Function concat

tensorflow/python/ops/array_ops.py:1328–1420

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 fro

(values, axis, name="concat")

@tf_export("concat")
@dispatch.add_dispatch_support
def concat(values, axis, name="concat"):
  """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:

  ```python
  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:

  ```python
  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:

  ```python
  [[[ 1,  2,  7,  4],
    [ 2,  3,  8,  4]],

   [[ 4,  4,  2, 10],
    [ 5,  3, 15, 11]]]
  ```

  Note: If you are concatenating along a new axis consider using stack.
  E.g.

  ```python