def test_network_by_global_fraction_deleting(self):
random_fractions = numpy.random.uniform(low=0, high=1, size=5)
for fraction in random_fractions:
for number_of_layers in [1, 3, 6, 10, 20]:
for shape in self.shapes:
network = get_random_network_mock(
number_of_layers=number_of_layers,
shape_of_layer=shape)
indicators = [
self.get_random_indicators(shape)
for x in xrange(number_of_layers)
]
network_after_deleting = deepcopy(network)
delete_weights_by_global_fraction(
network_after_deleting.weighted_layers, fraction,
indicators)
new_zeros = get_fraction_of_zeros_in_network(
network=network_after_deleting)
difference = abs(new_zeros - fraction)
network_size = sum([
layer.W.size
for layer in network_after_deleting.weighted_layers
])
assert_true(difference <= 1. / network_size)
def test_smallest_indicator_size(self):
for number_of_layers in [1, 3, 5, 7, 10, 20]:
for size_of_layer in [1, 10, 100, 500]:
network = get_random_network_mock(
number_of_layers=number_of_layers,
shape_of_layer=size_of_layer)
indicators = get_smallest_indicators(network.weighted_layers)
assert_equal(number_of_layers, len(indicators))
for layer in xrange(number_of_layers):
assert_equal(size_of_layer, len(indicators[layer]))
def test_sharpen_filters_indicators_shape(self):
for number_of_layers in [1, 3, 5, 7, 10, 20]:
for layers_shape in [(1, 1, 5, 5), (3, 5, 5, 30), (6, 1, 10, 10)]:
network = get_random_network_mock(
number_of_layers=number_of_layers,
shape_of_layer=layers_shape
)
indicators = get_filters_indicators(
layers=network.weighted_layers,
bilateral_filter_args=self.bilateral_filter_args
)
assert_equal(number_of_layers, len(indicators))
for layer in xrange(number_of_layers):
assert_equal(layers_shape, indicators[layer].shape)
def setUp(self):
self.network = get_random_network_mock()