(self, layer, I, F, O,
                   X=None, bias=None, bsum=None,
                   alpha=1.0, beta=0.0,
                   relu=False, brelu=False, slope=0.0, layer_op=None)
Forward propagate the inputs of a convolutional network layer to
        produce output.

        Arguments:
            layer: the conv layer as a parameter object
            I (CPUTensor): inputs
            F (CPUTensor): the weights (filters)
            O (CPUTensor): outputs

        Compounding Options:
            X: tensor to use in bprop_relu or beta
                can be same as O for beta accumulate (this is default when None)
                should be same shape as O
            bias: (K,1) tensor to use for adding bias to output
                O += bias
            bsum: (K,1) tensor to accumulate batch sum over (used in batchnorm or bprop_bias)
                bsum = sum(O.reshape(K,-1), axis=1)
                the sum operation is fully deterministic
            alpha, beta:
                O = alpha*O + beta*X
                O = alpha*O + beta*O   (if X==O)
            relu, slope: boolean flag to apply:
                O = max(O, 0) + beta*min(O, 0)
                can be combined with bias (where bias is added first)
            brelu, slope: boolean flag to apply:
                O *= (X > 0) + beta*(X < 0)
                can be combined with bsum tensor to output bprop_bias
Forward propagate the inputs of a convolutional network layer to
        produce output.
Forward propagate the inputs of a convolutional network layer to produce output .
def fprop_conv(self, layer, I, F, O,
                   X=None, bias=None, bsum=None,
                   alpha=1.0, beta=0.0,
                   relu=False, brelu=False, slope=0.0, layer_op=None):
        """
        Forward propagate the inputs of a convolutional network layer to
        produce output.

        Arguments:
            layer: the conv layer as a parameter object
            I (CPUTensor): inputs
            F (CPUTensor): the weights (filters)
            O (CPUTensor): outputs

        Compounding Options:
            X: tensor to use in bprop_relu or beta
                can be same as O for beta accumulate (this is default when None)
                should be same shape as O
            bias: (K,1) tensor to use for adding bias to output
                O += bias
            bsum: (K,1) tensor to accumulate batch sum over (used in batchnorm or bprop_bias)
                bsum = sum(O.reshape(K,-1), axis=1)
                the sum operation is fully deterministic
            alpha, beta:
                O = alpha*O + beta*X
                O = alpha*O + beta*O   (if X==O)
            relu, slope: boolean flag to apply:
                O = max(O, 0) + beta*min(O, 0)
                can be combined with bias (where bias is added first)
            brelu, slope: boolean flag to apply:
                O *= (X > 0) + beta*(X < 0)
                can be combined with bsum tensor to output bprop_bias
        """
        layer.xprop_conv(I, F, O, X, bias, bsum, alpha,
                         beta, relu, brelu, slope, layer_op=layer)
def fprop_conv ( self , layer , I , F , O , X = None , bias = None , bsum = None , alpha = 1.0 , beta = 0.0 , relu = False , brelu = False , slope = 0.0 , layer_op = None ) : layer . xprop_conv ( I , F , O , X , bias , bsum , alpha , beta , relu , brelu , slope , layer_op = layer )