def test_conv2dtranspose(self):
"""Compnents consisting Conv2DTranspose layer are retrieved"""
scope = self.get_scope()
with nn.variable_scope(scope) as vs:
input_ = nn.Input(shape=(32, 4, 8, 8), name='input')
layer = nn.get_layer('Conv2D')(filter_height=4,
filter_width=4,
n_filters=4,
strides=1,
with_bias=True,
name='Conv2D')
output = layer(input_)
layer = nn.get_layer('Conv2DTranspose')(filter_height=4,
filter_width=4,
n_filters=4,
strides=1,
with_bias=True,
output_shape=input_.shape,
name='Conv2DT')
output = layer(output)
filters = layer.get_parameter_variable('filter')
bias = layer.get_parameter_variable('bias')
with nn.variable_scope(vs, reuse=True):
self.assertIs(filters, nn.get_variable('Conv2DT/filter'))
self.assertIs(bias, nn.get_variable('Conv2DT/bias'))
self.assertIs(output, nn.get_tensor('Conv2DT/output'))
self.assertIs(input_, nn.get_input('input'))
def test_tile(self):
"""Compnents consisting Tile layer are retrieved"""
scope = self.get_scope()
with nn.variable_scope(scope):
input_ = nn.Input(shape=(32, ), name='input')
layer = nn.get_layer('Tile')(pattern=(1, 2), name='Tile')
output = layer(input_)
self.assertIs(output, nn.get_tensor('Tile/output'))
self.assertIs(input_, nn.get_input('input'))
def test_mean(self):
"""Compnents consisting Mean layer are retrieved"""
scope = self.get_scope()
with nn.variable_scope(scope):
input_ = nn.Input(shape=(32, 4, 8, 8), name='input')
layer = nn.get_layer('Mean')(axis=[1, 2], name='Mean')
output = layer(input_)
self.assertIs(output, nn.get_tensor('Mean/output'))
self.assertIs(input_, nn.get_input('input'))
def test_true_div(self):
"""Compnents consisting truediv layer are retrieved"""
scope = self.get_scope()
with nn.variable_scope(scope):
input_ = nn.Input(shape=(32, 4, 8, 8), name='input')
layer = nn.get_layer('TrueDiv')(denom=1.0, name='TrueDiv')
output = layer(input_)
self.assertIs(output, nn.get_tensor('TrueDiv/output'))
self.assertIs(input_, nn.get_input('input'))
def _test_layer_io(self, layer_name, input_shape):
scope = '{}/{}'.format(self.get_scope(), layer_name)
with nn.variable_scope(scope) as vs:
input_ = nn.Input(shape=input_shape, name='input')
layer = nn.get_layer(layer_name)(name=layer_name)
output = layer(input_)
with nn.variable_scope(vs, reuse=True):
self.assertIs(input_, nn.get_input('input'))
self.assertIs(output,
nn.get_tensor('{}/output'.format(layer_name)))
def test_sub(self):
"""Compnents consisting Sub layer are retrieved"""
scope = self.get_scope()
with nn.variable_scope(scope):
input_ = [
nn.Input(shape=(32, 4), name='input'),
nn.Input(shape=(32, 4), name='input'),
]
layer = nn.get_layer('Sub')(name='Sub')
output = layer(input_)
self.assertIs(output, nn.get_tensor('Sub/output'))
def test_concat(self):
"""Compnents consisting Concat layer are retrieved"""
scope = self.get_scope()
with nn.variable_scope(scope):
input_ = [
nn.Input(shape=(32, 4), name='input'),
nn.Input(shape=(32, 5), name='input'),
]
layer = nn.get_layer('Concat')(axis=1, name='Concat')
output = layer(input_)
self.assertIs(output, nn.get_tensor('Concat/output'))
def test_dense(self):
"""Compnents consisting Dense layer are retrieved"""
scope = self.get_scope()
with nn.variable_scope(scope) as vs:
input_ = nn.Input(shape=(32, 5), name='input')
layer = nn.get_layer('Dense')(n_nodes=4,
with_bias=True,
name='Dense')
output = layer(input_)
weight = layer.get_parameter_variable('weight')
bias = layer.get_parameter_variable('bias')
with nn.variable_scope(vs, reuse=True):
self.assertIs(weight, nn.get_variable('Dense/weight'))
self.assertIs(bias, nn.get_variable('Dense/bias'))
self.assertIs(output, nn.get_tensor('Dense/output'))
self.assertIs(input_, nn.get_input('input'))
def test_bn(self):
"""Compnents consisting BatchNormalization layer are retrieved"""
scope = self.get_scope()
with nn.variable_scope(scope) as vs:
input_ = nn.Input(shape=(32, 4), name='input')
layer = nn.get_layer('BatchNormalization')(name='BN')
output = layer(input_)
mean = layer.get_parameter_variable('mean')
var = layer.get_parameter_variable('var')
scale = layer.get_parameter_variable('scale')
offset = layer.get_parameter_variable('offset')
updates = layer.get_update_operations()
with nn.variable_scope(vs, reuse=True):
self.assertIs(mean, nn.get_variable('BN/mean'))
self.assertIs(var, nn.get_variable('BN/var'))
self.assertIs(scale, nn.get_variable('BN/scale'))
self.assertIs(offset, nn.get_variable('BN/offset'))
self.assertIs(output, nn.get_tensor('BN/output'))
self.assertIs(updates[0], nn.get_operation('BN/update_mean'))
self.assertIs(updates[1], nn.get_operation('BN/update_var'))
