Unpacks user-provided data tuple.
tf.keras.utils.unpack_x_y_sample_weight( data )
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)
.
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}
Arguments | |
---|---|
data | A tuple of the form (x,) , (x, y) , or (x, y, sample_weight) . |
Returns | |
---|---|
The unpacked tuple, with None s for y and sample_weight if they are not provided. |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/utils/unpack_x_y_sample_weight