TensorFlow2-高阶操作（一）：张量合并【concat/拼接（不创造新维度；拼接轴处维度可不同，其他轴维度必须相同）】【stack/堆叠（会创造新维度；各个轴的维度都必须相同）】

╰半夏微凉° 2023-09-29 10:35 7阅读 0赞

## 一、concat（拼接） ##

concat()是将tensor沿着指定维度连接起来。

tf.concat(values, axis, name='concat')

*  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.
    
     *  axis=0 代表在第0个维度拼接
     *  axis=1 代表在第1个维度拼接
 *  name：A name for the operation (optional).
 *  Returns：A Tensor resulting from concatenation of the input tensors.

### 1、axis=0 代表在第0个维度拼接 ###

import tensorflow as tf
    
    t1 = [[1, 2, 3], [4, 5, 6]]
    t2 = [[7, 8, 9], [10, 11, 12]]
    
    data1 = tf.concat([t1, t2], 0)
    
    print("data1 = \n", data1)

打印结果：

data1 = 
     tf.Tensor(
    [[ 1  2  3]
     [ 4  5  6]
     [ 7  8  9]
     [10 11 12]], shape=(4, 3), dtype=int32)

### 2、axis=1 代表在第1个维度拼接 ###

import tensorflow as tf
    
    t1 = [[1, 2, 3], [4, 5, 6]]
    t2 = [[7, 8, 9], [10, 11, 12]]
    
    data2 = tf.concat([t1, t2], 1)
    
    print("data2 = \n", data2)

打印结果：

data2 = 
     tf.Tensor(
    [[ 1  2  3  7  8  9]
     [ 4  5  6 10 11 12]], shape=(2, 6), dtype=int32)

## 二、stack ##

tf.stack(values, axis=0, name='stack')

*  **values**：list中的所有Tensor的形状和类型必须一致。（A list of Tensor objects with the **same shape** and the **same 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).
 *  **Returns**：output A stacked Tensor with the same type as values.

### 1、axis=0 代表在第0个维度堆叠 ###

![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBAdTAxMzI1MDg2MQ_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center]

import tensorflow as tf
    
    x = tf.constant([1, 4])
    y = tf.constant([2, 5])
    z = tf.constant([3, 6])
    data0 = tf.stack([x, y, z])
    
    print("x = \n", x)
    print("-" * 50)
    print("y = \n", y)
    print("-" * 50)
    print("z = \n", z)
    print("-" * 200)
    print("data0 = \n", data0)

打印结果：

x = 
     tf.Tensor([1 4], shape=(2,), dtype=int32)
    --------------------------------------------------
    y = 
     tf.Tensor([2 5], shape=(2,), dtype=int32)
    --------------------------------------------------
    z = 
     tf.Tensor([3 6], shape=(2,), dtype=int32)
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    data0 = 
     tf.Tensor(
    [[1 4]
     [2 5]
     [3 6]], shape=(3, 2), dtype=int32)
    
    Process finished with exit code 0

### 2、axis=1 代表在第1个维度堆叠 ###

import tensorflow as tf
    
    x = tf.constant([1, 4])
    y = tf.constant([2, 5])
    z = tf.constant([3, 6])
    data1 = tf.stack([x, y, z], axis=1)
    
    print("x = \n", x)
    print("-" * 50)
    print("y = \n", y)
    print("-" * 50)
    print("z = \n", z)
    print("-" * 200)
    print("data1 = \n", data1)

打印结果：

x = 
     tf.Tensor([1 4], shape=(2,), dtype=int32)
    --------------------------------------------------
    y = 
     tf.Tensor([2 5], shape=(2,), dtype=int32)
    --------------------------------------------------
    z = 
     tf.Tensor([3 6], shape=(2,), dtype=int32)
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    data1 = 
     tf.Tensor(
    [[1 2 3]
     [4 5 6]], shape=(2, 3), dtype=int32)
    
    Process finished with exit code 0

--------------------

参考资料：  
[Tensorflow的拼接操作实现——tf.concat()详解][Tensorflow_tf.concat]  
[tensorflow中的concat][tensorflow_concat]  
[tensorflow stack unstack操作][tensorflow stack unstack]  
[TensorFlow函数：tf.unstack][TensorFlow_tf.unstack]

[watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBAdTAxMzI1MDg2MQ_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center]: https://img-blog.csdnimg.cn/ad74a397b8bf42e5a4b97afb3ac646a2.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAdTAxMzI1MDg2MQ==,size_20,color_FFFFFF,t_70,g_se,x_16#pic_center
[Tensorflow_tf.concat]: https://blog.csdn.net/baidu_33216040/article/details/102576194
[tensorflow_concat]: https://blog.csdn.net/qq_42327424/article/details/88928753
[tensorflow stack unstack]: https://blog.csdn.net/bitcarmanlee/article/details/123716986
[TensorFlow_tf.unstack]: https://www.w3cschool.cn/tensorflow_python/tensorflow_python-bsky2o7k.html