def __init__(self, layer):
if not layer.is_block:
raise ValueError(
"Error: Convolution layer node is not in block node")
self.op_name = 'Convolution'
# initialize weights and input characteristics
self.input_parameter = layer.arguments[0]
self.weights_parameter = utilities.find_parameter_by_name(
layer.parameters, 'W', 0)
self.bias_parameter = utilities.find_parameter_by_name(
layer.parameters, 'b', 1)
# Get the hyper-parameters for the convolution.
# They are on the convolution node inside this block.
convolution_nodes = depth_first_search(
layer.block_root,
lambda x: utilities.op_name_equals(x, 'Convolution'))
self.attributes = convolution_nodes[0].attributes
self.convolution_method = 0
self.input_shape = self.input_parameter.shape
super().__init__(layer)
nodes = utilities.get_model_layers(layer.block_root)
if utilities.is_softmax_activation(nodes):
self.additional_layer_text = 'softmax'
else:
activation_type = utilities.get_cntk_activation_name(nodes)
if activation_type:
self.additional_layer_text = activation_type
def __init__(self, layer):
if not layer.is_block:
raise ValueError("Dense node is not a block node")
self.op_name = 'Dense'
super().__init__(layer)
internalNodes = utilities.get_model_layers(self.layer.block_root)
self.additional_layer_text = utilities.get_cntk_activation_name(internalNodes)
