71 lines
2.4 KiB
Python
71 lines
2.4 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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from keras.preprocessing import sequence
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from keras.optimizers import SGD, RMSprop, Adagrad
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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.core import Dense, Dropout, Activation
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from keras.layers.embeddings import Embedding
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from keras.layers.recurrent import LSTM, GRU
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from keras.datasets import imdb
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'''
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Train 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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Most configurations won't converge.
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- LSTM loss decrease during training can be quite different
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from what you see with CNNs/MLPs/etc. It's more or less a sigmoid
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instead of an inverse exponential.
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GPU command:
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THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_lstm.py
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250s/epoch on GPU (GT 650M), vs. 400s/epoch on CPU (2.4Ghz Core i7).
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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 = 16
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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, 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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model.add(Embedding(max_features, 256))
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model.add(LSTM(256, 128)) # try using a GRU instead, for fun
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model.add(Dropout(0.5))
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model.add(Dense(128, 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', optimizer='adam', class_mode="binary")
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print("Train...")
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model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=5, validation_split=0.1, show_accuracy=True)
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score = model.evaluate(X_test, y_test, batch_size=batch_size)
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print('Test score:', score)
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classes = model.predict_classes(X_test, batch_size=batch_size)
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acc = np_utils.accuracy(classes, y_test)
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print('Test accuracy:', acc)
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