tf.contrib.distributions.bijectors.masked_dense

tf.contrib.distributions.bijectors.masked_dense(
    inputs,
    units,
    num_blocks=None,
    exclusive=False,
    kernel_initializer=None,
    reuse=None,
    name=None,
    *args,
    **kwargs
)

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

A autoregressively masked dense layer. Analogous to tf.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.layers.dense arguments.
• **kwargs: tf.layers.dense keyword arguments.

Returns:

Output tensor.

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