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import numpy as np from keras.preprocessing import sequence from keras.models import Sequential, Model from keras.layers import Input, Dense, merge, Dropout, Lambda, Activation, Flatten, Embedding, LSTM, TimeDistributed, RepeatVector, Permute from keras.callbacks import EarlyStopping from keras.datasets import imdb from keras import backend as K n_words = 1000 (X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=n_words) print('Train seq: {}'.format(len(X_train))) print('Test seq: {}'.format(len(X_train))) Train seq: 25000 Test seq: 25000 print('Train example: \n{}'.format(X_train[0])) print('\nTest example: \n{}'.format(X_test[0])) # Note: the data is already preprocessed (words are mapped to vectors) Train example: [1, 14, 22, 16, 43, 530, 973, 2, 2, 65, 458, 2, 66, 2, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 2, 2, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2, 19, 14, 22, 4, 2, 2, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 2, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2, 2, 16, 480, 66, 2, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 2, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 2, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 2, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 2, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32] Test example: [1, 89, 27, 2, 2, 17, 199, 132, 5, 2, 16, 2, 24, 8, 760, 4, 2, 7, 4, 22, 2, 2, 16, 2, 17, 2, 7, 2, 2, 9, 4, 2, 8, 14, 991, 13, 877, 38, 19, 27, 239, 13, 100, 235, 61, 483, 2, 4, 7, 4, 20, 131, 2, 72, 8, 14, 251, 27, 2, 7, 308, 16, 735, 2, 17, 29, 144, 28, 77, 2, 18, 12] y_train array([1, 0, 0, ..., 0, 1, 0]) # Pad sequences with max_len max_len = 200 X_train = sequence.pad_sequences(X_train, maxlen=max_len) X_test = sequence.pad_sequences(X_test, maxlen=max_len) # Define network architecture and compile _input = Input(shape=[max_len], dtype='int32') # get the embedding layer embedded = Embedding( input_dim=n_words, output_dim=50, input_length=max_len, trainable=True, # mask_zero=masking, # weights=[embeddings] )(_input) activations = LSTM(100, return_sequences=True)(embedded) attention = TimeDistributed(Dense(1, activation='tanh'))(activations) attention = Flatten()(attention) attention = Activation('softmax')(attention) attention = RepeatVector(100)(attention) attention = Permute([2, 1])(attention) sent_representation = merge([activations, attention], mode='mul') sent_representation = Lambda(lambda xin: K.sum(xin, axis=1))(sent_representation) probabilities = Dense(1, activation='softmax')(sent_representation) model = Model(input=_input, output=probabilities) # model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=[]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() ____________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ==================================================================================================== input_14 (InputLayer) (None, 200) 0 ____________________________________________________________________________________________________ embedding_11 (Embedding) (None, 200, 50) 50000 input_14[0][0] ____________________________________________________________________________________________________ lstm_11 (LSTM) (None, 200, 100) 60400 embedding_11[0][0] ____________________________________________________________________________________________________ time_distributed_11 (TimeDistrib (None, 200, 1) 101 lstm_11[0][0] ____________________________________________________________________________________________________ flatten_10 (Flatten) (None, 200) 0 time_distributed_11[0][0] ____________________________________________________________________________________________________ activation_10 (Activation) (None, 200) 0 flatten_10[0][0] ____________________________________________________________________________________________________ repeat_vector_9 (RepeatVector) (None, 100, 200) 0 activation_10[0][0] ____________________________________________________________________________________________________ permute_8 (Permute) (None, 200, 100) 0 repeat_vector_9[0][0] ____________________________________________________________________________________________________ merge_7 (Merge) (None, 200, 100) 0 lstm_11[0][0] permute_8[0][0] ____________________________________________________________________________________________________ lambda_5 (Lambda) (None, 100) 0 merge_7[0][0] ____________________________________________________________________________________________________ dense_15 (Dense) (None, 1) 101 lambda_5[0][0] ==================================================================================================== Total params: 110,602 Trainable params: 110,602 Non-trainable params: 0 ____________________________________________________________________________________________________ callbacks = [EarlyStopping(monitor='val_acc', patience=3)] batch_size = 128 n_epochs = 100 model.fit(X_train, y_train, batch_size=batch_size, epochs=n_epochs, validation_split=0.2, callbacks=callbacks) Train on 20000 samples, validate on 5000 samples Epoch 1/100 20000/20000 [==============================] - 53s - loss: 7.9465 - acc: 0.5016 - val_loss: 8.0700 - val_acc: 0.4938 Epoch 2/100 20000/20000 [==============================] - 53s - loss: 7.9465 - acc: 0.5016 - val_loss: 8.0700 - val_acc: 0.4938 Epoch 3/100 20000/20000 [==============================] - 53s - loss: 7.9465 - acc: 0.5016 - val_loss: 8.0700 - val_acc: 0.4938 Epoch 4/100 12800/20000 [==================>...........] - ETA: 18s - loss: 7.9251 - acc: 0.5029 --------------------------------------------------------------------------- print('\nAccuracy on test set: {}'.format(model.evaluate(X_test, y_test)[1]) # Accuracy on test set: 0.81326 |
