contrib.bayesflow.stochastic_graph.surrogate_loss

tf.contrib.bayesflow.stochastic_graph.surrogate_loss

tf.contrib.bayesflow.stochastic_graph.surrogate_loss

surrogate_loss(
    sample_losses,
    stochastic_tensors=None,
    name='SurrogateLoss'
)

Defined in tensorflow/contrib/bayesflow/python/ops/stochastic_graph_impl.py.

See the guide: BayesFlow Stochastic Graph (contrib) > Stochastic Computation Graph Helper Functions

Surrogate loss for stochastic graphs.

This function will call loss_fn on each StochasticTensor upstream of sample_losses, passing the losses that it influenced.

Note that currently surrogate_loss does not work with StochasticTensors instantiated in while_loops or other control structures.

Args:

• sample_losses: a list or tuple of final losses. Each loss should be per example in the batch (and possibly per sample); that is, it should have dimensionality of 1 or greater. All losses should have the same shape.
• stochastic_tensors: a list of StochasticTensors to add loss terms for. If None, defaults to all StochasticTensors in the graph upstream of the Tensors in sample_losses.
• name: the name with which to prepend created ops.

Returns:

Tensor loss, which is the sum of sample_losses and the loss_fns returned by the StochasticTensors.

Raises:

• TypeError: if sample_losses is not a list or tuple, or if its elements are not Tensors.
• ValueError: if any loss in sample_losses does not have dimensionality 1 or greater.