75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
'''This example demonstrates the use of Convolution1D for text classification.
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Gets to 0.89 test accuracy after 2 epochs.
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90s/epoch on Intel i5 2.4Ghz CPU.
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10s/epoch on Tesla K40 GPU.
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'''
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from __future__ import print_function
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from keras.preprocessing import sequence
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Activation
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from keras.layers import Embedding
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from keras.layers import Conv1D, GlobalMaxPooling1D
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from keras.datasets import imdb
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# set parameters:
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max_features = 5000
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maxlen = 400
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batch_size = 32
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embedding_dims = 50
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filters = 250
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kernel_size = 3
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hidden_dims = 250
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epochs = 2
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print('Loading data...')
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(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
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print(len(x_train), 'train sequences')
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print(len(x_test), 'test sequences')
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print('Pad sequences (samples x time)')
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x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
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x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
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print('x_train shape:', x_train.shape)
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print('x_test shape:', x_test.shape)
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print('Build model...')
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model = Sequential()
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# we start off with an efficient embedding layer which maps
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# our vocab indices into embedding_dims dimensions
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model.add(Embedding(max_features,
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embedding_dims,
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input_length=maxlen))
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model.add(Dropout(0.2))
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# we add a Convolution1D, which will learn filters
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# word group filters of size filter_length:
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model.add(Conv1D(filters,
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kernel_size,
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padding='valid',
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activation='relu',
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strides=1))
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# we use max pooling:
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model.add(GlobalMaxPooling1D())
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# We add a vanilla hidden layer:
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model.add(Dense(hidden_dims))
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model.add(Dropout(0.2))
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model.add(Activation('relu'))
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# We project onto a single unit output layer, and squash it with a sigmoid:
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model.add(Dense(1))
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model.add(Activation('sigmoid'))
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model.compile(loss='binary_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=epochs,
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validation_data=(x_test, y_test))
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