I am using a Softmax activation function in the last layer of a neural network. But I have problems with a safe implementation of this function.
A naive implementation would be this one:
Vector y = mlp(x); // output of the neural network without softmax activation function
for(int f = 0; f < y.rows(); f++)
y(f) = exp(y(f));
y /= y.sum();
This does not work very well for > 100 hidden nodes because the y will be NaN in many cases (if y(f) > 709, exp(y(f)) will return inf). I came up with this version:
Vector y = mlp(x); // output of the neural network without softmax activation function
for(int f = 0; f < y.rows(); f++)
y(f) = safeExp(y(f), y.rows());
y /= y.sum();
where safeExp is defined as
double safeExp(double x, int div)
{
static const double maxX = std::log(std::numeric_limits<double>::max());
const double max = maxX / (double) div;
if(x > max)
x = max;
return std::exp(x);
}
This function limits the input of exp. In most of the cases this works but not in all cases and I did not really manage to find out in which cases it does not work. When I have 800 hidden neurons in the previous layer it does not work at all.
However, even if this worked I somehow "distort" the result of the ANN. Can you think of any other way to calculate the correct solution? Are there any C++ libraries or tricks that I can use to calculate the exact output of this ANN?
edit: The solution provided by Itamar Katz is:
Vector y = mlp(x); // output of the neural network without softmax activation function
double ymax = maximal component of y
for(int f = 0; f < y.rows(); f++)
y(f) = exp(y(f) - ymax);
y /= y.sum();
And it really is mathematically the same. In practice however, some small values become 0 because of the floating point precision. I wonder why nobody ever writes these implementation details down in textbooks.
