79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
'''This example demonstrates the use of Convolution1D for text classification.
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Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_cnn.py
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Get to 0.835 test accuracy after 2 epochs. 100s/epoch on K520 GPU.
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'''
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from __future__ import print_function
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import numpy as np
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np.random.seed(1337) # for reproducibility
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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.core import Dense, Dropout, Activation, Flatten
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from keras.layers.embeddings import Embedding
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from keras.layers.convolutional import Convolution1D, MaxPooling1D
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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 = 100
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batch_size = 32
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embedding_dims = 100
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nb_filter = 250
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filter_length = 3
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hidden_dims = 250
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nb_epoch = 2
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print('Loading data...')
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(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features,
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test_split=0.2)
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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, embedding_dims, input_length=maxlen))
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model.add(Dropout(0.25))
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# we add a Convolution1D, which will learn nb_filter
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# word group filters of size filter_length:
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model.add(Convolution1D(nb_filter=nb_filter,
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filter_length=filter_length,
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border_mode='valid',
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activation='relu',
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subsample_length=1))
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# we use standard max pooling (halving the output of the previous layer):
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model.add(MaxPooling1D(pool_length=2))
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# We flatten the output of the conv layer,
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# so that we can add a vanilla dense layer:
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model.add(Flatten())
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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(0.25))
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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='rmsprop',
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class_mode='binary')
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model.fit(X_train, y_train, batch_size=batch_size,
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nb_epoch=nb_epoch, show_accuracy=True,
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validation_data=(X_test, y_test))
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