tf.contrib.losses.log_loss( predictions, labels=None, weights=1.0, epsilon=1e-07, scope=None )
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
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
epsilon: A small increment to add to avoid taking a log of zero.
scope: The scope for the operations performed in computing the loss.
Tensor representing the loss value.
ValueError: If the shape of
predictionsdoesn't match that of
labelsor if the shape of
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Code samples licensed under the Apache 2.0 License.