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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SoftMax.c"
#else
static int nn_(SoftMax_updateOutput)(lua_State *L)
{
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_Tensor);
real *input_data, *output_data;
long nframe = 0, dim = 0;
long t, d;
if(input->nDimension == 1)
{
nframe = 1;
dim = input->size[0];
}
else if(input->nDimension == 2)
{
nframe = input->size[0];
dim = input->size[1];
}
else
THArgCheck(0, 2, "vector or matrix expected");
input = THTensor_(newContiguous)(input);
THTensor_(resizeAs)(output, input);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
for(t = 0; t < nframe; t++)
{
real inputMax = -THInf;
for(d = 0; d < dim; d++) {
if (input_data[d] >= inputMax) inputMax = input_data[d];
}
accreal sum = 0;
for(d = 0; d < dim; d++) {
real z = THExpMinusApprox(inputMax - input_data[d]);
output_data[d] = z;
sum += z;
}
for(d = 0; d < dim; d++) {
output_data[d] *= 1/sum;
}
input_data += dim;
output_data += dim;
}
THTensor_(free)(input);
return 1;
}
static int nn_(SoftMax_updateGradInput)(lua_State *L)
{
THTensor *gradOutput = luaT_checkudata(L, 3, torch_Tensor);
THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_Tensor);
THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_Tensor);
real *gradInput_data, *gradOutput_data, *output_data;
long nframe = 0, dim = 0;
long t, d;
if(output->nDimension == 1)
{
nframe = 1;
dim = output->size[0];
}
else if(output->nDimension == 2)
{
nframe = output->size[0];
dim = output->size[1];
}
else
THError("vector or matrix expected");
THTensor_(resizeAs)(gradInput, output);
gradInput_data = THTensor_(data)(gradInput);
output_data = THTensor_(data)(output);
gradOutput_data = THTensor_(data)(gradOutput);
for(t = 0; t < nframe; t++)
{
accreal sum = 0;
for(d = 0; d < dim; d++)
sum += (accreal)gradOutput_data[d] * output_data[d];
for(d = 0; d < dim; d++)
gradInput_data[d] = output_data[d] * (gradOutput_data[d] - sum);
gradInput_data += dim;
output_data += dim;
gradOutput_data += dim;
}
return 1;
}
static const struct luaL_Reg nn_(SoftMax__) [] = {
{"SoftMax_updateOutput", nn_(SoftMax_updateOutput)},
{"SoftMax_updateGradInput", nn_(SoftMax_updateGradInput)},
{NULL, NULL}
};
static void nn_(SoftMax_init)(lua_State *L)
{
luaT_pushmetatable(L, torch_Tensor);
luaT_registeratname(L, nn_(SoftMax__), "nn");
lua_pop(L,1);
}
#endif
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