b7df64ab2e
adding unitnorm constraint add regularizer and constraint to embedding adding unitnorm unittest
27 lines
1.0 KiB
Python
27 lines
1.0 KiB
Python
import unittest
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import numpy as np
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from keras.models import Sequential
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from keras.layers.core import Merge, Dense, Activation, Flatten
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from keras.layers.embeddings import Embedding
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from theano import function
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from keras.constraints import unitnorm
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class TestConcatenation(unittest.TestCase):
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def setUp(self):
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self.X1 = np.array([[1], [2]], dtype='int32')
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self.W1 = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]], dtype='float32')
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def test_unitnorm_constraint(self):
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lookup = Sequential()
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lookup.add(Embedding(3, 2, weights=[self.W1], W_constraint=unitnorm))
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lookup.add(Flatten())
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lookup.add(Dense(2, 1))
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lookup.add(Activation('sigmoid'))
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lookup.compile(loss='binary_crossentropy', optimizer='sgd', class_mode='binary')
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lookup.train(self.X1, np.array([[1], [0]], dtype='int32'))
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norm = np.linalg.norm(lookup.params[0].get_value(), axis=1)
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self.assertTrue(np.allclose(norm, np.ones_like(norm).astype('float32')))
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if __name__ == '__main__':
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unittest.main() |