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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SoftPlus.c"
#else
void THNN_(SoftPlus_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
accreal beta_,
accreal threshold_)
{
real beta = TH_CONVERT_ACCREAL_TO_REAL(beta_);
real threshold = TH_CONVERT_ACCREAL_TO_REAL(threshold_);
THTensor_(resizeAs)(output, input);
// f(x) = 1/beta * log(1 + exp(beta * x))
TH_TENSOR_APPLY2(real, output, real, input, \
*output_data = (*input_data * beta) > threshold ? *input_data : THLog1p(exp(*input_data * beta)) / beta;
);
}
void THNN_(SoftPlus_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THTensor *output,
accreal beta_,
accreal threshold_)
{
real beta = TH_CONVERT_ACCREAL_TO_REAL(beta_);
real threshold = TH_CONVERT_ACCREAL_TO_REAL(threshold_);
THNN_CHECK_NELEMENT(input, gradOutput);
THTensor_(resizeAs)(gradInput, output);
// d/dx[log(1+exp(k*x))/k] = exp(kx) / (exp(kx) + 1)
// SINCE
// y = (1/k)*log(1+exp(k*x)) --> x = (1/k)*log(exp(k*y)-1)
// THEREFORE:
// d/dx(f(x)) = (exp(k*y) - 1) / exp(k*y)
TH_TENSOR_APPLY3(real, gradInput, real, gradOutput, real, output,
real z = exp(*output_data * beta);
*gradInput_data = (*output_data * beta) > threshold ? *gradOutput_data : *gradOutput_data * (z - 1.)/z;
);
}
#endif
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