Imports

Procs

proc mrelu[T](t: var Tensor[T]): Source Edit
proc msigmoid[T: SomeFloat](t: var Tensor[T]): Logistic sigmoid activation function, f(x) = 1 / (1 + \exp(-x)) Note: Canonical sigmoid is not stable for large negative value Source Edit
proc mtanh[T: SomeFloat](t: var Tensor[T]): Source Edit
proc relu[T](t: Tensor[T]): Tensor[T] {.noinit.}: Source Edit
proc relu_backward[T](gradient: Tensor[T]; cached_tensor: Tensor[T]): Tensor[T] {. noinit.}: Source Edit
proc sigmoid[T: SomeFloat](t: Tensor[T]): Tensor[T] {.noinit.}: Logistic sigmoid activation function, f(x) = 1 / (1 + \exp(-x)) Note: Canonical sigmoid is not stable for large negative value Please use sigmoid_cross_entropy for the final layer for better stability and performance Source Edit
proc sigmoid_backward[T](gradient: Tensor[T]; cached_tensor: Tensor[T]): Tensor[ T] {.noinit.}: Source Edit
proc tanh[T: SomeFloat](t: Tensor[T]): Tensor[T] {.noinit.}: Source Edit
proc tanh_backward[T](gradient: Tensor[T]; cached_tensor: Tensor[T]): Tensor[T] {. noinit.}: Source Edit