1. 在jupyter notebook里写Python代码时，运行如何直接跳行，新增一个代码框

• Ctrl+Enter   运行当前单元格,焦点还在当前单元格
• Shift+Enter 运行当前单元格, 焦点跳到下一个单元格
• Alt+Enter    运行当前单元格, 在下方插入一个新的单元格

2. torch.rand_like报错

import torch
import numpy as np
data = [[1,2],[3,4]]
x_rand = torch.rand_like(x_data)
print(f"Rand Tensor:\n{x_rand}\n")

报错：“RuntimeError: "check_uniform_bounds" not implemented for 'Int'”

纠错：

import torch
import numpy as np
data = [[1,2],[3,4]]
x_rand = torch.rand_like(x_data,dtype=torch.float)
print(f"Rand Tensor:\n{x_rand}\n")

3. Jupyter Notebook 查看帮助文档

• 使用help查看文档：help(torch.cat)
• 使用？查看文档：torch.cat?
• 使用Shift+Tab快捷键查看文档：将光标置于cat上，点击Shift+Tab

4.  torch.cat报错

import torch
tensor = torch.ones(4,4)
tensor[:,2] = 0
print(tensor)
t1 = torch.cat(tensor,tensor,tensor,dim=1)
print(t1)

报错：

TypeError: cat() received an invalid combination of arguments - got (Tensor, Tensor, Tensor, dim=int), but expected one of: * (tuple of Tensors tensors, int dim, *, Tensor out) didn't match because some of the keywords were incorrect: dim * (tuple of Tensors tensors, name dim, *, Tensor out) didn't match because some of the keywords were incorrect: dim

纠错1：

import torch
tensor = torch.ones(4,4)
tensor[:,2] = 0
print(tensor)
t1 = torch.cat((tensor,tensor,tensor),dim=1)
print(t1)

纠错2： 

import torch
tensor = torch.ones(4,4)
tensor[:,2] = 0
print(tensor)
t1 = torch.cat([tensor,tensor,tensor],dim=1)
print(t1)

5.  函数后面莫忘记加括号

z = y * y * 3
out = z.mean()
out.backward()

  6.pytorch张量复制clone()和detach()

 https://blog.csdn.net/Answer3664/article/details/104417013/

 https://blog.csdn.net/m0_46653437/article/details/112547756/

 https://blog.csdn.net/guofei_fly/article/details/104486708/

• clone

clone()函数可以返回一个完全相同的tensor,新的tensor开辟新的内存，但是仍然留在计算图中。

clone操作在不共享数据内存的同时支持梯度回溯，所以常用在神经网络中某个单元需要重复使用的场景下。

• detach

detach()函数可以返回一个完全相同的tensor,新的tensor开辟与旧的tensor共享内存，新的tensor会脱离计算图，不会牵扯梯度计算。此外，一些原地操作(in-place, such as resize_ / resize_as_ / set_ / transpose_) 在两者任意一个执行都会引发错误。
detach操作在共享数据内存的脱离计算图，所以常用在神经网络中仅要利用张量数值，而不需要追踪导数的场景下。

7.

pred = x_tensor * w + b
plt.scatter(x, y)
plt.plot(x, pred)
plt.legend(['y=-3x+4','network'])

报错：

RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.

纠错：

x = np.linspace(0, 10, 100)
y = -3 * x + 4 + np.random.randint(-2, 2, size=100)
pred = x_tensor * w + b
plt.scatter(x, y)
plt.plot(x, pred.data.numpy())
plt.legend(['y=-3x+4', 'Network'])