contrib.losses.log_loss

tf.contrib.losses.log_loss

tf.contrib.losses.log_loss

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 was 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.

Args:

• 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.

Returns:

A scalar Tensor representing the loss value.

Raises:

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