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import numpy as np from keras.preprocessing import sequence from keras.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.layers import Embedding from keras.layers import LSTM from keras.callbacks import EarlyStopping from keras.datasets import imdb 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))) 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) # 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 model = Sequential() model.add(Embedding(n_words, 50, input_length=max_len)) model.add(Dropout(0.2)) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2)) model.add(Dense(250, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() 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) print('Accuracy on test set: {}'.format(model.evaluate(X_test, y_test)[1])) # Accuracy on test set: 0.82884 Using TensorFlow backend. Train seq: 25000 Test seq: 25000 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, 591, 202, 14, 31, 6, 717, 10, 10, 2, 2, 5, 4, 360, 7, 4, 177, 2, 394, 354, 4, 123, 9, 2, 2, 2, 10, 10, 13, 92, 124, 89, 488, 2, 100, 28, 2, 14, 31, 23, 27, 2, 29, 220, 468, 8, 124, 14, 286, 170, 8, 157, 46, 5, 27, 239, 16, 179, 2, 38, 32, 25, 2, 451, 202, 14, 6, 717] _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_1 (Embedding) (None, 200, 50) 50000 _________________________________________________________________ dropout_1 (Dropout) (None, 200, 50) 0 _________________________________________________________________ lstm_1 (LSTM) (None, 100) 60400 _________________________________________________________________ dense_1 (Dense) (None, 250) 25250 _________________________________________________________________ dropout_2 (Dropout) (None, 250) 0 _________________________________________________________________ dense_2 (Dense) (None, 1) 251 ================================================================= Total params: 135,901 Trainable params: 135,901 Non-trainable params: 0 _________________________________________________________________ Train on 20000 samples, validate on 5000 samples Epoch 1/100 20000/20000 [==============================] - 49s 2ms/step - loss: 0.5634 - acc: 0.6982 - val_loss: 0.4351 - val_acc: 0.8004 Epoch 2/100 20000/20000 [==============================] - 48s 2ms/step - loss: 0.4342 - acc: 0.8023 - val_loss: 0.3923 - val_acc: 0.8278 Epoch 3/100 4736/20000 [======>.......................] - ETA: 34s - loss: 0.4127 - acc: 0.8254 --------------------------------------------------------------------------- |
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