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. (deprecated)
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
: Pythonlist
-like of non-negative integer, scalars indicating the number of units in each hidden layer. Default: `[512, 512].shift_only
: Pythonbool
indicating if only theshift
term shall be computed (i.e. NICE bijector). Default:False
.activation
: Activation function (callable). Explicitly setting toNone
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
-likeTensor
of shift terms ("mu" in [Papamakarios et al. (2016)][1]).log_scale
:Float
-likeTensor
of log(scale) terms ("alpha" in [Papamakarios et al. (2016)][1]).
Raises:
NotImplementedError
: if rightmost dimension ofinputs
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