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tf.keras.utils.unpack_x_y_sample_weight

Unpacks user-provided data tuple.

This is a convenience utility to be used when overriding Model.train_step, Model.test_step, or Model.predict_step. This utility makes it easy to support data of the form (x,), (x, y), or (x, y, sample_weight).

Standalone usage:

features_batch = tf.ones((10, 5))
labels_batch = tf.zeros((10, 5))
data = (features_batch, labels_batch)
# `y` and `sample_weight` will default to `None` if not provided.
x, y, sample_weight = tf.keras.utils.unpack_x_y_sample_weight(data)
sample_weight is None
True

Example in overridden Model.train_step:

class MyModel(tf.keras.Model):

  def train_step(self, data):
    # If `sample_weight` is not provided, all samples will be weighted
    # equally.
    x, y, sample_weight = tf.keras.utils.unpack_x_y_sample_weight(data)

    with tf.GradientTape() as tape:
      y_pred = self(x, training=True)
      loss = self.compiled_loss(
        y, y_pred, sample_weight, regularization_losses=self.losses)
      trainable_variables = self.trainable_variables
      gradients = tape.gradient(loss, trainable_variables)
      self.optimizer.apply_gradients(zip(gradients, trainable_variables))

    self.compiled_metrics.update_state(y, y_pred, sample_weight)
    return {m.name: m.result() for m in self.metrics}

data A tuple of the form (x,), (x, y), or (x, y, sample_weight).

The unpacked tuple, with Nones for y and sample_weight if they are not provided.