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Loss base class.
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 = ops.convert_to_tensor(y_pred) y_true = math_ops.cast(y_true, y_pred.dtype) return K.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 https://www.tensorflow.org/alpha/tutorials/distribute/training_loops 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 https://www.tensorflow.org/alpha/tutorials/distribute/training_loops for more details on this. |
name | Optional name for the op. |
call
@abc.abstractmethod call( y_true, y_pred )
Invokes the Loss
instance.
Args | |
---|---|
y_true | Ground truth values, with the same shape as 'y_pred'. |
y_pred | The predicted values. |
from_config
@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
get_config()
__call__
__call__( y_true, y_pred, sample_weight=None )
Invokes the Loss
instance.
Args | |
---|---|
y_true | Ground truth values. shape = [batch_size, d0, .. dN] |
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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/losses/Loss