一、 tf.concat
官方解释文档：https://tensorflow.google.cn/api_docs/python/tf/concat
函数原型：
tf.concat(
values, # 要连接的张量
axis, # 指定的连接维度
name='concat'
)
官方解释：
values: A list of Tensor objects or a single Tensor.
axis: 0-D int32 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 to axis-th dimension. And negative axis
refers to axis + rank(values)-th dimension.
name: A name for the operation (optional).
这是将张量按指定维度进行连接的函数。如下面实例所示：
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]二、tf.stack()
官方解释：https://tensorflow.google.cn/api_docs/python/tf/stack
函数原型：
tf.stack(
values,
axis=0,
name='stack'
)
values: A list of Tensor objects with the same shape and type.
axis: An int. 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).
函数主要作用也是进行张量的连接，与上面那个函数的不同之处在于，这个函数支持在一个新的维度上进行连接。 如下面例子所示：
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]] # 注意，维度一在原来的x中是不存在的，也就是表明这个函数可以扩充维度