tf.squeeze

Removes dimensions of size 1 from the shape of a tensor.

tf.squeeze(     input, axis=None, name=None )

Used in the notebooks

Used in the guide Used in the tutorials

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] tf.shape(tf.squeeze(t))  # [2, 3]

Or, to remove specific size 1 dimensions:

# 't' is a tensor of shape [1, 2, 1, 3, 1, 1] tf.shape(tf.squeeze(t, [2, 4]))  # [1, 2, 3, 1]

Unlike the older op tf.compat.v1.squeeze, this op does not accept a deprecated squeeze_dims argument.

@tf.function def func(x):   print('x.shape:', x.shape)   known_axes = [i for i, size in enumerate(x.shape) if size == 1]   y = tf.squeeze(x, axis=known_axes)   print('shape of tf.squeeze(x, axis=known_axes):', y.shape)   y = tf.squeeze(x)   print('shape of tf.squeeze(x):', y.shape)   return 0  _ = func.get_concrete_function(tf.TensorSpec([None, 1, 2], dtype=tf.int32)) # Output is. # x.shape: (None, 1, 2) # shape of tf.squeeze(x, axis=known_axes): (None, 2) # shape of tf.squeeze(x): <unknown>

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).

A Tensor. Has the same type as input. Contains the same data as input, but has one or more dimensions of size 1 removed.

ValueError The input cannot be converted to a tensor, or the specified axis cannot be squeezed.