tf.expand_dims( input, axis=None, name=None, dim=None )
Defined in tensorflow/python/ops/array_ops.py
.
See the guide: Tensor Transformations > Shapes and Shaping
Inserts a dimension of 1 into a tensor's shape.
Given a tensor input
, this operation inserts a dimension of 1 at the dimension index axis
of input
's shape. The dimension index axis
starts at zero; if you specify a negative number for axis
it is counted backward from the end.
This operation is useful if you want to add a batch dimension to a single element. For example, if you have a single image of shape [height, width, channels]
, you can make it a batch of 1 image with expand_dims(image, 0)
, which will make the shape [1, height, width, channels]
.
Other examples:
# 't' is a tensor of shape [2] tf.shape(tf.expand_dims(t, 0)) # [1, 2] tf.shape(tf.expand_dims(t, 1)) # [2, 1] tf.shape(tf.expand_dims(t, -1)) # [2, 1] # 't2' is a tensor of shape [2, 3, 5] tf.shape(tf.expand_dims(t2, 0)) # [1, 2, 3, 5] tf.shape(tf.expand_dims(t2, 2)) # [2, 3, 1, 5] tf.shape(tf.expand_dims(t2, 3)) # [2, 3, 5, 1]
This operation requires that:
-1-input.dims() <= dim <= input.dims()
This operation is related to squeeze()
, which removes dimensions of size 1.
input
: A Tensor
.axis
: 0-D (scalar). Specifies the dimension index at which to expand the shape of input
. Must be in the range [-rank(input) - 1, rank(input)]
.name
: The name of the output Tensor
.dim
: 0-D (scalar). Equivalent to axis
, to be deprecated.A Tensor
with the same data as input
, but its shape has an additional dimension of size 1 added.
ValueError
: if both dim
and axis
are specified.
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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/api_docs/python/tf/expand_dims