tf.nn.batch_norm_with_global_normalization

tf.nn.batch_norm_with_global_normalization(
    t,
    m,
    v,
    beta,
    gamma,
    variance_epsilon,
    scale_after_normalization,
    name=None
)

Defined in tensorflow/python/ops/nn_impl.py.

See the guide: Neural Network > Normalization

Batch normalization.

This op is deprecated. See tf.nn.batch_normalization.

Args:

  • t: A 4D input Tensor.
  • m: A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.
  • v: A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.
  • beta: A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor.
  • gamma: A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor.
  • variance_epsilon: A small float number to avoid dividing by 0.
  • scale_after_normalization: A bool indicating whether the resulted tensor needs to be multiplied with gamma.
  • name: A name for this operation (optional).

Returns:

A batch-normalized t.