Calculates the mean and variance of `x`

.

tf.compat.v2.nn.moments( x, axes, shift=None, keepdims=False, name=None )

The mean and variance are calculated by aggregating the contents of `x`

across `axes`

. If `x`

is 1-D and `axes = [0]`

this is just the mean and variance of a vector.

Note:shift is currently not used; the true mean is computed and used.

When using these moments for batch normalization (see `tf.nn.batch_normalization`

):

- for so-called "global normalization", used with convolutional filters with shape
`[batch, height, width, depth]`

, pass`axes=[0, 1, 2]`

. - for simple batch normalization pass
`axes=[0]`

(batch only).

Args | |
---|---|

`x` | A `Tensor` . |

`axes` | Array of ints. Axes along which to compute mean and variance. |

`shift` | Not used in the current implementation. |

`keepdims` | produce moments with the same dimensionality as the input. |

`name` | Name used to scope the operations that compute the moments. |

Returns | |
---|---|

Two `Tensor` objects: `mean` and `variance` . |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/compat/v2/nn/moments