tf.keras.losses.CategoricalCrossentropy     View source on GitHub    Computes the crossentropy loss between the labels and predictions. Inherits From: Loss  View aliases  Main aliases 
tf.losses.CategoricalCrossentropy Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.losses.CategoricalCrossentropy  
tf.keras.losses.CategoricalCrossentropy(
    from_logits=False, label_smoothing=0, reduction=losses_utils.ReductionV2.AUTO,
    name='categorical_crossentropy'
)
 Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided in a one_hot representation. If you want to provide labels as integers, please use SparseCategoricalCrossentropy loss. There should be # classes floating point values per feature. In the snippet below, there is # classes floating pointing values per example. The shape of both y_pred and y_true are [batch_size, num_classes]. Standalone usage: 
y_true = [[0, 1, 0], [0, 0, 1]]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
# Using 'auto'/'sum_over_batch_size' reduction type.
cce = tf.keras.losses.CategoricalCrossentropy()
cce(y_true, y_pred).numpy()
1.177
 
# Calling with 'sample_weight'.
cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
0.814
 
# Using 'sum' reduction type.
cce = tf.keras.losses.CategoricalCrossentropy(
    reduction=tf.keras.losses.Reduction.SUM)
cce(y_true, y_pred).numpy()
2.354
 
# Using 'none' reduction type.
cce = tf.keras.losses.CategoricalCrossentropy(
    reduction=tf.keras.losses.Reduction.NONE)
cce(y_true, y_pred).numpy()
array([0.0513, 2.303], dtype=float32)
 Usage with the compile() API: model.compile(optimizer='sgd', loss=tf.keras.losses.CategoricalCrossentropy())

 


 Args
  from_logits   Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution. Note - Using from_logits=True is more numerically stable.  
  label_smoothing   Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. e.g. label_smoothing=0.2 means that we will use a value of 0.1 for label 0 and 0.9 for label 1"  
  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. Defaults to 'categorical_crossentropy'.    Methods 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.