mirror of
https://github.com/wassname/keras-contrib.git
synced 2026-07-19 11:24:38 +08:00
46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
from keras import backend as K
|
|
from keras_contrib import backend as KC
|
|
from keras_contrib import initializations
|
|
import pytest
|
|
import numpy as np
|
|
|
|
|
|
# 2D tensor test fixture
|
|
FC_SHAPE = (100, 100)
|
|
|
|
# 4D convolution in th order. This shape has the same effective shape as FC_SHAPE
|
|
CONV_SHAPE = (25, 25, 2, 2)
|
|
|
|
# The equivalent shape of both test fixtures
|
|
SHAPE = (100, 100)
|
|
|
|
|
|
def _runner(init, shape, target_mean=None, target_std=None,
|
|
target_max=None, target_min=None):
|
|
variable = init(shape)
|
|
output = K.get_value(variable)
|
|
lim = 1e-2
|
|
if target_std is not None:
|
|
assert abs(output.std() - target_std) < lim
|
|
if target_mean is not None:
|
|
assert abs(output.mean() - target_mean) < lim
|
|
if target_max is not None:
|
|
assert abs(output.max() - target_max) < lim
|
|
if target_min is not None:
|
|
assert abs(output.min() - target_min) < lim
|
|
|
|
|
|
'''
|
|
# Example :
|
|
|
|
@pytest.mark.parametrize('tensor_shape', [FC_SHAPE, CONV_SHAPE], ids=['FC', 'CONV'])
|
|
def test_uniform(tensor_shape):
|
|
_runner(initializations.uniform, tensor_shape, target_mean=0.,
|
|
target_max=0.05, target_min=-0.05)
|
|
|
|
'''
|
|
|
|
|
|
if __name__ == '__main__':
|
|
pytest.main([__file__])
|