tf版本：1.5.0

concat官方定義

Args:
values: A list of Tensor objects or a single Tensor. 單個張量或是一個關於張量的list
axis: 0-D int32 Tensor. Dimension along which to concatenate. Must be
in the range [-rank(values), rank(values)).維度，必須在values的維度之間的，否則無效，dtype = int32
name: A name for the operation (optional). 命名

Returns:
A Tensor resulting from concatenation of the input tensors.
返回一個 輸入張量的連線，連線方式按照axis所指維度

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   輸入的張量維度個數需要match所有的維度
  except `axis` must be equal.
 

例項

  For example:
import tensorflow as tf
  ```python
  #和其他axis一樣， 如果只有二維的話， axis=0 其實就是行，axis=1為列
  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]
  ```

  #如果要是用棧去 用一個new axis去連線 values的話 這裡推薦使用stack
  Note: If you are concatenating along a new axis consider using stack.
  E.g.

  ```python
  tf.concat([tf.expand_dims(t, axis) for t in tensors], axis)
  ```

  can be rewritten as

  ```python
  tf.stack(tensors, axis=axis)
  ```


  """