tf.nn.compute_average_loss

Scales per-example losses with sample_weights and computes their average.

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Compat aliases for migration

tf.compat.v1.nn.compute_average_loss

tf.nn.compute_average_loss(
    per_example_loss, sample_weight=None, global_batch_size=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Use TPUs Distributed training with TensorFlow	Custom training with tf.distribute.Strategy Custom training loop with Keras and MultiWorkerMirroredStrategy Parameter server training with ParameterServerStrategy

Usage with distribution strategy and custom training loop:

with strategy.scope():
  def compute_loss(labels, predictions, sample_weight=None):

    # If you are using a `Loss` class instead, set reduction to `NONE` so that
    # we can do the reduction afterwards and divide by global batch size.
    per_example_loss = tf.keras.losses.sparse_categorical_crossentropy(
        labels, predictions)

    # Compute loss that is scaled by sample_weight and by global batch size.
    return tf.nn.compute_average_loss(
        per_example_loss,
        sample_weight=sample_weight,
        global_batch_size=GLOBAL_BATCH_SIZE)

Args
`per_example_loss`	Per-example loss.
`sample_weight`	Optional weighting for each example.
`global_batch_size`	Optional global batch size value. Defaults to (size of first dimension of `losses`) * (number of replicas).

Returns
Scalar loss value, obtained by summing the `per_example_loss` and dividing by `global_batch_size`. If `global_batch_size` is zero, the result is zero.

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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/compute_average_loss