47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
import numpy as np
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import pytest
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np.random.seed(1337)
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from keras.models import Sequential, weighted_objective
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from keras.layers.core import TimeDistributedDense, Masking
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from keras import objectives
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from keras import backend as K
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def test_masking():
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if K._BACKEND == 'tensorflow':
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# skip this test for TF: not supported yet
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return
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X = np.array(
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[[[1, 1], [2, 1], [3, 1], [5, 5]],
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[[1, 5], [5, 0], [0, 0], [0, 0]]], dtype=np.int32)
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model = Sequential()
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model.add(Masking(mask_value=0, input_shape=(None, 2)))
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model.add(TimeDistributedDense(1, init='one'))
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model.compile(loss='mse', optimizer='sgd')
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y = model.predict(X)
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history = model.fit(X, 4 * y, nb_epoch=1, batch_size=2, verbose=1)
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assert history.history['loss'][0] == 285.
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def test_loss_masking():
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weighted_loss = weighted_objective(objectives.get('mae'))
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shape = (3, 4, 2)
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X = np.arange(24).reshape(shape)
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Y = 2 * X
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# Normally the trailing 1 is added by standardize_weights
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weights = np.ones((3,))
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mask = np.ones((3, 4))
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mask[1, 0] = 0
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out = K.eval(weighted_loss(K.variable(X),
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K.variable(Y),
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K.variable(weights),
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K.variable(mask)))
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if __name__ == '__main__':
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pytest.main([__file__])
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