/// <summary>
/// Functional interface for the batch normalization layer.
/// http://arxiv.org/abs/1502.03167
/// </summary>
/// <param name="inputs"></param>
/// <param name="axis"></param>
/// <param name="momentum"></param>
/// <param name="epsilon"></param>
/// <param name="center"></param>
/// <param name="scale"></param>
/// <param name="beta_initializer"></param>
/// <param name="gamma_initializer"></param>
/// <param name="moving_mean_initializer"></param>
/// <param name="moving_variance_initializer"></param>
/// <param name="training"></param>
/// <param name="trainable"></param>
/// <param name="name"></param>
/// <param name="renorm"></param>
/// <param name="renorm_momentum"></param>
/// <returns></returns>
public Tensor batch_normalization(Tensor inputs,
int axis = -1,
float momentum = 0.99f,
float epsilon = 0.001f,
bool center = true,
bool scale = true,
IInitializer beta_initializer = null,
IInitializer gamma_initializer = null,
IInitializer moving_mean_initializer = null,
IInitializer moving_variance_initializer = null,
Tensor training = null,
bool trainable = true,
string name = null,
bool renorm = false,
float renorm_momentum = 0.99f)
{
var layer = new BatchNormalization(
axis: axis,
momentum: momentum,
epsilon: epsilon,
center: center,
scale: scale,
beta_initializer: beta_initializer,
gamma_initializer: gamma_initializer,
moving_mean_initializer: moving_mean_initializer,
moving_variance_initializer: moving_variance_initializer,
renorm: renorm,
renorm_momentum: renorm_momentum,
trainable: trainable,
name: name);
return(layer.apply(inputs, training: training).Item1);
}
