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

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

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.

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