Login Register

tf.contrib.losses.get_regularization_losses()

tf.contrib.losses.get_regularization_losses(scope=None)

Gets the regularization losses.

Args:

scope: an optional scope for filtering the losses to return.

Returns:

A list of loss variables.

Links:

https://www.tensorflow.org/versions/master/api_docs/python/contrib.losses.html#get_regularization_losses

doc_TensorFlow

2016-10-14 13:07:10

Comments

Leave a Comment

Please login to continue.

Popular Articles

tf.contrib.losses.get_losses() tf.contrib.losses.get_losses(scope=None, loss_collection='losses') Gets the list of losses from the loss_collection. Args: scope: an optional scope

tf.contrib.losses.get_total_loss() tf.contrib.losses.get_total_loss(add_regularization_losses=True, name='total_loss') Returns a tensor whose value represents the total loss. Notice t

tf.contrib.losses.hinge_loss() tf.contrib.losses.hinge_loss(logits, target, scope=None) Method that returns the loss tensor for hinge loss. Args: logits: The logits, a float tens

tf.contrib.losses.add_loss() tf.contrib.losses.add_loss(*args, **kwargs) Adds a externally defined loss to the collection of losses. Args: loss: A loss Tensor. loss_collection

tf.contrib.losses.log_loss() tf.contrib.losses.log_loss(predictions, targets, weight=1.0, epsilon=1e-07, scope=None) Adds a Log Loss term to the training procedure. weight acts