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位置增加一個維度
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.
給定張量輸入，此操作在輸入形狀的維度索引軸處插入1的尺寸。 尺寸索引軸從零開始; 如果您指定軸的負數，則從最后向后計數。
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].
如果要將批量維度添加到單個元素，則此操作非常有用。 例如，如果您有一個單一的形狀[height，width，channels]，您可以使用expand_dims（image，0）使其成為1個圖像，這將使形狀[1，高度，寬度，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.
輸入：張量。
軸：0-D（標量）。 指定擴大輸入形狀的維度索引。
名稱：輸出名稱Tensor。
dim：0-D（標量）。 等同於軸，不推薦使用。
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.
從tensor中刪除所有大小是1的維度
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.
給定張量輸入，此操作返回相同類型的張量，並刪除所有尺寸為1的尺寸。 如果不想刪除所有尺寸1尺寸，可以通過指定squeeze_dims來刪除特定尺寸1尺寸。
如果不想刪除所有大小是1的維度，可以通過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).
輸入：張量。 輸入要擠壓。
squeeze_dims：可選的ints列表。 默認為[]。 如果指定，只能擠壓列出的尺寸。 維度索引從0開始。擠壓不是1的維度是一個錯誤。
名稱：操作的名稱（可選）。
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.
張量。 與輸入的類型相同。 包含與輸入相同的數據，但具有一個或多個刪除尺寸1的維度