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TensorFlow 1 version
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View source on GitHub
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Loss base class.
tf.keras.losses.Loss(
reduction=losses_utils.ReductionV2.AUTO, name=None
)
Used in the notebooks
| Used in the guide |
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To be implemented by subclasses:
call(): Contains the logic for loss calculation usingy_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 | |
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reduction
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(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.
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name
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Optional name for the op. |
Methods
call
@abc.abstractmethodcall( 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]
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y_pred
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The predicted values. shape = [batch_size, d0, .. dN]
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| Returns | |
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Loss values with the shape [batch_size, d0, .. dN-1].
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from_config
@classmethodfrom_config( config )
Instantiates a Loss from its config (output of get_config()).
| Args | |
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config
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Output of get_config().
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| Returns | |
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A Loss instance.
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get_config
get_config()
Returns the config dictionary for a Loss instance.
__call__
__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]
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y_pred
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The predicted values. shape = [batch_size, d0, .. dN]
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sample_weight
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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.)
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| Returns | |
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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.)
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| Raises | |
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ValueError
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If the shape of sample_weight is invalid.
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