def clone_cntk_layer(self, feature):
"""Returns a clone of the CNTK layer for per-layer forward prop validation"""
weightsParameter = utilities.find_parameter_by_name(
self.layer.parameters, 'W', 0)
biasParameter = utilities.find_parameter_by_name(
self.layer.parameters, 'b', 1)
internalNodes = utilities.get_model_layers(self.layer.block_root)
activationType = utilities.get_cntk_activation_op(internalNodes)
includeBias = biasParameter is not None
layer = Dense(self.layer.shape, activation=activationType, bias=includeBias)(feature)
layer.parameters[0].value = weightsParameter.value
if includeBias:
layer.parameters[1].value = biasParameter.value
return layer
def clone_cntk_layer(self, feature):
"""Returns a clone of the CNTK layer for per-layer forward prop validation"""
nodes = utilities.get_model_layers(self.layer.block_root)
activation = utilities.get_cntk_activation_op(nodes)
weightsShape = self.weights_parameter.shape
pad = self.attributes['autoPadding'][0] or (
self.attributes['autoPadding'][1] and self.attributes['autoPadding'][2])
bias = (self.bias_parameter is not None)
layer = Convolution((weightsShape[2], weightsShape[3]), weightsShape[0],
pad=pad, activation=activation, bias=bias)(feature)
layer.parameters[0].value = self.weights_parameter.value
if bias:
layer.parameters[1].value = self.bias_parameter.value
return layer