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
axis.
For example:
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]t=tf.ones([1,2,1,3,1,1])print(tf.shape(tf.squeeze(t)).numpy())[23]
Or, to remove specific size 1 dimensions:
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]t=tf.ones([1,2,1,3,1,1])print(tf.shape(tf.squeeze(t,[2,4])).numpy())[1231]
Args
input
A Tensor. The input to squeeze.
axis
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. Must be in the range
[-rank(input), rank(input)). Must be specified if input is a
RaggedTensor.
name
A name for the operation (optional).
squeeze_dims
Deprecated keyword argument that is now axis.
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]