deepy/networks/network.py
def register_layer(self, layer):
        """
        Register the layer so that it's param will be trained.
        But the output of the layer will not be stacked.
        """
        if type(layer) == Block:
            layer.fix()
        self.parameter_count += layer.parameter_count
        self.parameters.extend(layer.parameters)
        self.free_parameters.extend(layer.free_parameters)
        self.training_monitors.extend(layer.training_monitors)
        self.testing_monitors.extend(layer.testing_monitors)
        self.updates.extend(layer.updates)
        self.training_updates.extend(layer.training_updates)
        self.input_variables.extend(layer.external_inputs)
        self.target_variables.extend(layer.external_targets)

        self.training_callbacks.extend(layer.training_callbacks)
        self.testing_callbacks.extend(layer.testing_callbacks)
        self.epoch_callbacks.extend(layer.epoch_callbacks)
def register_layer(self, layer):
        """
        Register the layer so that it's param will be trained.
        But the output of the layer will not be stacked.
        """
        if type(layer) == Block:
            layer.fix()
        self.parameter_count += layer.parameter_count
        self.parameters.extend(layer.parameters)
        self.free_parameters.extend(layer.free_parameters)
        self.training_monitors.extend(layer.training_monitors)
        self.testing_monitors.extend(layer.testing_monitors)
        self.updates.extend(layer.updates)
        self.training_updates.extend(layer.training_updates)
        self.input_variables.extend(layer.external_inputs)
        self.target_variables.extend(layer.external_targets)

        self.training_callbacks.extend(layer.training_callbacks)
        self.testing_callbacks.extend(layer.testing_callbacks)
        self.epoch_callbacks.extend(layer.epoch_callbacks)
def register_layer ( self , layer ) : if type ( layer ) == Block : layer . fix ( ) self . parameter_count += layer . parameter_count self . parameters . extend ( layer . parameters ) self . free_parameters . extend ( layer . free_parameters ) self . training_monitors . extend ( layer . training_monitors ) self . testing_monitors . extend ( layer . testing_monitors ) self . updates . extend ( layer . updates ) self . training_updates . extend ( layer . training_updates ) self . input_variables . extend ( layer . external_inputs ) self . target_variables . extend ( layer . external_targets ) self . training_callbacks . extend ( layer . training_callbacks ) self . testing_callbacks . extend ( layer . testing_callbacks ) self . epoch_callbacks . extend ( layer . epoch_callbacks )
Register the layer so that it's param will be trained.
        But the output of the layer will not be stacked.
Register the layer so that it s param will be trained . But the output of the layer will not be stacked .