84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
'''Compare LSTM implementations on the IMDB sentiment classification task.
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consume_less='cpu' preprocesses input to the LSTM which typically results in
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faster computations at the expense of increased peak memory usage as the
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preprocessed input must be kept in memory.
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consume_less='mem' does away with the preprocessing, meaning that it might take
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a little longer, but should require less peak memory.
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consume_less='gpu' concatenates the input, output and forget gate's weights
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into one, large matrix, resulting in faster computation time as the GPU can
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utilize more cores, at the expense of reduced regularization because the same
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dropout is shared across the gates.
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Note that the relative performance of the different `consume_less` modes
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can vary depending on your device, your model and the size of your data.
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'''
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import time
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import numpy as np
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import matplotlib.pyplot as plt
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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 Embedding, Dense, LSTM
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from keras.datasets import imdb
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max_features = 20000
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max_length = 80
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embedding_dim = 256
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batch_size = 128
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epochs = 10
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modes = ['cpu', 'mem', 'gpu']
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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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X_train = sequence.pad_sequences(X_train, max_length)
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X_test = sequence.pad_sequences(X_test, max_length)
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# Compile and train different models while meauring performance.
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results = []
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for mode in modes:
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print('Testing mode: consume_less="{}"'.format(mode))
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model = Sequential()
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model.add(Embedding(max_features, embedding_dim, input_length=max_length, dropout=0.2))
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model.add(LSTM(embedding_dim, dropout_W=0.2, dropout_U=0.2, consume_less=mode))
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model.add(Dense(1, 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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start_time = time.time()
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history = model.fit(X_train, y_train,
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batch_size=batch_size,
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nb_epoch=epochs,
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validation_data=(X_test, y_test))
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average_time_per_epoch = (time.time() - start_time) / epochs
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results.append((history, average_time_per_epoch))
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# Compare models' accuracy, loss and elapsed time per epoch.
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plt.style.use('ggplot')
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ax1 = plt.subplot2grid((2, 2), (0, 0))
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ax1.set_title('Accuracy')
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ax1.set_ylabel('Validation Accuracy')
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ax1.set_xlabel('Epochs')
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ax2 = plt.subplot2grid((2, 2), (1, 0))
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ax2.set_title('Loss')
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ax2.set_ylabel('Validation Loss')
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ax2.set_xlabel('Epochs')
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ax3 = plt.subplot2grid((2, 2), (0, 1), rowspan=2)
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ax3.set_title('Time')
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ax3.set_ylabel('Seconds')
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for mode, result in zip(modes, results):
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ax1.plot(result[0].epoch, result[0].history['val_acc'], label=mode)
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ax2.plot(result[0].epoch, result[0].history['val_loss'], label=mode)
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ax1.legend()
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ax2.legend()
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ax3.bar(np.arange(len(results)), [x[1] for x in results],
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tick_label=modes, align='center')
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plt.tight_layout()
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plt.show()
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