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 as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight 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 weight vector. If the shape of weight matches the shape of predictions, then the loss of each measurable element of predictions is scaled by the corresponding value of weight.

Args:

• predictions: The predicted outputs.
• targets: The ground truth output tensor, same dimensions as 'predictions'.
• weight: 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.

Returns:

A scalar Tensor representing the loss value.

Raises:

• ValueError: If the shape of predictions doesn't match that of targets or if the shape of weight is invalid.