nn.internal #

fn compute_fans(shape []int) (int, int)

fn deriv_elu[T](gradient &vtl.Tensor[T], cached &vtl.Tensor[T], alpha T) !&vtl.Tensor[T]

deriv_elu computes the derivative of elu

fn deriv_leaky_relu[T](gradient &vtl.Tensor[T], cached &vtl.Tensor[T], alpha T) !&vtl.Tensor[T]

deriv_leaky_relu computes the derivative of leaky_relu

fn deriv_relu[T](gradient &vtl.Tensor[T], cached &vtl.Tensor[T]) !&vtl.Tensor[T]

deriv_relu computes the derivate of relu

fn deriv_sigmoid[T](gradient &vtl.Tensor[T], cached &vtl.Tensor[T]) !&vtl.Tensor[T]

deriv_sigmoid computes the derivative of sigmoid

fn deriv_tanh[T](gradient &vtl.Tensor[T], cached &vtl.Tensor[T]) !&vtl.Tensor[T]

deriv_tanh computes the derivative of tanh

fn dropout[T](input &vtl.Tensor[T], mask &vtl.Tensor[T], prob f64) !&vtl.Tensor[T]

fn dropout_backwards[T](gradient &vtl.Tensor[T], mask &vtl.Tensor[T], prob f64) !&vtl.Tensor[T]

fn elu[T](x &vtl.Tensor[T], alpha T) &vtl.Tensor[T]

elu activation function

fn kaiming_normal[T](shape []int) &vtl.Tensor[T]

fn kaiming_uniform[T](shape []int) &vtl.Tensor[T]

fn leaky_relu[T](x &vtl.Tensor[T], alpha T) &vtl.Tensor[T]

leaky_relu activation function

fn maxpool2d[T](input &vtl.Tensor[T], kernel []int, padding []int, stride []int) (&vtl.Tensor[int], &vtl.Tensor[T])

fn maxpool2d_backward[T](shape []int, max_indices &vtl.Tensor[int], grad_output &vtl.Tensor[T]) &vtl.Tensor[T]

fn mse[T](input &vtl.Tensor[T], target &vtl.Tensor[T]) !&vtl.Tensor[T]

mse squared error between the labels and the predictions

fn mse_backward[T](gradient &vtl.Tensor[T], cache &vtl.Tensor[T], target &vtl.Tensor[T]) ![]&vtl.Tensor[T]

fn relu[T](x &vtl.Tensor[T]) &vtl.Tensor[T]

relu activation function

fn sgd_optimize[T](mut value vtl.Tensor[T], gradient &vtl.Tensor[T], learning_rate f64) !

fn sigmoid[T](x &vtl.Tensor[T]) &vtl.Tensor[T]

sigmoid takes a real-valued number and squashes it to the range [0, 1]

fn sigmoid_cross_entropy[T](input &vtl.Tensor[T], target &vtl.Tensor[T]) !&vtl.Tensor[T]

sigmoid_cross_entropy computes the sigmoid cross entropy between the labels and the predictions

fn sigmoid_cross_entropy_backward[T](gradient &vtl.Tensor[T], cache &vtl.Tensor[T], target &vtl.Tensor[T]) ![]&vtl.Tensor[T]

fn softmax_cross_entropy_backward[T](gradient &vtl.Tensor[T], cache &vtl.Tensor[T], target &vtl.Tensor[T]) ![]&vtl.Tensor[T]

fn tanh[T](x &vtl.Tensor[T]) &vtl.Tensor[T]

tanh squashes a real-valued number to the range [-1, 1]

fn variance_scaled[T](shape []int, scale T, fan_mode FanMode, distribution Distribution) &vtl.Tensor[T]

enum Distribution {
	uniform
	normal
}

enum FanMode {
	fan_avg
	fan_in
	fan_out
}