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].
</td>
</tr><tr>
<td>shift_only</td>
<td>
Pythonboolindicating if only theshiftterm shall be
computed (i.e. NICE bijector). Default:False.
</td>
</tr><tr>
<td>activation</td>
<td>
Activation function (callable). Explicitly setting toNoneimplies a linear activation.
</td>
</tr><tr>
<td>name</td>
<td>
A name for ops managed by this function. Default:
"real_nvp_default_template".
</td>
</tr><tr>
<td>args</td>
<td>
<a href="../../../../tf/layers/dense"><code>tf.compat.v1.layers.dense</code></a> arguments.
</td>
</tr><tr>
<td>*kwargs`
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
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