def test_concat(self):
"""Compnents consisting Concat layer are retrieved"""
with nn.variable_scope(self.get_scope()):
layer = nn.fetch_layer('Concat')(axis=1, scope='Concat')
output = layer([
nn.Input(shape=(32, 4), name='input_1'),
nn.Input(shape=(32, 5), name='input_2'),
])
self.assertIs(output, nn.get_tensor('Concat/output'))
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.fetch_layer('TrueDiv')(denom=1.0, scope='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.fetch_layer(layer_name)(scope=layer_name)
output = layer(input_)
with nn.variable_scope(vs, reuse=True):
output_tensor_name = '{}/output'.format(layer_name)
self.assertIs(input_, nn.get_input('input'))
self.assertIs(output, nn.get_tensor(output_tensor_name))
def test_conv2dtranspose(self):
"""Compnents consisting Conv2DTranspose layer are retrieved"""
with nn.variable_scope(self.get_scope()) as vs:
input_ = nn.Input(shape=(32, 4, 8, 8), name='input')
layer = nn.fetch_layer('Conv2D')(
filter_height=4, filter_width=4, n_filters=4,
strides=1, with_bias=True, scope='Conv2D')
output = layer(input_)
layer = nn.fetch_layer('Conv2DTranspose')(
filter_height=4, filter_width=4, n_filters=4,
strides=1, with_bias=True, output_shape=input_.shape,
scope='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_dense(self):
"""Compnents consisting Dense layer are retrieved"""
with nn.variable_scope(self.get_scope()) as vs:
input_ = nn.Input(shape=(32, 5), name='input')
layer = nn.fetch_layer('Dense')(
n_nodes=4, with_bias=True, scope='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"""
base_scope, scope = self.get_scope(), 'BN'
with nn.variable_scope(base_scope) as vs:
input_ = nn.Input(shape=(32, 4), name='input')
layer = nn.fetch_layer('BatchNormalization')(scope=scope)
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'))