tf.stack
tf.stack
tf.stack
stack( values, axis=0, name='stack' )
Defined in tensorflow/python/ops/array_ops.py
.
See the guides: Layers (contrib) > Higher level ops for building neural network layers, Tensor Transformations > Slicing and Joining
Stacks a list of rank-R
tensors into one rank-(R+1)
tensor.
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' is [1, 4] # 'y' is [2, 5] # 'z' is [3, 6] stack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. stack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
This is the opposite of unstack. The numpy equivalent is
tf.stack([x, y, z]) = np.asarray([x, y, z])
Args:
-
values
: A list ofTensor
objects with the same shape and type. -
axis
: Anint
. The axis to stack along. Defaults to the first dimension. Supports negative indexes. -
name
: A name for this operation (optional).
Returns:
-
output
: A stackedTensor
with the same type asvalues
.
Raises:
-
ValueError
: Ifaxis
is out of the range [-(R+1), R+1).
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/stack