public static void Activation_Sigmoid(Tensor input, Tensor output)
{
__m256 one = Vector256.Create(1.0f);
for (int i = 0; i < output.xmm.Length; ++i)
{
output.xmm[i] = NN_utils.exp256_ps(input.xmm[i]);
var divisor = Avx2.Add(output.xmm[i], one);
output.xmm[i] = Avx2.Divide(output.xmm[i], divisor);
}
}
public static void Activation_Softmax(Tensor input, Tensor output)
{
float sum = 0.0f;
for (int i = 0; i < output.xmm.Length; ++i)
{
output.xmm[i] = NN_utils.exp256_ps(input.xmm[i]);
sum += NN_utils.hsums(output.xmm[i]).GetElement <float>(0);
}
sum = 1.0f / sum;
output.mul(sum, output);
}