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New stacked RNNs in Keras
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import keras | |
import numpy as np | |
timesteps = 60 | |
input_dim = 64 | |
samples = 10000 | |
batch_size = 128 | |
output_dim = 64 | |
# Test data. | |
x_np = np.random.random((samples, timesteps, input_dim)) | |
y_np = np.random.random((samples, output_dim)) | |
print('Classic stacked LSTM: 35s/epoch on CPU') | |
inputs = keras.Input((timesteps, input_dim)) | |
x = keras.layers.LSTM(output_dim, return_sequences=True)(inputs) | |
x = keras.layers.LSTM(output_dim, return_sequences=True)(x) | |
x = keras.layers.LSTM(output_dim)(x) | |
classic_model = keras.models.Model(inputs, x) | |
classic_model.compile(optimizer='rmsprop', loss='mse') | |
classic_model.fit(x_np, y_np, batch_size=batch_size, epochs=4) | |
print('New stacked LSTM: 30s/epoch on CPU (15pct faster)') | |
cells = [ | |
keras.layers.LSTMCell(output_dim), | |
keras.layers.LSTMCell(output_dim), | |
keras.layers.LSTMCell(output_dim), | |
] | |
inputs = keras.Input((timesteps, input_dim)) | |
x = keras.layers.RNN(cells)(inputs) | |
new_model = keras.models.Model(inputs, x) | |
new_model.compile(optimizer='rmsprop', loss='mse') | |
new_model.fit(x_np, y_np, batch_size=batch_size, epochs=4) |
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