tf.contrib.distributions.bijectors.real_nvp_default_template

tf.contrib.distributions.bijectors.real_nvp_default_template(
    hidden_layers,
    shift_only=False,
    activation=tf.nn.relu,
    name=None,
    *args,
    **kwargs
)

Defined in tensorflow/contrib/distributions/python/ops/bijectors/real_nvp.py.

Build a scale-and-shift function using a multi-layer neural network.

This will be wrapped in a make_template to ensure the variables are only created once. It takes the d-dimensional input x[0:d] and returns the D-d dimensional outputs loc ("mu") and log_scale ("alpha").

Arguments:

  • hidden_layers: Python list-like of non-negative integer, scalars indicating the number of units in each hidden layer. Default: `[512, 512].
  • shift_only: Python bool indicating if only the shift term shall be computed (i.e. NICE bijector). Default: False.
  • activation: Activation function (callable). Explicitly setting to None implies a linear activation.
  • name: A name for ops managed by this function. Default: "real_nvp_default_template".
  • *args: tf.layers.dense arguments.
  • **kwargs: tf.layers.dense keyword arguments.

Returns:

  • shift: Float-like Tensor of shift terms ("mu" in [Papamakarios et al. (2016)][1]).
  • log_scale: Float-like Tensor of log(scale) terms ("alpha" in [Papamakarios et al. (2016)][1]).

Raises:

  • NotImplementedError: if rightmost dimension of inputs is unknown prior to graph execution.

References

[1]: 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