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Quickly re-initialize weights of a Keras model
#!/usr/bin/env python
"""Demonstrate Keras model weight shuffling as fast alternative to re-creating a model."""
from __future__ import print_function
import numpy as np
from keras.layers import Dense
from keras.models import Sequential
def shuffle_weights(model, weights=None):
"""Randomly permute the weights in `model`, or the given `weights`.
This is a fast approximation of re-initializing the weights of a model.
Assumes weights are distributed independently of the dimensions of the weight tensors
(i.e., the weights have the same distribution along each dimension).
:param Model model: Modify the weights of the given model.
:param list(ndarray) weights: The model's weights will be replaced by a random permutation of these weights.
If `None`, permute the model's current weights.
"""
if weights is None:
weights = model.get_weights()
weights = [np.random.permutation(w.flat).reshape(w.shape) for w in weights]
# Faster, but less random: only permutes along the first dimension
# weights = [np.random.permutation(w) for w in weights]
model.set_weights(weights)
def main():
"""Train a simple single layer to learn the sum of three inputs.
Shuffle weights in a mock "cross-validation" loop instead of re-creating the model to save time.
"""
np.random.seed(42)
model = Sequential((
Dense(input_dim=3, output_dim=1, activation='linear'),
))
model.compile(loss='msle', optimizer='rmsprop')
X = np.random.random(size=(1000, 3))
y = np.sum(X, axis=1)
initial_weights = model.get_weights()
print('Initial weights:\n', initial_weights)
for rnd in xrange(3):
shuffle_weights(model, initial_weights)
print('\nRound {} starting weights:\n'.format(rnd), model.get_weights())
hist = model.fit(X, y, nb_epoch=50, verbose=0)
print('Learned weights:\n', model.get_weights(), '\nloss:', hist.history['loss'][-1])
if __name__ == '__main__':
main()
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