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Returns a tensor with a length 1 axis inserted at index axis
.
tf.expand_dims( input, axis, name=None )
Given a tensor input
, this operation inserts a dimension of length 1 at the dimension index axis
of input
's shape. The dimension index follows Python indexing rules: It's zero-based, a negative index it is counted backward from the end.
This operation is useful to:
If you have a single image of shape [height, width, channels]
:
image = tf.zeros([10,10,3])
You can add an outer batch
axis by passing axis=0
:
tf.expand_dims(image, axis=0).shape.as_list() [1, 10, 10, 3]
The new axis location matches Python list.insert(axis, 1)
:
tf.expand_dims(image, axis=1).shape.as_list() [10, 1, 10, 3]
Following standard Python indexing rules, a negative axis
counts from the end so axis=-1
adds an inner most dimension:
tf.expand_dims(image, -1).shape.as_list() [10, 10, 3, 1]
This operation requires that axis
is a valid index for input.shape
, following Python indexing rules:
-1-tf.rank(input) <= axis <= tf.rank(input)
This operation is related to:
tf.squeeze
, which removes dimensions of size 1.tf.reshape
, which provides more flexible reshaping capability.tf.sparse.expand_dims
, which provides this functionality for tf.SparseTensor
Args | |
---|---|
input | A Tensor . |
axis | Integer specifying the dimension index at which to expand the shape of input . Given an input of D dimensions, axis must be in range [-(D+1), D] (inclusive). |
name | Optional string. The name of the output Tensor . |
Returns | |
---|---|
A tensor with the same data as input , with an additional dimension inserted at the index specified by axis . |
Raises | |
---|---|
ValueError | If axis is not specified. |
InvalidArgumentError | If axis is out of range [-(D+1), D] . |
© 2020 The TensorFlow Authors. All rights reserved.
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/expand_dims