Convolution

Parametric chains convolution operator

struct Convolution (

size_t dim

alias Storage

) {

Variable!(T, dim + 2, Storage) weight;

Variable!(T, dim + 2, Storage) bias;

int nInput;

int nOutput;

bool useBias;

int[dim] kernel;

int[dim] stride;

int[dim] pad;

int[dim] dilation;

this(int nInput, int nOutput, int[dim] kernel, bool useBias = true);

this(int nInput, int nOutput, int[dim] kernel, int[dim] stride, int[dim] pad, int[dim] dilation, bool useBias = true);

void resetParameters();

auto opCall(Variable!(T, dim + 2, Storage) x);

}

Constructors

this this(int nInput, int nOutput, int[dim] kernel, bool useBias = true)
this this(int nInput, int nOutput, int[dim] kernel, int[dim] stride, int[dim] pad, int[dim] dilation, bool useBias = true)

Members

Functions

opCall auto opCall(Variable!(T, dim + 2, Storage) x)
resetParameters void resetParameters(): pytorch style init (LeCun uniform init)

Examples

1 import grain.testing;
2 import grain.utility;
3 import numir;
4 import mir.ndslice;
5 
6 auto conv = Convolution!(float, 2, HostStorage)(3, 4, [3, 3]);
7 auto x = uniform!float(2, 3, 4, 4).slice.variable(true);
8 auto y = conv(x);
9 auto gy = uniform!float(y.shape.castArray!size_t).slice.variable;
10 gradCheckChain!conv(x, gy, 1e-3, 5e-2, 5e-2);

Convolution

Constructors

Members

Functions

Examples

Meta

Source