tf.expand_dims()
Function
tf.expand_dims(input, axis=None, name=None, dim=None)
Inserts a dimension of 1 into a tensor's shape.
Add a dimension to the first axis
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.
Given a tensor input, this operation inserts a dimension of 1 at the dimension index axis of the input shape. The dimension index axis starts at zero; if you specify a negative number for the axis, it counts backwards 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].
This is useful if you want to add bulk dimensions to a single element. For example, if you have a single shape [height, width, channels], you can use expand_dims(image, 0) to make it 1 image, which will make the shape [1, height, width, channels].
For example:
# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]
# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
Args:
input: A Tensor.
axis: 0-D (scalar). Specifies the dimension index at which to expand the shape of input.
name: The name of the output Tensor.
dim: 0-D (scalar). Equivalent to axis, to be deprecated.
Input: Tensor.
axis: 0-D (scalar). Specifies the dimension index by which to expand the input shape.
name: Output name Tensor.
dim: 0-D (scalar). Equivalent to axis, deprecated.
Returns:
A Tensor with the same data as input, but its shape has an additional dimension of size 1 added.
tf.squeeze()
Function
tf.squeeze(input, squeeze_dims=None, name=None)
Removes dimensions of size 1 from the shape of a tensor.
Given a tensor input, this operation returns a tensor of the same type with all dimensions of size 1 removed. If you don’t want to remove all size 1 dimensions, you can remove specific size 1 dimensions by specifying squeeze_dims.
给定张量输入，此操作返回相同类型的张量，并删除所有尺寸为1的尺寸。 如果不想删除所有尺寸1尺寸，可以通过指定squeeze_dims来删除特定尺寸1尺寸。
如果不想删除所有大小是1的维度，可以通过squeeze_dims指定。
For example:
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]
Or, to remove specific size 1 dimensions:
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
Args:
input: A Tensor. The input to squeeze.
squeeze_dims: An optional list of ints. Defaults to []. If specified, only squeezes the dimensions listed. The dimension index starts at 0. It is an error to squeeze a dimension that is not 1.
name: A name for the operation (optional).
输入：张量。 输入要挤压。
squeeze_dims：可选的ints列表。 默认为[]。 如果指定，只能挤压列出的尺寸。 维度索引从0开始。挤压不是1的维度是一个错误。
名称：操作的名称（可选）。
Returns:
A Tensor. Has the same type as input. Contains the same data as input, but has one or more dimensions of size 1 removed.
张量。 与输入的类型相同。 包含与输入相同的数据，但具有一个或多个删除尺寸1的维度。