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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/BCECriterion.c"
#else
#define EPS 1e-12
void THNN_(BCECriterion_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *target,
THTensor *output,
bool sizeAverage,
THTensor *weights,
bool reduce)
{
THNN_CHECK_NELEMENT(input, target);
THNN_CHECK_NELEMENT(input, weights);
if (!reduce) {
THTensor_(resizeAs)(output, input);
TH_TENSOR_APPLY3(real, input, real, target, real, output,
real x = *input_data;
real y = *target_data;
THAssertMsg(x >= 0. && x <= 1.,
"input value should be between 0~1, but got %f",
(double) x);
*output_data = -(log(x + EPS) * y + log(1. - x + EPS) * (1. - y));
);
if (weights) {
THTensor_(cmul)(output, output, weights);
}
return;
}
THTensor_(resize1d)(output, 1);
real sum = 0;
if (weights) {
TH_TENSOR_APPLY3(real, input, real, target, real, weights,
real x = *input_data;
real y = *target_data;
real w = *weights_data;
THAssertMsg(x >= 0. && x <= 1.,
"input value should be between 0~1, but got %f",
(double) x);
sum -= (log(x + EPS) * y + log(1. - x + EPS) * (1. - y)) * w;
);
} else {
TH_TENSOR_APPLY2(real, input, real, target,
real x = *input_data;
real y = *target_data;
THAssertMsg(x >= 0. && x <= 1.,
"input value should be between 0~1, but got %f",
(double) x);
sum -= log(x + EPS) * y + log(1. - x + EPS) * (1. - y);
);
}
if (sizeAverage)
sum /= THTensor_(nElement)(input);
THTensor_(set1d)(output, 0, sum);
}
void THNN_(BCECriterion_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *target,
THTensor *gradOutput,
THTensor *gradInput,
bool sizeAverage,
THTensor *weights,
bool reduce)
{
THNN_CHECK_NELEMENT(input, target);
THNN_CHECK_NELEMENT(input, weights);
THTensor_(resizeAs)(gradInput, input);
if (!reduce) {
THNN_CHECK_NELEMENT(gradOutput, input);
TH_TENSOR_APPLY3(real, gradInput, real, input, real, target,
real x = *input_data;
real y = *target_data;
*gradInput_data = -(y - x) / ((1. - x + EPS) * (x + EPS));
);
if (weights) {
TH_TENSOR_APPLY3(real, gradInput, real, weights, real, gradOutput,
*gradInput_data = *gradInput_data * *weights_data * *gradOutput_data;
);
} else {
THTensor_(cmul)(gradInput, gradInput, gradOutput);
}
return;
}
THNN_CHECK_DIM_SIZE(gradOutput, 1, 0, 1);
real norm = (sizeAverage ? 1./((real)THTensor_(nElement)(input)) : 1.);
TH_TENSOR_APPLY3(real, gradInput, real, input, real, target,
real x = *input_data;
real y = *target_data;
*gradInput_data = - norm * (y - x) / ((1. - x + EPS) * (x + EPS)) * THTensor_fastGet1d(gradOutput, 0);
);
if(weights)
THTensor_(cmul)(gradInput, gradInput, weights);
}
#undef EPS
#endif
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