tf.fill | TensorFlow v2.16.1 (original) (raw)

tf.fill

Stay organized with collections Save and categorize content based on your preferences.

Creates a tensor filled with a scalar value.

View aliases

Compat aliases for migration

tf.compat.v1.fill

tf.fill(
    dims, value, name=None, layout=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Ragged tensors tf.data: Build TensorFlow input pipelines Subword tokenizers Unicode strings	Scalable model compression Multiple changepoint detection and Bayesian model selection Graph-based Neural Structured Learning in TFX Graph regularization for sentiment classification using synthesized graphs Bayesian Gaussian Mixture Model and Hamiltonian MCMC

See also tf.ones, tf.zeros, tf.one_hot, tf.eye.

This operation creates a tensor of shape dims and fills it with value.

For example:

tf.fill([2, 3], 9) <tf.Tensor: shape=(2, 3), dtype=int32, numpy= array([[9, 9, 9], [9, 9, 9]], dtype=int32)>

tf.fill evaluates at graph runtime and supports dynamic shapes based on other runtime tf.Tensors, unlike tf.constant(value, shape=dims), which embeds the value as a Const node.

Args
dims	A 1-D sequence of non-negative numbers. Represents the shape of the output tf.Tensor. Entries should be of type: int32, int64.
value	A value to fill the returned tf.Tensor.
name	Optional string. The name of the output tf.Tensor.
layout	Optional, tf.experimental.dtensor.Layout. If provided, the result is a DTensor with the provided layout.

Returns
A tf.Tensor with shape dims and the same dtype as value.

Raises
InvalidArgumentError	dims contains negative entries.
NotFoundError	dims contains non-integer entries.

numpy compatibility

Similar to np.full. In numpy, more parameters are supported. Passing a number argument as the shape (np.full(5, value)) is valid in numpy for specifying a 1-D shaped result, while TensorFlow does not support this syntax.