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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 GRU 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))) # 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(GRU(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 = 512 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: 0.83004 |
