64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
from __future__ import absolute_import
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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.utils.np_utils import accuracy
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from keras.models import Graph
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from keras.layers.core import Dense, Dropout
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from keras.layers.embeddings import Embedding
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from keras.layers.recurrent import LSTM
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from keras.datasets import imdb
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'''
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Train a Bidirectional LSTM on the IMDB sentiment classification task.
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GPU command:
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THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_bidirectional_lstm.py
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Output after 4 epochs on CPU: ~0.8146
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Time per epoch on CPU (Core i7): ~150s.
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'''
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max_features = 20000
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maxlen = 100 # cut texts after this number of words (among top max_features most common words)
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batch_size = 32
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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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y_train = np.array(y_train)
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y_test = np.array(y_test)
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print('Build model...')
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model = Graph()
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model.add_input(name='input', input_shape=(1,), dtype=int)
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model.add_node(Embedding(max_features, 128, input_length=maxlen),
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name='embedding', input='input')
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model.add_node(LSTM(64), name='forward', input='embedding')
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model.add_node(LSTM(64, go_backwards=True), name='backward', input='embedding')
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model.add_node(Dropout(0.5), name='dropout', inputs=['forward', 'backward'])
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model.add_node(Dense(1, activation='sigmoid'), name='sigmoid', input='dropout')
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model.add_output(name='output', input='sigmoid')
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# try using different optimizers and different optimizer configs
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model.compile('adam', {'output': 'binary_crossentropy'})
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print("Train...")
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model.fit({'input': X_train, 'output': y_train},
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batch_size=batch_size,
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nb_epoch=4)
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acc = accuracy(y_test,
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np.round(np.array(model.predict({'input': X_test},
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batch_size=batch_size)['output'])))
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print('Test accuracy:', acc)
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