View source on GitHub |
Inserts a dimension of 1 into a tensor's shape.
tf.compat.v2.expand_dims(
input, axis, name=None
)
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.
Args | |
---|---|
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 (optional).
|
Returns | |
---|---|
A Tensor with the same data as input , but its shape has an additional
dimension of size 1 added.
|