parameters: (self, feature)
return_statement: return layer
docstring: Returns a clone of the CNTK layer for per-layer forward prop validation
docstring_summary: Returns a clone of the CNTK layer for per-layer forward prop validation
docstring_tokens: Returns a clone of the CNTK layer for per - layer forward prop validation
function: 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
function_tokens: def clone_cntk_layer ( self , feature ) : 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