(self, layer, I, E, U, alpha=1.0, beta=0.0, grad_bias=None, layer_op=None)
Compute the updated gradient for a convolutional network layer.

        Arguments:
            layer: the conv layer as a parameter object
            I (CPUTensor): the inputs
            E (CPUTensor): the errors
            U (CPUTensor): the updates
            alpha (float): linear scaling
            beta  (float): scaled accumulation
Compute the updated gradient for a convolutional network layer.
Compute the updated gradient for a convolutional network layer .
def update_conv(self, layer, I, E, U, alpha=1.0, beta=0.0, grad_bias=None, layer_op=None):
        """
        Compute the updated gradient for a convolutional network layer.

        Arguments:
            layer: the conv layer as a parameter object
            I (CPUTensor): the inputs
            E (CPUTensor): the errors
            U (CPUTensor): the updates
            alpha (float): linear scaling
            beta  (float): scaled accumulation
        """
        assert layer.sizeI == I.size
        assert layer.sizeO == E.size
        assert layer.sizeF == U.size
        layer.update_conv(I, E, U, alpha, beta, grad_bias=grad_bias, layer_op=layer_op)
def update_conv ( self , layer , I , E , U , alpha = 1.0 , beta = 0.0 , grad_bias = None , layer_op = None ) : assert layer . sizeI == I . size assert layer . sizeO == E . size assert layer . sizeF == U . size layer . update_conv ( I , E , U , alpha , beta , grad_bias = grad_bias , layer_op = layer_op )