'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'

def update_conv(self, layer, I, E, U, alpha=1.0, beta=0.0, 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, layer_op=layer)
