tf.stack

最新推荐文章于 2022-11-11 11:35:34 发布

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本文详细介绍了TensorFlow中stack函数的使用方法，包括如何将一组具有相同形状和类型的张量沿指定轴堆叠成一个新的张量。通过具体示例展示了不同axis参数设置下输出张量的形状变化，并对比了stack与unstack操作的区别，以及与numpy中stack函数的等效性。

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def stack(values, axis=0, name="stack"):
  """Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
  
  See also `tf.concat`, `tf.tile`, `tf.repeat`.

  Packs the list of tensors in `values` into a tensor with rank one higher than
  each tensor in `values`, by packing them along the `axis` dimension.
  Given a list of length `N` of tensors of shape `(A, B, C)`;

  if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
  if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
  Etc.

  For example:

  >>> x = tf.constant([1, 4])
  >>> y = tf.constant([2, 5])
  >>> z = tf.constant([3, 6])
  >>> tf.stack([x, y, z])
  <tf.Tensor: shape=(3, 2), dtype=int32, numpy=
  array([[1, 4],
         [2, 5],
         [3, 6]], dtype=int32)>
  >>> tf.stack([x, y, z], axis=1)
  <tf.Tensor: shape=(2, 3), dtype=int32, numpy=
  array([[1, 2, 3],
         [4, 5, 6]], dtype=int32)>

  This is the opposite of unstack.  The numpy equivalent is `np.stack`

  >>> np.array_equal(np.stack([x, y, z]), tf.stack([x, y, z]))
  True

  Args:
    values: A list of `Tensor` objects with the same shape and type.
    axis: An `int`. The axis to stack along. Defaults to the first dimension.
      Negative values wrap around, so the valid range is `[-(R+1), R+1)`.
    name: A name for this operation (optional).

  Returns:
    output: A stacked `Tensor` with the same type as `values`.

  Raises:
    ValueError: If `axis` is out of the range [-(R+1), R+1).
  """