LostTech.TensorFlow : API Documentation

Type BatchNormalization

Namespace tensorflow.contrib.distributions.bijectors

Parent Bijector

Interfaces IBatchNormalization

Compute `Y = g(X) s.t.

X = g^-1(Y) = (Y - mean(Y)) / std(Y)`.

Applies Batch Normalization [(Ioffe and Szegedy, 2015)][1] to samples from a data distribution. This can be used to stabilize training of normalizing flows ([Papamakarios et al., 2016][3]; [Dinh et al., 2017][2])

When training Deep Neural Networks (DNNs), it is common practice to normalize or whiten features by shifting them to have zero mean and scaling them to have unit variance.

The `inverse()` method of the `BatchNormalization` bijector, which is used in the log-likelihood computation of data samples, implements the normalization procedure (shift-and-scale) using the mean and standard deviation of the current minibatch.

Conversely, the `forward()` method of the bijector de-normalizes samples (e.g. `X*std(Y) + mean(Y)` with the running-average mean and standard deviation computed at training-time. De-normalization is useful for sampling. During training time, `BatchNorm.inverse` and `BatchNorm.forward` are not guaranteed to be inverses of each other because `inverse(y)` uses statistics of the current minibatch, while `forward(x)` uses running-average statistics accumulated from training. In other words, `BatchNorm.inverse(BatchNorm.forward(...))` and `BatchNorm.forward(BatchNorm.inverse(...))` will be identical when `training=False` but may be different when `training=True`.

#### References

[1]: Sergey Ioffe and Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In _International Conference on Machine Learning_, 2015. https://arxiv.org/abs/1502.03167

[2]: Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density Estimation using Real NVP. In _International Conference on Learning Representations_, 2017. https://arxiv.org/abs/1605.08803

[3]: George Papamakarios, Theo Pavlakou, and Iain Murray. Masked Autoregressive Flow for Density Estimation. In _Neural Information Processing Systems_, 2017. https://arxiv.org/abs/1705.07057
Show Example
dist = tfd.TransformedDistribution(

y = tfd.MultivariateNormalDiag(loc=1., scale=2.).sample(100) # ~ N(1, 2) x = dist.bijector.inverse(y) # ~ N(0, 1) y = dist.sample() # ~ N(1, 2)


Public properties

BatchNormalization batchnorm get; set;

object dtype get;

object dtype_dyn get;

object forward_min_event_ndims get;

object forward_min_event_ndims_dyn get;

IList<object> graph_parents get;

object graph_parents_dyn get;

object inverse_min_event_ndims get;

object inverse_min_event_ndims_dyn get;

bool is_constant_jacobian get;

object is_constant_jacobian_dyn get;

object name get;

object name_dyn get;

object PythonObject get;

bool validate_args get;

object validate_args_dyn get;