidden layer. if second_layer_width > 0: model.add(Dense(second_layer_width)) model.add(Activation(activation_func)) if dropout_rate > 0: model.add(Dropout(0.5)) ## Last layer has the same dimension as the number of classes model.add(Dense(10)) ## For classification, the activation is softmax model.add(Activation('softmax')) ## Define optimizer. In this tutorial/codelab, we select SGD. ## You can also use other methods, e.g., opt = RMSprop() opt = SGD(lr=learning_rate, clipnorm=5.) ## Define loss function = 'categorical_crossentropy' or 'mean_squared_error' model.compile(loss=loss_function, optimizer=opt, metrics=["accuracy"]) return model # ## Define Training Procedure # ### Tutorial/workshop activity 7: Mini-batch # # Each group uses different types of ingredients: # 1. Group A uses mini-batch of size 128. # 2. Group B uses mini-batch of size 256. # 3. Group C uses mini-batch of size 512. # # [See results](#Observe-Training-Process) # # ### Tutorial/workshop activity 8: Learning Rate # # Each group uses different types of ingredients: # 1. Group A uses learning rate of 0.1. # 2. Group B uses learning rate of 0.01. # 3. Group C uses learning rate of 0.5. # # [See results](#Observe-Training-Process) # In[ ]: def TrainModel(data=None, epochs=20): ################################################################ # Activity 7 (Mini-batch): # Group A uses mini-batch of size 128. # batch = 128 # # Group B uses mini-batch of size 256. # batch = 256 # # Group C uses mini-batch of size 512. # batch = 512 ################################################################ batch=128 start_time = time.time() model = DefineModel() if data is None: print("Must provide data.") return x_train, x_test, y_train, y_test = data print('Start training.') ## Use the first 55,000 (out of 60,000) samples to train, last 5,500 samples to validate. history = model.fit(x_train[:55000], y_train[:55000], nb_epoch=epochs, batch_size=batch, validation_data=(x_train[55000:], y_train[55000:])) print("Training took {0} seconds.".format(time.time() - start_time)) return model, history # ## Start Training # In[ ]: trained_model, training_history = TrainModel(data=[x_train, x_test, y_train, y_test]) # ## Define Plotting # In[ ]: def PlotHistory(train_value, test_value, value_is_loss_or_acc): f, ax = plt.subplots() ax.plot([None] + train_value, 'o-') ax.plot([None] + test_value, 'x-') ## Plot legend and use the best location automatically: loc = 0. ax.legend(['Train ' + value_is_loss_or_acc, 'Validation ' + value_is_loss_or_acc], loc = 0) ax.set_title('Training/Validation ' + value_is_loss_or_acc + ' per Epoch') ax.set_xlabel('Epoch') ax.set_ylabel(value_is_loss_or_acc) # ## Observe Training Process # In[ ]: PlotHistory(training_history.history['loss'], training_history.history['val_loss'], 'Loss') PlotHistory(training_history.history['acc'], training_history.history['val_acc'], 'Accuracy') # ## Observe Regularization Effects # # In[ ]: def drawWeightHistogram(x): ## the histogram of the data fig = plt.subplots() n, bins, patches = plt.hist(x, 50) plt.xlim(-0.5, 0.5) plt.xlabel('Weight') plt.ylabel('Count') zero_counts = (x == 0.0).sum() plt.title("Weight Histogram. Num of '0's: %d" % zero_counts) # In[ ]: w1 = trained_model.layers[0].get_weights()[0].flatten() drawWeightHistogram(w1) # ## Define Testing Procedure # # In[ ]: def TestModel(model=None, data=None): if model is None: print("Must provide a trained model.") return if data is None: print("Must provide data.") return x_test, y_test = data scores = model.evaluate(x_test, y_test) return scores # ## Test Trained Model # # In[ ]: test_score = TestModel(model=trained_model, data=[x_test, y_test]) print("Test loss {:.4f}, accuracy {:.2f}%".format(test_score[0], test_score[1] * 100)) # In[ ]: def ShowInputImage(data): """Visualize input image.""" plot = plt.figure() plot.set_size_inches(2,2) plt.imshow(np.reshape(-data, (28,28)), cmap='Greys_r') plt.title("Input") plt.axis('off') plt.show() def ShowHiddenLayerOutput(input_data, target_layer_num): """Visualize output from the target hidden layer.""" from keras import backend as K ## Backend converter: to TensorFlow target_layer = K.function(trained_model.inputs, [trained_model.layers[target_layer_num].output]) ## Extract output from the target hidden layer. target_layer_out = target_layer([input_data]) plot = plt.figure() plot.set_size_inches(2,2) plt.imshow(np.reshape(-target_layer_out[0][0], (16,-1)), cmap='Greys_r') plt.title("Hidden layer " + str(target_layer_num)) plt.axis('off') plt.show() def ShowFinalOutput(input_data): """Calculate final prediction.""" from keras import backend as K ## Backend converter: to TensorFlow ## Calculate final prediction. last_layer = K.function(trained_model.inputs, [trained_model.layers[-1].output]) last_layer_out = last_layer([input_data]) print("Final prediction: " + str(np.argmax(last_layer_out[0][0])) ) ShowInputImage(x_test[0]) ShowHiddenLayerOutput(x_test, 1) ShowFinalOutput(x_test) # In[ ]: ================================================ FILE: sklearn_test.py ================================================ from sklearn import datasets iris = datasets.load_iris() digits = datasets.load_digits() from sklearn import svm clf = svm.SVC(gamma=0.001, C=100.) clf.fit(digits.data[:-1], digits.target[:-1]) print clf print clf.predict(digits.data[-3:-1]) ================================================ FILE: tensor_flow_test.py ================================================ import os; import inspect; import tensorflow; print(os.path.dirname(inspect.getfile(tensorflow))) ================================================ FILE: xgboost/.gitignore ================================================ .ipynb_checkpoints/* *.csv ================================================ FILE: xgboost/tree_xgboost.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of Tree and [xgboost](https://github.com/dmlc/xgboost)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ruizhang/anaconda3/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import xgboost as xgb\n", "\n", "import numpy as np\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import r2_score\n", "\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n", "from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor, BaggingRegressor\n", "\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Cvxlf4ttHB7Sip4VoWWkT0HJOka3RRnQRaVblSpW5ZF7VE+koTofh2X1xKpbF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