\\n\",\n \"1. Group A proceed w/o pre-processing datasets.\\n\",\n \"2. Group B proceed w/ normalizing datasets into the range of [0, 1].\\n\",\n \"3. Group C proceed w/ standardizing datasets by z-scoring (de-mean, uni-variance).\\n\",\n \"\\n\",\n \"[See results](#Observe-Training-Process)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def PreprocessDataset():\\n\",\n \" from sklearn import preprocessing\\n\",\n \" ## Load dataset\\n\",\n \" (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n\",\n \" ## Transform labels to one-hot\\n\",\n \" ## i.e., from '7' to [0,0,0,0,0,0,0,1,0,0]\\n\",\n \" y_train = np_utils.to_categorical(y_train, 10)\\n\",\n \" y_test = np_utils.to_categorical(y_test, 10)\\n\",\n \" \\n\",\n \" ## Process features. Set numeric type\\n\",\n \" x_train = x_train.astype('float32')\\n\",\n \" x_test = x_test.astype('float32')\\n\",\n \" ## Reshape from a matrix of 28 x 28 pixels to 1-D vector of 784 dimensions\\n\",\n \" x_train = np.reshape(x_train, (60000, 784))\\n\",\n \" x_test = np.reshape(x_test, (10000, 784))\\n\",\n \"\\n\",\n \" ################################################################\\n\",\n \" # Activity 1 (Pre-processing):\\n\",\n \" # Group A: w/o pre-processing datasets.\\n\",\n \" #\\n\",\n \" # Group B: Min-Max Normalize value to [0, 1]\\n\",\n \" # x_train /= 255\\n\",\n \" # x_test /= 255\\n\",\n \" #\\n\",\n \" # Group C: proceed w/ standardizing datasets by z-scoring (de-mean, uni-variance).\\n\",\n \" # x_train = preprocessing.scale(x_train)\\n\",\n \" # x_test = preprocessing.scale(x_test)\\n\",\n \" ################################################################ \\n\",\n \" ## YOUR TURN: CHANGE HERE\\n\",\n \" x_train /= 255\\n\",\n \" x_test /= 255\\n\",\n \"\\n\",\n \" return x_train, x_test, y_train, y_test\\n\",\n \"\\n\",\n \"x_train, x_test, y_train, y_test = PreprocessDataset()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"## Show part of training data: features and labels\\n\",\n \"## Each row is a sample, and each column represents a feature.\\n\",\n \"print(\\\"{:^43}\\\".format(\\\"x\\\"), \\\"|\\\", \\\"{:^4}\\\".format(\\\"y\\\"))\\n\",\n \"print(\\\"=\\\"*50)\\n\",\n \"for sample_id in range(10):\\n\",\n \" print(\\\"{:.2f} {:.2f} ... {:.2f} {:.2f} {:.2f} ... {:.2f} {:.2f}\\\".format(\\n\",\n \" x_train[sample_id][0], x_train[sample_id][1],\\n\",\n \" x_train[sample_id][156], x_train[sample_id][157], x_train[sample_id][158],\\n\",\n \" x_train[sample_id][-2], x_train[sample_id][-1]), \\\"| \\\",\\n\",\n \" \\\"{:.0f}\\\".format(y_train[sample_id][0]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Recipes for Neural Nets\\n\",\n \"\\n\",\n \"In this section you will play with useful recipes for designing a neural net.\\n\",\n \"### Tutorial/workshop activity 2: Network Structure\\n\",\n \"\\n\",\n \"Each group uses different types of ingredients:
\\n\",\n \"1. Group A uses **1** layer.\\n\",\n \"2. Group B uses **2** layers of a **tower-shaped** (same width) network. \\n\",\n \"3. Group C uses **2** layers of a **pyramid-shaped** (shrink width) network.\\n\",\n \"\\n\",\n \"[See results](#Observe-Training-Process)\\n\",\n \"\\n\",\n \"### Tutorial/workshop activity 3: Activation Function\\n\",\n \"\\n\",\n \"Each group uses different types of ingredients:\\n\",\n \"1. Group A uses Relu.\\n\",\n \"2. Group B uses Sigmoid.\\n\",\n \"3. Group C uses Tanh.\\n\",\n \"\\n\",\n \"[See results](#Observe-Training-Process)\\n\",\n \"\\n\",\n \"### Tutorial/workshop activity 4: Loss Function\\n\",\n \"\\n\",\n \"Each group uses different types of ingredients:\\n\",\n \"1. Group A uses cross entropy.\\n\",\n \"2. Group B uses cross entropy.\\n\",\n \"3. Group C uses squared error.\\n\",\n \"\\n\",\n \"[See results](#Observe-Training-Process)\\n\",\n \"\\n\",\n \"### Tutorial/workshop activity 5: Dropout\\n\",\n \"\\n\",\n \"Each group uses different types of ingredients:\\n\",\n \"1. Group A uses 0% dropout.\\n\",\n \"2. Group B uses 50% dropout.\\n\",\n \"3. Group C uses 90% dropout.\\n\",\n \"\\n\",\n \"[See results](#Observe-Training-Process)\\n\",\n \"\\n\",\n \"### Tutorial/workshop activity 6: Regularization\\n\",\n \"\\n\",\n \"Each group uses different types of ingredients:\\n\",\n \"1. Group A uses L1-norm.\\n\",\n \"2. Group B uses L2-norm.\\n\",\n \"3. Group C uses no regularization.\\n\",\n \"\\n\",\n \"[See results](#Observe-Training-Process)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def DefineModel():\\n\",\n \"\\n\",\n \" ################################################################\\n\",\n \" # Activity 2 (Network Structure):\\n\",\n \" # Group A: uses only 1 layer\\n\",\n \" # second_layer_width = 0\\n\",\n \" #\\n\",\n \" # Group B: uses 2 layers of a tower-shaped (same width) network.\\n\",\n \" # second_layer_width = 128\\n\",\n \" #\\n\",\n \" # Group C: uses 2 layers of a pyramid-shaped (shrink width) network.\\n\",\n \" # second_layer_width = 64\\n\",\n \" ################################################################\\n\",\n \" first_layer_width = 128\\n\",\n \" second_layer_width = 64 \\n\",\n \" \\n\",\n \" ################################################################\\n\",\n \" # Activity 3 (Activation Function):\\n\",\n \" # Group A uses ReLU.\\n\",\n \" # activation_func = 'relu' \\n\",\n \" # \\n\",\n \" # Group B uses Sigmoid.\\n\",\n \" # activation_func = 'sigmoid'\\n\",\n \" #\\n\",\n \" # Group C uses Tanh.\\n\",\n \" # activation_func = 'tanh'\\n\",\n \" ################################################################\\n\",\n \" activation_func = 'relu' \\n\",\n \"\\n\",\n \" ################################################################ \\n\",\n \" # Activity 4 (Loss Function):\\n\",\n \" # Group A uses cross entropy.\\n\",\n \" # loss_function = 'categorical_crossentropy'\\n\",\n \" # \\n\",\n \" # Group B uses cross entropy.\\n\",\n \" # loss_function = 'categorical_crossentropy'\\n\",\n \" # \\n\",\n \" # Group C uses squared error.\\n\",\n \" # loss_function = 'mean_squared_error'\\n\",\n \" ################################################################ \\n\",\n \" loss_function = 'categorical_crossentropy'\\n\",\n \" \\n\",\n \" ################################################################# \\n\",\n \" # Activity 5 (Dropout):\\n\",\n \" # Group A uses 0% dropout.\\n\",\n \" #\\n\",\n \" # Group B uses 50% dropout.\\n\",\n \" # dropout_rate = 0.5\\n\",\n \" #\\n\",\n \" # Group C uses 90% dropout.\\n\",\n \" # dropout_rate = 0.9\\n\",\n \" ################################################################# \\n\",\n \" dropout_rate = 0.0\\n\",\n \" \\n\",\n \" ################################################################ \\n\",\n \" # Activity 6 (Regularization):\\n\",\n \" # Group A uses L1 regularizer\\n\",\n \" # weight_regularizer = l1(0.01)\\n\",\n \" #\\n\",\n \" # Group B uses L2 regularizer\\n\",\n \" # weight_regularizer = l2(0.01)\\n\",\n \" # \\n\",\n \" # Group C uses no regularizer\\n\",\n \" # weight_regularizer = None\\n\",\n \" ################################################################\\n\",\n \" weight_regularizer = None\\n\",\n \"\\n\",\n \" ################################################################ \\n\",\n \" # Activity 8 (Learning Rate):\\n\",\n \" # Group A uses learning rate of 0.1.\\n\",\n \" # learning_rate = 0.1\\n\",\n \" # \\n\",\n \" # Group B uses learning rate of 0.01.\\n\",\n \" # learning_rate = 0.01\\n\",\n \" #\\n\",\n \" # Group C uses learning rate of 0.5. \\n\",\n \" # learning_rate = 0.5\\n\",\n \" ################################################################\\n\",\n \" learning_rate = 0.1\\n\",\n \" \\n\",\n \" ## Initialize model.\\n\",\n \" model = Sequential()\\n\",\n \"\\n\",\n \" ## First hidden layer with 'first_layer_width' neurons. \\n\",\n \" ## Also need to specify input dimension.\\n\",\n \" ## 'Dense' means fully-connected.\\n\",\n \" model.add(Dense(first_layer_width, input_dim=784, W_regularizer=weight_regularizer))\\n\",\n \" model.add(Activation(activation_func))\\n\",\n \" if dropout_rate > 0:\\n\",\n \" model.add(Dropout(0.5))\\n\",\n \"\\n\",\n \" ## Second hidden layer.\\n\",\n \" if second_layer_width > 0:\\n\",\n \" model.add(Dense(second_layer_width))\\n\",\n \" model.add(Activation(activation_func))\\n\",\n \" if dropout_rate > 0:\\n\",\n \" model.add(Dropout(0.5)) \\n\",\n \" \\n\",\n \" ## Last layer has the same dimension as the number of classes\\n\",\n \" model.add(Dense(10))\\n\",\n \" ## For classification, the activation is softmax\\n\",\n \" model.add(Activation('softmax'))\\n\",\n \" ## Define optimizer. In this tutorial/codelab, we select SGD.\\n\",\n \" ## You can also use other methods, e.g., opt = RMSprop()\\n\",\n \" opt = SGD(lr=learning_rate, clipnorm=5.)\\n\",\n \" ## Define loss function = 'categorical_crossentropy' or 'mean_squared_error'\\n\",\n \" model.compile(loss=loss_function, optimizer=opt, metrics=[\\\"accuracy\\\"])\\n\",\n \"\\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Define Training Procedure\\n\",\n \"### Tutorial/workshop activity 7: Mini-batch\\n\",\n \"\\n\",\n \"Each group uses different types of ingredients:\\n\",\n \"1. Group A uses mini-batch of size 128.\\n\",\n \"2. Group B uses mini-batch of size 256.\\n\",\n \"3. Group C uses mini-batch of size 512.\\n\",\n \"\\n\",\n \"[See results](#Observe-Training-Process)\\n\",\n \"\\n\",\n \"### Tutorial/workshop activity 8: Learning Rate\\n\",\n \"\\n\",\n \"Each group uses different types of ingredients:\\n\",\n \"1. Group A uses learning rate of 0.1.\\n\",\n \"2. Group B uses learning rate of 0.01.\\n\",\n \"3. Group C uses learning rate of 0.5.\\n\",\n \"\\n\",\n \"[See results](#Observe-Training-Process)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def TrainModel(data=None, epochs=20):\\n\",\n \" ################################################################\\n\",\n \" # Activity 7 (Mini-batch):\\n\",\n \" # Group A uses mini-batch of size 128.\\n\",\n \" # batch = 128\\n\",\n \" #\\n\",\n \" # Group B uses mini-batch of size 256.\\n\",\n \" # batch = 256\\n\",\n \" # \\n\",\n \" # Group C uses mini-batch of size 512.\\n\",\n \" # batch = 512\\n\",\n \" ################################################################\\n\",\n \" batch=128\\n\",\n \" start_time = time.time()\\n\",\n \" model = DefineModel()\\n\",\n \" if data is None:\\n\",\n \" print(\\\"Must provide data.\\\")\\n\",\n \" return\\n\",\n \" x_train, x_test, y_train, y_test = data\\n\",\n \" print('Start training.')\\n\",\n \" ## Use the first 55,000 (out of 60,000) samples to train, last 5,500 samples to validate.\\n\",\n \" history = model.fit(x_train[:55000], y_train[:55000], nb_epoch=epochs, batch_size=batch,\\n\",\n \" validation_data=(x_train[55000:], y_train[55000:]))\\n\",\n \" print(\\\"Training took {0} seconds.\\\".format(time.time() - start_time))\\n\",\n \" return model, history\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Start Training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"trained_model, training_history = TrainModel(data=[x_train, x_test, y_train, y_test])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Define Plotting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def PlotHistory(train_value, test_value, value_is_loss_or_acc):\\n\",\n \" f, ax = plt.subplots()\\n\",\n \" ax.plot([None] + train_value, 'o-')\\n\",\n \" ax.plot([None] + test_value, 'x-')\\n\",\n \" ## Plot legend and use the best location automatically: loc = 0.\\n\",\n \" ax.legend(['Train ' + value_is_loss_or_acc, 'Validation ' + value_is_loss_or_acc], loc = 0) \\n\",\n \" ax.set_title('Training/Validation ' + value_is_loss_or_acc + ' per Epoch')\\n\",\n \" ax.set_xlabel('Epoch')\\n\",\n \" ax.set_ylabel(value_is_loss_or_acc) \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Observe Training Process\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"PlotHistory(training_history.history['loss'], training_history.history['val_loss'], 'Loss')\\n\",\n \"PlotHistory(training_history.history['acc'], training_history.history['val_acc'], 'Accuracy')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Observe Regularization Effects\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def drawWeightHistogram(x):\\n\",\n \" ## the histogram of the data\\n\",\n \" fig = plt.subplots()\\n\",\n \" n, bins, patches = plt.hist(x, 50)\\n\",\n \" plt.xlim(-0.5, 0.5)\\n\",\n \" plt.xlabel('Weight')\\n\",\n \" plt.ylabel('Count')\\n\",\n \" zero_counts = (x == 0.0).sum()\\n\",\n \" plt.title(\\\"Weight Histogram. Num of '0's: %d\\\" % zero_counts)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"w1 = trained_model.layers[0].get_weights()[0].flatten()\\n\",\n \"drawWeightHistogram(w1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Define Testing Procedure\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def TestModel(model=None, data=None):\\n\",\n \" if model is None:\\n\",\n \" print(\\\"Must provide a trained model.\\\")\\n\",\n \" return\\n\",\n \" if data is None:\\n\",\n \" print(\\\"Must provide data.\\\")\\n\",\n \" return\\n\",\n \" x_test, y_test = data\\n\",\n \" scores = model.evaluate(x_test, y_test)\\n\",\n \" return scores\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Test Trained Model\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"test_score = TestModel(model=trained_model, data=[x_test, y_test])\\n\",\n \"print(\\\"Test loss {:.4f}, accuracy {:.2f}%\\\".format(test_score[0], test_score[1] * 100))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def ShowInputImage(data):\\n\",\n \" \\\"\\\"\\\"Visualize input image.\\\"\\\"\\\"\\n\",\n \" plot = plt.figure()\\n\",\n \" plot.set_size_inches(2,2)\\n\",\n \" plt.imshow(np.reshape(-data, (28,28)), cmap='Greys_r')\\n\",\n \" plt.title(\\\"Input\\\")\\n\",\n \" plt.axis('off')\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"def ShowHiddenLayerOutput(input_data, target_layer_num):\\n\",\n \" \\\"\\\"\\\"Visualize output from the target hidden layer.\\\"\\\"\\\"\\n\",\n \" from keras import backend as K\\n\",\n \" ## Backend converter: to TensorFlow\\n\",\n \" target_layer = K.function(trained_model.inputs, [trained_model.layers[target_layer_num].output])\\n\",\n \" ## Extract output from the target hidden layer.\\n\",\n \" target_layer_out = target_layer([input_data])\\n\",\n \" plot = plt.figure()\\n\",\n \" plot.set_size_inches(2,2)\\n\",\n \" plt.imshow(np.reshape(-target_layer_out[0][0], (16,-1)), cmap='Greys_r')\\n\",\n \" plt.title(\\\"Hidden layer \\\" + str(target_layer_num))\\n\",\n \" plt.axis('off')\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"def ShowFinalOutput(input_data):\\n\",\n \" \\\"\\\"\\\"Calculate final prediction.\\\"\\\"\\\"\\n\",\n \" from keras import backend as K\\n\",\n \" ## Backend converter: to TensorFlow\\n\",\n \" ## Calculate final prediction.\\n\",\n \" last_layer = K.function(trained_model.inputs, [trained_model.layers[-1].output])\\n\",\n \" last_layer_out = last_layer([input_data])\\n\",\n \" print(\\\"Final prediction: \\\" + str(np.argmax(last_layer_out[0][0])) )\\n\",\n \"\\n\",\n \"ShowInputImage(x_test[0])\\n\",\n \"ShowHiddenLayerOutput(x_test, 1)\\n\",\n \"ShowFinalOutput(x_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"celltoolbar\": \"Raw Cell Format\",\n \"kernelspec\": {\n \"display_name\": \"Python [conda root]\",\n \"language\": \"python\",\n \"name\": \"conda-root-py\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "Quora_questions_similar/.gitignore", "content": "# Created by .ignore support plugin (hsz.mobi)\n*.npy\n*.json\n*.h5\n*.csv" }, { "path": "Quora_questions_similar/CNN_Question_pair.py", "content": "# coding: utf-8\n\n# # Predicting Duplicate Questions\n\n# In[5]:\n\nimport pandas as pd\nimport numpy as np\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem import SnowballStemmer\nimport re\nfrom sklearn.metrics import accuracy_score\nimport matplotlib.pyplot as plt\nimport datetime, time, json\nfrom string import punctuation\n\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import Sequential\nfrom keras.layers import Embedding, Dense, Dropout, Reshape, Merge, BatchNormalization, TimeDistributed, Lambda, \\\n Activation, LSTM, Flatten, Convolution1D, GRU, MaxPooling1D\nfrom keras.regularizers import l2\nfrom keras.callbacks import Callback, ModelCheckpoint, EarlyStopping\nfrom keras import initializers\nfrom keras import backend as K\nfrom keras.optimizers import SGD\nfrom collections import defaultdict\n\n# In[6]:\n\ntrain = pd.read_csv(\"train.csv\")[:100]\ntest = pd.read_csv(\"test.csv\")[:100]\n\n# In[7]:\n\ntrain.head(6)\n\n# In[8]:\n\ntest.head()\n\n# In[9]:\n\nprint(train.shape)\nprint(test.shape)\n\n# In[10]:\n\n# Check for any null values\nprint(train.isnull().sum())\nprint(test.isnull().sum())\n\n# In[11]:\n\n# Add the string 'empty' to empty strings\ntrain = train.fillna('empty')\ntest = test.fillna('empty')\n\n# In[12]:\n\nprint(train.isnull().sum())\nprint(test.isnull().sum())\n\n# In[13]:\n\n# Preview some of the pairs of questions\nfor i in range(6):\n print(train.question1[i])\n print(train.question2[i])\n print()\n\n# In[14]:\n\nstop_words = ['the', 'a', 'an', 'and', 'but', 'if', 'or', 'because', 'as', 'what', 'which', 'this', 'that', 'these',\n 'those', 'then',\n 'just', 'so', 'than', 'such', 'both', 'through', 'about', 'for', 'is', 'of', 'while', 'during', 'to',\n 'What', 'Which',\n 'Is', 'If', 'While', 'This']\n\n\n# In[191]:\n\ndef text_to_wordlist(text, remove_stop_words=True, stem_words=False):\n # Clean the text, with the option to remove stop_words and to stem words.\n\n # Convert words to lower case and split them\n # text = text.lower()\n\n # Clean the text\n text = re.sub(r\"[^A-Za-z0-9]\", \" \", text)\n text = re.sub(r\"what's\", \"\", text)\n text = re.sub(r\"What's\", \"\", text)\n text = re.sub(r\"\\'s\", \" \", text)\n text = re.sub(r\"\\'ve\", \" have \", text)\n text = re.sub(r\"can't\", \"cannot \", text)\n text = re.sub(r\"n't\", \" not \", text)\n text = re.sub(r\"I'm\", \"I am\", text)\n text = re.sub(r\" m \", \" am \", text)\n text = re.sub(r\"\\'re\", \" are \", text)\n text = re.sub(r\"\\'d\", \" would \", text)\n text = re.sub(r\"\\'ll\", \" will \", text)\n text = re.sub(r\"\\0k \", \"0000 \", text)\n text = re.sub(r\" e g \", \" eg \", text)\n text = re.sub(r\" b g \", \" bg \", text)\n text = re.sub(r\"\\0s\", \"0\", text)\n text = re.sub(r\" 9 11 \", \"911\", text)\n text = re.sub(r\"e-mail\", \"email\", text)\n text = re.sub(r\"\\s{2,}\", \" \", text)\n text = re.sub(r\"quikly\", \"quickly\", text)\n text = re.sub(r\" usa \", \" America \", text)\n text = re.sub(r\" USA \", \" America \", text)\n text = re.sub(r\" u s \", \" America \", text)\n text = re.sub(r\" uk \", \" England \", text)\n text = re.sub(r\" UK \", \" England \", text)\n text = re.sub(r\"india\", \"India\", text)\n text = re.sub(r\"china\", \"China\", text)\n text = re.sub(r\"chinese\", \"Chinese\", text)\n text = re.sub(r\"imrovement\", \"improvement\", text)\n text = re.sub(r\"intially\", \"initially\", text)\n text = re.sub(r\"quora\", \"Quora\", text)\n text = re.sub(r\" dms \", \"direct messages \", text)\n text = re.sub(r\"demonitization\", \"demonetization\", text)\n text = re.sub(r\"actived\", \"active\", text)\n text = re.sub(r\"kms\", \" kilometers \", text)\n text = re.sub(r\"KMs\", \" kilometers \", text)\n text = re.sub(r\" cs \", \" computer science \", text)\n text = re.sub(r\" upvotes \", \" up votes \", text)\n text = re.sub(r\" iPhone \", \" phone \", text)\n text = re.sub(r\"\\0rs \", \" rs \", text)\n text = re.sub(r\"calender\", \"calendar\", text)\n text = re.sub(r\"ios\", \"operating system\", text)\n text = re.sub(r\"gps\", \"GPS\", text)\n text = re.sub(r\"gst\", \"GST\", text)\n text = re.sub(r\"programing\", \"programming\", text)\n text = re.sub(r\"bestfriend\", \"best friend\", text)\n text = re.sub(r\"dna\", \"DNA\", text)\n text = re.sub(r\"III\", \"3\", text)\n text = re.sub(r\"the US\", \"America\", text)\n text = re.sub(r\"Astrology\", \"astrology\", text)\n text = re.sub(r\"Method\", \"method\", text)\n text = re.sub(r\"Find\", \"find\", text)\n text = re.sub(r\"banglore\", \"Banglore\", text)\n text = re.sub(r\" J K \", \" JK \", text)\n\n # Remove punctuation from text\n text = ''.join([c for c in text if c not in punctuation])\n\n # Optionally, remove stop words\n if remove_stop_words:\n text = text.split()\n text = [w for w in text if not w in stop_words]\n text = \" \".join(text)\n\n # Optionally, shorten words to their stems\n if stem_words:\n text = text.split()\n stemmer = SnowballStemmer('english')\n stemmed_words = [stemmer.stem(word) for word in text]\n text = \" \".join(stemmed_words)\n\n # Return a list of words\n return (text)\n\n\n# In[192]:\n\ndef process_questions(question_list, questions, question_list_name, dataframe):\n '''transform questions and display progress'''\n for question in questions:\n question_list.append(text_to_wordlist(question))\n if len(question_list) % 100000 == 0:\n progress = len(question_list) / len(dataframe) * 100\n print(\"{} is {}% complete.\".format(question_list_name, round(progress, 1)))\n\n\n# In[193]:\n\ntrain_question1 = []\nprocess_questions(train_question1, train.question1, 'train_question1', train)\n\n# In[194]:\n\ntrain_question2 = []\nprocess_questions(train_question2, train.question2, 'train_question2', train)\n\n# In[165]:\n\ntest_question1 = []\nprocess_questions(test_question1, test.question1, 'test_question1', test)\n\n# In[166]:\n\ntest_question2 = []\nprocess_questions(test_question2, test.question2, 'test_question2', test)\n\n# In[195]:\n\n# Preview some transformed pairs of questions\ni = 0\nfor i in range(i, i + 10):\n print(train_question1[i])\n print(train_question2[i])\n print()\n\n# In[168]:\n\n# Find the length of questions\nlengths = []\nfor question in train_question1:\n lengths.append(len(question.split()))\n\nfor question in train_question2:\n lengths.append(len(question.split()))\n\n# Create a dataframe so that the values can be inspected\nlengths = pd.DataFrame(lengths, columns=['counts'])\n\n# In[169]:\n\nlengths.counts.describe()\n\n# In[170]:\n\nprint(np.percentile(lengths.counts, 99.0))\nprint(np.percentile(lengths.counts, 99.4))\nprint(np.percentile(lengths.counts, 99.5))\nprint(np.percentile(lengths.counts, 99.9))\n\n# In[171]:\n\n# tokenize the words for all of the questions\nall_questions = train_question1 + train_question2 + test_question1 + test_question2\ntokenizer = Tokenizer()\ntokenizer.fit_on_texts(all_questions)\nprint(\"Fitting is complete.\")\ntrain_question1_word_sequences = tokenizer.texts_to_sequences(train_question1)\nprint(\"train_question1 is complete.\")\ntrain_question2_word_sequences = tokenizer.texts_to_sequences(train_question2)\nprint(\"train_question2 is complete\")\n\n# In[172]:\n\ntest_question1_word_sequences = tokenizer.texts_to_sequences(test_question1)\nprint(\"test_question1 is complete.\")\ntest_question2_word_sequences = tokenizer.texts_to_sequences(test_question2)\nprint(\"test_question2 is complete.\")\n\n# In[173]:\n\nword_index = tokenizer.word_index\nprint(\"Words in index: %d\" % len(word_index))\n\n# In[174]:\n\n# Pad the questions so that they all have the same length.\n\nmax_question_len = 36\n\ntrain_q1 = pad_sequences(train_question1_word_sequences,\n maxlen=max_question_len)\nprint(\"train_q1 is complete.\")\n\ntrain_q2 = pad_sequences(train_question2_word_sequences,\n maxlen=max_question_len)\nprint(\"train_q2 is complete.\")\n\n# In[175]:\n\ntest_q1 = pad_sequences(test_question1_word_sequences,\n maxlen=max_question_len,\n padding='post',\n truncating='post')\nprint(\"test_q1 is complete.\")\n\ntest_q2 = pad_sequences(test_question2_word_sequences,\n maxlen=max_question_len,\n padding='post',\n truncating='post')\nprint(\"test_q2 is complete.\")\n\n# In[30]:\n\ny_train = train.is_duplicate\n\n# In[31]:\n\n# Load GloVe to use pretrained vectors\n\n# Note for Kaggle users: Uncomment this - it couldn't be used on Kaggle\n\n# From this link: https://nlp.stanford.edu/projects/glove/\n# embeddings_index = {}\n# with open('glove.840B.300d.txt', encoding='utf-8') as f:\n# for line in f:\n# values = line.split(' ')\n# word = values[0]\n# embedding = np.asarray(values[1:], dtype='float32')\n# embeddings_index[word] = embedding\n#\n# print('Word embeddings:', len(embeddings_index)) #151,250\n\n\n# In[176]:\n\n# Need to use 300 for embedding dimensions to match GloVe's vectors.\nembedding_dim = 300\n\n# Note for Kaggle users: Uncomment this too, because it relate to the code for GloVe.\n\nnb_words = len(word_index)\n# word_embedding_matrix = np.zeros((nb_words + 1, embedding_dim))\n# for word, i in word_index.items():\n# embedding_vector = embeddings_index.get(word)\n# if embedding_vector is not None:\n# # words not found in embedding index will be all-zeros.\n# word_embedding_matrix[i] = embedding_vector\n#\n# print('Null word embeddings: %d' % np.sum(np.sum(word_embedding_matrix, axis=1) == 0)) #75,334\n\n\n# In[177]:\n\nunits = 128 # Number of nodes in the Dense layers\ndropout = 0.25 # Percentage of nodes to drop\nnb_filter = 32 # Number of filters to use in Convolution1D\nfilter_length = 3 # Length of filter for Convolution1D\n# Initialize weights and biases for the Dense layers\nweights = initializers.TruncatedNormal(mean=0.0, stddev=0.05, seed=2)\nbias = bias_initializer = 'zeros'\n\nmodel1 = Sequential()\nmodel1.add(Embedding(nb_words + 1,\n embedding_dim,\n # weights = [word_embedding_matrix], Commented out for Kaggle\n input_length=max_question_len,\n trainable=False))\n\nmodel1.add(Convolution1D(filters=nb_filter,\n kernel_size=filter_length,\n padding='same'))\nmodel1.add(BatchNormalization())\nmodel1.add(Activation('relu'))\nmodel1.add(Dropout(dropout))\n\nmodel1.add(Convolution1D(filters=nb_filter,\n kernel_size=filter_length,\n padding='same'))\nmodel1.add(BatchNormalization())\nmodel1.add(Activation('relu'))\nmodel1.add(Dropout(dropout))\n\nmodel1.add(Flatten())\n\nmodel2 = Sequential()\nmodel2.add(Embedding(nb_words + 1,\n embedding_dim,\n # weights = [word_embedding_matrix],\n input_length=max_question_len,\n trainable=False))\n\nmodel2.add(Convolution1D(filters=nb_filter,\n kernel_size=filter_length,\n padding='same'))\nmodel2.add(BatchNormalization())\nmodel2.add(Activation('relu'))\nmodel2.add(Dropout(dropout))\n\nmodel2.add(Convolution1D(filters=nb_filter,\n kernel_size=filter_length,\n padding='same'))\nmodel2.add(BatchNormalization())\nmodel2.add(Activation('relu'))\nmodel2.add(Dropout(dropout))\n\nmodel2.add(Flatten())\n\nmodel3 = Sequential()\nmodel3.add(Embedding(nb_words + 1,\n embedding_dim,\n # weights = [word_embedding_matrix],\n input_length=max_question_len,\n trainable=False))\nmodel3.add(TimeDistributed(Dense(embedding_dim)))\nmodel3.add(BatchNormalization())\nmodel3.add(Activation('relu'))\nmodel3.add(Dropout(dropout))\nmodel3.add(Lambda(lambda x: K.max(x, axis=1), output_shape=(embedding_dim,)))\n\nmodel4 = Sequential()\nmodel4.add(Embedding(nb_words + 1,\n embedding_dim,\n # weights = [word_embedding_matrix],\n input_length=max_question_len,\n trainable=False))\n\nmodel4.add(TimeDistributed(Dense(embedding_dim)))\nmodel4.add(BatchNormalization())\nmodel4.add(Activation('relu'))\nmodel4.add(Dropout(dropout))\nmodel4.add(Lambda(lambda x: K.max(x, axis=1), output_shape=(embedding_dim,)))\n\nmodela = Sequential()\nmodela.add(Merge([model1, model2], mode='concat'))\nmodela.add(Dense(units * 2, kernel_initializer=weights, bias_initializer=bias))\nmodela.add(BatchNormalization())\nmodela.add(Activation('relu'))\nmodela.add(Dropout(dropout))\n\nmodela.add(Dense(units, kernel_initializer=weights, bias_initializer=bias))\nmodela.add(BatchNormalization())\nmodela.add(Activation('relu'))\nmodela.add(Dropout(dropout))\n\nmodelb = Sequential()\nmodelb.add(Merge([model3, model4], mode='concat'))\nmodelb.add(Dense(units * 2, kernel_initializer=weights, bias_initializer=bias))\nmodelb.add(BatchNormalization())\nmodelb.add(Activation('relu'))\nmodelb.add(Dropout(dropout))\n\nmodelb.add(Dense(units, kernel_initializer=weights, bias_initializer=bias))\nmodelb.add(BatchNormalization())\nmodelb.add(Activation('relu'))\nmodelb.add(Dropout(dropout))\n\nmodel = Sequential()\nmodel.add(Merge([modela, modelb], mode='concat'))\nmodel.add(Dense(units * 2, kernel_initializer=weights, bias_initializer=bias))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(dropout))\n\nmodel.add(Dense(units, kernel_initializer=weights, bias_initializer=bias))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(dropout))\n\nmodel.add(Dense(units, kernel_initializer=weights, bias_initializer=bias))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(dropout))\n\nmodel.add(Dense(1, kernel_initializer=weights, bias_initializer=bias))\nmodel.add(BatchNormalization())\nmodel.add(Activation('sigmoid'))\n\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n# In[178]:\n\n# save the best weights for predicting the test question pairs\nsave_best_weights = 'question_pairs_weights.h5'\n\nt0 = time.time()\ncallbacks = [ModelCheckpoint(save_best_weights, monitor='val_loss', save_best_only=True),\n EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='auto')]\nhistory = model.fit([train_q1, train_q2, train_q1, train_q2],\n y_train,\n batch_size=256,\n epochs=2, # Use 100, I reduce it for Kaggle,\n validation_split=0.15,\n verbose=True,\n shuffle=True,\n callbacks=callbacks)\nt1 = time.time()\nprint(\"Minutes elapsed: %f\" % ((t1 - t0) / 60.))\n\n# In[179]:\n\n# Aggregate the summary statistics\nsummary_stats = pd.DataFrame({'epoch': [i + 1 for i in history.epoch],\n 'train_acc': history.history['acc'],\n 'valid_acc': history.history['val_acc'],\n 'train_loss': history.history['loss'],\n 'valid_loss': history.history['val_loss']})\n\n# In[180]:\n\nsummary_stats\n\n# In[181]:\n\nplt.plot(summary_stats.train_loss) # blue\nplt.plot(summary_stats.valid_loss) # green\nplt.show()\n\n# In[182]:\n\n# Find the minimum validation loss during the training\nmin_loss, idx = min((loss, idx) for (idx, loss) in enumerate(history.history['val_loss']))\nprint('Minimum loss at epoch', '{:d}'.format(idx + 1), '=', '{:.4f}'.format(min_loss))\nmin_loss = round(min_loss, 4)\n\n# In[183]:\n\n# Make predictions with the best weights\nmodel.load_weights(save_best_weights)\npredictions = model.predict([test_q1, test_q2, test_q1, test_q2], verbose=True)\n\n# In[184]:\n\n# Create submission\nsubmission = pd.DataFrame(predictions, columns=['is_duplicate'])\nsubmission.insert(0, 'test_id', test.test_id)\nfile_name = 'submission_{}.csv'.format(min_loss)\nsubmission.to_csv(file_name, index=False)\n\n# In[185]:\n\nsubmission.head(10)" }, { "path": "Quora_questions_similar/README.md", "content": "\n\n**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*\n\n- [Reference](#reference)\n\n\n\n# Reference\n\nhttps://data.quora.com/First-Quora-Dataset-Release-Question-Pairs\n\nhttps://engineering.quora.com/Semantic-Question-Matching-with-Deep-Learning\n\nhttps://github.com/bradleypallen/keras-quora-question-pairs" }, { "path": "Quora_questions_similar/lstm_embedding.py", "content": "'''\nSingle model may achieve LB scores at around 0.29+ ~ 0.30+\nAverage ensembles can easily get 0.28+ or less\nDon't need to be an expert of feature engineering\nAll you need is a GPU!!!!!!!\n\nThe code is tested on Keras 2.0.0 using Tensorflow backend, and Python 2.7\n\nAccording to experiments by kagglers, Theano backend with GPU may give bad LB scores while\n the val_loss seems to be fine, so try Tensorflow backend first please\n'''\n\n########################################\n## import packages\n########################################\nimport os\nimport re\nimport csv\nimport codecs\nimport numpy as np\nimport pandas as pd\n\nfrom nltk.corpus import stopwords\nfrom nltk.stem import SnowballStemmer\nfrom string import punctuation\n\nfrom gensim.models import KeyedVectors\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation\nfrom keras.layers.merge import concatenate\nfrom keras.models import Model\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint\n\nimport sys\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\n########################################\n## set directories and parameters\n########################################\nBASE_DIR = '/Users/ruizhang/Documents/NLP_dataset/quora'\nGoolgeNews = '/Users/ruizhang/Documents/NLP_dataset'\nEMBEDDING_FILE = GoolgeNews + 'GoogleNews-vectors-negative300.bin'\nTRAIN_DATA_FILE = BASE_DIR + 'train.csv'\nTEST_DATA_FILE = BASE_DIR + 'test.csv'\nMAX_SEQUENCE_LENGTH = 30\nMAX_NB_WORDS = 200000\nEMBEDDING_DIM = 300\nVALIDATION_SPLIT = 0.1\n\nnum_lstm = np.random.randint(175, 275)\nnum_dense = np.random.randint(100, 150)\nrate_drop_lstm = 0.15 + np.random.rand() * 0.25\nrate_drop_dense = 0.15 + np.random.rand() * 0.25\n\nact = 'relu'\nre_weight = True # whether to re-weight classes to fit the 17.5% share in test set\n\nSTAMP = 'lstm_%d_%d_%.2f_%.2f' % (num_lstm, num_dense, rate_drop_lstm, \\\n rate_drop_dense)\n\n########################################\n## index word vectors\n########################################\nprint('Indexing word vectors')\n\nword2vec = KeyedVectors.load_word2vec_format(EMBEDDING_FILE, \\\n binary=True)\nprint('Found %s word vectors of word2vec' % len(word2vec.vocab))\n\n########################################\n## process texts in datasets\n########################################\nprint('Processing text dataset')\n\n\n# The function \"text_to_wordlist\" is from\n# https://www.kaggle.com/currie32/quora-question-pairs/the-importance-of-cleaning-text\ndef text_to_wordlist(text, remove_stopwords=False, stem_words=False):\n # Clean the text, with the option to remove stopwords and to stem words.\n\n # Convert words to lower case and split them\n text = text.lower().split()\n\n # Optionally, remove stop words\n if remove_stopwords:\n stops = set(stopwords.words(\"english\"))\n text = [w for w in text if not w in stops]\n\n text = \" \".join(text)\n\n # Clean the text\n text = re.sub(r\"[^A-Za-z0-9^,!.\\/'+-=]\", \" \", text)\n text = re.sub(r\"what's\", \"what is \", text)\n text = re.sub(r\"\\'s\", \" \", text)\n text = re.sub(r\"\\'ve\", \" have \", text)\n text = re.sub(r\"can't\", \"cannot \", text)\n text = re.sub(r\"n't\", \" not \", text)\n text = re.sub(r\"i'm\", \"i am \", text)\n text = re.sub(r\"\\'re\", \" are \", text)\n text = re.sub(r\"\\'d\", \" would \", text)\n text = re.sub(r\"\\'ll\", \" will \", text)\n text = re.sub(r\",\", \" \", text)\n text = re.sub(r\"\\.\", \" \", text)\n text = re.sub(r\"!\", \" ! \", text)\n text = re.sub(r\"\\/\", \" \", text)\n text = re.sub(r\"\\^\", \" ^ \", text)\n text = re.sub(r\"\\+\", \" + \", text)\n text = re.sub(r\"\\-\", \" - \", text)\n text = re.sub(r\"\\=\", \" = \", text)\n text = re.sub(r\"'\", \" \", text)\n text = re.sub(r\"(\\d+)(k)\", r\"\\g<1>000\", text)\n text = re.sub(r\":\", \" : \", text)\n text = re.sub(r\" e g \", \" eg \", text)\n text = re.sub(r\" b g \", \" bg \", text)\n text = re.sub(r\" u s \", \" american \", text)\n text = re.sub(r\"\\0s\", \"0\", text)\n text = re.sub(r\" 9 11 \", \"911\", text)\n text = re.sub(r\"e - mail\", \"email\", text)\n text = re.sub(r\"j k\", \"jk\", text)\n text = re.sub(r\"\\s{2,}\", \" \", text)\n\n # Optionally, shorten words to their stems\n if stem_words:\n text = text.split()\n stemmer = SnowballStemmer('english')\n stemmed_words = [stemmer.stem(word) for word in text]\n text = \" \".join(stemmed_words)\n\n # Return a list of words\n return (text)\n\n\ntexts_1 = []\ntexts_2 = []\nlabels = []\nwith codecs.open(TRAIN_DATA_FILE, encoding='utf-8') as f:\n reader = csv.reader(f, delimiter=',')\n header = next(reader)\n for values in reader:\n texts_1.append(text_to_wordlist(values[3]))\n texts_2.append(text_to_wordlist(values[4]))\n labels.append(int(values[5]))\nprint('Found %s texts in train.csv' % len(texts_1))\n\ntest_texts_1 = []\ntest_texts_2 = []\ntest_ids = []\nwith codecs.open(TEST_DATA_FILE, encoding='utf-8') as f:\n reader = csv.reader(f, delimiter=',')\n header = next(reader)\n for values in reader:\n test_texts_1.append(text_to_wordlist(values[1]))\n test_texts_2.append(text_to_wordlist(values[2]))\n test_ids.append(values[0])\nprint('Found %s texts in test.csv' % len(test_texts_1))\n\ntokenizer = Tokenizer(num_words=MAX_NB_WORDS)\ntokenizer.fit_on_texts(texts_1 + texts_2 + test_texts_1 + test_texts_2)\n\nsequences_1 = tokenizer.texts_to_sequences(texts_1)\nsequences_2 = tokenizer.texts_to_sequences(texts_2)\ntest_sequences_1 = tokenizer.texts_to_sequences(test_texts_1)\ntest_sequences_2 = tokenizer.texts_to_sequences(test_texts_2)\n\nword_index = tokenizer.word_index\nprint('Found %s unique tokens' % len(word_index))\n\ndata_1 = pad_sequences(sequences_1, maxlen=MAX_SEQUENCE_LENGTH)\ndata_2 = pad_sequences(sequences_2, maxlen=MAX_SEQUENCE_LENGTH)\nlabels = np.array(labels)\nprint('Shape of data tensor:', data_1.shape)\nprint('Shape of label tensor:', labels.shape)\n\ntest_data_1 = pad_sequences(test_sequences_1, maxlen=MAX_SEQUENCE_LENGTH)\ntest_data_2 = pad_sequences(test_sequences_2, maxlen=MAX_SEQUENCE_LENGTH)\ntest_ids = np.array(test_ids)\n\n########################################\n## prepare embeddings\n########################################\nprint('Preparing embedding matrix')\n\nnb_words = min(MAX_NB_WORDS, len(word_index)) + 1\n\nembedding_matrix = np.zeros((nb_words, EMBEDDING_DIM))\nfor word, i in word_index.items():\n if word in word2vec.vocab:\n embedding_matrix[i] = word2vec.word_vec(word)\nprint('Null word embeddings: %d' % np.sum(np.sum(embedding_matrix, axis=1) == 0))\n\n########################################\n## sample train/validation data\n########################################\n# np.random.seed(1234)\nperm = np.random.permutation(len(data_1))\nidx_train = perm[:int(len(data_1) * (1 - VALIDATION_SPLIT))]\nidx_val = perm[int(len(data_1) * (1 - VALIDATION_SPLIT)):]\n\ndata_1_train = np.vstack((data_1[idx_train], data_2[idx_train]))\ndata_2_train = np.vstack((data_2[idx_train], data_1[idx_train]))\nlabels_train = np.concatenate((labels[idx_train], labels[idx_train]))\n\ndata_1_val = np.vstack((data_1[idx_val], data_2[idx_val]))\ndata_2_val = np.vstack((data_2[idx_val], data_1[idx_val]))\nlabels_val = np.concatenate((labels[idx_val], labels[idx_val]))\n\nweight_val = np.ones(len(labels_val))\nif re_weight:\n weight_val *= 0.472001959\n weight_val[labels_val == 0] = 1.309028344\n\n########################################\n## define the model structure\n########################################\nembedding_layer = Embedding(nb_words,\n EMBEDDING_DIM,\n weights=[embedding_matrix],\n input_length=MAX_SEQUENCE_LENGTH,\n trainable=False)\nlstm_layer = LSTM(num_lstm, dropout=rate_drop_lstm, recurrent_dropout=rate_drop_lstm)\n\nsequence_1_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\nembedded_sequences_1 = embedding_layer(sequence_1_input)\nx1 = lstm_layer(embedded_sequences_1)\n\nsequence_2_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\nembedded_sequences_2 = embedding_layer(sequence_2_input)\ny1 = lstm_layer(embedded_sequences_2)\n\nmerged = concatenate([x1, y1])\nmerged = Dropout(rate_drop_dense)(merged)\nmerged = BatchNormalization()(merged)\n\nmerged = Dense(num_dense, activation=act)(merged)\nmerged = Dropout(rate_drop_dense)(merged)\nmerged = BatchNormalization()(merged)\n\npreds = Dense(1, activation='sigmoid')(merged)\n\n########################################\n## add class weight\n########################################\nif re_weight:\n class_weight = {0: 1.309028344, 1: 0.472001959}\nelse:\n class_weight = None\n\n########################################\n## train the model\n########################################\nmodel = Model(inputs=[sequence_1_input, sequence_2_input], \\\n outputs=preds)\nmodel.compile(loss='binary_crossentropy',\n optimizer='nadam',\n metrics=['acc'])\n# model.summary()\nprint(STAMP)\n\nearly_stopping = EarlyStopping(monitor='val_loss', patience=3)\nbst_model_path = STAMP + '.h5'\nmodel_checkpoint = ModelCheckpoint(bst_model_path, save_best_only=True, save_weights_only=True)\n\nhist = model.fit([data_1_train, data_2_train], labels_train, \\\n validation_data=([data_1_val, data_2_val], labels_val, weight_val), \\\n epochs=200, batch_size=2048, shuffle=True, \\\n class_weight=class_weight, callbacks=[early_stopping, model_checkpoint])\n\nmodel.load_weights(bst_model_path)\nbst_val_score = min(hist.history['val_loss'])\n\n########################################\n## make the submission\n########################################\nprint('Start making the submission before fine-tuning')\n\npreds = model.predict([test_data_1, test_data_2], batch_size=8192, verbose=1)\npreds += model.predict([test_data_2, test_data_1], batch_size=8192, verbose=1)\npreds /= 2\n\nsubmission = pd.DataFrame({'test_id':test_ids, 'is_duplicate':preds.ravel()})\nsubmission.to_csv('%.4f_'%(bst_val_score)+STAMP+'.csv', index=False)\n" }, { "path": "Quora_questions_similar/similar_questions.py", "content": "from __future__ import print_function\nimport numpy as np\nimport csv, datetime, time, json\nfrom zipfile import ZipFile\nfrom os.path import expanduser, exists\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import Model\nfrom keras.layers import Input, TimeDistributed, Dense, Lambda, concatenate,Dropout, BatchNormalization\nfrom keras.layers.embeddings import Embedding\nfrom keras.regularizers import l2\nfrom keras.callbacks import Callback, ModelCheckpoint\nfrom keras.utils.data_utils import get_file\nfrom keras import backend as K\nfrom sklearn.model_selection import train_test_split\n\n# Initialize global variables\nKERAS_DATASETS_DIR = expanduser('~/.keras/datasets/')\nQUESTION_PAIRS_FILE_URL = 'http://qim.ec.quoracdn.net/quora_duplicate_questions.tsv'\nQUESTION_PAIRS_FILE = 'quora_duplicate_questions.tsv'\nGLOVE_ZIP_FILE_PATH = '/Users/ruizhang/Documents/NLP_dataset/'\nGLOVE_ZIP_FILE = 'glove.840B.300d.zip'\nGLOVE_FILE = 'glove.840B.300d.txt'\nQ1_TRAINING_DATA_FILE = 'q1_train.npy'\nQ2_TRAINING_DATA_FILE = 'q2_train.npy'\nLABEL_TRAINING_DATA_FILE = 'label_train.npy'\nWORD_EMBEDDING_MATRIX_FILE = 'word_embedding_matrix.npy'\nNB_WORDS_DATA_FILE = 'nb_words.json'\nMAX_NB_WORDS = 200000\nMAX_SEQUENCE_LENGTH = 25\nEMBEDDING_DIM = 300\nMODEL_WEIGHTS_FILE = 'question_pairs_weights.h5'\nVALIDATION_SPLIT = 0.1\nTEST_SPLIT = 0.1\nRNG_SEED = 13371447\nNB_EPOCHS = 5\nDROPOUT = 0.1\nBATCH_SIZE = 32\nOPTIMIZER = 'adam'\n\n# If the dataset, embedding matrix and word count exist in the local directory\nif exists(Q1_TRAINING_DATA_FILE) and exists(Q2_TRAINING_DATA_FILE) and exists(LABEL_TRAINING_DATA_FILE) and exists(\n NB_WORDS_DATA_FILE) and exists(WORD_EMBEDDING_MATRIX_FILE):\n # Then load them\n q1_data = np.load(open(Q1_TRAINING_DATA_FILE, 'rb'))\n q2_data = np.load(open(Q2_TRAINING_DATA_FILE, 'rb'))\n labels = np.load(open(LABEL_TRAINING_DATA_FILE, 'rb'))\n word_embedding_matrix = np.load(open(WORD_EMBEDDING_MATRIX_FILE, 'rb'))\n with open(NB_WORDS_DATA_FILE, 'r') as f:\n nb_words = json.load(f)['nb_words']\nelse:\n # Else download and extract questions pairs data\n if not exists(KERAS_DATASETS_DIR + QUESTION_PAIRS_FILE):\n get_file(QUESTION_PAIRS_FILE, QUESTION_PAIRS_FILE_URL)\n\n print(\"Processing\", QUESTION_PAIRS_FILE)\n\n question1 = []\n question2 = []\n is_duplicate = []\n with open(KERAS_DATASETS_DIR + QUESTION_PAIRS_FILE, encoding='utf-8') as csvfile:\n reader = csv.DictReader(csvfile, delimiter='\\t')\n for row in reader:\n question1.append(row['question1'])\n question2.append(row['question2'])\n is_duplicate.append(row['is_duplicate'])\n\n print('Question pairs: %d' % len(question1))\n\n # Build tokenized word index\n questions = question1 + question2\n tokenizer = Tokenizer(nb_words=MAX_NB_WORDS)\n tokenizer.fit_on_texts(questions)\n question1_word_sequences = tokenizer.texts_to_sequences(question1)\n question2_word_sequences = tokenizer.texts_to_sequences(question2)\n word_index = tokenizer.word_index\n\n print(\"Words in index: %d\" % len(word_index))\n\n # Download and process GloVe embeddings\n if not exists(KERAS_DATASETS_DIR + GLOVE_ZIP_FILE):\n zipfile = ZipFile(GLOVE_ZIP_FILE_PATH+GLOVE_ZIP_FILE)\n zipfile.extract(GLOVE_FILE, path=KERAS_DATASETS_DIR)\n\n print(\"Processing\", GLOVE_FILE)\n\n embeddings_index = {}\n with open(KERAS_DATASETS_DIR + GLOVE_FILE, encoding='utf-8') as f:\n for line in f:\n values = line.split(' ')\n word = values[0]\n embedding = np.asarray(values[1:], dtype='float32')\n embeddings_index[word] = embedding\n\n print('Word embeddings: %d' % len(embeddings_index))\n\n # Prepare word embedding matrix\n nb_words = min(MAX_NB_WORDS, len(word_index))\n word_embedding_matrix = np.zeros((nb_words + 1, EMBEDDING_DIM))\n for word, i in word_index.items():\n if i > MAX_NB_WORDS:\n continue\n embedding_vector = embeddings_index.get(word)\n if embedding_vector is not None:\n word_embedding_matrix[i] = embedding_vector\n\n print('Null word embeddings: %d' % np.sum(np.sum(word_embedding_matrix, axis=1) == 0))\n\n # Prepare training data tensors\n q1_data = pad_sequences(question1_word_sequences, maxlen=MAX_SEQUENCE_LENGTH)\n q2_data = pad_sequences(question2_word_sequences, maxlen=MAX_SEQUENCE_LENGTH)\n labels = np.array(is_duplicate, dtype=int)\n print('Shape of question1 data tensor:', q1_data.shape)\n print('Shape of question2 data tensor:', q2_data.shape)\n print('Shape of label tensor:', labels.shape)\n\n # Persist training and configuration data to files\n np.save(open(Q1_TRAINING_DATA_FILE, 'wb'), q1_data)\n np.save(open(Q2_TRAINING_DATA_FILE, 'wb'), q2_data)\n np.save(open(LABEL_TRAINING_DATA_FILE, 'wb'), labels)\n np.save(open(WORD_EMBEDDING_MATRIX_FILE, 'wb'), word_embedding_matrix)\n with open(NB_WORDS_DATA_FILE, 'w') as f:\n json.dump({'nb_words': nb_words}, f)\n\n# Partition the dataset into train and test sets\nX = np.stack((q1_data, q2_data), axis=1)\ny = labels\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SPLIT, random_state=RNG_SEED)\nQ1_train = X_train[:, 0]\nQ2_train = X_train[:, 1]\nQ1_test = X_test[:, 0]\nQ2_test = X_test[:, 1]\n\n# Define the model\nquestion1 = Input(shape=(MAX_SEQUENCE_LENGTH,))\nquestion2 = Input(shape=(MAX_SEQUENCE_LENGTH,))\nprint(question1.shape)\nprint(question2.shape)\n\nq1 = Embedding(nb_words + 1,\n EMBEDDING_DIM,\n weights=[word_embedding_matrix],\n input_length=MAX_SEQUENCE_LENGTH,\n trainable=False)(question1)\nprint(word_embedding_matrix.shape)\n\nprint(q1.shape)\nq1 = TimeDistributed(Dense(EMBEDDING_DIM, activation='relu'))(q1)\nq1 = Lambda(lambda x: K.max(x, axis=1), output_shape=(EMBEDDING_DIM,))(q1)\n\nq2 = Embedding(nb_words + 1,\n EMBEDDING_DIM,\n weights=[word_embedding_matrix],\n input_length=MAX_SEQUENCE_LENGTH,\n trainable=False)(question2)\nprint(q2.shape)\nprint(nb_words)\nq2 = TimeDistributed(Dense(EMBEDDING_DIM, activation='relu'))(q2)\nq2 = Lambda(lambda x: K.max(x, axis=1), output_shape=(EMBEDDING_DIM,))(q2)\n\nmerged = concatenate([q1,q2])\nprint(merged.shape)\n\nmerged = Dense(128, activation='relu')(merged)\nmerged = Dropout(DROPOUT)(merged)\nmerged = BatchNormalization()(merged)\nmerged = Dense(64, activation='relu')(merged)\nmerged = Dropout(DROPOUT)(merged)\nmerged = BatchNormalization()(merged)\n# merged = Dense(200, activation='relu')(merged)\n# merged = Dropout(DROPOUT)(merged)\n# merged = BatchNormalization()(merged)\n# merged = Dense(200, activation='relu')(merged)\n# merged = Dropout(DROPOUT)(merged)\n# merged = BatchNormalization()(merged)\n\nis_duplicate = Dense(1, activation='sigmoid')(merged)\n\nmodel = Model(inputs=[question1, question2], outputs=is_duplicate)\n\nmodel.compile(loss='binary_crossentropy', optimizer=OPTIMIZER, metrics=['accuracy'])\n\n# Train the model, checkpointing weights with best validation accuracy\nprint(\"Starting training at\", datetime.datetime.now())\nt0 = time.time()\ncallbacks = [ModelCheckpoint(MODEL_WEIGHTS_FILE, monitor='val_acc', save_best_only=True)]\nhistory = model.fit([Q1_train, Q2_train],\n y_train,\n epochs=NB_EPOCHS,\n validation_split=VALIDATION_SPLIT,\n verbose=2,\n batch_size=BATCH_SIZE,\n callbacks=callbacks)\nt1 = time.time()\nprint(\"Training ended at\", datetime.datetime.now())\nprint(\"Minutes elapsed: %f\" % ((t1 - t0) / 60.))\n\n# Print best validation accuracy and epoch\nmax_val_acc, idx = max((val, idx) for (idx, val) in enumerate(history.history['val_acc']))\nprint('Maximum validation accuracy = {0:.4f} (epoch {1:d})'.format(max_val_acc, idx + 1))\n\n# Evaluate the model with best validation accuracy on the test partition\nmodel.load_weights(MODEL_WEIGHTS_FILE)\nloss, accuracy = model.evaluate([Q1_test, Q2_test], y_test, verbose=0)\nprint('Test loss = {0:.4f}, test accuracy = {1:.4f}'.format(loss, accuracy))" }, { "path": "README.md", "content": "\n\n**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*\n\n- [Basic algorithm/Framework](#basic-algorithmframework)\n - [Basics Machine learning](#basics-machine-learning)\n - [CNN](#cnn)\n - [RNN](#rnn)\n - [TensorFlow](#tensorflow)\n - [Pytorch](#pytorch)\n - [GBDT](#gbdt)\n - [SVM](#svm)\n- [Applications](#applications)\n - [NLP](#nlp)\n - [Search/Rank/CTR](#searchrankctr)\n - [Text Classification](#text-classification)\n - [Recommendations](#recommendations)\n - [Industrial machine Learning Application design](#industrial-machine-learning-application-design)\n- [Machine learning implementation in large scale system](#machine-learning-implementation-in-large-scale-system)\n - [Deep Learning/AI Chip Design](#deep-learningai-chip-design)\n- [Reference Materails](#reference-materails)\n - [Kaggle](#kaggle)\n - [Book](#book)\n - [Tutorial](#tutorial)\n - [Courses](#courses)\n - [Workshop](#workshop)\n - [Blog](#blog)\n - [Blog Posts](#blog-posts)\n - [Code and Framework](#code-and-framework)\n - [Open Source](#open-source)\n\n\n\n# Basic algorithm/Framework\n\n## Basics Machine learning\n[Basics Machine learning](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/Basics_Machine_learning.md)\n\n## CNN\n[CNN](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/CNN.md)\n\n## RNN\n[RNN](https://github.com/zhangruiskyline/DeepLearning/blob/master/doc/RNN.md)\n\n## TensorFlow\n\n[TensorFlow and Keras](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/Tensorflow_Keras.md)\n\n## Pytorch\n\n[Pytorch](https://github.com/zhangruiskyline/DeepLearning/blob/master/doc/pytorch.md)\n\n\n## GBDT\n[Tree(decision_tree,xgboost)](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/decision_tree.md)\n\n## SVM\n[SVM](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/SVM.md)\n\n\n# Applications\n\n## NLP\n\n* [NLP](https://github.com/zhangruiskyline/DeepLearning/blob/master/doc/NLP.md)\n\n## Search/Rank/CTR\n\n* [Search/Rank/CTR](https://github.com/zhangruiskyline/DeepLearning/blob/master/doc/Search.md)\n\n## Text Classification\n\n* [Summary of Text Classification](https://github.com/zhangruiskyline/text_classification)\n\n* 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/)\n\n* Bidirectional LSTM and one level attentional RNN. [blog](https://richliao.github.io/supervised/classification/2016/12/26/textclassifier-RNN/)\n\n## Recommendations\n* [Recommendation using DL](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/Recommendation.md)\n\n## Industrial machine Learning Application design\n\n[Industrial machine Learning Application design](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/industrial_design.md)\n\n# Machine learning implementation in large scale system\n[Machine learning implementation in large scale system](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/system.md)\n\n## Deep Learning/AI Chip Design\n\n[Deep Learning/AI Chip Design](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/AI_chip.md)\n\n\n# Reference Materails\n\n## Kaggle\n\n* [Kaggle experience](https://github.com/zhangruiskyline/DeepLearning_Intro/blob/master/doc/Kaggle.md)\n\n## Book\n* [Deep Learning: An MIT Press Book](http://www.deeplearningbook.org)\n * By Ian Goodfellow and Yoshua Bengio and Aaron Courville\n\n## Tutorial\n* [Stanford Deep Learning Tutorial](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial)\n\n* [A Guide to Deep Learning](http://yerevann.com/a-guide-to-deep-learning/)\n\n* [Reinforcement Learning- OpenAI Gym](https://gym.openai.com/)\n\n* [Fast AI Tutorial](http://course.fast.ai)\n\n* [Introduction to Boosted Trees](http://xgboost.readthedocs.io/en/latest/model.html)\n\n## Courses\n\n* [Stanford: CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu)\n * [CS231n Chinese Translation](https://zhuanlan.zhihu.com/p/21930884?refer=intelligentunit)\n\n* [Berkeley: CS 294: Deep Reinforcement Learning](http://rll.berkeley.edu/deeprlcourse/)\n\n* [MIT: Deep Learning Self Driving Car](http://selfdrivingcars.mit.edu/)\n\n* [Oxford: Deep Learning on NLP](https://github.com/oxford-cs-deepnlp-2017)\n\n* [UCL: Reinforcement learning (By D.Silver)](http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html)\n\n* [Stanford: CS224n: Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/)\n\n* [CMU:Scalable Machine Learning (By A. Smola)](http://alex.smola.org/teaching/berkeley2012/)\n\n## Workshop\n\n* [Applied Deep Learning Workshop London 2017](https://github.com/telecombcn-dl/2017-persontyle)\n\n* [Deep Learning course: lecture slides and lab notebooks](https://github.com/m2dsupsdlclass/lectures-labs)\n\n\n## Blog\n\n* [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)\n\n* [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/)\n\n* [Word2Vec Resource (by Chris McCormick)](http://mccormickml.com/2016/04/27/word2vec-resources/#alex-minnaars-tutorials)\n\n* [AI, Deep Learning and NLP (By Denny Britz in Google Brain)](http://www.wildml.com)\n\n\n### Blog Posts\n\n* [Understand CNN on NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/)\n * [IMPLEMENTING A CNN FOR TEXT CLASSIFICATION IN TENSORFLOW](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)\n\n* [Understanding RNN](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/)\n\n* [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)\n\n* [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)\n\n## Code and Framework\n\n* [LEARNING REINFORCEMENT LEARNING (WITH CODE, EXERCISES AND SOLUTIONS)](http://www.wildml.com/2016/10/learning-reinforcement-learning/)\n * [Github link](https://github.com/dennybritz/reinforcement-learning)\n\n### Open Source\n\n* Prophet: forecasting at scale by Facebook\n\nFacebook is open sourcing [Prophet](https://research.fb.com/prophet-forecasting-at-scale/), a forecasting tool available\n\nsome interesting project based on it\n1. [Forecasting iPad sales using Facebook's Prophet package](https://github.com/natebrix/prophet-ipad)\n" }, { "path": "TensorFlow_Examples/.gitignore", "content": "# random crap\n*~\n*.swp\n\\#*\\#\n.\\#*\n\n# python crap\n*.pyc\n__pycache__/\n\nevaluations/*.pdf\n\n# Dataset files and folders\nml-100k\nml-100k.zip\nMNIST_data/\nimages_resize\nlabs/04_conv_nets_2/VOCdevkit/\nlabs/04_conv_nets_2/VOCtrainval_06-Nov-2007.tar\nlabs/04_conv_nets_2/voc_representaions.h5\nlabs/04_conv_nets_2/voc_representations.h5\nglove100*\nsimple_seq2seq_pretrained.h5\nsimple_seq2seq_partially_pretrained.h5\n\n# generated data\nresults\ntsne.png\nsimple_seq2seq_checkpoint.h5\n\n# editors crap\n.vscode\n.ipynb_checkpoints/\n\n# tf summary folders\ntensorflow_summaries\n\n# VS Code jupyter temp files\nkernel-*.json\ntags\n\n\ninstallation_instructions.md\n" }, { "path": "TensorFlow_Examples/LICENSE", "content": "MIT License\n\nCopyright (c) 2017 \n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "TensorFlow_Examples/_config.yml", "content": "theme: jekyll-theme-slate" }, { "path": "TensorFlow_Examples/assets/css/style.scss", "content": "---\n---\n\n@import \"{{ site.theme }}\";\n\n#header_wrap {\n display: none;\n}\n" }, { "path": "TensorFlow_Examples/labs/02_recommendation/Explicit_Feedback_Neural_Recommender_System.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"# Explicit Feedback Neural Recommender Systems\\n\",\n \"\\n\",\n \"Goals:\\n\",\n \"- Understand recommender data\\n\",\n \"- Build different models architectures using Keras\\n\",\n \"- Retrieve Embeddings and visualize them\\n\",\n \"- Add metadata information as input to the model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import os.path as op\\n\",\n \"\\n\",\n \"from zipfile import ZipFile\\n\",\n \"try:\\n\",\n \" from urllib.request import urlretrieve\\n\",\n \"except ImportError: # Python 2 compat\\n\",\n \" from urllib import urlretrieve\\n\",\n \"\\n\",\n \"\\n\",\n \"ML_100K_URL = \\\"http://files.grouplens.org/datasets/movielens/ml-100k.zip\\\"\\n\",\n \"ML_100K_FILENAME = ML_100K_URL.rsplit('/', 1)[1]\\n\",\n \"ML_100K_FOLDER = 'ml-100k'\\n\",\n \"\\n\",\n \"if not op.exists(ML_100K_FILENAME):\\n\",\n \" print('Downloading %s to %s...' % (ML_100K_URL, ML_100K_FILENAME))\\n\",\n \" urlretrieve(ML_100K_URL, ML_100K_FILENAME)\\n\",\n \"\\n\",\n \"if not op.exists(ML_100K_FOLDER):\\n\",\n \" print('Extracting %s to %s...' % (ML_100K_FILENAME, ML_100K_FOLDER))\\n\",\n \" ZipFile(ML_100K_FILENAME).extractall('.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Ratings file\\n\",\n \"\\n\",\n \"Each line contains a rated movie: \\n\",\n \"- a user\\n\",\n \"- an item\\n\",\n \"- a rating from 1 to 5 stars\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(100000, 4)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" user_id item_id rating timestamp\\n\",\n \"0 196 242 3 881250949\\n\",\n \"1 186 302 3 891717742\\n\",\n \"2 22 377 1 878887116\\n\",\n \"3 244 51 2 880606923\\n\",\n \"4 166 346 1 886397596\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"all_ratings = pd.read_csv(op.join(ML_100K_FOLDER, 'u.data'), sep='\\\\t',\\n\",\n \" names=[\\\"user_id\\\", \\\"item_id\\\", \\\"rating\\\", \\\"timestamp\\\"])\\n\",\n \"print(all_ratings.shape)\\n\",\n \"all_ratings.head()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Item metadata file\\n\",\n \"\\n\",\n \"The item metadata file contains metadata like the name of the movie or the date it was released\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | user_id | \\n\",\n \"item_id | \\n\",\n \"rating | \\n\",\n \"timestamp | \\n\",\n \"
|---|---|---|---|---|
| 0 | \\n\",\n \"196 | \\n\",\n \"242 | \\n\",\n \"3 | \\n\",\n \"881250949 | \\n\",\n \"
| 1 | \\n\",\n \"186 | \\n\",\n \"302 | \\n\",\n \"3 | \\n\",\n \"891717742 | \\n\",\n \"
| 2 | \\n\",\n \"22 | \\n\",\n \"377 | \\n\",\n \"1 | \\n\",\n \"878887116 | \\n\",\n \"
| 3 | \\n\",\n \"244 | \\n\",\n \"51 | \\n\",\n \"2 | \\n\",\n \"880606923 | \\n\",\n \"
| 4 | \\n\",\n \"166 | \\n\",\n \"346 | \\n\",\n \"1 | \\n\",\n \"886397596 | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" name date genre \\\\\\n\",\n \"1 Toy Story (1995) 01-Jan-1995 01-Jan-1997 \\n\",\n \"2 GoldenEye (1995) 01-Jan-1995 01-Jan-1997 \\n\",\n \"3 Four Rooms (1995) 01-Jan-1995 01-Jan-1997 \\n\",\n \"4 Get Shorty (1995) 01-Jan-1995 01-Jan-1997 \\n\",\n \"5 Copycat (1995) 01-Jan-1995 01-Jan-1997 \\n\",\n \"\\n\",\n \" url f0 f1 f2 f3 f4 f5 \\\\\\n\",\n \"1 http://us.imdb.com/M/title-exact?Toy%20Story%2... 0 0 0 1 1 1 \\n\",\n \"2 http://us.imdb.com/M/title-exact?GoldenEye%20(... 0 1 1 0 0 0 \\n\",\n \"3 http://us.imdb.com/M/title-exact?Four%20Rooms%... 0 0 0 0 0 0 \\n\",\n \"4 http://us.imdb.com/M/title-exact?Get%20Shorty%... 0 1 0 0 0 1 \\n\",\n \"5 http://us.imdb.com/M/title-exact?Copycat%20(1995) 0 0 0 0 0 0 \\n\",\n \"\\n\",\n \" ... f9 f10 f11 f12 f13 f14 f15 f16 f17 f18 \\n\",\n \"1 ... 0 0 0 0 0 0 0 0 0 0 \\n\",\n \"2 ... 0 0 0 0 0 0 0 1 0 0 \\n\",\n \"3 ... 0 0 0 0 0 0 0 1 0 0 \\n\",\n \"4 ... 0 0 0 0 0 0 0 0 0 0 \\n\",\n \"5 ... 0 0 0 0 0 0 0 1 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 23 columns]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"names = [\\\"name\\\", \\\"date\\\", \\\"genre\\\", \\\"url\\\"]\\n\",\n \"names += [\\\"f\\\" + str(x) for x in range(19)] # unused feature names\\n\",\n \"\\n\",\n \"items = pd.read_csv(op.join(ML_100K_FOLDER, 'u.item'), sep='|', encoding='latin-1',\\n\",\n \" names=names)\\n\",\n \"# fix a missing value\\n\",\n \"items.fillna(value=\\\"01-Jan-1997\\\", inplace=True)\\n\",\n \"items.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Data preprocessing\\n\",\n \"\\n\",\n \"To understand well the distribution of the data, the following statistics are computed:\\n\",\n \"- the number of users\\n\",\n \"- the number of items\\n\",\n \"- the rating distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(100000, 4)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"count 100000.000000\\n\",\n \"mean 3.529860\\n\",\n \"std 1.125674\\n\",\n \"min 1.000000\\n\",\n \"25% 3.000000\\n\",\n \"50% 4.000000\\n\",\n \"75% 4.000000\\n\",\n \"max 5.000000\\n\",\n \"Name: rating, dtype: float64\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"print(all_ratings.shape)\\n\",\n \"\\n\",\n \"all_ratings['rating'].describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"943\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"max_user_id = all_ratings['user_id'].max()\\n\",\n \"max_user_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1682\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"max_item_id = all_ratings['item_id'].max()\\n\",\n \"max_item_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"count 80000.000000\\n\",\n \"mean 425.538538\\n\",\n \"std 331.169279\\n\",\n \"min 1.000000\\n\",\n \"25% 174.000000\\n\",\n \"50% 322.000000\\n\",\n \"75% 631.000000\\n\",\n \"max 1682.000000\\n\",\n \"Name: item_id, dtype: float64\\n\",\n \"count 20000.000000\\n\",\n \"mean 425.496500\\n\",\n \"std 329.318736\\n\",\n \"min 1.000000\\n\",\n \"25% 176.000000\\n\",\n \"50% 321.000000\\n\",\n \"75% 632.000000\\n\",\n \"max 1680.000000\\n\",\n \"Name: item_id, dtype: float64\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"ratings_train, ratings_test = train_test_split(\\n\",\n \" all_ratings, test_size=0.2, random_state=0)\\n\",\n \"\\n\",\n \"user_id_train = ratings_train['user_id']\\n\",\n \"item_id_train = ratings_train['item_id']\\n\",\n \"rating_train = ratings_train['rating']\\n\",\n \"\\n\",\n \"print(item_id_train.describe())\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"user_id_test = ratings_test['user_id']\\n\",\n \"item_id_test = ratings_test['item_id']\\n\",\n \"rating_test = ratings_test['rating']\\n\",\n \"\\n\",\n \"print(item_id_test.describe())\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"# Explicit feedback: supervised ratings prediction\\n\",\n \"\\n\",\n \"For each pair of (user, item) try to predict the rating the user would give to the item.\\n\",\n \"\\n\",\n \"This is the classical setup for building recommender systems from offline data with explicit supervision signal. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Predictive ratings as a regression problem\\n\",\n \"\\n\",\n \"The following code implements the following architecture:\\n\",\n \"\\n\",\n \"| \\n\",\n \" | name | \\n\",\n \"date | \\n\",\n \"genre | \\n\",\n \"url | \\n\",\n \"f0 | \\n\",\n \"f1 | \\n\",\n \"f2 | \\n\",\n \"f3 | \\n\",\n \"f4 | \\n\",\n \"f5 | \\n\",\n \"... | \\n\",\n \"f9 | \\n\",\n \"f10 | \\n\",\n \"f11 | \\n\",\n \"f12 | \\n\",\n \"f13 | \\n\",\n \"f14 | \\n\",\n \"f15 | \\n\",\n \"f16 | \\n\",\n \"f17 | \\n\",\n \"f18 | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | \\n\",\n \"Toy Story (1995) | \\n\",\n \"01-Jan-1995 | \\n\",\n \"01-Jan-1997 | \\n\",\n \"http://us.imdb.com/M/title-exact?Toy%20Story%2... | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"... | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"
| 2 | \\n\",\n \"GoldenEye (1995) | \\n\",\n \"01-Jan-1995 | \\n\",\n \"01-Jan-1997 | \\n\",\n \"http://us.imdb.com/M/title-exact?GoldenEye%20(... | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"... | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"
| 3 | \\n\",\n \"Four Rooms (1995) | \\n\",\n \"01-Jan-1995 | \\n\",\n \"01-Jan-1997 | \\n\",\n \"http://us.imdb.com/M/title-exact?Four%20Rooms%... | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"... | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"
| 4 | \\n\",\n \"Get Shorty (1995) | \\n\",\n \"01-Jan-1995 | \\n\",\n \"01-Jan-1997 | \\n\",\n \"http://us.imdb.com/M/title-exact?Get%20Shorty%... | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"... | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"
| 5 | \\n\",\n \"Copycat (1995) | \\n\",\n \"01-Jan-1995 | \\n\",\n \"01-Jan-1997 | \\n\",\n \"http://us.imdb.com/M/title-exact?Copycat%20(1995) | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"... | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"
5 rows × 23 columns
\\n\",\n \"![](\\\"images/rec_archi_1.svg\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using TensorFlow backend.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from keras.layers import Input, Embedding, Flatten, merge, Dense, Dropout, Lambda\\n\",\n \"from keras.models import Model\\n\",\n \"import keras.backend as K\\n\",\n \"from keras_fixes import dot_mode\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[[1 2 3]]]\\n\",\n \"[[[4 5 6]]]\\n\",\n \"[[[ 1. 2. 3. 4. 5. 6.]]]\\n\",\n \"[[[ 32.]]]\\n\",\n \"[[[[ 0.97463191]]]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# test the merge in keras\\n\",\n \"from keras.layers import Input, merge\\n\",\n \"from keras.models import Model\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"input_a = np.reshape([1, 2, 3], (1, 1, 3))\\n\",\n \"input_b = np.reshape([4, 5, 6], (1, 1, 3))\\n\",\n \"\\n\",\n \"print(input_a)\\n\",\n \"print(input_b)\\n\",\n \"\\n\",\n \"a = Input(shape=(1, 3))\\n\",\n \"b = Input(shape=(1, 3))\\n\",\n \"\\n\",\n \"\\n\",\n \"concat = merge([a, b], mode='concat', concat_axis=-1)\\n\",\n \"dot = merge([a, b], mode='dot', dot_axes=2)\\n\",\n \"cos = merge([a, b], mode='cos', dot_axes=2)\\n\",\n \"\\n\",\n \"model_concat = Model(input=[a, b], output=concat)\\n\",\n \"model_dot = Model(input=[a, b], output=dot)\\n\",\n \"model_cos = Model(input=[a, b], output=cos)\\n\",\n \"\\n\",\n \"\\n\",\n \"print(model_concat.predict([input_a, input_b]))\\n\",\n \"print(model_dot.predict([input_a, input_b]))\\n\",\n \"print(model_cos.predict([input_a, input_b]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# For each sample we input the integer identifiers\\n\",\n \"# of a single user and a single item\\n\",\n \"user_id_input = Input(shape=[1], name='user')\\n\",\n \"item_id_input = Input(shape=[1], name='item')\\n\",\n \"\\n\",\n \"\\n\",\n \"embedding_size = 32\\n\",\n \"user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,\\n\",\n \" input_length=1, name='user_embedding')(user_id_input)\\n\",\n \"item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,\\n\",\n \" input_length=1, name='item_embedding')(item_id_input)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# reshape from shape: (batch_size, input_length, embedding_size)\\n\",\n \"# to shape: (batch_size, input_length * embedding_size) which is\\n\",\n \"# equal to shape: (batch_size, embedding_size)\\n\",\n \"user_vecs = Flatten()(user_embedding)\\n\",\n \"item_vecs = Flatten()(item_embedding)\\n\",\n \"\\n\",\n \"\\n\",\n \"y = merge([user_vecs, item_vecs], mode=dot_mode, output_shape=(1,))\\n\",\n \"\\n\",\n \"\\n\",\n \"model = Model(input=[user_id_input, item_id_input], output=y)\\n\",\n \"model.compile(optimizer='adam', loss='mae')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-0.00425739 0.00101695 0.00587618 ..., -0.00157842 0.00947668\\n\",\n \" 0.00450012]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Useful for debugging the output shape of model\\n\",\n \"initial_train_preds = model.predict([user_id_train, item_id_train])\\n\",\n \"initial_train_preds.shape\\n\",\n \"\\n\",\n \"print(initial_train_preds)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Model error\\n\",\n \"\\n\",\n \"Using `initial_train_preds`, compute the model errors:\\n\",\n \"- mean absolute error\\n\",\n \"- mean squared error\\n\",\n \"\\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`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Random init MSE: 13.732\\n\",\n \"Random init MAE: 3.531\\n\"\n ]\n }\n ],\n \"source\": [\n \"# %load solutions/compute_errors.py\\n\",\n \"squared_differences = np.square(initial_train_preds - rating_train.values)\\n\",\n \"absolute_differences = np.abs(initial_train_preds - rating_train.values)\\n\",\n \"\\n\",\n \"print(\\\"Random init MSE: %0.3f\\\" % np.mean(squared_differences))\\n\",\n \"print(\\\"Random init MAE: %0.3f\\\" % np.mean(absolute_differences))\\n\",\n \"\\n\",\n \"# You may also use sklearn metrics to do so with less numpy engineering \\n\",\n \"#from sklearn.metrics import mean_squared_error, mean_absolute_error\\n\",\n \"#\\n\",\n \"#print(\\\"Random init MSE: %0.3f\\\" % mean_squared_error(initial_train_preds, rating_train))\\n\",\n \"#print(\\\"Random init MAE: %0.3f\\\" % mean_absolute_error(initial_train_preds, rating_train))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Monitoring runs\\n\",\n \"\\n\",\n \"Keras enables to monitor various variables during training. \\n\",\n \"\\n\",\n \"`history.history` returned by the `model.fit` function is a dictionary\\n\",\n \"containing the `'loss'` and validation loss `'val_loss'` after each epoch\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 72000 samples, validate on 8000 samples\\n\",\n \"Epoch 1/6\\n\",\n \"3s - loss: 2.9310 - val_loss: 1.4521\\n\",\n \"Epoch 2/6\\n\",\n \"3s - loss: 1.1063 - val_loss: 0.9898\\n\",\n \"Epoch 3/6\\n\",\n \"3s - loss: 0.9439 - val_loss: 0.9467\\n\",\n \"Epoch 4/6\\n\",\n \"2s - loss: 0.9201 - val_loss: 0.9336\\n\",\n \"Epoch 5/6\\n\",\n \"2s - loss: 0.9123 - val_loss: 0.9291\\n\",\n \"Epoch 6/6\\n\",\n \"2s - loss: 0.9088 - val_loss: 0.9264\\n\",\n \"CPU times: user 24.6 s, sys: 5.59 s, total: 30.2 s\\n\",\n \"Wall time: 19.5 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# Training the model\\n\",\n \"history = model.fit([user_id_train, item_id_train], rating_train,\\n\",\n \" batch_size=64, nb_epoch=6, validation_split=0.1,\\n\",\n \" shuffle=True, verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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WwisMHd33P3BuBR4MqTWE9OmFFexp76RpZs2pXtUkSki0vVOf6Pm9kK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\\nkRyj4BcRyTEKfhGRHPP/AYBbEOEKv9eZAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"![](\\\"images/rec_archi_2.svg\\\")
\\n\",\n \"\\n\",\n \"\\n\",\n \"### Exercise\\n\",\n \"\\n\",\n \"- The following code has **4 errors** that prevent it from working correctly. **Correct them and explain** why they are critical.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# %load solutions/deep_explicit_feedback_recsys.py\\n\",\n \"# For each sample we input the integer identifiers\\n\",\n \"# of a single user and a single item\\n\",\n \"user_id_input = Input(shape=[1], name='user')\\n\",\n \"item_id_input = Input(shape=[1], name='item')\\n\",\n \"\\n\",\n \"embedding_size = 50\\n\",\n \"user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,\\n\",\n \" input_length=1, name='user_embedding')(user_id_input)\\n\",\n \"item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,\\n\",\n \" input_length=1, name='item_embedding')(item_id_input)\\n\",\n \"\\n\",\n \"# reshape from shape: (batch_size, input_length, embedding_size)\\n\",\n \"# to shape: (batch_size, input_length * embedding_size) which is\\n\",\n \"# equal to shape: (batch_size, embedding_size)\\n\",\n \"user_vecs = Flatten()(user_embedding)\\n\",\n \"item_vecs = Flatten()(item_embedding)\\n\",\n \"\\n\",\n \"input_vecs = merge([user_vecs, item_vecs], mode='concat')\\n\",\n \"\\n\",\n \"input_vecs = Dropout(0.2)(input_vecs)\\n\",\n \"\\n\",\n \"x = Dense(64, activation='relu')(input_vecs)\\n\",\n \"## output dimension for 1-d rating\\n\",\n \"## tanh activation squashes the outputs between -1 and 1\\n\",\n \"## when we want to predict values between 1 and 5\\n\",\n \"y = Dense(1)(x)\\n\",\n \"model = Model(input=[user_id_input, item_id_input], output=y)\\n\",\n \"## A binary crossentropy loss is only useful for binary\\n\",\n \"## classification, while we are in regression (use mse or mae)\\n\",\n \"model.compile(optimizer='adam', loss='mae')\\n\",\n \"\\n\",\n \"initial_train_preds = model.predict([user_id_train, item_id_train])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 72000 samples, validate on 8000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"4s - loss: 0.9496 - val_loss: 0.7715\\n\",\n \"Epoch 2/5\\n\",\n \"4s - loss: 0.7486 - val_loss: 0.7603\\n\",\n \"Epoch 3/5\\n\",\n \"3s - loss: 0.7347 - val_loss: 0.7496\\n\",\n \"Epoch 4/5\\n\",\n \"5s - loss: 0.7220 - val_loss: 0.7460\\n\",\n \"Epoch 5/5\\n\",\n \"5s - loss: 0.7118 - val_loss: 0.7397\\n\",\n \"CPU times: user 33.4 s, sys: 9.85 s, total: 43.3 s\\n\",\n \"Wall time: 24 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"history = model.fit([user_id_train, item_id_train], rating_train,\\n\",\n \" batch_size=64, nb_epoch=5, validation_split=0.1,\\n\",\n \" shuffle=True, verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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5lZyjjxm5mljBO/mVnKOPGbmaWME7+ZWco48ZuZpYwTv5lZyjjxm5ml\\nTJ+JX9IKSW2SevyidJXcK2mzpHWSFpTVLZG0Kam7s5qBm5nZ8ankiP+HwJJj1F8NTEtey4DvAUjK\\nAvcl9bOAGyXNOpFgzczsxPWZ+CPiWWDnMZpcB/xTlLwInCHpbGAhsDki3oqIQ8BDSVszM6ujalzj\\nnwBsLVtvTcp6K++RpGWSWiS1tLe3VyEsMzPryaD5425ELI+I5ohobmpqqnc4ZmanrIYq9LENmFS2\\nPjEpy/VSbmZmdVSNI/7HgT9M7u65GNgVEe8Ba4BpkqZKGgIsTdqamVkd9XnEL+lBYBEwTlIrcBel\\no3ki4n7gCeAaYDOwH7glqctLugN4CsgCKyJiwwC8hy7/9psPOO/MUTQOrcaJjJnZqanPDBkRN/ZR\\nH8DXeql7gtLEMOAO5Yvc/IOX6CgEC6eOYdH0JhZNH8+5TY1IqkUIZmYnBZXy9uDS3NwcLS0t/dqm\\nUAxeensHqze1s3pTG6+/vxeAiaOHs3j6eBZNb+KT545lxBCfDZjZqUfS2ohorqjtqZL4u2v9YD/P\\nvN7Oqo3t/GLzdg50FBjSkOGiqWNYNH08i6c3MXWczwbM7NTgxN/NR/kCa97+gFWb2li9qY032/cB\\nMHnMCBZPb2LRjPF88uNjGZbLVm2fZma15MTfh60797N6UxurN7Xzize3c7CjyNCGDJ88dyyLziv9\\nbWDKuMYB27+ZWbU58ffDwY4Cv3x7Z3I20M7b20tnA1PHNXb9gfiiqWN8NmBmg5oT/wnYsn1f6Wzg\\n9XZeeHMHH+WLDMtl+NS540qXhaaPZ9KYEXWJzcysN078VXKwo8ALb+1g9cY2Vm1q5zc79wNwblNj\\n8gfi8Xxi6miGNvhswMzqy4l/AEQEb2/fx+pN7aza1MZLb+/kUL7IiCFZPnXuuOSyUBMTR/tswMxq\\nrz+J3ze1V0gSH28aycebRvLvL53K/kN5XnhzR9dE8PRr7wMwbfxIFs8Yz6LzmmieMoYhDYPmOXhm\\nZoCP+KsiInizfV/XnUIvvb2DjkLQOCTLJb8zrjQRTG/i7NOH1ztUMztF+Yi/xiTxO+NH8jvjR3Lr\\nZR9n30d5nn9zB6s2tfHMpnb+5dXS2cCMs0ZxxfQmFk8fz4UfG00u67MBM6s9H/EPsIjgjba9rN7U\\nxqqN7azZspN8MRg1tIFLp43rumX0zNOG1TtUMzuJ+Y+7g9iegx38YvMOnnm9NBH8dvdBAGaefVrX\\n7aILJp9Bg88GzKwfnPhPEhHBpvf3sGpj6cFyLe98QKEYnDasgcumle4SumJ6E+NH+WzAzI7Nif8k\\ntftgB794Y3vXp4jb9nwEwJwJp7HovPEsntHEvEmjyWb8YDkzO5IT/ykgInj1vd1dj5n+1W8+pFAM\\nTh+e4/Lzmlh0XulsYNzIofUO1cwGASf+U9Cu/R08t7k9mQja2b63dDYwd+LpLEq+b+CCiWf4bMAs\\npZz4T3HFYulsYNXG0jOF/u03H1AMGD2idDawePp4Lj+viTGNQ+odqpnVSNUTv6QlwP+k9N25P4iI\\n73Sr/xPgpmS1AZgJNEXETklbgD1AAchXEthxJ/6/vwIKHZBtgEz5K9vDeq6P+gbI5nqpLy/rrU3Z\\nejbXRx+d/fTQRwVfFPPBvkM8t3k7qze28czr7ezYdwgJLph4BouSzw2cP+F0Mj4bMDtlVTXxS8oC\\nrwNXAq3AGuDGiHi1l/bXAv8xIn43Wd8CNEfE9krfwHEn/kdvhY4DUMyXvQpHrhfyx64vFqDYcXg9\\niv2Po5pUPoH0PaFFJsf+PHx4MNhxoMCuj4J8ZMhkc4weNZyxo0Yw7vSRDB0ypOfJSJkKJs3u5ceY\\n9DINyXvoo02mATLd991Q0cRnZtX/5O5CYHNEvJV0/hBwHdBj4gduBB6sZOdV9/kfVL/PYhGih8mh\\n0NHH5JE/cgLprU1XP7310dlP9/pubQqlNirmaSzmaRyRZ0KxQEf+EHv3f8TeA7s4uOd9Duwq8F5r\\ngeEN0NgQNCjIRB5FIXnlUbFAJvLVH8vj0dNEpH5MOplKJp2++iwrUyaZjNRtWccozxxjmV62q2Q/\\nx+i7xz6oQXydyxzuQ9nkZyYZ68yR7azmKkn8E4CtZeutwEU9NZQ0AlgC3FFWHMDTkgrA30fE8l62\\nXQYsA5g8eXIFYdVIJgNkSpdrTkI5YHTyKhSDda0fsmpTO89sauOV1l3H3FYUaaBIlgINFMhSPPKn\\nSstZigzPBEOzRYZkigzNBEMzRYYoGJIt/cwl5UNUJJcpln6q1L6B0nIuUyRHgYZMkKNIg0r7aaBI\\nVqWfR+6/SCY6Yygky8nPQoFMvkAmPkJJmaJApphMblFAxcMTHVGaQFUsHjm5RqEW/0wppV4mhG6v\\nI+qyhyeUo8qTyeSovnqrK5+QemrfuQ8doy7pt9fJrbf30UtdbjjM+fyAj3y1n9VzLfCLiNhZVnZp\\nRGyTNB74maSNEfFs9w2TCWE5lC71VDkuA7IZMX/yaOZPHs0fX3keO/cdYvvej+goFMkXgnyxyKF8\\n6We+EKXyYhxZXwjyyXpH0i5fKNJRTH4WDrfvKJbWDyQ/82X9da53FMr217XfUllH/nC/xRr9RuSy\\noiGToSErctkMDYJhWRiaLTIsWySXgVwGGjIilxG5TJDLimwGchK5LDR0tSm1a0jaNQgasqJBkZRz\\neDkLWUFOpe0yKvWR7eyH0nI2IxoyQZYoLQuySR8ZRWlZIpOhqy6r5GA/ikBAxJHLJOtdy72Vd2/T\\nvT96Lu96FQ73USz0UN5HXbGnvopl7aNbX93qinkoHOpWXuylfec+opf2vdUVTuzycOP4QZP4twGT\\nytYnJmU9WUq3yzwRsS352SZpJaVLR0clfqu9MY1DTpo7f4rF8ommfJIo9jhBdZS168j3Vt9Z3ll3\\n5ASW7z4RdW1fan+oGOwvBB35IoWkn0LZJFYoRFd/+WIcse9ayyYTUGmSySaT1eFJriFTmuiyGdGQ\\nzZDLlOpz2cP1DZkM2WxpwmvIZpK+kj46t8se3q58n9msyCbtsmWvo9czvdb3t63qeSnpmBNY2aTR\\nva5GKkn8a4BpkqZSSvhLgX/XvZGk04ErgJvLyhqBTETsSZavAv6iGoFbumQyYmgmy9BT5HmyheLh\\niaVzQuicUDonka76YmlC6ShvW7ZN5wRYKB6eaMonoo5ubcsnoo5iqW15vx1l+9t/KH9kPN376GFi\\nq9XZWV8yoqJJ4vB6JjmrSiYoJXXJZHbkeoasytpmy+rL1nvst9c4sowY0sCVZwz82PT53ygi8pLu\\nAJ6idDvniojYIOm2pP7+pOn1wL9ExL6yzc8EViYzbwPwQET8tJpvwOxk1Pkf/VSZyMp1np11ThjF\\nYufkFRQiKHRNENE1YXTVHbVe7Lm+c7kYFApFCkGpbbHU/xH1Za98sUihWNb2iLqj2+aLRT7K91bf\\nfbl4VF2+n7Ng06ihXDnrzAH6lznMH+AyMxtAvU18PU10AOc2jTyu/fiLWMzMBolMRgwZZB+e9EPf\\nzcxSxonfzCxlnPjNzFLGid/MLGWc+M3MUsaJ38wsZZz4zcxSxonfzCxlnPjNzFLGid/MLGWc+M3M\\nUsaJ38wsZZz4zcxSxonfzCxlnPjNzFLGid/MLGWc+M3MUqaixC9piaRNkjZLurOH+kWSdkl6OXl9\\nu9Jtzcystvr86kVJWeA+4EqgFVgj6fGIeLVb0+ci4veOc1szM6uRSo74FwKbI+KtiDgEPARcV2H/\\nJ7KtmZkNgEoS/wRga9l6a1LW3ackrZP0pKTZ/dwWScsktUhqaW9vryAsMzM7HtX64+6vgMkRMRf4\\nO+Cx/nYQEcsjojkimpuamqoUlpmZdVdJ4t8GTCpbn5iUdYmI3RGxN1l+AshJGlfJtmZmVluVJP41\\nwDRJUyUNAZYCj5c3kHSWJCXLC5N+d1SyrZmZ1Vafd/VERF7SHcBTQBZYEREbJN2W1N8PfAG4XVIe\\nOAAsjYgAetx2gN6LmZlVQKX8PLg0NzdHS0tLvcMwMztpSFobEc2VtPUnd83MUsaJ38wsZZz4zcxS\\nxonfzCxlnPjNzFLGid/MLGWc+M3MUsaJ38wsZZz4zcxSxonfzCxlnPjNzFLGid/MLGWc+M3MUsaJ\\n38wsZZz4zcxSxonfzCxlnPjNzFLGid/MLGUqSvySlkjaJGmzpDt7qL9J0jpJv5b0vKQLyuq2JOUv\\nS/L3KZqZ1VmfX7YuKQvcB1wJtAJrJD0eEa+WNXsbuCIiPpB0NbAcuKisfnFEbK9i3GZmdpwqOeJf\\nCGyOiLci4hDwEHBdeYOIeD4iPkhWXwQmVjdMMzOrlkoS/wRga9l6a1LWmz8CnixbD+BpSWslLet/\\niGZmVk19XurpD0mLKSX+S8uKL42IbZLGAz+TtDEinu1h22XAMoDJkydXMywzMytTyRH/NmBS2frE\\npOwIkuYCPwCui4gdneURsS352QaspHTp6CgRsTwimiOiuampqfJ3YGZm/VJJ4l8DTJM0VdIQYCnw\\neHkDSZOBHwNfjojXy8obJY3qXAauAtZXK3gzM+u/Pi/1RERe0h3AU0AWWBERGyTdltTfD3wbGAt8\\nVxJAPiKagTOBlUlZA/BARPx0QN6JmZlVRBFR7xiO0tzcHC0tvuXfzKxSktYmB9x98id3zcxSxonf\\nzCxlnPjNzFLGid/MLGWc+M3MUsaJ38wsZZz4zcxSxonfzCxlnPjNzFLGid/MLGWc+M3MUsaJ38ws\\nZZz4zcxSxonfzCxlnPjNzFLGid/MLGWc+M3MUsaJ38wsZZz4zcxSpqLEL2mJpE2SNku6s4d6Sbo3\\nqV8naUGl25qZWW31mfglZYH7gKuBWcCNkmZ1a3Y1MC15LQO+149tzcyshio54l8IbI6ItyLiEPAQ\\ncF23NtcB/xQlLwJnSDq7wm3NzKyGGipoMwHYWrbeClxUQZsJFW4LgKRllM4WAPZK2lRBbD0ZB2w/\\nzm0HkuPqH8fVP46rf07FuD5WacNKEn9NRMRyYPmJ9iOpJSKaqxBSVTmu/nFc/eO4+iftcVWS+LcB\\nk8rWJyZllbTJVbCtmZnVUCXX+NcA0yRNlTQEWAo83q3N48AfJnf3XAzsioj3KtzWzMxqqM8j/ojI\\nS7oDeArIAisiYoOk25L6+4EngGuAzcB+4JZjbTsg7+SwE75cNEAcV/84rv5xXP2T6rgUEbXYj5mZ\\nDRL+5K6ZWco48ZuZpcxJmfhP5BESdY5rkaRdkl5OXt+uUVwrJLVJWt9Lfb3Gq6+46jVekyStkvSq\\npA2SvtFDm5qPWYVx1XzMJA2T9EtJryRx/XkPbeoxXpXEVZffsWTfWUn/Jumfe6gb2PGKiJPqRemP\\nxG8CHweGAK8As7q1uQZ4EhBwMfDSIIlrEfDPdRizy4EFwPpe6ms+XhXGVa/xOhtYkCyPAl4fJL9j\\nlcRV8zFLxmBkspwDXgIuHgTjVUlcdfkdS/b9x8ADPe1/oMfrZDziP5FHSNQ7rrqIiGeBncdoUo/x\\nqiSuuoiI9yLiV8nyHuA1Sp9CL1fzMaswrppLxmBvsppLXt3vGqnHeFUSV11Imgh8FvhBL00GdLxO\\nxsTf2+Mh+tumHnEBfCo5dXtS0uwBjqlS9RivStV1vCRNAeZTOlosV9cxO0ZcUIcxSy5bvAy0AT+L\\niEExXhXEBfX5HbsH+C9AsZf6AR2vkzHxn8x+BUyOiLnA3wGP1Tmewa6u4yVpJPAo8M2I2F3LfR9L\\nH3HVZcwiohAR8yh9On+hpDm12G9fKoir5uMl6feAtohYO9D76s3JmPhP5BESdY0rInZ3nnpGxBNA\\nTtK4AY6rEvUYrz7Vc7wk5Sgl1x9FxI97aFKXMesrrnr/jkXEh8AqYEm3qrr+jvUWV53G6xLgc5K2\\nULok/LuS/k+3NgM6Xidj4j+RR0jUNS5JZ0lSsryQ0vjvGOC4KlGP8epTvcYr2ec/AK9FxN29NKv5\\nmFUSVz3GTFKTpDOS5eHAlcDGbs3qMV59xlWP8YqIP42IiRExhVKe+NeIuLlbswEdr0HzdM5KxQk8\\nQmIQxPUF4HZJeeAAsDSSP+EPJEkPUrp7YZykVuAuSn/oqtt4VRhXXcaL0hHZl4FfJ9eHAb4FTC6L\\nrR5jVklc9Rizs4H/rdIXL2WAhyPin+v9f7LCuOr1O3aUWo6XH9lgZpYyJ+OlHjMzOwFO/GZmKePE\\nb2aWMk6JRX72AAAAG0lEQVT8ZmYp48RvZpYyTvxmZinjxG9mljL/HyyZDlb/wK1BAAAAAElFTkSu\\nQmCC\\n\",\n \"text/plain\": [\n \"![](\\\"images/rec_archi_3.svg\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(80000, 20000)\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# transform the date (string) into an int representing the release year\\n\",\n \"parsed_dates = [int(film_date[-4:])\\n\",\n \" for film_date in items[\\\"date\\\"].tolist()]\\n\",\n \"\\n\",\n \"items['parsed_date'] = pd.Series(parsed_dates, index=items.index)\\n\",\n \"max_date = max(items['parsed_date'])\\n\",\n \"min_date = min(items['parsed_date'])\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import scale\\n\",\n \"\\n\",\n \"items['scaled_date'] = scale(items['parsed_date'].astype('float64'))\\n\",\n \"item_meta_train = items[\\\"scaled_date\\\"][item_id_train]\\n\",\n \"item_meta_test = items[\\\"scaled_date\\\"][item_id_test]\\n\",\n \"\\n\",\n \"len(item_meta_train), len(item_meta_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"count 1.682000e+03\\n\",\n \"mean -2.415758e-15\\n\",\n \"std 1.000297e+00\\n\",\n \"min -4.730390e+00\\n\",\n \"25% 2.533563e-01\\n\",\n \"50% 3.937435e-01\\n\",\n \"75% 4.639372e-01\\n\",\n \"max 6.043244e-01\\n\",\n \"Name: scaled_date, dtype: float64\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"items[\\\"scaled_date\\\"].describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 110,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# For each sample we input the integer identifiers\\n\",\n \"# of a single user and a single item\\n\",\n \"user_id_input = Input(shape=[1], name='user')\\n\",\n \"item_id_input = Input(shape=[1], name='item')\\n\",\n \"meta_input = Input(shape=[1], name='meta_item')\\n\",\n \"\\n\",\n \"embedding_size = 50\\n\",\n \"user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,\\n\",\n \" input_length=1, name='user_embedding')(user_id_input)\\n\",\n \"item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,\\n\",\n \" input_length=1, name='item_embedding')(item_id_input)\\n\",\n \"\\n\",\n \"\\n\",\n \"# reshape from shape: (batch_size, input_length, embedding_size)\\n\",\n \"# to shape: (batch_size, input_length * embedding_size) which is\\n\",\n \"# equal to shape: (batch_size, embedding_size)\\n\",\n \"user_vecs = Flatten()(user_embedding)\\n\",\n \"item_vecs = Flatten()(item_embedding)\\n\",\n \"\\n\",\n \"input_vecs = merge([user_vecs, item_vecs, meta_input], mode='concat')\\n\",\n \"\\n\",\n \"x = Dense(64, activation='relu')(input_vecs)\\n\",\n \"x = Dropout(0.5)(x)\\n\",\n \"x = Dense(32, activation='relu')(x)\\n\",\n \"y = Dense(1)(x)\\n\",\n \"\\n\",\n \"model = Model(input=[user_id_input, item_id_input, meta_input], output=y)\\n\",\n \"model.compile(optimizer='adam', loss='mae')\\n\",\n \"\\n\",\n \"initial_train_preds = model.predict([user_id_train, item_id_train, item_meta_train])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 111,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 72000 samples, validate on 8000 samples\\n\",\n \"Epoch 1/15\\n\",\n \"5s - loss: 0.9475 - val_loss: 0.7691\\n\",\n \"Epoch 2/15\\n\",\n \"5s - loss: 0.7587 - val_loss: 0.7587\\n\",\n \"Epoch 3/15\\n\",\n \"4s - loss: 0.7363 - val_loss: 0.7465\\n\",\n \"Epoch 4/15\\n\",\n \"4s - loss: 0.7168 - val_loss: 0.7383\\n\",\n \"Epoch 5/15\\n\",\n \"4s - loss: 0.7025 - val_loss: 0.7347\\n\",\n \"Epoch 6/15\\n\",\n \"5s - loss: 0.6885 - val_loss: 0.7349\\n\",\n \"Epoch 7/15\\n\",\n \"7s - loss: 0.6772 - val_loss: 0.7380\\n\",\n \"Epoch 8/15\\n\",\n \"6s - loss: 0.6691 - val_loss: 0.7381\\n\",\n \"Epoch 9/15\\n\",\n \"4s - loss: 0.6621 - val_loss: 0.7315\\n\",\n \"Epoch 10/15\\n\",\n \"4s - loss: 0.6560 - val_loss: 0.7317\\n\",\n \"Epoch 11/15\\n\",\n \"5s - loss: 0.6519 - val_loss: 0.7316\\n\",\n \"Epoch 12/15\\n\",\n \"5s - loss: 0.6458 - val_loss: 0.7324\\n\",\n \"Epoch 13/15\\n\",\n \"5s - loss: 0.6404 - val_loss: 0.7299\\n\",\n \"Epoch 14/15\\n\",\n \"5s - loss: 0.6374 - val_loss: 0.7306\\n\",\n \"Epoch 15/15\\n\",\n \"5s - loss: 0.6315 - val_loss: 0.7400\\n\",\n \"CPU times: user 1min 50s, sys: 35.8 s, total: 2min 26s\\n\",\n \"Wall time: 1min 20s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"history = model.fit([user_id_train, item_id_train, item_meta_train], rating_train,\\n\",\n \" batch_size=64, nb_epoch=15, validation_split=0.1,\\n\",\n \" shuffle=True, verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 112,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Final test Loss: 0.973\\n\",\n \"Final test Loss: 0.723\\n\"\n ]\n }\n ],\n \"source\": [\n \"test_preds = model.predict([user_id_test, item_id_test, item_meta_test])\\n\",\n \"print(\\\"Final test Loss: %0.3f\\\" % mean_squared_error(test_preds, rating_test))\\n\",\n \"print(\\\"Final test Loss: %0.3f\\\" % mean_absolute_error(test_preds, rating_test))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### A recommendation function for a given user\\n\",\n \"\\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\",\n \"- we use the `model.predict` to compute the ratings a user would have given to all items\\n\",\n \"- we build a reco function that sorts these items and exclude those the user has already seen\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 113,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def recommend(user_id, top_n=10):\\n\",\n \" item_ids = range(1, max_item_id)\\n\",\n \" seen_movies = list(all_ratings[all_ratings[\\\"user_id\\\"]==user_id][\\\"item_id\\\"])\\n\",\n \" item_ids = list(filter(lambda x: x not in seen_movies, item_ids))\\n\",\n \" \\n\",\n \" print(\\\"user \\\"+str(user_id) +\\\" has seen \\\"+str(len(seen_movies)) + \\\" movies. \\\"+\\n\",\n \" \\\"Computing ratings for \\\"+str(len(item_ids))+ \\\" other movies\\\")\\n\",\n \" \\n\",\n \" item_ids = np.array(item_ids)\\n\",\n \" user = np.zeros_like(item_ids)\\n\",\n \" user[:]=user_id\\n\",\n \" items_meta = items[\\\"scaled_date\\\"][item_ids].values\\n\",\n \"\\n\",\n \" rating_preds = model.predict([user, item_ids, items_meta])\\n\",\n \"\\n\",\n \" item_ids = np.argsort(rating_preds[:,0])[::-1].tolist()\\n\",\n \" rec_items = item_ids[:top_n]\\n\",\n \" return [(items[\\\"name\\\"][movie], rating_preds[movie][0]) for movie in rec_items]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 114,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"user 3 has seen 54 movies. Computing ratings for 1627 other movies\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[('A Chef in Love (1996)', 5.0031729),\\n\",\n \" ('Hurricane Streets (1998)', 4.7923875),\\n\",\n \" (\\\"Pharaoh's Army (1995)\\\", 4.7363672),\\n\",\n \" ('Ladybird Ladybird (1994)', 4.4955535),\\n\",\n \" ('Swept from the Sea (1997)', 4.2269187),\\n\",\n \" ('Spitfire Grill, The (1996)', 4.187326),\\n\",\n \" ('NeverEnding Story III, The (1994)', 4.1698661),\\n\",\n \" ('Nixon (1995)', 4.1500397),\\n\",\n \" ('Mary Poppins (1964)', 4.1487379),\\n\",\n \" ('Palmetto (1998)', 4.1305041)]\"\n ]\n },\n \"execution_count\": 114,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"recommend(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Home assignment: Predicting ratings as a classification problem\\n\",\n \"\\n\",\n \"In this dataset, the ratings all belong to a finite set of possible values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 115,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4, 5])\"\n ]\n },\n \"execution_count\": 115,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"np.unique(rating_train)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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 \"\\n\",\n \"- setting the final layer to output class membership probabities using a softmax activation with 5 outputs;\\n\",\n \"- optimize the categorical cross-entropy classification loss instead of a regression loss such as MSE or MAE.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 116,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Random init MSE: 4.012\\n\",\n \"Random init MAE: 1.706\\n\",\n \"Train on 72000 samples, validate on 8000 samples\\n\",\n \"Epoch 1/6\\n\",\n \"7s - loss: 1.3425 - val_loss: 1.2857\\n\",\n \"Epoch 2/6\\n\",\n \"6s - loss: 1.2584 - val_loss: 1.2631\\n\",\n \"Epoch 3/6\\n\",\n \"5s - loss: 1.2348 - val_loss: 1.2545\\n\",\n \"Epoch 4/6\\n\",\n \"5s - loss: 1.2191 - val_loss: 1.2574\\n\",\n \"Epoch 5/6\\n\",\n \"5s - loss: 1.2086 - val_loss: 1.2557\\n\",\n \"Epoch 6/6\\n\",\n \"5s - loss: 1.1997 - val_loss: 1.2546\\n\",\n \"Final test MSE: 1.198\\n\",\n \"Final test MAE: 0.736\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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ccw0VNHZjZGxlfwdx6CI/uhqwM626Grvbjc3THyfWVygwwUDYMOFqfl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\\nrkJggg==\\n\",\n \"text/plain\": [\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" user_id item_id rating timestamp\\n\",\n \"count 90570.000000 90570.000000 90570.000000 9.057000e+04\\n\",\n \"mean 461.494038 428.104891 3.523827 8.835073e+08\\n\",\n \"std 266.004364 333.088029 1.126073 5.341684e+06\\n\",\n \"min 1.000000 1.000000 1.000000 8.747247e+08\\n\",\n \"25% 256.000000 174.000000 3.000000 8.794484e+08\\n\",\n \"50% 442.000000 324.000000 4.000000 8.828143e+08\\n\",\n \"75% 682.000000 636.000000 4.000000 8.882049e+08\\n\",\n \"max 943.000000 1682.000000 5.000000 8.932866e+08\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_train = pd.read_csv(op.join(ML_100K_FOLDER, 'ua.base'), sep='\\\\t',\\n\",\n \" names=[\\\"user_id\\\", \\\"item_id\\\", \\\"rating\\\", \\\"timestamp\\\"])\\n\",\n \"data_test = pd.read_csv(op.join(ML_100K_FOLDER, 'ua.test'), sep='\\\\t',\\n\",\n \" names=[\\\"user_id\\\", \\\"item_id\\\", \\\"rating\\\", \\\"timestamp\\\"])\\n\",\n \"\\n\",\n \"data_train.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | user_id | \\n\",\n \"item_id | \\n\",\n \"rating | \\n\",\n \"timestamp | \\n\",\n \"
|---|---|---|---|---|
| count | \\n\",\n \"90570.000000 | \\n\",\n \"90570.000000 | \\n\",\n \"90570.000000 | \\n\",\n \"9.057000e+04 | \\n\",\n \"
| mean | \\n\",\n \"461.494038 | \\n\",\n \"428.104891 | \\n\",\n \"3.523827 | \\n\",\n \"8.835073e+08 | \\n\",\n \"
| std | \\n\",\n \"266.004364 | \\n\",\n \"333.088029 | \\n\",\n \"1.126073 | \\n\",\n \"5.341684e+06 | \\n\",\n \"
| min | \\n\",\n \"1.000000 | \\n\",\n \"1.000000 | \\n\",\n \"1.000000 | \\n\",\n \"8.747247e+08 | \\n\",\n \"
| 25% | \\n\",\n \"256.000000 | \\n\",\n \"174.000000 | \\n\",\n \"3.000000 | \\n\",\n \"8.794484e+08 | \\n\",\n \"
| 50% | \\n\",\n \"442.000000 | \\n\",\n \"324.000000 | \\n\",\n \"4.000000 | \\n\",\n \"8.828143e+08 | \\n\",\n \"
| 75% | \\n\",\n \"682.000000 | \\n\",\n \"636.000000 | \\n\",\n \"4.000000 | \\n\",\n \"8.882049e+08 | \\n\",\n \"
| max | \\n\",\n \"943.000000 | \\n\",\n \"1682.000000 | \\n\",\n \"5.000000 | \\n\",\n \"8.932866e+08 | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" user_id item_id rating timestamp\\n\",\n \"0 1 1 5 874965758\\n\",\n \"1 1 2 3 876893171\\n\",\n \"2 1 3 4 878542960\\n\",\n \"3 1 4 3 876893119\\n\",\n \"4 1 5 3 889751712\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_train.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# data_test.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"n_users=944, n_items=1683\\n\"\n ]\n }\n ],\n \"source\": [\n \"max_user_id = max(data_train['user_id'].max(), data_test['user_id'].max())\\n\",\n \"max_item_id = max(data_train['item_id'].max(), data_test['item_id'].max())\\n\",\n \"\\n\",\n \"n_users = max_user_id + 1\\n\",\n \"n_items = max_item_id + 1\\n\",\n \"\\n\",\n \"print('n_users=%d, n_items=%d' % (n_users, n_items))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Implicit feedback data\\n\",\n \"\\n\",\n \"Consider ratings >= 4 as positive feed back and ignore the rest:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"pos_data_train = data_train.query(\\\"rating >= 4\\\")\\n\",\n \"pos_data_test = data_test.query(\\\"rating >= 4\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Because the median rating is around 3.5, this cut will remove approximately half of the ratings from the datasets:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"49906\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pos_data_train['rating'].count()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5469\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pos_data_test['rating'].count()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## The Triplet Loss\\n\",\n \"\\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 \"\\n\",\n \"The matching scores can be use to rank items to recommend to a specific user.\\n\",\n \"\\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 \"\\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\",\n \"| \\n\",\n \" | user_id | \\n\",\n \"item_id | \\n\",\n \"rating | \\n\",\n \"timestamp | \\n\",\n \"
|---|---|---|---|---|
| 0 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"5 | \\n\",\n \"874965758 | \\n\",\n \"
| 1 | \\n\",\n \"1 | \\n\",\n \"2 | \\n\",\n \"3 | \\n\",\n \"876893171 | \\n\",\n \"
| 2 | \\n\",\n \"1 | \\n\",\n \"3 | \\n\",\n \"4 | \\n\",\n \"878542960 | \\n\",\n \"
| 3 | \\n\",\n \"1 | \\n\",\n \"4 | \\n\",\n \"3 | \\n\",\n \"876893119 | \\n\",\n \"
| 4 | \\n\",\n \"1 | \\n\",\n \"5 | \\n\",\n \"3 | \\n\",\n \"889751712 | \\n\",\n \"
![](\\\"images/rec_archi_implicit_2.svg\\\")
\\n\",\n \"\\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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"\\n\",\n \"def identity_loss(y_true, y_pred):\\n\",\n \" \\\"\\\"\\\"Ignore y_true and return the mean of y_pred\\n\",\n \" \\n\",\n \" This is a hack to work-around the design of the Keras API that is\\n\",\n \" not really suited to train networks with a triplet loss by default.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return tf.reduce_mean(y_pred + 0 * y_true)\\n\",\n \"\\n\",\n \"\\n\",\n \"def margin_comparator_loss(inputs, margin=1.):\\n\",\n \" \\\"\\\"\\\"Comparator loss for a pair of precomputed similarities\\n\",\n \" \\n\",\n \" If the inputs are cosine similarities, they each have range in\\n\",\n \" (-1, 1), therefore their difference have range in (-2, 2). Using\\n\",\n \" a margin of 1. can therefore make sense.\\n\",\n \"\\n\",\n \" If the input similarities are not normalized, it can be beneficial\\n\",\n \" to use larger values for the margin of the comparator loss.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" positive_pair_sim, negative_pair_sim = inputs\\n\",\n \" return tf.maximum(negative_pair_sim - positive_pair_sim + margin, 0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using TensorFlow backend.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from keras.models import Model\\n\",\n \"from keras.layers import Embedding, Flatten, Input, Dense, merge\\n\",\n \"from keras.regularizers import l2\\n\",\n \"from keras_fixes import dot_mode, cos_mode\\n\",\n \"\\n\",\n \"\\n\",\n \"def build_models(n_users, n_items, latent_dim=64, l2_reg=0):\\n\",\n \" \\\"\\\"\\\"Build a triplet model and its companion similarity model\\n\",\n \" \\n\",\n \" The triplet model is used to train the weights of the companion\\n\",\n \" similarity model. The triplet model takes 1 user, 1 positive item\\n\",\n \" (relative to the selected user) and one negative item and is\\n\",\n \" trained with comparator loss.\\n\",\n \" \\n\",\n \" The similarity model takes one user and one item as input and return\\n\",\n \" compatibility score (aka the match score).\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Common architectural components for the two models:\\n\",\n \" # - symbolic input placeholders\\n\",\n \" user_input = Input((1,), name='user_input')\\n\",\n \" positive_item_input = Input((1,), name='positive_item_input')\\n\",\n \" negative_item_input = Input((1,), name='negative_item_input')\\n\",\n \"\\n\",\n \" # - embeddings\\n\",\n \" l2_reg = None if l2_reg == 0 else l2(l2_reg)\\n\",\n \" user_layer = Embedding(n_users, latent_dim, input_length=1,\\n\",\n \" name='user_embedding', W_regularizer=l2_reg)\\n\",\n \" \\n\",\n \" # The following embedding parameters will be shared to encode both\\n\",\n \" # the positive and negative items.\\n\",\n \" item_layer = Embedding(n_items, latent_dim, input_length=1,\\n\",\n \" name=\\\"item_embedding\\\", W_regularizer=l2_reg)\\n\",\n \"\\n\",\n \" user_embedding = Flatten()(user_layer(user_input))\\n\",\n \" positive_item_embedding = Flatten()(item_layer(positive_item_input))\\n\",\n \" negative_item_embedding = Flatten()(item_layer(negative_item_input))\\n\",\n \"\\n\",\n \" # - similarity computation between embeddings\\n\",\n \" positive_similarity = merge([user_embedding, positive_item_embedding],\\n\",\n \" mode=cos_mode, output_shape=(1,),\\n\",\n \" name=\\\"positive_similarity\\\")\\n\",\n \" negative_similarity = merge([user_embedding, negative_item_embedding],\\n\",\n \" mode=cos_mode, output_shape=(1,),\\n\",\n \" name=\\\"negative_similarity\\\")\\n\",\n \"\\n\",\n \" # The triplet network model, only used for training\\n\",\n \" triplet_loss = merge([positive_similarity, negative_similarity],\\n\",\n \" mode=margin_comparator_loss, output_shape=(1,),\\n\",\n \" name='comparator_loss')\\n\",\n \"\\n\",\n \" triplet_model = Model(input=[user_input,\\n\",\n \" positive_item_input,\\n\",\n \" negative_item_input],\\n\",\n \" output=triplet_loss)\\n\",\n \" \\n\",\n \" # The match-score model, only use at inference to rank items for a given\\n\",\n \" # model: the model weights are shared with the triplet_model therefore\\n\",\n \" # we do not need to train it and therefore we do not need to plug a loss\\n\",\n \" # and an optimizer.\\n\",\n \" match_model = Model(input=[user_input, positive_item_input],\\n\",\n \" output=positive_similarity)\\n\",\n \" \\n\",\n \" return triplet_model, match_model\\n\",\n \"\\n\",\n \"\\n\",\n \"triplet_model, match_model = build_models(n_users, n_items, latent_dim=64,\\n\",\n \" l2_reg=1e-6)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Exercise:\\n\",\n \"\\n\",\n \"How many trainable parameters does each model. Count the shared parameters only once per model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"____________________________________________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # Connected to \\n\",\n \"====================================================================================================\\n\",\n \"user_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"positive_item_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"user_embedding (Embedding) (None, 1, 64) 60416 user_input[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"item_embedding (Embedding) (None, 1, 64) 107712 positive_item_input[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_1 (Flatten) (None, 64) 0 user_embedding[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_2 (Flatten) (None, 64) 0 item_embedding[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"positive_similarity (Merge) (None, 1) 0 flatten_1[0][0] \\n\",\n \" flatten_2[0][0] \\n\",\n \"====================================================================================================\\n\",\n \"Total params: 168,128\\n\",\n \"Trainable params: 168,128\\n\",\n \"Non-trainable params: 0\\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(match_model.summary())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"____________________________________________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # Connected to \\n\",\n \"====================================================================================================\\n\",\n \"user_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"positive_item_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"negative_item_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"user_embedding (Embedding) (None, 1, 64) 60416 user_input[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"item_embedding (Embedding) (None, 1, 64) 107712 positive_item_input[0][0] \\n\",\n \" negative_item_input[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_1 (Flatten) (None, 64) 0 user_embedding[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_2 (Flatten) (None, 64) 0 item_embedding[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_3 (Flatten) (None, 64) 0 item_embedding[1][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"positive_similarity (Merge) (None, 1) 0 flatten_1[0][0] \\n\",\n \" flatten_2[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"negative_similarity (Merge) (None, 1) 0 flatten_1[0][0] \\n\",\n \" flatten_3[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"comparator_loss (Merge) (None, 1) 0 positive_similarity[0][0] \\n\",\n \" negative_similarity[0][0] \\n\",\n \"====================================================================================================\\n\",\n \"Total params: 168,128\\n\",\n \"Trainable params: 168,128\\n\",\n \"Non-trainable params: 0\\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(triplet_model.summary())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# %load solutions/triplet_parameter_count.py\\n\",\n \"# Analysis:\\n\",\n \"#\\n\",\n \"# Both models have exactly the same number of parameters,\\n\",\n \"# namely the parameters of the 2 embeddings:\\n\",\n \"#\\n\",\n \"# - user embedding: n_users x embedding_dim\\n\",\n \"# - item embedding: n_items x embedding_dim\\n\",\n \"#\\n\",\n \"# The triplet model uses the same item embedding twice,\\n\",\n \"# once to compute the positive similarity and the other\\n\",\n \"# time to compute the negative similarity. However because\\n\",\n \"# those two nodes in the computation graph share the same\\n\",\n \"# instance of the item embedding layer, the item embedding\\n\",\n \"# weight matrix is shared by the two branches of the\\n\",\n \"# graph and therefore the total number of parameters for\\n\",\n \"# each model is in both cases:\\n\",\n \"#\\n\",\n \"# (n_users x embedding_dim) + (n_items x embedding_dim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Quality of Ranked Recommendations\\n\",\n \"\\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 \"\\n\",\n \"- compute matching scores for items (except the movies that the user has already seen in the training set),\\n\",\n \"- compare to the positive feedback actually collected on the test set using the ROC AUC ranking metric,\\n\",\n \"- average ROC AUC scores across users to get the average performance of the recommender model on the test set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import roc_auc_score\\n\",\n \"\\n\",\n \"\\n\",\n \"def average_roc_auc(match_model, data_train, data_test):\\n\",\n \" \\\"\\\"\\\"Compute the ROC AUC for each user and average over users\\\"\\\"\\\"\\n\",\n \" max_user_id = max(data_train['user_id'].max(), data_test['user_id'].max())\\n\",\n \" max_item_id = max(data_train['item_id'].max(), data_test['item_id'].max())\\n\",\n \" user_auc_scores = []\\n\",\n \" for user_id in range(1, max_user_id + 1):\\n\",\n \" pos_item_train = data_train[data_train['user_id'] == user_id]\\n\",\n \" pos_item_test = data_test[data_test['user_id'] == user_id]\\n\",\n \" \\n\",\n \" # Consider all the items already seen in the training set\\n\",\n \" all_item_ids = np.arange(1, max_item_id + 1)\\n\",\n \" items_to_rank = np.setdiff1d(all_item_ids, pos_item_train['item_id'].values)\\n\",\n \" \\n\",\n \" # Ground truth: return 1 for each item positively present in the test set\\n\",\n \" # and 0 otherwise.\\n\",\n \" expected = np.in1d(items_to_rank, pos_item_test['item_id'].values)\\n\",\n \" \\n\",\n \" if np.sum(expected) >= 1:\\n\",\n \" # At least one positive test value to rank\\n\",\n \" repeated_user_id = np.empty_like(items_to_rank)\\n\",\n \" repeated_user_id.fill(user_id)\\n\",\n \"\\n\",\n \" predicted = match_model.predict([repeated_user_id, items_to_rank],\\n\",\n \" batch_size=4096)\\n\",\n \" user_auc_scores.append(roc_auc_score(expected, predicted))\\n\",\n \"\\n\",\n \" return sum(user_auc_scores) / len(user_auc_scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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 \"\\n\",\n \"A random (untrained) model should yield 0.50 ROC AUC on average. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.49808857713281779\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"average_roc_auc(match_model, pos_data_train, pos_data_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Training the Triplet Model\\n\",\n \"\\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 \"\\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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def sample_triplets(pos_data, max_item_id, random_seed=0):\\n\",\n \" \\\"\\\"\\\"Sample negatives at random\\\"\\\"\\\"\\n\",\n \" rng = np.random.RandomState(random_seed)\\n\",\n \" user_ids = pos_data['user_id'].values\\n\",\n \" pos_item_ids = pos_data['item_id'].values\\n\",\n \"\\n\",\n \" neg_item_ids = rng.randint(low=1, high=max_item_id + 1,\\n\",\n \" size=len(user_ids))\\n\",\n \"\\n\",\n \" return [user_ids, pos_item_ids, neg_item_ids]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's train the triplet model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/1\\n\",\n \"5s - loss: 0.7700\\n\",\n \"Epoch 1/15: test ROC AUC: 0.8427\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3884\\n\",\n \"Epoch 2/15: test ROC AUC: 0.8787\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3588\\n\",\n \"Epoch 3/15: test ROC AUC: 0.8972\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3440\\n\",\n \"Epoch 4/15: test ROC AUC: 0.9042\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3358\\n\",\n \"Epoch 5/15: test ROC AUC: 0.9098\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3288\\n\",\n \"Epoch 6/15: test ROC AUC: 0.9130\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3306\\n\",\n \"Epoch 7/15: test ROC AUC: 0.9157\\n\",\n \"Epoch 1/1\\n\",\n \"6s - loss: 0.3246\\n\",\n \"Epoch 8/15: test ROC AUC: 0.9167\\n\",\n \"Epoch 1/1\\n\",\n \"6s - loss: 0.3225\\n\",\n \"Epoch 9/15: test ROC AUC: 0.9167\\n\",\n \"Epoch 1/1\\n\",\n \"5s - loss: 0.3204\\n\",\n \"Epoch 10/15: test ROC AUC: 0.9178\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3190\\n\",\n \"Epoch 11/15: test ROC AUC: 0.9194\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3191\\n\",\n \"Epoch 12/15: test ROC AUC: 0.9193\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3185\\n\",\n \"Epoch 13/15: test ROC AUC: 0.9198\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3218\\n\",\n \"Epoch 14/15: test ROC AUC: 0.9216\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3170\\n\",\n \"Epoch 15/15: test ROC AUC: 0.9230\\n\"\n ]\n }\n ],\n \"source\": [\n \"# we plug the identity loss and the a fake target variable ignored by\\n\",\n \"# the model to be able to use the Keras API to train the triplet model\\n\",\n \"triplet_model.compile(loss=identity_loss, optimizer=\\\"adam\\\")\\n\",\n \"fake_y = np.ones_like(pos_data_train['user_id'])\\n\",\n \"\\n\",\n \"n_epochs = 15\\n\",\n \"\\n\",\n \"for i in range(n_epochs):\\n\",\n \" # Sample new negatives to build different triplets at each epoch\\n\",\n \" triplet_inputs = sample_triplets(pos_data_train, max_item_id,\\n\",\n \" random_seed=i)\\n\",\n \"\\n\",\n \" # Fit the model incrementally by doing a single pass over the\\n\",\n \" # sampled triplets.\\n\",\n \" triplet_model.fit(triplet_inputs, fake_y, shuffle=True,\\n\",\n \" batch_size=64, nb_epoch=1, verbose=2)\\n\",\n \" \\n\",\n \" # Monitor the convergence of the model\\n\",\n \" test_auc = average_roc_auc(match_model, pos_data_train, pos_data_test)\\n\",\n \" print(\\\"Epoch %d/%d: test ROC AUC: %0.4f\\\"\\n\",\n \" % (i + 1, n_epochs, test_auc))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Training a Deep Matching Model on Implicit Feedback\\n\",\n \"\\n\",\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 \"\\n\",\n \"![](\\\"images/rec_archi_implicit_1.svg\\\")
\\n\",\n \"\\n\",\n \"\\n\",\n \"### Exercise to complete as a home assignment:\\n\",\n \"\\n\",\n \"- Implement a `deep_match_model`, `deep_triplet_model` pair of models\\n\",\n \" for the architecture described in the schema. The last layer of\\n\",\n \" the embedded Multi Layer Perceptron outputs a single scalar that\\n\",\n \" encodes the similarity between a user and a candidate item.\\n\",\n \"\\n\",\n \"- Evaluate the resulting model by computing the per-user average\\n\",\n \" ROC AUC score on the test feedback data.\\n\",\n \" \\n\",\n \" - Check that the AUC ROC score is close to 0.50 for a randomly\\n\",\n \" initialized model.\\n\",\n \" \\n\",\n \" - Check that you can reach at least 0.91 ROC AUC with this deep\\n\",\n \" model (you might need to adjust the hyperparameters).\\n\",\n \" \\n\",\n \" \\n\",\n \"Hints:\\n\",\n \"\\n\",\n \"- it is possible to reuse the code to create embeddings from the previous model\\n\",\n \" definition;\\n\",\n \"\\n\",\n \"- the concatenation between user and the positive item embedding can be\\n\",\n \" obtained with:\\n\",\n \"\\n\",\n \"```py\\n\",\n \" positive_embeddings_pair = merge([user_embedding, positive_item_embedding],\\n\",\n \" mode='concat',\\n\",\n \" name=\\\"positive_embeddings_pair\\\")\\n\",\n \" negative_embeddings_pair = merge([user_embedding, negative_item_embedding],\\n\",\n \" mode='concat',\\n\",\n \" name=\\\"negative_embeddings_pair\\\")\\n\",\n \"```\\n\",\n \"\\n\",\n \"- those embedding pairs should be fed to a shared MLP instance to compute the similarity scores.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/1\\n\",\n \"5s - loss: 0.4747\\n\",\n \"Epoch 1/15: test ROC AUC: 0.8554\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3814\\n\",\n \"Epoch 2/15: test ROC AUC: 0.8623\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3742\\n\",\n \"Epoch 3/15: test ROC AUC: 0.8637\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3668\\n\",\n \"Epoch 4/15: test ROC AUC: 0.8662\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3480\\n\",\n \"Epoch 5/15: test ROC AUC: 0.8740\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3282\\n\",\n \"Epoch 6/15: test ROC AUC: 0.8871\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.3113\\n\",\n \"Epoch 7/15: test ROC AUC: 0.8986\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.2918\\n\",\n \"Epoch 8/15: test ROC AUC: 0.9048\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.2824\\n\",\n \"Epoch 9/15: test ROC AUC: 0.9098\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.2735\\n\",\n \"Epoch 10/15: test ROC AUC: 0.9116\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.2691\\n\",\n \"Epoch 11/15: test ROC AUC: 0.9137\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.2659\\n\",\n \"Epoch 12/15: test ROC AUC: 0.9158\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.2597\\n\",\n \"Epoch 13/15: test ROC AUC: 0.9161\\n\",\n \"Epoch 1/1\\n\",\n \"78s - loss: 0.2609\\n\",\n \"Epoch 14/15: test ROC AUC: 0.9159\\n\",\n \"Epoch 1/1\\n\",\n \"4s - loss: 0.2544\\n\",\n \"Epoch 15/15: test ROC AUC: 0.9189\\n\"\n ]\n }\n ],\n \"source\": [\n \"# %load solutions/deep_implicit_feedback_recsys.py\\n\",\n \"from keras.models import Model, Sequential\\n\",\n \"from keras.layers import Embedding, Flatten, Input, Dense, Dropout, merge\\n\",\n \"from keras.regularizers import l2\\n\",\n \"\\n\",\n \"\\n\",\n \"def make_interaction_mlp(input_dim, n_hidden=1, hidden_size=64,\\n\",\n \" dropout=0, l2_reg=None):\\n\",\n \" \\\"\\\"\\\"Build the shared multi layer perceptron\\\"\\\"\\\"\\n\",\n \" mlp = Sequential()\\n\",\n \" if n_hidden == 0:\\n\",\n \" # Plug the output unit directly: this is a simple\\n\",\n \" # linear regression model. Not dropout required.\\n\",\n \" mlp.add(Dense(1, input_dim=input_dim,\\n\",\n \" activation='relu', W_regularizer=l2_reg))\\n\",\n \" else:\\n\",\n \" mlp.add(Dense(hidden_size, input_dim=input_dim,\\n\",\n \" activation='relu', W_regularizer=l2_reg))\\n\",\n \" mlp.add(Dropout(dropout))\\n\",\n \" for i in range(n_hidden - 1):\\n\",\n \" mlp.add(Dense(hidden_size, activation='relu',\\n\",\n \" W_regularizer=l2_reg))\\n\",\n \" mlp.add(Dropout(dropout))\\n\",\n \" mlp.add(Dense(1, activation='relu', W_regularizer=l2_reg))\\n\",\n \" return mlp\\n\",\n \"\\n\",\n \"\\n\",\n \"def build_models(n_users, n_items, user_dim=32, item_dim=64,\\n\",\n \" n_hidden=1, hidden_size=64, dropout=0, l2_reg=0):\\n\",\n \" \\\"\\\"\\\"Build models to train a deep triplet network\\\"\\\"\\\"\\n\",\n \" user_input = Input((1,), name='user_input')\\n\",\n \" positive_item_input = Input((1,), name='positive_item_input')\\n\",\n \" negative_item_input = Input((1,), name='negative_item_input')\\n\",\n \"\\n\",\n \" l2_reg = None if l2_reg == 0 else l2(l2_reg)\\n\",\n \" user_layer = Embedding(n_users, user_dim, input_length=1,\\n\",\n \" name='user_embedding', W_regularizer=l2_reg)\\n\",\n \"\\n\",\n \" # The following embedding parameters will be shared to encode both\\n\",\n \" # the positive and negative items.\\n\",\n \" item_layer = Embedding(n_items, item_dim, input_length=1,\\n\",\n \" name=\\\"item_embedding\\\", W_regularizer=l2_reg)\\n\",\n \"\\n\",\n \" user_embedding = Flatten()(user_layer(user_input))\\n\",\n \" positive_item_embedding = Flatten()(item_layer(positive_item_input))\\n\",\n \" negative_item_embedding = Flatten()(item_layer(negative_item_input))\\n\",\n \"\\n\",\n \"\\n\",\n \" # Similarity computation between embeddings using a MLP similarity\\n\",\n \" positive_embeddings_pair = merge([user_embedding, positive_item_embedding],\\n\",\n \" mode='concat',\\n\",\n \" name=\\\"positive_embeddings_pair\\\")\\n\",\n \" positive_embeddings_pair = Dropout(dropout)(positive_embeddings_pair)\\n\",\n \" negative_embeddings_pair = merge([user_embedding, negative_item_embedding],\\n\",\n \" mode='concat',\\n\",\n \" name=\\\"negative_embeddings_pair\\\")\\n\",\n \" negative_embeddings_pair = Dropout(dropout)(negative_embeddings_pair)\\n\",\n \"\\n\",\n \" # Instanciate the shared similarity architecture\\n\",\n \" interaction_layers = make_interaction_mlp(\\n\",\n \" user_dim + item_dim, n_hidden=n_hidden, hidden_size=hidden_size,\\n\",\n \" dropout=dropout, l2_reg=l2_reg)\\n\",\n \"\\n\",\n \" positive_similarity = interaction_layers(positive_embeddings_pair)\\n\",\n \" negative_similarity = interaction_layers(negative_embeddings_pair)\\n\",\n \"\\n\",\n \" # The triplet network model, only used for training\\n\",\n \" triplet_loss = merge([positive_similarity, negative_similarity],\\n\",\n \" mode=margin_comparator_loss, output_shape=(1,),\\n\",\n \" name='comparator_loss')\\n\",\n \"\\n\",\n \" deep_triplet_model = Model(input=[user_input,\\n\",\n \" positive_item_input,\\n\",\n \" negative_item_input],\\n\",\n \" output=triplet_loss)\\n\",\n \"\\n\",\n \" # The match-score model, only used at inference\\n\",\n \" deep_match_model = Model(input=[user_input, positive_item_input],\\n\",\n \" output=positive_similarity)\\n\",\n \"\\n\",\n \" return deep_match_model, deep_triplet_model\\n\",\n \"\\n\",\n \"\\n\",\n \"hyper_parameters = dict(\\n\",\n \" user_dim=32,\\n\",\n \" item_dim=64,\\n\",\n \" n_hidden=2,\\n\",\n \" hidden_size=128,\\n\",\n \" dropout=0.1,\\n\",\n \" l2_reg=0\\n\",\n \")\\n\",\n \"deep_match_model, deep_triplet_model = build_models(n_users, n_items,\\n\",\n \" **hyper_parameters)\\n\",\n \"\\n\",\n \"\\n\",\n \"deep_triplet_model.compile(loss=identity_loss, optimizer='adam')\\n\",\n \"fake_y = np.ones_like(pos_data_train['user_id'])\\n\",\n \"\\n\",\n \"n_epochs = 15\\n\",\n \"\\n\",\n \"for i in range(n_epochs):\\n\",\n \" # Sample new negatives to build different triplets at each epoch\\n\",\n \" triplet_inputs = sample_triplets(pos_data_train, max_item_id,\\n\",\n \" random_seed=i)\\n\",\n \"\\n\",\n \" # Fit the model incrementally by doing a single pass over the\\n\",\n \" # sampled triplets.\\n\",\n \" deep_triplet_model.fit(triplet_inputs, fake_y, shuffle=True,\\n\",\n \" batch_size=64, nb_epoch=1, verbose=2)\\n\",\n \"\\n\",\n \" # Monitor the convergence of the model\\n\",\n \" test_auc = average_roc_auc(deep_match_model, pos_data_train, pos_data_test)\\n\",\n \" print(\\\"Epoch %d/%d: test ROC AUC: %0.4f\\\"\\n\",\n \" % (i + 1, n_epochs, test_auc))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Exercise:\\n\",\n \"\\n\",\n \"Count the number of parameters in `deep_match_model` and `deep_triplet_model`. Which model has the largest number of parameters?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"____________________________________________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # Connected to \\n\",\n \"====================================================================================================\\n\",\n \"user_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"positive_item_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"user_embedding (Embedding) (None, 1, 32) 30208 user_input[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"item_embedding (Embedding) (None, 1, 64) 107712 positive_item_input[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_4 (Flatten) (None, 32) 0 user_embedding[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_5 (Flatten) (None, 64) 0 item_embedding[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"positive_embeddings_pair (Merge) (None, 96) 0 flatten_4[0][0] \\n\",\n \" flatten_5[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"dropout_1 (Dropout) (None, 96) 0 positive_embeddings_pair[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"sequential_1 (Sequential) (None, 1) 29057 dropout_1[0][0] \\n\",\n \"====================================================================================================\\n\",\n \"Total params: 166,977\\n\",\n \"Trainable params: 166,977\\n\",\n \"Non-trainable params: 0\\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(deep_match_model.summary())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"____________________________________________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # Connected to \\n\",\n \"====================================================================================================\\n\",\n \"user_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"positive_item_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"negative_item_input (InputLayer) (None, 1) 0 \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"user_embedding (Embedding) (None, 1, 32) 30208 user_input[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"item_embedding (Embedding) (None, 1, 64) 107712 positive_item_input[0][0] \\n\",\n \" negative_item_input[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_4 (Flatten) (None, 32) 0 user_embedding[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_5 (Flatten) (None, 64) 0 item_embedding[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"flatten_6 (Flatten) (None, 64) 0 item_embedding[1][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"positive_embeddings_pair (Merge) (None, 96) 0 flatten_4[0][0] \\n\",\n \" flatten_5[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"negative_embeddings_pair (Merge) (None, 96) 0 flatten_4[0][0] \\n\",\n \" flatten_6[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"dropout_1 (Dropout) (None, 96) 0 positive_embeddings_pair[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"dropout_2 (Dropout) (None, 96) 0 negative_embeddings_pair[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"sequential_1 (Sequential) (None, 1) 29057 dropout_1[0][0] \\n\",\n \" dropout_2[0][0] \\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"comparator_loss (Merge) (None, 1) 0 sequential_1[1][0] \\n\",\n \" sequential_1[2][0] \\n\",\n \"====================================================================================================\\n\",\n \"Total params: 166,977\\n\",\n \"Trainable params: 166,977\\n\",\n \"Non-trainable params: 0\\n\",\n \"____________________________________________________________________________________________________\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(deep_triplet_model.summary())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# %load solutions/deep_triplet_parameter_count.py\\n\",\n \"# Analysis:\\n\",\n \"#\\n\",\n \"# Both models have again exactly the same number of parameters,\\n\",\n \"# namely the parameters of the 2 embeddings:\\n\",\n \"#\\n\",\n \"# - user embedding: n_users x user_dim\\n\",\n \"# - item embedding: n_items x item_dim\\n\",\n \"#\\n\",\n \"# and the parameters of the MLP model used to compute the\\n\",\n \"# similarity score of an (user, item) pair:\\n\",\n \"#\\n\",\n \"# - first hidden layer weights: (user_dim + item_dim) * hidden_size\\n\",\n \"# - first hidden biases: hidden_size\\n\",\n \"# - extra hidden layers weights: hidden_size * hidden_size\\n\",\n \"# - extra hidden layers biases: hidden_size\\n\",\n \"# - output layer weights: hidden_size * 1\\n\",\n \"# - output layer biases: 1\\n\",\n \"#\\n\",\n \"# The triplet model uses the same item embedding layer\\n\",\n \"# twice and the same MLP instance twice:\\n\",\n \"# once to compute the positive similarity and the other\\n\",\n \"# time to compute the negative similarity. However because\\n\",\n \"# those two lanes in the computation graph share the same\\n\",\n \"# instances for the item embedding layer and for the MLP,\\n\",\n \"# their parameters are shared.\\n\",\n \"#\\n\",\n \"# Reminder: MLP stands for multi-layer perceptron, which is a\\n\",\n \"# common short-hand for Feed Forward Fully Connected Neural\\n\",\n \"# Network.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Possible Extensions\\n\",\n \"\\n\",\n \"You can implement any of the following ideas if you want to get a deeper understanding of recommender systems.\\n\",\n \"\\n\",\n \"\\n\",\n \"### Leverage User and Item metadata\\n\",\n \"\\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\",\n \"\\n\",\n \"### Better Ranking Metrics\\n\",\n \"\\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 \"\\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 \"\\n\",\n \"You can read up online about those metrics and try to implement them here.\\n\",\n \"\\n\",\n \"\\n\",\n \"### Hard Negatives Sampling\\n\",\n \"\\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 \"\\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 \"\\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\",\n \"\\n\",\n \"### Factorization Machines\\n\",\n \"\\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 \"\\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 \"\\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\",\n \"\\n\",\n \"## References:\\n\",\n \"\\n\",\n \" [1] Wsabie: Scaling Up To Large Vocabulary Image Annotation\\n\",\n \" Jason Weston, Samy Bengio, Nicolas Usunier, 2011\\n\",\n \" https://research.google.com/pubs/pub37180.html\\n\",\n \"\\n\",\n \" [2] Factorization Machines, Steffen Rendle, 2010\\n\",\n \" https://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf\\n\",\n \"\\n\",\n \" [3] Factorization Machines with libFM, Steffen Rendle, 2012\\n\",\n \" in ACM Trans. Intell. Syst. Technol., 3(3), May.\\n\",\n \" http://doi.acm.org/10.1145/2168752.2168771\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
},
{
"path": "TensorFlow_Examples/labs/02_recommendation/solutions/classification.py",
"content": "# For each sample we input the integer identifiers\n# of a single user and a single item\nuser_id_input = Input(shape=[1], name='user')\nitem_id_input = Input(shape=[1], name='item')\n\nembedding_size = 16\ndense_size = 128\ndropout_embedding = 0.5\ndropout_hidden = 0.2\n\nuser_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,\n input_length=1, name='user_embedding')(user_id_input)\nitem_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)\nuser_vecs = Flatten()(user_embedding)\nitem_vecs = Flatten()(item_embedding)\n\ninput_vecs = merge([user_vecs, item_vecs], mode='concat')\ninput_vecs = Dropout(dropout_embedding)(input_vecs)\n\nx = Dense(dense_size, activation='relu')(input_vecs)\nx = Dropout(dropout_hidden)(x)\nx = Dense(dense_size, activation='relu')(x)\ny = Dense(output_dim=5, activation='softmax')(x)\n\nmodel = Model(input=[user_id_input, item_id_input], output=y)\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy')\n\ninitial_train_preds = model.predict([user_id_train, item_id_train]).argmax(axis=1) + 1\nprint(\"Random init MSE: %0.3f\" % mean_squared_error(initial_train_preds, rating_train))\nprint(\"Random init MAE: %0.3f\" % mean_absolute_error(initial_train_preds, rating_train))\n\n\nhistory = model.fit([user_id_train, item_id_train], rating_train - 1,\n batch_size=64, nb_epoch=6, validation_split=0.1,\n shuffle=True)\n\nplt.plot(history.history['loss'], label='train')\nplt.plot(history.history['val_loss'], label='validation')\nplt.ylim(0, 2)\nplt.legend(loc='best')\nplt.title('loss');\n\ntest_preds = model.predict([user_id_test, item_id_test]).argmax(axis=1) + 1\nprint(\"Final test MSE: %0.3f\" % mean_squared_error(test_preds, rating_test))\nprint(\"Final test MAE: %0.3f\" % mean_absolute_error(test_preds, rating_test))\n"
},
{
"path": "TensorFlow_Examples/labs/02_recommendation/solutions/compute_errors.py",
"content": "squared_differences = np.square(initial_train_preds - rating_train.values)\nabsolute_differences = np.abs(initial_train_preds - rating_train.values)\n\nprint(\"Random init MSE: %0.3f\" % np.mean(squared_differences))\nprint(\"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))\n"
},
{
"path": "TensorFlow_Examples/labs/02_recommendation/solutions/deep_explicit_feedback_recsys.py",
"content": "# For each sample we input the integer identifiers\n# of a single user and a single item\nuser_id_input = Input(shape=[1], name='user')\nitem_id_input = Input(shape=[1], name='item')\n\nembedding_size = 30\nuser_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,\n input_length=1, name='user_embedding')(user_id_input)\nitem_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)\nuser_vecs = Flatten()(user_embedding)\nitem_vecs = Flatten()(item_embedding)\n\ninput_vecs = merge([user_vecs, item_vecs], mode='concat')\n## Error 1: Dropout was too high, preventing any training\ninput_vecs = Dropout(0.5)(input_vecs)\n\nx = Dense(64, activation='relu')(input_vecs)\n\n## Error 2: output dimension was 2 where we predict only 1-d rating\n## Error 3: tanh activation squashes the outputs between -1 and 1\n## when we want to predict values between 1 and 5\ny = Dense(1)(x)\n\nmodel = Model(input=[user_id_input, item_id_input], output=y)\n## Error 4: A binary crossentropy loss is only useful for binary\n## classification, while we are in regression (use mse or mae)\nmodel.compile(optimizer='adam', loss='mae')\n\ninitial_train_preds = model.predict([user_id_train, item_id_train])\n"
},
{
"path": "TensorFlow_Examples/labs/02_recommendation/solutions/deep_implicit_feedback_recsys.py",
"content": "from keras.models import Model, Sequential\nfrom keras.layers import Embedding, Flatten, Input, Dense, Dropout, merge\nfrom keras.regularizers import l2\n\n\ndef 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\ndef 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\nhyper_parameters = dict(\n user_dim=32,\n item_dim=64,\n n_hidden=1,\n hidden_size=128,\n dropout=0.1,\n l2_reg=0\n)\ndeep_match_model, deep_triplet_model = build_models(n_users, n_items,\n **hyper_parameters)\n\n\ndeep_triplet_model.compile(loss=identity_loss, optimizer='adam')\nfake_y = np.ones_like(pos_data_train['user_id'])\n\nn_epochs = 15\n\nfor 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)\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"
},
{
"path": "TensorFlow_Examples/labs/02_recommendation/solutions/deep_triplet_parameter_count.py",
"content": "# 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."
},
{
"path": "TensorFlow_Examples/labs/02_recommendation/solutions/ml-1m.py",
"content": "import matplotlib.pyplot as plt\n\nfrom pathlib import Path\nfrom zipfile import ZipFile\nfrom urllib.request import urlretrieve\n\nfrom keras.layers import Input, Embedding, Flatten, merge, Dense, Dropout, Lambda\nfrom keras.models import Model\nimport keras.backend as K\nimport pandas as pd\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error\n\nML_1M_URL = \"http://files.grouplens.org/datasets/movielens/ml-1m.zip\"\nML_1M_FILENAME = Path(ML_1M_URL.rsplit('/', 1)[1])\nML_1M_FOLDER = Path('ml-1m')\n\nif not ML_1M_FILENAME.exists():\n print('Downloading %s to %s...' % (ML_1M_URL, ML_1M_FILENAME))\n urlretrieve(ML_1M_URL, ML_1M_FILENAME.name)\n\nif not ML_1M_FOLDER.exists():\n print('Extracting %s to %s...' % (ML_1M_FILENAME, ML_1M_FOLDER))\n ZipFile(ML_1M_FILENAME.name).extractall('.')\n\n\nall_ratings = pd.read_csv(ML_1M_FOLDER / 'ratings.dat', sep='::',\n names=[\"user_id\", \"item_id\", \"rating\", \"timestamp\"])\nall_ratings.head()\n\nmax_user_id = all_ratings['user_id'].max()\nmax_item_id = all_ratings['item_id'].max()\n\nfrom sklearn.model_selection import train_test_split\n\nratings_train, ratings_test = train_test_split(\n all_ratings, test_size=0.2, random_state=0)\n\nuser_id_train = ratings_train['user_id']\nitem_id_train = ratings_train['item_id']\nrating_train = ratings_train['rating']\n\nuser_id_test = ratings_test['user_id']\nitem_id_test = ratings_test['item_id']\nrating_test = ratings_test['rating']\n\nuser_id_input = Input(shape=[1], name='user')\nitem_id_input = Input(shape=[1], name='item')\n\nembedding_size = 32\nuser_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,\n input_length=1, name='user_embedding')(user_id_input)\nitem_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)\nuser_vecs = Flatten()(user_embedding)\nitem_vecs = Flatten()(item_embedding)\n\ninput_vecs = merge([user_vecs, item_vecs], mode='concat')\ninput_vecs = Dropout(0.5)(input_vecs)\n\ndense_size = 64\nx = Dense(dense_size, activation='relu')(input_vecs)\nx = Dense(dense_size, activation='relu')(input_vecs)\ny = Dense(output_dim=1)(x)\n\nmodel = Model(input=[user_id_input, item_id_input], output=y)\nmodel.compile(optimizer='adam', loss='mae')\n\ninitial_train_preds = model.predict([user_id_train, item_id_train])\n\nhistory = model.fit([user_id_train, item_id_train], rating_train,\n batch_size=64, nb_epoch=15, validation_split=0.1,\n shuffle=True)\n\ntest_preds = model.predict([user_id_test, item_id_test])\nprint(\"Final test MSE: %0.3f\" % mean_squared_error(test_preds, rating_test))\nprint(\"Final test MAE: %0.3f\" % mean_absolute_error(test_preds, rating_test))\n"
},
{
"path": "TensorFlow_Examples/labs/02_recommendation/solutions/similarity.py",
"content": "EPSILON = 1e-07\n\ndef cosine(x, y):\n dot_pdt = np.dot(x, y.T)\n norms = np.linalg.norm(x) * np.linalg.norm(y)\n return dot_pdt / (norms + EPSILON)\n\n# Computes cosine similarities between x and all item embeddings\ndef cosine_similarities(x):\n dot_pdts = np.dot(item_embeddings, x)\n norms = np.linalg.norm(x) * np.linalg.norm(item_embeddings, axis=1)\n return dot_pdts / (norms + EPSILON)\n\n# Computes euclidean distances between x and all item embeddings\ndef euclidean_distances(x):\n return np.linalg.norm(item_embeddings - x, axis=1)\n\n# Computes top_n most similar items to an idx, \ndef most_similar(idx, top_n=10, mode='euclidean'):\n sorted_indexes=0\n if mode=='euclidean':\n dists = euclidean_distances(item_embeddings[idx])\n sorted_indexes = np.argsort(dists)\n idxs = sorted_indexes[0:top_n]\n return list(zip(items[\"name\"][idxs], dists[idxs]))\n else:\n sims = cosine_similarities(item_embeddings[idx])\n # [::-1] makes it possible to reverse the order of a numpy\n # array, this is required because most similar items have\n # a larger cosine similarity value\n sorted_indexes = np.argsort(sims)[::-1]\n idxs = sorted_indexes[0:top_n]\n return list(zip(items[\"name\"][idxs], sims[idxs]))\n\n# sanity checks:\nprint(\"cosine of item 1 and item 1: \"\n + str(cosine(item_embeddings[1], item_embeddings[1])))\neuc_dists = euclidean_distances(item_embeddings[1])\nprint(euc_dists.shape)\nprint(euc_dists[1:5])\nprint()\n\n# Test on movie 181: Return of the Jedi\nprint(\"Items closest to 'Return of the Jedi':\")\nfor title, dist in most_similar(181, mode=\"euclidean\"):\n print(title, dist)\n\n\n# We observe that the embedding is poor at representing similarities\n# between movies, as most distance/similarities are very small/big \n# One may notice a few clusters though\n# it's interesting to plot the following distributions\n# plt.hist(euc_dists)\n\n# The reason for that is that the number of ratings is low and the embedding\n# does not automatically capture semantic relationships in that context. \n# Better representations arise with higher number of ratings, and less overfitting models\n"
},
{
"path": "TensorFlow_Examples/labs/02_recommendation/solutions/triplet_parameter_count.py",
"content": "# 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)"
},
{
"path": "TensorFlow_Examples/labs/03_LSTM_NLP/NLP_word_vectors_classification.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Text classification using Neural Networks\\n\",\n \"\\n\",\n \"The goal of this notebook is to learn to use Neural Networks for text classification.\\n\",\n \"\\n\",\n \"In this notebook, we will:\\n\",\n \"- Train a shallow model with learning embeddings\\n\",\n \"- Download pre-trained embeddings from Glove\\n\",\n \"- Use these pre-trained embeddings\\n\",\n \"\\n\",\n \"However keep in mind:\\n\",\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\",\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\",\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\",\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\",\n \"\\n\",\n \"### 20 Newsgroups Dataset\\n\",\n \"\\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/\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"from sklearn.datasets import fetch_20newsgroups\\n\",\n \"\\n\",\n \"newsgroups_train = fetch_20newsgroups(subset='train')\\n\",\n \"newsgroups_test = fetch_20newsgroups(subset='test')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sample_idx = 1000\\n\",\n \"print(newsgroups_train[\\\"data\\\"][sample_idx])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"target_names = newsgroups_train[\\\"target_names\\\"]\\n\",\n \"\\n\",\n \"target_id = newsgroups_train[\\\"target\\\"][sample_idx]\\n\",\n \"print(\\\"Class of previous message:\\\", target_names[target_id])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Here are all the possible classes:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"target_names\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Preprocessing text for the (supervised) CBOW model\\n\",\n \"\\n\",\n \"We will implement a simple classification model in Keras. Raw text requires (sometimes a lot of) preprocessing.\\n\",\n \"\\n\",\n \"The following cells uses Keras to preprocess text:\\n\",\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\",\n \"- sequences have different lengths, so we pad them (add 0s at the end until the sequence is of length `1000`)\\n\",\n \"- we convert the output classes as 1-hot encodings\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from keras.preprocessing.text import Tokenizer\\n\",\n \"\\n\",\n \"MAX_NB_WORDS = 20000\\n\",\n \"\\n\",\n \"# get the raw text data\\n\",\n \"texts_train = newsgroups_train[\\\"data\\\"]\\n\",\n \"texts_test = newsgroups_test[\\\"data\\\"]\\n\",\n \"\\n\",\n \"# finally, vectorize the text samples into a 2D integer tensor\\n\",\n \"tokenizer = Tokenizer(nb_words=MAX_NB_WORDS, char_level=False)\\n\",\n \"tokenizer.fit_on_texts(texts_train)\\n\",\n \"sequences = tokenizer.texts_to_sequences(texts_train)\\n\",\n \"sequences_test = tokenizer.texts_to_sequences(texts_test)\\n\",\n \"\\n\",\n \"word_index = tokenizer.word_index\\n\",\n \"print('Found %s unique tokens.' % len(word_index))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Tokenized sequences are converted to list of token ids (with an integer code):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sequences[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The tokenizer object stores a mapping (vocabulary) from word strings to token ids that can be inverted to reconstruct the original message (without formatting):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"type(tokenizer.word_index), len(tokenizer.word_index)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"index_to_word = dict((i, w) for w, i in tokenizer.word_index.items())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"\\\" \\\".join([index_to_word[i] for i in sequences[0]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's have a closer look at the tokenized sequences:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"seq_lens = [len(s) for s in sequences]\\n\",\n \"print(\\\"average length: %0.1f\\\" % np.mean(seq_lens))\\n\",\n \"print(\\\"max length: %d\\\" % max(seq_lens))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"plt.hist(seq_lens, bins=50);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's zoom on the distribution of regular sized posts. The vast majority of the posts have less than 1000 symbols:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plt.hist([l for l in seq_lens if l < 3000], bins=50);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's truncate and pad all the sequences to 1000 symbols to build the training set:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from keras.preprocessing.sequence import pad_sequences\\n\",\n \"\\n\",\n \"\\n\",\n \"MAX_SEQUENCE_LENGTH = 1000\\n\",\n \"\\n\",\n \"# pad sequences with 0s\\n\",\n \"x_train = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\\n\",\n \"x_test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH)\\n\",\n \"print('Shape of data tensor:', x_train.shape)\\n\",\n \"print('Shape of data test tensor:', x_test.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from keras.utils.np_utils import to_categorical\\n\",\n \"y_train = newsgroups_train[\\\"target\\\"]\\n\",\n \"y_test = newsgroups_test[\\\"target\\\"]\\n\",\n \"\\n\",\n \"y_train = to_categorical(np.asarray(y_train))\\n\",\n \"print('Shape of label tensor:', y_train.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### A simple supervised CBOW model in Keras\\n\",\n \"\\n\",\n \"The following computes a very simple model, as described in [fastText](https://github.com/facebookresearch/fastText):\\n\",\n \"\\n\",\n \"![](\\\"images/fasttext.svg\\\")
\\n\",\n \"\\n\",\n \"- Build an embedding layer mapping each word to a vector representation\\n\",\n \"- Compute the vector representation of all words in each sequence and average them\\n\",\n \"- Add a dense layer to output 20 classes (+ softmax)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from keras.layers import Dense, Input, Flatten\\n\",\n \"from keras.layers import GlobalAveragePooling1D, Embedding\\n\",\n \"from keras.models import Model\\n\",\n \"\\n\",\n \"EMBEDDING_DIM = 50\\n\",\n \"N_CLASSES = len(target_names)\\n\",\n \"\\n\",\n \"# input: a sequence of MAX_SEQUENCE_LENGTH integers\\n\",\n \"sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\\n\",\n \"\\n\",\n \"embedding_layer = Embedding(MAX_NB_WORDS, EMBEDDING_DIM,\\n\",\n \" input_length=MAX_SEQUENCE_LENGTH,\\n\",\n \" trainable=True)\\n\",\n \"embedded_sequences = embedding_layer(sequence_input)\\n\",\n \"\\n\",\n \"average = GlobalAveragePooling1D()(embedded_sequences)\\n\",\n \"predictions = Dense(N_CLASSES, activation='softmax')(average)\\n\",\n \"\\n\",\n \"model = Model(sequence_input, predictions)\\n\",\n \"model.compile(loss='categorical_crossentropy',\\n\",\n \" optimizer='adam', metrics=['acc'], verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"model.fit(x_train, y_train, validation_split=0.1,\\n\",\n \" nb_epoch=10, batch_size=128, verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"**Exercice**\\n\",\n \" - compute model accuracy on test set\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# %load solutions/accuracy.py\\n\",\n \"output_test = model.predict(x_test)\\n\",\n \"test_casses = np.argmax(output_test, axis=-1)\\n\",\n \"print(\\\"test accuracy:\\\", np.mean(test_casses == y_test))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Building more complex models\\n\",\n \"\\n\",\n \"\\n\",\n \"- From the previous template, build more complex models using:\\n\",\n \" - 1d convolution and 1d maxpooling. Note that you will still need a GloabalAveragePooling or Flatten after the convolutions\\n\",\n \" - Recurrent neural networks through LSTM (you will need to reduce sequence length before)\\n\",\n \" \\n\",\n \" \\n\",\n \"![](\\\"images/unrolled_rnn_one_output_2.svg\\\")
\\n\",\n \"\\n\",\n \"\\n\",\n \" - more intermediate layers, combination of dense, conv, recurrent\\n\",\n \" - different recurrent (GRU, RNN)\\n\",\n \" - bidirectional LSTMs\\n\",\n \"\\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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# %load solutions/lstm.py\\n\",\n \"from keras.layers import LSTM, Conv1D, MaxPooling1D\\n\",\n \"\\n\",\n \"# input: a sequence of MAX_SEQUENCE_LENGTH integers\\n\",\n \"sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\\n\",\n \"embedded_sequences = embedding_layer(sequence_input)\\n\",\n \"\\n\",\n \"# 1D convolution with 64 output channels\\n\",\n \"x = Conv1D(64, 5)(embedded_sequences)\\n\",\n \"# MaxPool divides the length of the sequence by 5\\n\",\n \"x = MaxPooling1D(5)(x)\\n\",\n \"x = Conv1D(64, 5)(x)\\n\",\n \"x = MaxPooling1D(5)(x)\\n\",\n \"# LSTM layer with a hidden size of 64\\n\",\n \"x = LSTM(64)(x)\\n\",\n \"predictions = Dense(20, activation='softmax')(x)\\n\",\n \"\\n\",\n \"model = Model(sequence_input, predictions)\\n\",\n \"model.compile(loss='categorical_crossentropy',\\n\",\n \" optimizer='adam',\\n\",\n \" metrics=['acc'])\\n\",\n \"\\n\",\n \"# You will get large speedups with these models by using a GPU\\n\",\n \"# The model might take a lot of time to converge, and even more\\n\",\n \"# if you add dropout (needed to prevent overfitting)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# %load solutions/conv1d.py\\n\",\n \"from keras.layers import Conv1D, MaxPooling1D, Flatten\\n\",\n \"\\n\",\n \"sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\\n\",\n \"embedded_sequences = embedding_layer(sequence_input)\\n\",\n \"\\n\",\n \"# A 1D convolution with 128 output channels\\n\",\n \"x = Conv1D(128, 5, activation='relu')(embedded_sequences)\\n\",\n \"# MaxPool divides the length of the sequence by 5\\n\",\n \"x = MaxPooling1D(5)(x)\\n\",\n \"# A 1D convolution with 64 output channels\\n\",\n \"x = Conv1D(64, 5, activation='relu')(x)\\n\",\n \"# MaxPool divides the length of the sequence by 5\\n\",\n \"x = MaxPooling1D(5)(x)\\n\",\n \"x = Flatten()(x)\\n\",\n \"\\n\",\n \"predictions = Dense(20, activation='softmax')(x)\\n\",\n \"\\n\",\n \"model = Model(sequence_input, predictions)\\n\",\n \"model.compile(loss='categorical_crossentropy',\\n\",\n \" optimizer='adam',\\n\",\n \" metrics=['acc'])\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"model.fit(x_train, y_train, validation_split=0.1,\\n\",\n \" nb_epoch=10, batch_size=128, verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Loading pre-trained embeddings\\n\",\n \"\\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 \"\\n\",\n \"We extracted the `100 000` most frequent words. They have a dimension of `100`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"embeddings_index = {}\\n\",\n \"embeddings_vectors = []\\n\",\n \"f = open('glove100K.100d.txt', 'rb')\\n\",\n \"\\n\",\n \"word_idx = 0\\n\",\n \"for line in f:\\n\",\n \" values = line.decode('utf-8').split()\\n\",\n \" word = values[0]\\n\",\n \" vector = np.asarray(values[1:], dtype='float32')\\n\",\n \" embeddings_index[word] = word_idx\\n\",\n \" embeddings_vectors.append(vector)\\n\",\n \" word_idx = word_idx + 1\\n\",\n \"f.close()\\n\",\n \"\\n\",\n \"inv_index = {v: k for k, v in embeddings_index.items()}\\n\",\n \"print(\\\"found %d different words in the file\\\" % word_idx)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Stack all embeddings in a large numpy array\\n\",\n \"glove_embeddings = np.vstack(embeddings_vectors)\\n\",\n \"glove_norms = np.linalg.norm(glove_embeddings, axis=-1, keepdims=True)\\n\",\n \"glove_embeddings_normed = glove_embeddings / glove_norms\\n\",\n \"print(glove_embeddings.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def get_emb(word):\\n\",\n \" idx = embeddings_index.get(word)\\n\",\n \" if idx is None:\\n\",\n \" return None\\n\",\n \" else:\\n\",\n \" return glove_embeddings[idx]\\n\",\n \"\\n\",\n \" \\n\",\n \"def get_normed_emb(word):\\n\",\n \" idx = embeddings_index.get(word)\\n\",\n \" if idx is None:\\n\",\n \" return None\\n\",\n \" else:\\n\",\n \" return glove_embeddings_normed[idx]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"get_emb(\\\"computer\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Finding most similar words\\n\",\n \"\\n\",\n \"**Exercice**\\n\",\n \"\\n\",\n \"Build a function to find most similar words, given a word as query:\\n\",\n \"- lookup the vector for the query word in the Glove index;\\n\",\n \"- compute the cosine similarity between a word embedding and all other words;\\n\",\n \"- display the top 10 most similar words.\\n\",\n \"\\n\",\n \"**Bonus**\\n\",\n \"\\n\",\n \"Change your function so that it takes multiple words as input (by averaging them)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# %load solutions/most_similar.py\\n\",\n \"def most_similar(words, topn=10):\\n\",\n \" query_emb = 0\\n\",\n \" # If we have a list of words instead of one word\\n\",\n \" # (bonus question)\\n\",\n \" if type(words) == list:\\n\",\n \" for word in words:\\n\",\n \" query_emb += get_emb(word) \\n\",\n \" else:\\n\",\n \" query_emb = get_emb(words)\\n\",\n \" \\n\",\n \" query_emb = query_emb / np.linalg.norm(query_emb)\\n\",\n \" \\n\",\n \" # Large numpy vector with all cosine similarities\\n\",\n \" # between emb and all other words\\n\",\n \" cosines = np.dot(glove_embeddings_normed, query_emb)\\n\",\n \" \\n\",\n \" # topn most similar indexes corresponding to cosines\\n\",\n \" idxs = np.argsort(cosines)[::-1][:topn]\\n\",\n \" \\n\",\n \" # pretty return with word and similarity\\n\",\n \" return [(inv_index[idx], cosines[idx]) for idx in idxs]\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"most_similar(\\\"cpu\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"most_similar(\\\"pitt\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"most_similar(\\\"jolie\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Predict the future better than tarot:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"np.dot(get_normed_emb('aniston'), get_normed_emb('pitt'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"np.dot(get_normed_emb('jolie'), get_normed_emb('pitt'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"most_similar(\\\"1\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# bonus: yangtze is a chinese river\\n\",\n \"most_similar([\\\"river\\\", \\\"chinese\\\"])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Displaying vectors with t-SNE\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.manifold import TSNE\\n\",\n \"\\n\",\n \"word_emb_tsne = TSNE(perplexity=30).fit_transform(glove_embeddings_normed[:1000])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"plt.figure(figsize=(40, 40))\\n\",\n \"axis = plt.gca()\\n\",\n \"np.set_printoptions(suppress=True)\\n\",\n \"plt.scatter(word_emb_tsne[:, 0], word_emb_tsne[:, 1], marker=\\\".\\\", s=1)\\n\",\n \"\\n\",\n \"for idx in range(1000):\\n\",\n \" plt.annotate(inv_index[idx],\\n\",\n \" xy=(word_emb_tsne[idx, 0], word_emb_tsne[idx, 1]),\\n\",\n \" xytext=(0, 0), textcoords='offset points')\\n\",\n \"plt.savefig(\\\"tsne.png\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Using pre-trained embeddings in our model\\n\",\n \"\\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\",\n \"- We initialize embedding matrix from the model with Glove embeddings:\\n\",\n \" - take all words from our 20 Newgroup vocabulary (`MAX_NB_WORDS = 20000`), and look up their Glove embedding \\n\",\n \" - place the Glove embedding at the corresponding index in the matrix\\n\",\n \" - if the word is not in the Glove vocabulary, we only place zeros in the matrix\\n\",\n \"- We may fix these embeddings or fine-tune them\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"EMBEDDING_DIM = 100\\n\",\n \"\\n\",\n \"# prepare embedding matrix\\n\",\n \"nb_words_in_matrix = 0\\n\",\n \"nb_words = min(MAX_NB_WORDS, len(word_index))\\n\",\n \"embedding_matrix = np.zeros((nb_words, EMBEDDING_DIM))\\n\",\n \"for word, i in word_index.items():\\n\",\n \" if i >= MAX_NB_WORDS:\\n\",\n \" continue\\n\",\n \" embedding_vector = get_emb(word)\\n\",\n \" if embedding_vector is not None:\\n\",\n \" # words not found in embedding index will be all-zeros.\\n\",\n \" embedding_matrix[i] = embedding_vector\\n\",\n \" nb_words_in_matrix = nb_words_in_matrix + 1\\n\",\n \" \\n\",\n \"print(\\\"added %d words in the embedding matrix\\\" % nb_words_in_matrix)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Build a layer with pre-trained embeddings:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"pretrained_embedding_layer = Embedding(\\n\",\n \" MAX_NB_WORDS, EMBEDDING_DIM,\\n\",\n \" weights=[embedding_matrix],\\n\",\n \" input_length=MAX_SEQUENCE_LENGTH,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### A model with pre-trained Embeddings\\n\",\n \"\\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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\\n\",\n \"embedded_sequences = pretrained_embedding_layer(sequence_input)\\n\",\n \"average = GlobalAveragePooling1D()(embedded_sequences)\\n\",\n \"predictions = Dense(N_CLASSES, activation='softmax')(average)\\n\",\n \"\\n\",\n \"model = Model(sequence_input, predictions)\\n\",\n \"\\n\",\n \"# We don't want to fine-tune embeddings\\n\",\n \"model.layers[1].trainable=False\\n\",\n \"\\n\",\n \"model.compile(loss='categorical_crossentropy',\\n\",\n \" optimizer='adam', metrics=['acc'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"model.fit(x_train, y_train, validation_split=0.1,\\n\",\n \" nb_epoch=10, batch_size=128, verbose=2)\\n\",\n \"\\n\",\n \"# Note, on this type of task, this technique will \\n\",\n \"# degrade results as we train much less parameters\\n\",\n \"# and we average a large number pre-trained embeddings.\\n\",\n \"# You will notice much less overfitting then!\\n\",\n \"# Using convolutions / LSTM will help\\n\",\n \"# It is also advisable to treat seperately pre-trained\\n\",\n \"# embeddings and words out of vocabulary.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Reality check\\n\",\n \"\\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\",\n \"- Naive Bayes approach, using scikit-learn http://scikit-learn.org/stable/datasets/twenty_newsgroups.html\\n\",\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 \"\\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 \"\\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.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Going further\\n\",\n \"\\n\",\n \"- Compare pre-trained embeddings vs specifically trained embeddings\\n\",\n \"- Train your own wordvectors in any language using [gensim's word2vec](https://radimrehurek.com/gensim/models/word2vec.html)\\n\",\n \"- Check [Keras Examples](https://github.com/fchollet/keras/tree/master/examples) on `imdb` sentiment analysis\\n\",\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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
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{
"path": "TensorFlow_Examples/labs/03_conv_nets/ConvNets_with_TensorFlow_rendered.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Extracting MNIST_data/train-images-idx3-ubyte.gz\\n\",\n \"Extracting MNIST_data/train-labels-idx1-ubyte.gz\\n\",\n \"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\\n\",\n \"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"'/Users/ruizhang/.keras/models/resnet50_weights_tf_dim_ordering_tf_kernels.h5'\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"## Run this cell once before the lab to download\\n\",\n \"## the mnist dataset and the pre-trained ResNet50 model. \\n\",\n \"\\n\",\n \"## Mnist\\n\",\n \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n \"mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\\n\",\n \"\\n\",\n \"## Keras pre-trained weights\\n\",\n \"from keras.utils.data_utils import get_file\\n\",\n \"get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',\\n\",\n \" 'https://github.com/fchollet/deep-learning-models/releases'+\\n\",\n \" '/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5',\\n\",\n \" cache_subdir='models',\\n\",\n \" md5_hash='a7b3fe01876f51b976af0dea6bc144eb')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"# Convolutional Neural Networks\\n\",\n \"\\n\",\n \"Objectives:\\n\",\n \"- TensorFlow tutorial\\n\",\n \"- Application of convolution on images\\n\",\n \"- First conv net on MNIST with TensorFlow\\n\",\n \"- Use a pre-trained ResNet with Keras for transfer learning (second notebook)\\n\",\n \"\\n\",\n \"Home assignment: fine-tuning a resnet on GPU (third notebook)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## TensorFlow 101\\n\",\n \"\\n\",\n \"TensorFlow is a symbolic graph computation engine, that allows automatic differentiation of each node\\n\",\n \"- https://www.tensorflow.org \\n\",\n \"- https://www.tensorflow.org/tutorials/mnist/tf/\\n\",\n \"\\n\",\n \"TensorFlow builds where nodes may be:\\n\",\n \"- **constant:** constants tensors, such as a learning rate\\n\",\n \"- **Variables:** any tensor, such as parameters of the models\\n\",\n \"- **Placeholders:** placeholders for inputs and outputs of your models\\n\",\n \"- many other types of nodes (functions, loss, ...)\\n\",\n \"\\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\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"a = tf.constant(3)\\n\",\n \"b = tf.constant(2)\\n\",\n \"c = a + b\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"![](\\\"images/basic_seq2seq.png\\\")
\\n\",\n \"\\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 \"\\n\",\n \"\\n\",\n \"For a given source sequence - target sequence pair, we will:\\n\",\n \"- tokenize the source and target sequences;\\n\",\n \"- reverse the order of the source sequence;\\n\",\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\",\n \"- build the output sequence by appending the `_EOS` token to the source sequence.\\n\",\n \"\\n\",\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:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"**Exercise**\\n\",\n \"- Build a function which adapts a pair of tokenized sequences to the framework above.\\n\",\n \"- The function should have a reverse_source as an option.\\n\",\n \"\\n\",\n \"*Note*: \\n\",\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\",\n \"- Don't forget to insert the `_GO` and `_EOS` special symbols at the right locations.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# %load solutions/make_input_output.py\\n\",\n \"def make_input_output(source_tokens, target_tokens, reverse_source=True):\\n\",\n \" if reverse_source:\\n\",\n \" source_tokens = source_tokens[::-1]\\n\",\n \" input_tokens = source_tokens + [GO] + target_tokens\\n\",\n \" output_tokens = target_tokens + [EOS]\\n\",\n \" return input_tokens, output_tokens\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"input_tokens, output_tokens = make_input_output(\\n\",\n \" ['cent', 'vingt', 'et', 'un'],\\n\",\n \" ['1', '2', '1'],\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['un', 'et', 'vingt', 'cent', '_GO', '1', '2', '1']\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"input_tokens\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['1', '2', '1', '_EOS']\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"output_tokens\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Vectorization of the parallel corpus\\n\",\n \"\\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: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"all_tokenized_sequences = tokenized_fr_train + tokenized_num_train\\n\",\n \"shared_vocab, rev_shared_vocab = build_vocabulary(all_tokenized_sequences)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"max_length = 20 # found by introspection of our training set\\n\",\n \"\\n\",\n \"def vectorize_corpus(source_sequences, target_sequences, shared_vocab,\\n\",\n \" word_level_source=True, word_level_target=True,\\n\",\n \" max_length=max_length):\\n\",\n \" assert len(source_sequences) == len(target_sequences)\\n\",\n \" n_sequences = len(source_sequences)\\n\",\n \" source_ids = np.empty(shape=(n_sequences, max_length), dtype=np.int32)\\n\",\n \" source_ids.fill(shared_vocab[PAD])\\n\",\n \" target_ids = np.empty(shape=(n_sequences, max_length), dtype=np.int32)\\n\",\n \" target_ids.fill(shared_vocab[PAD])\\n\",\n \" numbered_pairs = zip(range(n_sequences), source_sequences, target_sequences)\\n\",\n \" for i, source_seq, target_seq in numbered_pairs:\\n\",\n \" source_tokens = tokenize(source_seq, word_level=word_level_source)\\n\",\n \" target_tokens = tokenize(target_seq, word_level=word_level_target)\\n\",\n \" \\n\",\n \" in_tokens, out_tokens = make_input_output(source_tokens, target_tokens)\\n\",\n \" \\n\",\n \" in_token_ids = [shared_vocab.get(t, UNK) for t in in_tokens]\\n\",\n \" source_ids[i, -len(in_token_ids):] = in_token_ids\\n\",\n \" \\n\",\n \" out_token_ids = [shared_vocab.get(t, UNK) for t in out_tokens]\\n\",\n \" target_ids[i, -len(out_token_ids):] = out_token_ids\\n\",\n \" return source_ids, target_ids\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"X_train, Y_train = vectorize_corpus(fr_train, num_train, shared_vocab,\\n\",\n \" word_level_target=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10000, 20)\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_train.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10000, 20)\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Y_train.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'deux mille huit cent quatre vingt deux'\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fr_train[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'2882'\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"num_train[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0, 0, 0, 0, 0, 0, 0, 0, 18, 38, 28, 14, 22, 23, 18, 1, 6,\\n\",\n \" 12, 12, 6], dtype=int32)\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_train[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 12,\\n\",\n \" 12, 6, 2], dtype=int32)\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Y_train[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This looks good. In particular we can note:\\n\",\n \"\\n\",\n \"- the PAD=0 symbol at the beginning of the two sequences,\\n\",\n \"- the input sequence has the GO=1 symbol to separate the source from the target,\\n\",\n \"- the output sequence is a shifted version of the target and ends with EOS=2.\\n\",\n \"\\n\",\n \"Let's vectorize the validation and test set to be able to evaluate our models:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"X_val, Y_val = vectorize_corpus(fr_val, num_val, shared_vocab,\\n\",\n \" word_level_target=False)\\n\",\n \"X_test, Y_test = vectorize_corpus(fr_test, num_test, shared_vocab,\\n\",\n \" word_level_target=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"((5000, 20), (5000, 20))\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_val.shape, Y_val.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"((5000, 20), (5000, 20))\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_test.shape, Y_test.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### A simple homogeneous Seq2Seq architecture\\n\",\n \"\\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 \"\\n\",\n \"We may GRU recurrent cell instead of LSTM because it is slightly faster to compute and should give comparable results.\\n\",\n \"\\n\",\n \"**Exercise:**\\n\",\n \"- Build a Seq2Seq model:\\n\",\n \" - Start with an Embedding layer;\\n\",\n \" - Add a single GRU layer: the GRU layer should yield a sequence of output vectors, one at each timestep;\\n\",\n \" - Add a Dense layer to adapt the ouput dimension of the GRU layer to the dimension of the output vocabulary;\\n\",\n \" - Don't forget to insert some Dropout layer(s), especially after the Embedding layer.\\n\",\n \"\\n\",\n \"Note:\\n\",\n \"- The output dimension of the Embedding layer should be smaller than usual be cause we have small vocabulary size;\\n\",\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\",\n \"- Your model should output a shape `[batch, sequence_length, vocab_size]`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# %load solutions/simple_seq2seq.py\\n\",\n \"from keras.models import Sequential\\n\",\n \"from keras.layers import Embedding, Dropout, GRU, Dense\\n\",\n \"\\n\",\n \"vocab_size = len(shared_vocab)\\n\",\n \"simple_seq2seq = Sequential()\\n\",\n \"simple_seq2seq.add(Embedding(vocab_size, 32, input_length=max_length))\\n\",\n \"simple_seq2seq.add(Dropout(0.2))\\n\",\n \"simple_seq2seq.add(GRU(64, return_sequences=True))\\n\",\n \"simple_seq2seq.add(Dense(vocab_size, activation='softmax'))\\n\",\n \"\\n\",\n \"# Here we use the sparse_categorical_crossentropy loss to be able to pass\\n\",\n \"# integer-coded output for the token ids without having to convert to one-hot\\n\",\n \"# codes\\n\",\n \"simple_seq2seq.compile(optimizer='adam', loss='sparse_categorical_crossentropy')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's use a callback mechanism to automatically snapshot the best model found so far on the validation set:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from keras.callbacks import ModelCheckpoint\\n\",\n \"from keras.models import load_model\\n\",\n \"\\n\",\n \"\\n\",\n \"best_model_fname = \\\"simple_seq2seq_checkpoint.h5\\\"\\n\",\n \"best_model_cb = ModelCheckpoint(best_model_fname, monitor='val_loss',\\n\",\n \" save_best_only=True, verbose=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/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\",\n \" warnings.warn('The `nb_epoch` argument in `fit` '\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 10000 samples, validate on 5000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"Epoch 00000: val_loss improved from inf to 0.60855, saving model to simple_seq2seq_checkpoint.h5\\n\",\n \"23s - loss: 1.0676 - val_loss: 0.6086\\n\",\n \"Epoch 2/5\\n\",\n \"Epoch 00001: val_loss improved from 0.60855 to 0.56051, saving model to simple_seq2seq_checkpoint.h5\\n\",\n \"20s - loss: 0.5827 - val_loss: 0.5605\\n\",\n \"Epoch 3/5\\n\",\n \"Epoch 00002: val_loss improved from 0.56051 to 0.53664, saving model to simple_seq2seq_checkpoint.h5\\n\",\n \"23s - loss: 0.5548 - val_loss: 0.5366\\n\",\n \"Epoch 4/5\\n\",\n \"Epoch 00003: val_loss improved from 0.53664 to 0.48266, saving model to simple_seq2seq_checkpoint.h5\\n\",\n \"22s - loss: 0.5156 - val_loss: 0.4827\\n\",\n \"Epoch 5/5\\n\",\n \"Epoch 00004: val_loss improved from 0.48266 to 0.43141, saving model to simple_seq2seq_checkpoint.h5\\n\",\n \"20s - loss: 0.4649 - val_loss: 0.4314\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"![](\\\"figs/skyarrow.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"In this exercise we will implement the algorithm in Keras and test it on some example images to see its effect.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt \\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using TensorFlow backend.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from keras.applications import vgg16\\n\",\n \"from keras.layers import Input\\n\",\n \"from dream import *\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We will use the same image for the example. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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6BqbtRKE6y26yxUPB4xI6cGG5ZScBgOIfpIKkIyo+86xCDPDWfXXFDn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\\nQmCC\\n\",\n \"text/plain\": [\n \"![](\\\"figs/style-equation.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"style_weight = 0.5\\n\",\n \"content_weight = 0.5\\n\",\n \"\\n\",\n \"# combine these loss functions into a single scalar\\n\",\n \"loss = K.variable(0.)\\n\",\n \"\\n\",\n \"# loss for content layers\\n\",\n \"for layer_name in content_layers:\\n\",\n \" layer_features = outputs_dict[layer_name]\\n\",\n \"\\n\",\n \" # we get the content features from the content image and the combination image\\n\",\n \" base_image_features = layer_features[0, :, :, :]\\n\",\n \" combination_features = layer_features[2, :, :, :]\\n\",\n \"\\n\",\n \" # content_loss is defined in style.py\\n\",\n \" cl = content_loss(base_image_features,combination_features)\\n\",\n \" loss += (content_weight / len(content_layers)) * cl\\n\",\n \"\\n\",\n \"# loss for style layers\\n\",\n \"for layer_name in style_layers:\\n\",\n \" layer_features = outputs_dict[layer_name]\\n\",\n \" \\n\",\n \" # get style features from style and combination images\\n\",\n \" style_reference_features = layer_features[1, :, :, :]\\n\",\n \" combination_features = layer_features[2, :, :, :]\\n\",\n \" \\n\",\n \" # style_loss is defined in style.py\\n\",\n \" sl = style_loss(style_reference_features, combination_features,img_nrows,img_ncols)\\n\",\n \" loss += (style_weight / len(style_layers)) * sl\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"total_variation_weight = 1.0\\n\",\n \"# total_variation_loss is defined in style.py - preserves local coherence\\n\",\n \"loss += total_variation_weight*total_variation_loss(combination_image,img_nrows,img_ncols)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Next, we get the function that computes the gradient of the combination image with respect to the total loss.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# get the gradients of the generated image wrt the loss\\n\",\n \"grads = K.gradients(loss, combination_image)\\n\",\n \"\\n\",\n \"outputs = [loss]\\n\",\n \"if isinstance(grads, (list, tuple)):\\n\",\n \" outputs += grads\\n\",\n \"else:\\n\",\n \" outputs.append(grads)\\n\",\n \"\\n\",\n \"# final function that will give the gradients of the generated image wrt the loss\\n\",\n \"f_outputs = K.function([combination_image], outputs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"And now we iterate. At each iteration, the combination image ```x``` will be updated based on the gradients.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(0, 'Current loss value:', 3.4465788e+10)\\n\",\n \"(1, 'Current loss value:', 2.430669e+10)\\n\",\n \"(2, 'Current loss value:', 2.1680191e+10)\\n\",\n \"(3, 'Current loss value:', 2.0499026e+10)\\n\",\n \"(4, 'Current loss value:', 1.9802112e+10)\\n\",\n \"(5, 'Current loss value:', 1.9322927e+10)\\n\",\n \"(6, 'Current loss value:', 1.8981853e+10)\\n\",\n \"(7, 'Current loss value:', 1.872947e+10)\\n\",\n \"(8, 'Current loss value:', 1.8534095e+10)\\n\",\n \"(9, 'Current loss value:', 1.8380681e+10)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# the Evaluator class is defined in style.py\\n\",\n \"evaluator = Evaluator(img_nrows,img_ncols,f_outputs)\\n\",\n \"\\n\",\n \"ims = []\\n\",\n \"iterations = 10\\n\",\n \"\\n\",\n \"# run scipy-based optimization (L-BFGS) over the pixels of the generated image\\n\",\n \"# so as to minimize the neural style loss\\n\",\n \"\\n\",\n \"# we start with random image - this will be updated to be the combination of content and style images\\n\",\n \"x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.\\n\",\n \"\\n\",\n \"for i in range(iterations): \\n\",\n \" x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),\\n\",\n \" fprime=evaluator.grads, maxfun=20)\\n\",\n \" \\n\",\n \" print(i,'Current loss value:', min_val)\\n\",\n \" # deprocess_image is defined in style.py\\n\",\n \" img = deprocess_image(x.copy(),img_nrows,img_ncols)\\n\",\n \" \\n\",\n \" # append image to display later\\n\",\n \" ims.append(img)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We will plot the images in the last 5 iterations.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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lzNnUk7j332OYyOh+Q8aYZux1m9ukBxPxheiPHwFxfY6Stn6f0AJ5/c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\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+qo6Gzh9X96ga4+IzdG7Ow8pSLqWeL877ycetjG66+dp0RU5NS2BOrw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\\nhBBCCHGG/R/kYCYx+23TiwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"![](\\\"../figs/ssd_overview.PNG\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.rcParams['figure.figsize'] = (8, 8)\\n\",\n \"plt.rcParams['image.interpolation'] = 'nearest'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pickle\\n\",\n \"from ssd import SSD300\\n\",\n \"from ssd_utils import BBoxUtility, Generator\\n\",\n \"\\n\",\n \"SEED = 4242\\n\",\n \"np.random.seed(SEED)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Load the anchor bounding boxes and initialize the bounding box utilities:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"NUM_CLASSES = 21\\n\",\n \"input_shape = (300, 300, 3)\\n\",\n \"nms_thresh = 0.4\\n\",\n \"priors = pickle.load(open('../data/files/prior_boxes_ssd300.pkl', 'rb'))\\n\",\n \"bbox_util = BBoxUtility(NUM_CLASSES, priors, nms_thresh = nms_thresh)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's look at the anchor boxes:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"print(np.shape(priors))\\n\",\n \"print (priors[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"priors[i] = [xmin, ymin, xmax, ymax, varxc, varyc, varw, varh]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"pascal_gt = '../data/files/VOC2007.pkl'\\n\",\n \"if not os.path.isfile(pascal_gt):\\n\",\n \" from PASCAL_VOC.get_data_from_XML import XML_preprocessor\\n\",\n \" data = XML_preprocessor('../../../../data/VOCdevkit/VOC2007/Annotations/').data\\n\",\n \" pickle.dump(data,open(pascal_gt,'wb'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We load the annotations file:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"gt = pickle.load(open('../data/files/VOC2007.pkl', 'rb'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's take a peek at this file:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"key1 = gt.keys()[0]\\n\",\n \"print (key1)\\n\",\n \"print (gt[key1][0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"train_perc = 0.8\\n\",\n \"keys = sorted(gt.keys())\\n\",\n \"\\n\",\n \"num_train = int(round(train_perc * len(keys)))\\n\",\n \"train_keys = keys[:num_train]\\n\",\n \"val_keys = keys[num_train:]\\n\",\n \"num_val = len(val_keys)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Initialize the generator. Here we must indicate the path where the raw jpeg files are stored and the batch size we want to use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"path_prefix = '../../../../data/VOCdevkit/VOC2007/JPEGImages/'\\n\",\n \"batch_size = 64\\n\",\n \"gen = Generator(gt, bbox_util, batch_size, path_prefix,\\n\",\n \" train_keys, val_keys,\\n\",\n \" (input_shape[0], input_shape[1]), do_crop=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"model = SSD300(input_shape, num_classes=NUM_CLASSES)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from keras.callbacks import ModelCheckpoint\\n\",\n \"mc = ModelCheckpoint('../data/checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5', \\n\",\n \" verbose=2,save_weights_only=True,monitor='val_loss')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from keras.optimizers import Adam\\n\",\n \"from ssd_training import MultiboxLoss\\n\",\n \"\\n\",\n \"base_lr = 1e-3\\n\",\n \"optim = Adam(lr=base_lr)\\n\",\n \"model.compile(optimizer=optim,\\n\",\n \" loss=MultiboxLoss(NUM_CLASSES, neg_pos_ratio=2.0).compute_loss)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We start training the model here. We will only do 2 epochs to be able to see some intermediate outputs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"nb_epoch = 2\\n\",\n \"# 1 epoch - 500s running on titanX or K80 GPU (batch size 64)\\n\",\n \"history = model.fit_generator(gen.generate(True), gen.train_batches,\\n\",\n \" nb_epoch, verbose=2,\\n\",\n \" callbacks=[mc],\\n\",\n \" validation_data=gen.generate(False),\\n\",\n \" nb_val_samples=gen.val_batches)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Once it finishes the 2 epochs, run it again for longer. We will see the performance of the model in the next lab session.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### While we are waiting...\\n\",\n \"\\n\",\n \"#### 1. Loss function\\n\",\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 ($L_{conf}$).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"![](\\\"../figs/total_loss.PNG\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's see the two loss components:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"![](\\\"../figs/loc_loss.PNG\\\")
\\n\",\n \"\\n\",\n \"![](\\\"../figs/smoothl1.PNG\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"$L_{loc}$ 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 \"\\n\",\n \"$L_{conf}$ is the multiclass softmax loss. Check the implementation in ```ssd_training.py```.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"![](\\\"../figs/class_loss.PNG\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Finally, let's check the implementation of total loss computation in ```ssd_training.py``` to see how these two are combined.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"#### 2. Model architecture\\n\",\n \"\\n\",\n \"In the paper, the authors give the following figure for the model architecture:\\n\",\n \"\\n\",\n \"![](\\\"../figs/ssd_arch.PNG\\\")
\\n\",\n \"\\n\",\n \"Now let's look at ```ssd.py``` to see how this is implemented in keras.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"voc_classes = ['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle',\\n\",\n \" 'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable',\\n\",\n \" 'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant',\\n\",\n \" 'Sheep', 'Sofa', 'Train', 'Tvmonitor']\\n\",\n \"\\n\",\n \"def display_boxes(img,preds,th):\\n\",\n \" # Parse the outputs.\\n\",\n \" det_label = preds[:, 0]\\n\",\n \" det_conf = preds[:, 1]\\n\",\n \" det_xmin = preds[:, 2]\\n\",\n \" det_ymin = preds[:, 3]\\n\",\n \" det_xmax = preds[:, 4]\\n\",\n \" det_ymax = preds[:, 5]\\n\",\n \"\\n\",\n \" # Get detections with confidence higher than 0.6.\\n\",\n \" top_indices = [i for i, conf in enumerate(det_conf) if conf >= score_thresh]\\n\",\n \"\\n\",\n \" top_conf = det_conf[top_indices]\\n\",\n \" top_label_indices = det_label[top_indices].tolist()\\n\",\n \" top_xmin = det_xmin[top_indices]\\n\",\n \" top_ymin = det_ymin[top_indices]\\n\",\n \" top_xmax = det_xmax[top_indices]\\n\",\n \" top_ymax = det_ymax[top_indices]\\n\",\n \"\\n\",\n \" colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()\\n\",\n \"\\n\",\n \" plt.imshow(img / 255.)\\n\",\n \" plt.axis('off')\\n\",\n \" currentAxis = plt.gca()\\n\",\n \"\\n\",\n \" for i in range(top_conf.shape[0]):\\n\",\n \" xmin = int(round(top_xmin[i] * img.shape[1]))\\n\",\n \" ymin = int(round(top_ymin[i] * img.shape[0]))\\n\",\n \" xmax = int(round(top_xmax[i] * img.shape[1]))\\n\",\n \" ymax = int(round(top_ymax[i] * img.shape[0]))\\n\",\n \" score = top_conf[i]\\n\",\n \" label = int(top_label_indices[i])\\n\",\n \" label_name = voc_classes[label - 1]\\n\",\n \" display_txt = '{:0.2f}, {}'.format(score, label_name)\\n\",\n \" coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1\\n\",\n \" color = colors[label]\\n\",\n \" currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))\\n\",\n \" currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})\\n\",\n \" \\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now let's take a few validation images and test how it works\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from scipy.misc import imread\\n\",\n \"from keras.applications.imagenet_utils import preprocess_input\\n\",\n \"from keras.preprocessing import image\\n\",\n \"\\n\",\n \"inputs = []\\n\",\n \"images = []\\n\",\n \"\\n\",\n \"idxs = [0,2,10,15]\\n\",\n \"for idx in idxs:\\n\",\n \" img_path = path_prefix + sorted(val_keys)[idx]\\n\",\n \" img = image.load_img(img_path, target_size=(300, 300))\\n\",\n \" img = image.img_to_array(img)\\n\",\n \" images.append(imread(img_path))\\n\",\n \" inputs.append(img.copy())\\n\",\n \"inputs = preprocess_input(np.array(inputs))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"preds = model.predict(inputs, batch_size=1, verbose=0)\\n\",\n \"results = bbox_util.detection_out(preds)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"score_thresh = 0.7\\n\",\n \"for i, img in enumerate(images):\\n\",\n \" display_boxes(img,results[i],score_thresh)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"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.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
},
{
"path": "Workshop/sessions/dream.py",
"content": "from keras.preprocessing.image import load_img, img_to_array\r\nimport numpy as np\r\nfrom scipy.optimize import fmin_l_bfgs_b\r\nfrom keras.applications import vgg16\r\nfrom keras import backend as K\r\nfrom keras.layers import Input\r\n\r\n\r\ndef preprocess_image(image_path,img_height,img_width):\r\n img = load_img(image_path, target_size=(img_height, img_width))\r\n img = img_to_array(img)\r\n img = np.expand_dims(img, axis=0)\r\n img = vgg16.preprocess_input(img)\r\n return img\r\n\r\n# util function to convert a tensor into a valid image\r\ndef deprocess_image(x,img_height,img_width):\r\n if K.image_dim_ordering() == 'th':\r\n x = x.reshape((3, img_height, img_width))\r\n x = x.transpose((1, 2, 0))\r\n else:\r\n x = x.reshape((img_height, img_width, 3))\r\n # Remove zero-center by mean pixel\r\n x[:, :, 0] += 103.939\r\n x[:, :, 1] += 116.779\r\n x[:, :, 2] += 123.68\r\n # 'BGR'->'RGB'\r\n x = x[:, :, ::-1]\r\n x = np.clip(x, 0, 255).astype('uint8')\r\n return x\r\n\r\ndef continuity_loss(x,img_height,img_width):\r\n\r\n a = K.square(x[:, :img_height - 1, :img_width - 1, :] -\r\n x[:, 1:, :img_width - 1, :])\r\n b = K.square(x[:, :img_height - 1, :img_width - 1, :] -\r\n x[:, :img_height - 1, 1:, :])\r\n return K.sum(K.pow(a + b, 1.25))\r\n\r\n\r\ndef eval_loss_and_grads(x,img_size,f_outputs):\r\n x = x.reshape((1,) + img_size)\r\n outs = f_outputs([x])\r\n loss_value = outs[0]\r\n if len(outs[1:]) == 1:\r\n grad_values = outs[1].flatten().astype('float64')\r\n else:\r\n grad_values = np.array(outs[1:]).flatten().astype('float64')\r\n return loss_value, grad_values\r\n\r\n# this Evaluator class makes it possible\r\n# to compute loss and gradients in one pass\r\n# while retrieving them via two separate functions,\r\n# \"loss\" and \"grads\". This is done because scipy.optimize\r\n# requires separate functions for loss and gradients,\r\n# but computing them separately would be inefficient.\r\n\r\n\r\nclass Evaluator(object):\r\n\r\n def __init__(self,img_size,f_outputs):\r\n self.loss_value = None\r\n self.grad_values = None\r\n self.img_size = img_size\r\n self.f_outputs = f_outputs\r\n\r\n def loss(self, x):\r\n assert self.loss_value is None\r\n loss_value, grad_values = eval_loss_and_grads(x,self.img_size,\r\n self.f_outputs)\r\n self.loss_value = loss_value\r\n self.grad_values = grad_values\r\n return self.loss_value\r\n\r\n def grads(self, x):\r\n assert self.loss_value is not None\r\n grad_values = np.copy(self.grad_values)\r\n self.loss_value = None\r\n self.grad_values = None\r\n return grad_values\r\n"
},
{
"path": "Workshop/sessions/glove_download.sh",
"content": "#!/bin/bash\nmkdir -p ../../data/glove\npushd ../../data/glove\n#wget http://nlp.stanford.edu/data/glove.6B.zip\n#tar -xvf glove.6B.zip\nunzip glove.6B.zip\npopd\n"
},
{
"path": "Workshop/sessions/style.py",
"content": "'''Neural style transfer with Keras.\r\n# References\r\n - [A Neural Algorithm of Artistic Style](http://arxiv.org/abs/1508.06576)\r\n'''\r\n\r\nfrom keras.preprocessing.image import load_img, img_to_array\r\nimport numpy as np\r\nfrom scipy.optimize import fmin_l_bfgs_b\r\nfrom keras.applications import vgg16\r\nfrom keras import backend as K\r\n\r\n\r\n# util function to open, resize and format pictures into appropriate tensors\r\ndef preprocess_image(image_path,img_nrows,img_ncols):\r\n img = load_img(image_path, target_size=(img_nrows, img_ncols))\r\n img = img_to_array(img)\r\n img = np.expand_dims(img, axis=0)\r\n img = vgg16.preprocess_input(img)\r\n return img\r\n\r\n# util function to convert a tensor into a valid image\r\ndef deprocess_image(x,img_nrows,img_ncols):\r\n\r\n x = x.reshape((img_nrows, img_ncols, 3))\r\n # Remove zero-center by mean pixel\r\n x[:, :, 0] += 103.939\r\n x[:, :, 1] += 116.779\r\n x[:, :, 2] += 123.68\r\n # 'BGR'->'RGB'\r\n x = x[:, :, ::-1]\r\n x = np.clip(x, 0, 255).astype('uint8')\r\n return x\r\n\r\ndef gram_matrix(x):\r\n assert K.ndim(x) == 3\r\n if K.image_dim_ordering() == 'th':\r\n features = K.batch_flatten(x)\r\n else:\r\n features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))\r\n gram = K.dot(features, K.transpose(features))\r\n return gram\r\n\r\n# the \"style loss\" is designed to maintain\r\n# the style of the reference image in the generated image.\r\n# It is based on the gram matrices (which capture style) of\r\n# feature maps from the style reference image\r\n# and from the generated image\r\n\r\n\r\ndef style_loss(style, combination,img_nrows,img_ncols):\r\n assert K.ndim(style) == 3\r\n assert K.ndim(combination) == 3\r\n S = gram_matrix(style)\r\n C = gram_matrix(combination)\r\n channels = 3\r\n size = img_nrows * img_ncols\r\n return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))\r\n\r\n# an auxiliary loss function\r\n# designed to maintain the \"content\" of the\r\n# base image in the generated image\r\ndef content_loss(base, combination):\r\n return K.sum(K.square(combination - base))\r\n\r\n# the 3rd loss function, total variation loss,\r\n# designed to keep the generated image locally coherent\r\n\r\ndef total_variation_loss(x,img_nrows,img_ncols):\r\n assert K.ndim(x) == 4\r\n a = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, 1:, :img_ncols - 1, :])\r\n b = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, :img_nrows - 1, 1:, :])\r\n return K.sum(K.pow(a + b, 1.25))\r\n\r\n\r\n\r\ndef eval_loss_and_grads(x,img_nrows,img_ncols,f_outputs):\r\n if K.image_dim_ordering() == 'th':\r\n x = x.reshape((1, 3, img_nrows, img_ncols))\r\n else:\r\n x = x.reshape((1, img_nrows, img_ncols, 3))\r\n outs = f_outputs([x])\r\n loss_value = outs[0]\r\n if len(outs[1:]) == 1:\r\n grad_values = outs[1].flatten().astype('float64')\r\n else:\r\n grad_values = np.array(outs[1:]).flatten().astype('float64')\r\n return loss_value, grad_values\r\n\r\n# this Evaluator class makes it possible\r\n# to compute loss and gradients in one pass\r\n# while retrieving them via two separate functions,\r\n# \"loss\" and \"grads\". This is done because scipy.optimize\r\n# requires separate functions for loss and gradients,\r\n# but computing them separately would be inefficient.\r\n\r\nclass Evaluator(object):\r\n\r\n def __init__(self,img_nrows,img_ncols,f_outputs):\r\n self.loss_value = None\r\n self.grads_values = None\r\n self.img_nrows = img_nrows\r\n self.img_ncols = img_ncols\r\n self.f_outputs = f_outputs\r\n\r\n def loss(self, x):\r\n assert self.loss_value is None\r\n loss_value, grad_values = eval_loss_and_grads(x,self.img_nrows,\r\n self.img_ncols,\r\n self.f_outputs)\r\n self.loss_value = loss_value\r\n self.grad_values = grad_values\r\n return self.loss_value\r\n\r\n def grads(self, x):\r\n assert self.loss_value is not None\r\n grad_values = np.copy(self.grad_values)\r\n self.loss_value = None\r\n self.grad_values = None\r\n return grad_values\r\n"
},
{
"path": "Workshop/sessions/tracking/README.md",
"content": "\n\n**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*\n\n- [SORT](#sort)\n - [Introduction](#introduction)\n - [License](#license)\n - [Citing SORT](#citing-sort)\n - [Usage:](#usage)\n\n\n\nSORT\n=====\n\nA simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences.\nSee an example [video here](https://motchallenge.net/movies/ETH-Linthescher-SORT.mp4).\n\nBy Alex Bewley \n[DynamicDetection.com](http://www.dynamicdetection.com)\n\n*Note: This fork has been modified for educational purposes*\n\n### Introduction\n\nSORT 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.\n\nSORT 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/).\n\nThis code has been tested on Mac OSX 10.10, and Ubuntu 14.04, with Python 2.7 (anaconda).\n\n**Note:** A significant proportion of SORT's accuracy is attributed to the detections.\nFor 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.\n\n### License\n\nSORT 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).\n\n### Citing SORT\n\nIf you find this repo useful in your research, please consider citing:\n\n @inproceedings{Bewley2016_sort,\n author={Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben},\n booktitle={2016 IEEE International Conference on Image Processing (ICIP)},\n title={Simple online and realtime tracking},\n year={2016},\n pages={3464-3468},\n keywords={Benchmark testing;Complexity theory;Detectors;Kalman filters;Target tracking;Visualization;Computer Vision;Data Association;Detection;Multiple Object Tracking},\n doi={10.1109/ICIP.2016.7533003}\n }\n\n\n### Usage:\n\nTo run the tracker with the provided detections:\n\n- Download the [2D MOT 2015 benchmark dataset](https://motchallenge.net/data/2D_MOT_2015/#download)\n\t```\n\twget https://motchallenge.net/data/2DMOT2015.zip\n\tunzip 2DMOT2015.zip\n\t```"
},
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"path": "Workshop/sessions/tracking/data/ADL-Rundle-6/det.txt",
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},
{
"path": "Workshop/sessions/tracking/sort.py",
"content": "\"\"\"\n SORT: A Simple, Online and Realtime Tracker\n Copyright (C) 2016 Alex Bewley alex@dynamicdetection.com\n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program. If not, see \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)\n\n# In[ ]:\n\ndef 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\nx_train, x_test, y_train, y_test = PreprocessDataset()\n\n\n# In[ ]:\n\n## Show part of training data: features and labels\n## Each row is a sample, and each column represents a feature.\nprint(\"{:^43}\".format(\"x\"), \"|\", \"{:^4}\".format(\"y\"))\nprint(\"=\"*50)\nfor 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]))\n\n\n# ## 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)\n\n# In[ ]:\n\ndef 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\n\n\n# ## 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)\n\n# In[ ]:\n\ndef 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\n\n\n# ## Start Training\n\n# In[ ]:\n\ntrained_model, training_history = TrainModel(data=[x_train, x_test, y_train, y_test])\n\n\n# ## Define Plotting\n\n# In[ ]:\n\ndef 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 \n\n\n# ## Observe Training Process\n\n# In[ ]:\n\nPlotHistory(training_history.history['loss'], training_history.history['val_loss'], 'Loss')\nPlotHistory(training_history.history['acc'], training_history.history['val_acc'], 'Accuracy')\n\n\n# ## Observe Regularization Effects\n# \n\n# In[ ]:\n\ndef 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)\n\n\n# In[ ]:\n\nw1 = trained_model.layers[0].get_weights()[0].flatten()\ndrawWeightHistogram(w1)\n\n\n# ## Define Testing Procedure\n# \n\n# In[ ]:\n\ndef 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\n\n\n# ## Test Trained Model\n# \n\n# In[ ]:\n\ntest_score = TestModel(model=trained_model, data=[x_test, y_test])\nprint(\"Test loss {:.4f}, accuracy {:.2f}%\".format(test_score[0], test_score[1] * 100))\n\n\n# In[ ]:\n\ndef 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 \ndef 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\ndef 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\nShowInputImage(x_test[0])\nShowHiddenLayerOutput(x_test, 1)\nShowFinalOutput(x_test)\n\n\n# In[ ]:\n\n\n\n" }, { "path": "sklearn_test.py", "content": "from sklearn import datasets\niris = datasets.load_iris()\ndigits = datasets.load_digits()\nfrom sklearn import svm\nclf = svm.SVC(gamma=0.001, C=100.)\nclf.fit(digits.data[:-1], digits.target[:-1])\nprint clf\n\nprint clf.predict(digits.data[-3:-1])" }, { "path": "tensor_flow_test.py", "content": "import os;\nimport inspect;\nimport tensorflow;\n\nprint(os.path.dirname(inspect.getfile(tensorflow)))\n" }, { "path": "xgboost/.gitignore", "content": ".ipynb_checkpoints/*\n*.csv" }, { "path": "xgboost/tree_xgboost.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Comparison of Tree and [xgboost](https://github.com/dmlc/xgboost)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/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\",\n \" \\\"This module will be removed in 0.20.\\\", DeprecationWarning)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Populating the interactive namespace from numpy and matplotlib\\n\"\n ]\n }\n ],\n \"source\": [\n \"import xgboost as xgb\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"from sklearn.cross_validation import train_test_split\\n\",\n \"from sklearn.metrics import r2_score\\n\",\n \"\\n\",\n \"from sklearn.tree import DecisionTreeRegressor\\n\",\n \"from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\\n\",\n \"from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor, BaggingRegressor\\n\",\n \"\\n\",\n \"%pylab inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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8MSaSglhyIiIiItqFq1ODeVpDvoYUdX46qGne7QYISvHOglV6zw43Oz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\\nAAAASUVORK5CYII=\\n\",\n 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