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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/LogSigmoid.c"
#else
void THNN_(LogSigmoid_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THTensor *buffer)
{
THTensor_(resizeAs)(output, input);
THTensor_(resizeAs)(buffer, input);
//Use the LogSumExp trick to make this stable against overflow
TH_TENSOR_APPLY3(real, output, real, input, real, buffer,
real max_elem = fmax(0, -*input_data);
real z = exp(-max_elem) + exp(-*input_data - max_elem);
*buffer_data = z;
*output_data = -(max_elem + log(z));
);
}
void THNN_(LogSigmoid_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THTensor *buffer)
{
THNN_CHECK_NELEMENT(input, gradOutput);
THTensor_(resizeAs)(gradInput, buffer);
/* deriv of -max(0,-x) - log(e(0 - max(0,-x)) + e(-x - max(0,-x)) is
* -max_deriv - (-max_deriv*e(0-max(0,-x)) + (-1 - max_deriv)*e(-x - max(0,-x)))/z
* where z = e(0 - max(0,-x)) + e(-x - max(0,-x))
* which simplifies to
* -max_deriv - (z-1)/z if x is >= 0 or
* -max_deriv + (z-1)/z if x is < 0
*/
TH_TENSOR_APPLY3(real, input, real, gradInput, real, buffer,
real z = *buffer_data;
real max_deriv = 0.0;
real sign = -1.0;
if (*input_data < 0){
max_deriv = -1.0;
sign = 1.0;
}
*gradInput_data = -max_deriv - sign*((z - 1.0)/ z);
);
THTensor_(cmul)(gradInput, gradOutput, gradInput);
}
#endif
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