tf.contrib.distributions.bijectors.masked_dense
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A autoregressively masked dense layer. (deprecated)
tf.contrib.distributions.bijectors.masked_dense(
inputs, units, num_blocks=None, exclusive=False, kernel_initializer=None,
reuse=None, name=None, *args, **kwargs
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2018-10-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use tfp.distributions
instead of tf.contrib.distributions
.
Analogous to tf.compat.v1.layers.dense
.
See [Germain et al. (2015)][1] for detailed explanation.
Arguments
inputs
Tensor input.
units
Python int
scalar representing the dimensionality of the output
space.
num_blocks
Python int
scalar representing the number of blocks for the
MADE masks.
exclusive
Python bool
scalar representing whether to zero the diagonal of
the mask, used for the first layer of a MADE.
kernel_initializer
Initializer function for the weight matrix. If None
(default), weights are initialized using the
tf.glorot_random_initializer
.
reuse
Python bool
scalar representing whether to reuse the weights of a
previous layer by the same name.
name
Python str
used to describe ops managed by this function.
*args
tf.compat.v1.layers.dense
arguments.
**kwargs
tf.compat.v1.layers.dense
keyword arguments.
Raises
NotImplementedError
if rightmost dimension of inputs
is unknown prior to
graph execution.
References
[1]: Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle. MADE:
Masked Autoencoder for Distribution Estimation. In International
Conference on Machine Learning , 2015. https://arxiv.org/abs/1502.03509
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
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