(self, dtype,
                   N, C, K,
                   D=1, H=1, W=1,
                   T=1, R=1, S=1,
                   pad_d=0, pad_h=0, pad_w=0,
                   str_d=1, str_h=1, str_w=1,
                   dil_d=1, dil_h=1, dil_w=1)
return ConvLayer(self, dtype, N, C, K, D, H, W, T, R, S,
                         pad_d, pad_h, pad_w, str_d, str_h, str_w,
                         dil_d, dil_h, dil_w)
Create a new ConvLayer parameter object.
        This then is passed as an argument to all the convolution operations.

        N: Number of images in mini-batch
        C: Number of input feature maps
        K: Number of output feature maps

        D: Depth  of input image
        H: Height of input image
        W: Width  of input image

        T: Depth  of filter kernel
        R: Height of filter kernel
        S: Width  of filter kernel

        padding: amount of zero-padding around the given edge
        strides: factor to step the filters by in a given direction
        dilation: dilation factor for each dimension

        dtype: need to know dtype to setup proper kernels and params.

        bsum: calculate the sum along the batchnorm axis for fprop or bprop
              outputs an fp32 tensor of size Kx1
Create a new ConvLayer parameter object.
        This then is passed as an argument to all the convolution operations.
Create a new ConvLayer parameter object . This then is passed as an argument to all the convolution operations .
def conv_layer(self, dtype,
                   N, C, K,
                   D=1, H=1, W=1,
                   T=1, R=1, S=1,
                   pad_d=0, pad_h=0, pad_w=0,
                   str_d=1, str_h=1, str_w=1,
                   dil_d=1, dil_h=1, dil_w=1):
        """
        Create a new ConvLayer parameter object.
        This then is passed as an argument to all the convolution operations.

        N: Number of images in mini-batch
        C: Number of input feature maps
        K: Number of output feature maps

        D: Depth  of input image
        H: Height of input image
        W: Width  of input image

        T: Depth  of filter kernel
        R: Height of filter kernel
        S: Width  of filter kernel

        padding: amount of zero-padding around the given edge
        strides: factor to step the filters by in a given direction
        dilation: dilation factor for each dimension

        dtype: need to know dtype to setup proper kernels and params.

        bsum: calculate the sum along the batchnorm axis for fprop or bprop
              outputs an fp32 tensor of size Kx1

        """
        return ConvLayer(self, dtype, N, C, K, D, H, W, T, R, S,
                         pad_d, pad_h, pad_w, str_d, str_h, str_w,
                         dil_d, dil_h, dil_w)
def conv_layer ( self , dtype , N , C , K , D = 1 , H = 1 , W = 1 , T = 1 , R = 1 , S = 1 , pad_d = 0 , pad_h = 0 , pad_w = 0 , str_d = 1 , str_h = 1 , str_w = 1 , dil_d = 1 , dil_h = 1 , dil_w = 1 ) : return ConvLayer ( self , dtype , N , C , K , D , H , W , T , R , S , pad_d , pad_h , pad_w , str_d , str_h , str_w , dil_d , dil_h , dil_w )