tf.compat.v1.squeeze

Removes dimensions of size 1 from the shape of a tensor. (deprecated arguments)

tf.compat.v1.squeeze(
    input, axis=None, name=None, squeeze_dims=None
)

Warning: SOME ARGUMENTS ARE DEPRECATED: (squeeze_dims). They will be removed in a future version. Instructions for updating: Use the axis argument instead

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())
[2 3]

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())
[1 2 3 1]

Note: if input is a tf.RaggedTensor, then this operation takes O(N) time, where N is the number of elements in the squeezed dimensions.

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.

Raises:

ValueError: When both squeeze_dims and axis are specified.