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【Pytroch】中view、randint、requires_grad和detach方法、Embedding、RMSE和MAE

梁存
2023-12-01

1 view方法

import torch
a = torch.Tensor([[[1,2,3],
                   [4,5,6]]])
b = torch.Tensor([1,2,3,4,5,6])

print(a.view(-1))
print(b.view(-1))

————
结果:

tensor([1., 2., 3., 4., 5., 6.])
tensor([1., 2., 3., 4., 5., 6.])

2 randint方法

a = torch.randint(low=0, high=10, size=(10,1))
print(a)

————
结果:

tensor([[3],
        [9],
        [6],
        [9],
        [1],
        [0],
        [9],
        [8],
        [4],
        [2]])

3 clamp方法

a = torch.clamp(a, 3, 5)
print(a)

————
结果:

tensor([[3],
        [5],
        [5],
        [5],
        [3],
        [3],
        [5],
        [5],
        [4],
        [3]])

4 requires_grad方法

import torch
 
a = torch.tensor([1, 2, 3.], requires_grad=True)
print(a)
out = a.tanh()
print(out)
c = out.data  # 需要走注意的是,通过.data “分离”得到的的变量会和原来的变量共用同样的数据,而且新分离得到的张量是不可求导的,c发生了变化,原来的张量也会发生变化
c.zero_() # 改变c的值,原来的out也会改变
print(c.requires_grad)
print(c)
print(out.requires_grad)
print(out)
print("----------------------------------------------")
 
out.sum().backward()  # 对原来的out求导,
print(a.grad)  # 不会报错,但是结果却并不正确
 
# #输出
# tensor([1., 2., 3.], requires_grad=True)
# tensor([0.7616, 0.9640, 0.9951], grad_fn=<TanhBackward>)
# False
# tensor([0., 0., 0.])
# True
# tensor([0., 0., 0.], grad_fn=<TanhBackward>)
# ----------------------------------------------
# tensor([1., 1., 1.])

————
结果:

tensor([1., 2., 3.], requires_grad=True)
tensor([0.7616, 0.9640, 0.9951], grad_fn=<TanhBackward>)
False
tensor([0., 0., 0.])
True
tensor([0., 0., 0.], grad_fn=<TanhBackward>)
----------------------------------------------
tensor([1., 1., 1.])

5 detach方法

import torch
 
a = torch.tensor([1, 2, 3.], requires_grad=True)
print(a)
out = a.tanh()
print(out)

#需要走注意的是,通过.detach() “分离”得到的的变量会和原来的变量共用同样的数据,
#而且新分离得到的张量是不可求导的,c发生了变化,原来的张量也会发生变化
c = out.detach()  

c.zero_()  # 改变c的值,原来的out也会改变
print(c.requires_grad)
print(c)
print(out.requires_grad)
print(out)
print("----------------------------------------------")
 
out.sum().backward()  # 对原来的out求导,
print(a.grad)  # 此时会报错,错误结果参考下面,显示梯度计算所需要的张量已经被“原位操作inplace”所更改了。

————
结果:

tensor([1., 2., 3.], requires_grad=True)
tensor([0.7616, 0.9640, 0.9951], grad_fn=<TanhBackward>)
False
tensor([0., 0., 0.])
True
tensor([0., 0., 0.], grad_fn=<TanhBackward>)
----------------------------------------------

报错情况:
17 print("----------------------------------------------")
     18 
---> 19 out.sum().backward()  # 对原来的out求导,
     20 print(a.grad)  # 此时会报错,错误结果参考下面,显示梯度计算所需要的张量已经被“原位操作inplace”所更改了。
     21 

6 matmul+cuda方法

import torch

device = 'cuda:0'

a = torch.zeros(2, 3).to(device)
print(type(a))

b = torch.ones(3, 4).to(device)
print(type(b))

c = torch.matmul(a, b)
print(c)

————
结果:

<class 'torch.Tensor'>
<class 'torch.Tensor'>
tensor([[0., 0., 0., 0.],
        [0., 0., 0., 0.]], device='cuda:0')

7 Embedding模型使用

import torch
from torch import nn
embedding1 = nn.Embedding(10, 3)
embedding2 = nn.Embedding(8, 4)
inputs = torch.LongTensor([[1, 2, 4, 5], [4, 3, 2, 9]])
print(inputs)
outputs = embedding1(inputs)
print(outputs)

————
结果:

tensor([[1, 2, 4, 5],
        [4, 3, 2, 9]])
tensor([[[ 2.4182e+00,  5.1324e-01, -2.3636e-01],
         [ 9.6180e-01,  1.3771e+00,  2.2036e+00],
         [ 1.7772e-02,  3.0630e-01, -7.9741e-01],
         [-9.2795e-01,  1.9076e+00,  3.5437e+00]],

        [[ 1.7772e-02,  3.0630e-01, -7.9741e-01],
         [ 1.6840e-03,  5.2214e-01,  7.9724e-01],
         [ 9.6180e-01,  1.3771e+00,  2.2036e+00],
         [ 1.7058e-01,  5.4104e-01,  1.4526e+00]]],
       grad_fn=<EmbeddingBackward>)

8 计算RMSE和MAE

import math
a1 = np.array([0.7, 0.8, 1.2, 1])
a2 = np.array([1, 1, 1, 1])

error = a1 - a2
error

square_error = [i**2 for i in error]
abs_error = [abs(i) for i in error]

#RMSE
math.sqrt(np.array(square_error).sum()/len(square_error))
#MAE
np.array(abs_error).sum()/len(abs_error)

————
结果:

0.206155281280883#RMSE
0.175#MAE
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