tf.expand_dims和tf.squeeze函数

from http://blog.csdn.net/qq_31780525/article/details/72280284

tf.expand_dims()

Function

tf.expand_dims(input, axis=None, name=None, dim=None)

Inserts a dimension of 1 into a tensor’s shape. 
在第axis位置增加一个维度

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.

给定张量输入，此操作在输入形状的维度索引轴处插入1的尺寸。 尺寸索引轴从零开始; 如果您指定轴的负数，则从最后向后计数。

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].

如果要将批量维度添加到单个元素，则此操作非常有用。 例如，如果您有一个单一的形状[height，width，channels]，您可以使用expand_dims（image，0）使其成为1个图像，这将使形状[1，高度，宽度，通道]。

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.

输入：张量。
轴：0-D（标量）。 指定扩大输入形状的维度索引。
名称：输出名称Tensor。
dim：0-D（标量）。 等同于轴，不推荐使用。

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. 
从tensor中删除所有大小是1的维度

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