tf.nn.avg_pool3d

Batch normalization.

tf.nn.batch_norm_with_global_normalization(
    input, mean, variance, beta, gamma, variance_epsilon, scale_after_normalization,
    name=None
)

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

Args
`input`	A 4D input Tensor.
`mean`	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.
`variance`	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`.

References:

Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift: Ioffe et al., 2015 (pdf)

TensorFlow

tf.nn / avg_pool3d

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tf.nn / avg_pool3d

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References: