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nzw
61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
from keras import backend as K
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from keras_contrib import backend as KC
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from keras_contrib import initializers
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import pytest
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import numpy as np
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# 2D tensor test fixture
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FC_SHAPE = (100, 100)
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# 4D convolution in th order. This shape has the same effective shape as
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# FC_SHAPE
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CONV_SHAPE = (25, 25, 2, 2)
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# The equivalent shape of both test fixtures
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SHAPE = (100, 100)
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def _runner(init, shape, target_mean=None, target_std=None,
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target_max=None, target_min=None, upper_bound=None, lower_bound=None):
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variable = init(shape)
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if not isinstance(variable, np.ndarray):
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output = K.get_value(variable)
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else:
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output = variable
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lim = 1e-2
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if target_std is not None:
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assert abs(output.std() - target_std) < lim
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if target_mean is not None:
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assert abs(output.mean() - target_mean) < lim
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if target_max is not None:
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assert abs(output.max() - target_max) < lim
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if target_min is not None:
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assert abs(output.min() - target_min) < lim
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if upper_bound is not None:
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assert output.max() < upper_bound
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if lower_bound is not None:
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assert output.min() > lower_bound
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'''
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# Example :
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@pytest.mark.parametrize('tensor_shape', [FC_SHAPE, CONV_SHAPE], ids=['FC', 'CONV'])
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def test_uniform(tensor_shape):
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_runner(initializations.uniform, tensor_shape, target_mean=0.,
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target_max=0.05, target_min=-0.05)
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'''
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@pytest.mark.parametrize('tensor_shape', [FC_SHAPE, CONV_SHAPE], ids=['FC', 'CONV'])
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def test_cai(tensor_shape):
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# upper and lower bounds are proved in original paper
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_runner(initializers.ConvolutionAware(), tensor_shape,
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upper_bound=1, lower_bound=-1)
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if __name__ == '__main__':
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pytest.main([__file__])
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