58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
'''Trains a LSTM on the IMDB sentiment classification task.
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The dataset is actually too small for LSTM to be of any advantage
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compared to simpler, much faster methods such as TF-IDF + LogReg.
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Notes:
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- RNNs are tricky. Choice of batch size is important,
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choice of loss and optimizer is critical, etc.
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Some configurations won't converge.
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- LSTM loss decrease patterns during training can be quite different
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from what you see with CNNs/MLPs/etc.
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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.utils import np_utils
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Activation, Embedding
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from keras.layers import LSTM, SimpleRNN, GRU
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from keras.datasets import imdb
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max_features = 20000
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maxlen = 80 # 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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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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model.add(Embedding(max_features, 128, dropout=0.2))
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model.add(LSTM(128, dropout_W=0.2, dropout_U=0.2)) # try using a GRU instead, for fun
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model.add(Dense(1))
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model.add(Activation('sigmoid'))
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# try using different optimizers and different optimizer configs
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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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print('Train...')
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model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=15,
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validation_data=(X_test, y_test))
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score, acc = model.evaluate(X_test, y_test,
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batch_size=batch_size)
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print('Test score:', score)
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print('Test accuracy:', acc)
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