tf.concat(
values,
axis,
name='concat'
)
Defined in tensorflow/python/ops/array_ops.py
.
Concatenates tensors along one dimension.
Concatenates the list of tensors values
along dimension axis
. If
values[i].shape = [D0, D1, ... Daxis(i), ...Dn]
, the concatenated
result has shape
[D0, D1, ... Raxis, ...Dn]
where
Raxis = sum(Daxis(i))
That is, the data from the input tensors is joined along the axis
dimension.
The number of dimensions of the input tensors must match, and all dimensions
except axis
must be equal.
For example:
t1 = [[1, 2, 3], [4, 5, 6]]
t2 = [[7, 8, 9], [10, 11, 12]]
tf.concat([t1, t2], 0) # [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
tf.concat([t1, t2], 1) # [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
# tensor t3 with shape [2, 3]
# tensor t4 with shape [2, 3]
tf.shape(tf.concat([t3, t4], 0)) # [4, 3]
tf.shape(tf.concat([t3, t4], 1)) # [2, 6]
As in Python, the axis
could also be negative numbers. Negative axis
are interpreted as counting from the end of the rank, i.e.,
axis + rank(values)
-th dimension.
For example:
t1 = [[[1, 2], [2, 3]], [[4, 4], [5, 3]]]
t2 = [[[7, 4], [8, 4]], [[2, 10], [15, 11]]]
tf.concat([t1, t2], -1)
would produce:
[[[ 1, 2, 7, 4],
[ 2, 3, 8, 4]],
[[ 4, 4, 2, 10],
[ 5, 3, 15, 11]]]
tf.concat([tf.expand_dims(t, axis) for t in tensors], axis)
can be rewritten as
tf.stack(tensors, axis=axis)
Args:
values
: A list ofTensor
objects or a singleTensor
.axis
: 0-Dint32
Tensor
. Dimension along which to concatenate. Must be in the range[-rank(values), rank(values))
. As in Python, indexing for axis is 0-based. Positive axis in the rage of[0, rank(values))
refers toaxis
-th dimension. And negative axis refers toaxis + rank(values)
-th dimension.name
: A name for the operation (optional).
Returns:
A Tensor
resulting from concatenation of the input tensors.