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PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

心中带点小风骚 • • Python • 阅读 217

文章目录

本文参加新星计划人工智能(Pytorch)赛道：https://bbs.csdn.net/topics/613989052

一、PyTorch 简介

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

二、PyTorch 软件框架

1. Anaconda 下载

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

2. Anaconda 安装

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

conda --version

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

3. Anaconda Navigator 打不开问题（不适用所有）

4. PyTorch 环境创建

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --set show_channel_urls yes
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/

conda create -n pytorch python=3.7

conda info --envs

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

activate pytorch

5. PyTorch 下载

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

pip3 install torch torchvision torchaudio

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

6. Jupyter 中配置 PyTorch

conda activate pytorch #pytorch3.8 是之前建立的环境名称，可修改为自己建立名称

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

conda install nb_conda

jupyter notbook

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

三、PyTorch 基本使用方法

import torch
torch.__version__
#'1.13.1+cpu'

x = torch.empty(5, 3)
x
#tensor([[8.9082e-39, 9.9184e-39, 8.4490e-39],
#        [9.6429e-39, 1.0653e-38, 1.0469e-38],
#        [4.2246e-39, 1.0378e-38, 9.6429e-39],
#        [9.2755e-39, 9.7346e-39, 1.0745e-38],
#        [1.0102e-38, 9.9184e-39, 6.2342e-19]])

x = torch.rand(5, 3)
x
#tensor([[0.1452, 0.4816, 0.4507],
#        [0.1991, 0.1799, 0.5055],
#        [0.6840, 0.6698, 0.3320],
#        [0.5095, 0.7218, 0.6996],
#        [0.2091, 0.1717, 0.0504]])

x = torch.zeros(5, 3, dtype=torch.long)
x
#tensor([[0, 0, 0],
#        [0, 0, 0],
#        [0, 0, 0],
#        [0, 0, 0],
#        [0, 0, 0]])

x = torch.tensor([5.5, 3])
x
#tensor([5.5000, 3.0000])

x.size()
#torch.Size([5, 3])

x = torch.randn(4, 4)
y = x.view(16)
z = x.view(-1, 8) 
print(x.size(), y.size(), z.size())
#torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])

import numpy as np
a = torch.ones(5)
b = a.numpy()
b
#array([1., 1., 1., 1., 1.], dtype=float32)

import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
b
#tensor([1., 1., 1., 1., 1.], dtype=torch.float64)

四、tensor 的几种形状

import torch
from torch import tensor

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

1. Scalar（标量）

x = tensor(42.)
x
#tensor(42.)

x.dim()
#0

x.item()
#42.0

2. Vector（向量）

v = tensor([1.5, -0.5, 3.0])
v
#tensor([ 1.5000, -0.5000,  3.0000])

v.dim()
#1

v.size()
#torch.Size([3])

3. Matrix（矩阵）

M = tensor([[1., 2.], [3., 4.]])
M
#tensor([[1., 2.],
#        [3., 4.]])

M.matmul(M)
#tensor([[ 7., 10.],
#        [15., 22.]])

M * M
#tensor([[ 1.,  4.],
#        [ 9., 16.]])

五、PyTorch 的 autograd 机制

1. autograd 机制

x = torch.randn(3,4,requires_grad=True)
x
#tensor([[-0.4847,  0.7512, -1.0082,  2.2007],
#        [ 1.0067,  0.3669, -1.5128, -1.3823],
#        [ 0.8001, -1.6713,  0.0755,  0.9826]], requires_grad=True)

x = torch.randn(3,4)
x.requires_grad=True
x
#tensor([[ 0.6438,  0.4278,  0.8278, -0.1493],
#        [-0.8396,  1.3533,  0.6111,  1.8616],
#        [-1.0954,  1.8096,  1.3869, -1.7984]], requires_grad=True)

b = torch.randn(3,4,requires_grad=True)
t = x + b
y = t.sum()
y
#tensor(7.9532, grad_fn=<SumBackward0>)

y.backward()
b.grad
#tensor([[1., 1., 1., 1.],
#        [1., 1., 1., 1.],
#        [1., 1., 1., 1.]])

x.requires_grad, b.requires_grad, t.requires_grad
#(True, True, True)

2. 举例说明

PyTorch 之 简介、相关软件框架、基本使用方法、tensor 的几种形状和 autograd 机制

x = torch.rand(1)
b = torch.rand(1, requires_grad = True)
w = torch.rand(1, requires_grad = True)
y = w * x 
z = y + b 

x.requires_grad, b.requires_grad, w.requires_grad, y.requires_grad#注意y也是需要的
#(False, True, True, True)

x.is_leaf, w.is_leaf, b.is_leaf, y.is_leaf, z.is_leaf
#(True, True, True, False, False)

z.backward(retain_graph=True)#如果不清空会累加起来

w.grad
#tensor([0.7954])

b.grad
#tensor([1.])

3. 一个简单的线性回归模型

x_values = [i for i in range(11)]
x_train = np.array(x_values, dtype=np.float32)
x_train = x_train.reshape(-1, 1)
x_train.shape
#(11, 1)

y_values = [2*i + 1 for i in x_values]
y_train = np.array(y_values, dtype=np.float32)
y_train = y_train.reshape(-1, 1)
y_train.shape
#(11, 1)

import torch
import torch.nn as nn

class LinearRegressionModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinearRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, output_dim)  
​
    def forward(self, x):
        out = self.linear(x)
        return out
input_dim = 1
output_dim = 1
​
model = LinearRegressionModel(input_dim, output_dim)

model
#LinearRegressionModel(
#  (linear): Linear(in_features=1, out_features=1, bias=True)
#)

epochs = 1000
learning_rate = 0.01
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()

for epoch in range(epochs):
    epoch += 1
    # 注意转行成tensor
    inputs = torch.from_numpy(x_train)
    labels = torch.from_numpy(y_train)
​
    # 梯度要清零每一次迭代
    optimizer.zero_grad() 
​
    # 前向传播
    outputs = model(inputs)
​
    # 计算损失
    loss = criterion(outputs, labels)
​
    # 返向传播
    loss.backward()
​
    # 更新权重参数
    optimizer.step()
    if epoch % 50 == 0:
        print('epoch {}, loss {}'.format(epoch, loss.item()))
#epoch 50, loss 0.22157077491283417
#epoch 100, loss 0.12637567520141602
#epoch 150, loss 0.07208002358675003
#epoch 200, loss 0.04111171141266823
#epoch 250, loss 0.023448562249541283
#epoch 300, loss 0.01337424572557211
#epoch 350, loss 0.007628156337887049
#epoch 400, loss 0.004350822884589434
#epoch 450, loss 0.0024815555661916733
#epoch 500, loss 0.0014153871452435851
#epoch 550, loss 0.000807293108664453
#epoch 600, loss 0.00046044986811466515
#epoch 650, loss 0.00026261876337230206
#epoch 700, loss 0.0001497901976108551
#epoch 750, loss 8.543623698642477e-05
#epoch 800, loss 4.8729089030530304e-05
#epoch 900, loss 1.58514467329951e-05
#epoch 950, loss 9.042541933013126e-06
#epoch 1000, loss 5.158052317710826e-06

predicted = model(torch.from_numpy(x_train).requires_grad_()).data.numpy()
predicted
#array([[ 0.9957756],
#       [ 2.9963837],
#       [ 4.996992 ],
#       [ 6.9976   ],
#       [ 8.998208 ],
#       [10.9988165],
#       [12.999424 ],
#       [15.000032 ],
#       [17.00064  ],
#       [19.00125  ],
#       [21.001858 ]], dtype=float32)

torch.save(model.state_dict(), 'model.pkl')
model.load_state_dict(torch.load('model.pkl'))
#<All keys matched successfully>

import torch
import torch.nn as nn
import numpy as np
​​
class LinearRegressionModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinearRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, output_dim)  
​
    def forward(self, x):
        out = self.linear(x)
        return out
​
input_dim = 1
output_dim = 1
​
model = LinearRegressionModel(input_dim, output_dim)​
​
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
​
​criterion = nn.MSELoss()
​
​learning_rate = 0.01
​
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
​
epochs = 1000
for epoch in range(epochs):
    epoch += 1
    inputs = torch.from_numpy(x_train).to(device)
    labels = torch.from_numpy(y_train).to(device)
​
    optimizer.zero_grad() 
​
    outputs = model(inputs)
​
    loss = criterion(outputs, labels)
​
    loss.backward()
​
    optimizer.step()
​
    if epoch % 50 == 0:
        print('epoch {}, loss {}'.format(epoch, loss.item()))
#epoch 50, loss 0.057580433785915375
#epoch 100, loss 0.03284168243408203
#epoch 150, loss 0.01873171515762806
#epoch 200, loss 0.010683886706829071
#epoch 250, loss 0.006093675270676613
#epoch 300, loss 0.0034756092354655266
#epoch 350, loss 0.0019823340699076653
#epoch 400, loss 0.0011306683300063014
#epoch 450, loss 0.0006449012435041368
#epoch 500, loss 0.0003678193606901914
#epoch 550, loss 0.0002097855758620426
#epoch 600, loss 0.00011965946032432839
#epoch 650, loss 6.825226591899991e-05
#epoch 700, loss 3.892400854965672e-05
#epoch 750, loss 2.2203324988367967e-05
#epoch 800, loss 1.2662595509027597e-05
#epoch 850, loss 7.223141892609419e-06
#epoch 900, loss 4.118806373298867e-06
#epoch 950, loss 2.349547230551252e-06
#epoch 1000, loss 1.3400465377344517e-06

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