tf.keras.losses.Loss     View source on GitHub    Loss base class.  View aliases  Main aliases 
tf.losses.Loss Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.losses.Loss  
tf.keras.losses.Loss(
    reduction=losses_utils.ReductionV2.AUTO, name=None
)
 To be implemented by subclasses:  
call(): Contains the logic for loss calculation using y_true, y_pred.  Example subclass implementation: class MeanSquaredError(Loss):

  def call(self, y_true, y_pred):
    y_pred = tf.convert_to_tensor_v2(y_pred)
    y_true = tf.cast(y_true, y_pred.dtype)
    return tf.reduce_mean(math_ops.square(y_pred - y_true), axis=-1)
 When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, please use 'SUM' or 'NONE' reduction types, and reduce losses explicitly in your training loop. Using 'AUTO' or 'SUM_OVER_BATCH_SIZE' will raise an error. Please see this custom training tutorial for more details on this. You can implement 'SUM_OVER_BATCH_SIZE' using global batch size like: with strategy.scope():
  loss_obj = tf.keras.losses.CategoricalCrossentropy(
      reduction=tf.keras.losses.Reduction.NONE)
  ....
  loss = (tf.reduce_sum(loss_obj(labels, predictions)) *
          (1. / global_batch_size))

 


 Args
  reduction   (Optional) Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see this custom training tutorial for more details.  
  name   Optional name for the op.    Methods call View source 
@abc.abstractmethod
call(
    y_true, y_pred
)
 Invokes the Loss instance.
 


 Args
  y_true   Ground truth values. shape = [batch_size, d0, .. dN], except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]  
  y_pred   The predicted values. shape = [batch_size, d0, .. dN]   
 


 Returns   Loss values with the shape [batch_size, d0, .. dN-1].  
 from_config View source 
@classmethod
from_config(
    config
)
 Instantiates a Loss from its config (output of get_config()).
 


 Args
  config   Output of get_config().   
 


 Returns   A Loss instance.  
 get_config View source 
get_config()
 Returns the config dictionary for a Loss instance. __call__ View source 
__call__(
    y_true, y_pred, sample_weight=None
)
 Invokes the Loss instance.
 


 Args
  y_true   Ground truth values. shape = [batch_size, d0, .. dN], except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]  
  y_pred   The predicted values. shape = [batch_size, d0, .. dN]  
  sample_weight   Optional sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_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 sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each loss element of y_pred is scaled by the corresponding value of sample_weight. (Note ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)   
 


 Returns   Weighted loss float Tensor. If reduction is NONE, this has shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)  

 


 Raises
  ValueError   If the shape of sample_weight is invalid.