【tensorflow】tf.squeeze用法

tf.squeeze用法

• 作用
  tf.squeeze的作用是移除给定的tensor中大小为1的维度。
  源码是：

def squeeze(input, axis=None, name=None, squeeze_dims=None):
  # pylint: disable=redefined-builtin
  """Removes dimensions of size 1 from the shape of a tensor.

  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]


  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))`.
    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.
  """
  if squeeze_dims is not None:
    if axis is not None:
      raise ValueError("Cannot specify both 'squeeze_dims' and 'axis'")
    axis = squeeze_dims
  if np.isscalar(axis):
    axis = [axis]
  return gen_array_ops.squeeze(input, axis, name)

举例说明用法：

import tensorflow as tf
a = tf.ones(shape=[3,1,4,1,2,1,5])
b = tf.squeeze(a)
sess = tf.InteractiveSession()

print(a.shape)

print(b)

输出为：

(3, 1, 4, 1, 2, 1, 5)
Tensor("Squeeze:0", shape=(3, 4, 2, 5), dtype=float32)