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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)
.
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}
Args | |
---|---|
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.
|