tf.contrib.losses.log_loss( predictions, labels=None, weights=1.0, epsilon=1e-07, scope=None )
Defined in tensorflow/contrib/losses/python/losses/loss_ops.py
.
See the guide: Losses (contrib) > Loss operations for use in neural networks.
Adds a Log Loss term to the training procedure. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-12-30. Instructions for updating: Use tf.losses.log_loss instead. Note that the order of the predictions and labels arguments has been changed.
weights
acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weights
is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the weights
vector. If the shape of weights
matches the shape of predictions
, then the loss of each measurable element of predictions
is scaled by the corresponding value of weights
.
predictions
: The predicted outputs.labels
: The ground truth output tensor, same dimensions as 'predictions'.weights
: Coefficients for the loss a scalar, a tensor of shape [batch_size] or a tensor whose shape matches predictions
.epsilon
: A small increment to add to avoid taking a log of zero.scope
: The scope for the operations performed in computing the loss.A scalar Tensor
representing the loss value.
ValueError
: If the shape of predictions
doesn't match that of labels
or if the shape of weights
is invalid.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/losses/log_loss