Batch Normalization layer from http://arxiv.org/abs/1502.03167

Inherits From: `BatchNormalization`

, `Layer`

tf.layers.BatchNormalization( axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer=tf.zeros_initializer(), gamma_initializer=tf.ones_initializer(), moving_mean_initializer=tf.zeros_initializer(), moving_variance_initializer=tf.ones_initializer(), beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, renorm=False, renorm_clipping=None, renorm_momentum=0.99, fused=None, trainable=True, virtual_batch_size=None, adjustment=None, name=None, **kwargs )

"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift"

Sergey Ioffe, Christian Szegedy

Keras APIs handle BatchNormalization updates to the moving_mean and moving_variance as part of their `fit()`

and `evaluate()`

loops. However, if a custom training loop is used with an instance of `Model`

, these updates need to be explicitly included. Here's a simple example of how it can be done:

# model is an instance of Model that contains BatchNormalization layer. update_ops = model.get_updates_for(None) + model.get_updates_for(features) train_op = optimizer.minimize(loss) train_op = tf.group([train_op, update_ops])

Arguments | |
---|---|

`axis` | An `int` or list of `int` , the axis or axes that should be normalized, typically the features axis/axes. For instance, after a `Conv2D` layer with `data_format="channels_first"` , set `axis=1` . If a list of axes is provided, each axis in `axis` will be normalized simultaneously. Default is `-1` which uses the last axis. Note: when using multi-axis batch norm, the `beta` , `gamma` , `moving_mean` , and `moving_variance` variables are the same rank as the input Tensor, with dimension size 1 in all reduced (non-axis) dimensions). |

`momentum` | Momentum for the moving average. |

`epsilon` | Small float added to variance to avoid dividing by zero. |

`center` | If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. |

`scale` | If True, multiply by `gamma` . If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu` ), this can be disabled since the scaling can be done by the next layer. |

`beta_initializer` | Initializer for the beta weight. |

`gamma_initializer` | Initializer for the gamma weight. |

`moving_mean_initializer` | Initializer for the moving mean. |

`moving_variance_initializer` | Initializer for the moving variance. |

`beta_regularizer` | Optional regularizer for the beta weight. |

`gamma_regularizer` | Optional regularizer for the gamma weight. |

`beta_constraint` | An optional projection function to be applied to the `beta` weight after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. |

`gamma_constraint` | An optional projection function to be applied to the `gamma` weight after being updated by an `Optimizer` . |

`renorm` | Whether to use Batch Renormalization (https://arxiv.org/abs/1702.03275). This adds extra variables during training. The inference is the same for either value of this parameter. |

`renorm_clipping` | A dictionary that may map keys 'rmax', 'rmin', 'dmax' to scalar `Tensors` used to clip the renorm correction. The correction `(r, d)` is used as `corrected_value = normalized_value * r + d` , with `r` clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin, dmax are set to inf, 0, inf, respectively. |

`renorm_momentum` | Momentum used to update the moving means and standard deviations with renorm. Unlike `momentum` , this affects training and should be neither too small (which would add noise) nor too large (which would give stale estimates). Note that `momentum` is still applied to get the means and variances for inference. |

`fused` | if `None` or `True` , use a faster, fused implementation if possible. If `False` , use the system recommended implementation. |

`trainable` | Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). |

`virtual_batch_size` | An `int` . By default, `virtual_batch_size` is `None` , which means batch normalization is performed across the whole batch. When `virtual_batch_size` is not `None` , instead perform "Ghost Batch Normalization", which creates virtual sub-batches which are each normalized separately (with shared gamma, beta, and moving statistics). Must divide the actual batch size during execution. |

`adjustment` | A function taking the `Tensor` containing the (dynamic) shape of the input tensor and returning a pair (scale, bias) to apply to the normalized values (before gamma and beta), only during training. For example, if axis==-1, `adjustment = lambda shape: ( tf.random.uniform(shape[-1:], 0.93, 1.07), tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized value by up to 7% up or down, then shift the result by up to 0.1 (with independent scaling and bias for each feature but shared across all examples), and finally apply gamma and/or beta. If `None` , no adjustment is applied. Cannot be specified if virtual_batch_size is specified. |

`name` | A string, the name of the layer. |

Attributes | |
---|---|

`graph` | DEPRECATED FUNCTION |

`scope_name` |

© 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/layers/BatchNormalization