tf.stack(
values,
axis=0,
name='stack'
)
Defined in tensorflow/python/ops/array_ops.py
.
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 = tf.constant([1, 4])
y = tf.constant([2, 5])
z = tf.constant([3, 6])
tf.stack([x, y, z]) # [[1, 4], [2, 5], [3, 6]] (Pack along first dim.)
tf.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.stack([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. Negative values wrap around, so the valid range is[-(R+1), R+1)
.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).