tf.estimator.WarmStartSettings

Build a supervised_input_receiver_fn for raw features and labels.

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Compat aliases for migration

tf.compat.v1.estimator.experimental.build_raw_supervised_input_receiver_fn

tf.estimator.experimental.build_raw_supervised_input_receiver_fn(
    features, labels, default_batch_size=None
)

This function wraps tensor placeholders in a supervised_receiver_fn with the expectation that the features and labels appear precisely as the model_fn expects them. Features and labels can therefore be dicts of tensors, or raw tensors.

Args
`features`	a dict of string to `Tensor` or `Tensor`.
`labels`	a dict of string to `Tensor` or `Tensor`.
`default_batch_size`	the number of query examples expected per batch. Leave unset for variable batch size (recommended).

Returns
A supervised_input_receiver_fn.

Raises
`ValueError`	if features and labels have overlapping keys.

TensorFlow

tf.estimator / WarmStartSettings

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tf.estimator / WarmStartSettings

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