Francois Chollet
47 linhas
1.3 KiB
Python
47 linhas
1.3 KiB
Python
import numpy as np
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import pytest
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from keras.models import Sequential
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from keras.engine.training import _weighted_masked_objective
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from keras.layers import TimeDistributed, Masking, Dense
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from keras.utils.test_utils import keras_test
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from keras import losses
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from keras import backend as K
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@keras_test
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def test_masking():
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np.random.seed(1337)
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x = np.array([[[1], [1]],
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[[0], [0]]])
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model = Sequential()
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model.add(Masking(mask_value=0, input_shape=(2, 1)))
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model.add(TimeDistributed(Dense(1, kernel_initializer='one')))
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model.compile(loss='mse', optimizer='sgd')
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y = np.array([[[1], [1]],
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[[1], [1]]])
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loss = model.train_on_batch(x, y)
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assert loss == 0
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@keras_test
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def test_loss_masking():
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weighted_loss = _weighted_masked_objective(losses.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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