# Model\_Grammar

## python List拷贝复制

### 直接赋值：

**如果用 = 直接赋值，是非拷贝方法。**

**这两个列表是等价的，修改其中任何一个列表都会影响到另一个列表。**

```
old = [1,[1,2,3],3]
new = []
for i in range(len(old)):
    new.append(old[i])

new[0] = 3
new[1][0] = 3

'''
-----------------------
Before:
[1, [1, 2, 3], 3]
[1, [1, 2, 3], 3]
After:
[3, [3, 2, 3], 3]
[3, [3, 2, 3], 3]
-----------------------
'''
```

### 浅拷贝：

#### 1.copy()方法

**对于List来说，其第一层，是实现了深拷贝，但对于其内嵌套的List，仍然是浅拷贝。**

**因为嵌套的List保存的是地址，复制过去的时候是把地址复制过去了，嵌套的List在内存中指向的还是同一个。**

```
old = [1,[1,2,3],3]
new = old.copy()

new[0] = 3
new[1][0] =3

'''
---------------------
Before:
[1, [1, 2, 3], 3]
[1, [1, 2, 3], 3]
After:
[1, [3, 2, 3], 3]
[3, [3, 2, 3], 3]
---------------------
'''
```

#### 2.使用列表生成式

**使用列表生成式产生新列表也是一个浅拷贝方法，只对第一层实现深拷贝。**

```
old = [1,[1,2,3],3]
new = [i for i in old]

new[0] = 3
new[1][0] = 3

'''
----------------------
Before
[1, [1, 2, 3], 3]
[1, [1, 2, 3], 3]
After
[1, [3, 2, 3], 3]
[3, [3, 2, 3], 3]
----------------------
'''
```

#### 3.for循环遍历

**通过for循环遍历，将元素一个个添加到新列表中。这也是一个浅拷贝方法，只对第一层实现深拷贝。**

```
old = [1,[1,2,3],3]
new = []
for i in range(len(old)):
    new.append(old[i])

new[0] = 3
new[1][0] = 3

'''
-----------------------
Before:
[1, [1, 2, 3], 3]
[1, [1, 2, 3], 3]
After:
[1, [3, 2, 3], 3]
[3, [3, 2, 3], 3]
-----------------------
'''
```

#### **4.使用切片**

**通过使用 \[ : ] 切片，可以浅拷贝整个列表，同样的，只对第一层实现深拷贝。**

```
old = [1,[1,2,3],3]
new = old[:]

new[0] = 3
new[1][0] = 3

'''
------------------
Before:
[1, [1, 2, 3], 3]
[1, [1, 2, 3], 3]
After:
[1, [3, 2, 3], 3]
[3, [3, 2, 3], 3]
------------------
'''
```

### 深拷贝：

**如果用deepcopy()方法，则无论多少层，无论怎样的形式，得到的新列表都是和原来无关的，这是最安全最清爽最有效的方法。**

**需要import copy**

```
import copy
old = [1,[1,2,3],3]
new = copy.deepcopy(old)

new[0] = 3
new[1][0] = 3

'''
-----------------------
Before:
[1, [1, 2, 3], 3]
[1, [1, 2, 3], 3]
After:
[1, [1, 2, 3], 3]
[3, [3, 2, 3], 3]
-----------------------
'''
```

本文转自 <https://blog.csdn.net/qq_24502469/article/details/104185122>，如有侵权，请联系删除。

## reshape中的-1

在深度学习对模型代码的脉络整理中，输入输出的维度变化是一个最重要的线索，其中tensorflow中的api——reshape扮演了重要的角色。reshape中有个参数-1比较难以理解，这篇文章主要讲解其用法。

官方文档中这样解释它：

> If one component of`shape`is the special value -1, the size of that dimension is computed so that the total size remains constant. In particular, a`shape`of`[-1]`flattens into 1-D. At most one component of`shape`can be -1

中文释意为：如果shape参数中包含特殊值-1，维度的尺寸在数组总大小保持不变的基础上进行计算。特殊的，如果shape参数为【-1】，即将该数组打平。

打平很好理解，即无论该数组形状是什么样的，统一变为一维。打平顺序默认为先行后列。

> 如果shape参数中包含特殊值-1，维度的尺寸在数组总大小保持不变的基础上进行计算

这句如何理解呢？举例来看吧。

```
import tensorflow as tf
#(3,2,3)
t1 = [[[1, 1, 1],
       [2, 2, 2]],
      [[3, 3, 3],
       [4, 4, 4]],
      [[5, 5, 5],
       [6, 6, 6]]]

t1 = tf.constant(t1)
sess = tf.Session()
result = sess.run(tf.reshape(t1, [2,-1]))
print(result)
```

最后输出结果为：

```
[[1 1 1 2 2 2 3 3 3]
 [4 4 4 5 5 5 6 6 6]]
```

即，从维度(3,2,3)变为(2,9)。

这个变化按照什么规则呢？

首先3\*2\*3=18，数组总大小为18。shape参数【2，-1】，这个参数意义为reshape后的数组为二维数组，其中第一维度值为2。为了保持总大小不变，18除以2为9。所以变化后的数组形状为(2,9)

看了很多博客，没有领会意思。后来发现官方文档写的很清楚。一定要养成不会先看官方文档，再结合各种博客理解的方法论。

本文转自 <https://zhuanlan.zhihu.com/p/343541487>，如有侵权，请联系删除。


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