Deep learning framework pytorch rapid development and actual combat chapter3

报错

问题1

The main reason for this is that there are two in the environmentlibiomp5md.dll文件
①General environment will be placedconda文件夹的envs下
②或者是直接cmd下输入以下代码

conda info --envs

Find the corresponding environment and remove the following one

问题2：data[0]报错

The following codes have been corrected！！！

将data[0]改成item（）就可以了

    if (epoch+1) % 5 == 0:
        # 修改后
        print ('Epoch [%d/%d], Loss: %.4f' %(epoch+1, num_epochs, loss.item()))
        #修改前：
        #% (epoch + 1, num_epochs, loss.data[0]))

（一） 线性回归

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable



# Hyper Parameters
input_size = 1
output_size = 1
num_epochs = 1000
learning_rate = 0.001

x_train = np.array([[2.3], [4.4], [3.7], [6.1], [7.3], [2.1],[5.6], [7.7], [8.7], [4.1],

                    [6.7], [6.1], [7.5], [2.1], [7.2],

                    [5.6], [5.7], [7.7], [3.1]], dtype=np.float32)

#xtrain生成矩阵数据

y_train = np.array([[3.7], [4.76], [4.], [7.1], [8.6], [3.5],[5.4], [7.6], [7.9], [5.3],

                    [7.3], [7.5], [8.5], [3.2], [8.7],

                    [6.4], [6.6], [7.9], [5.3]], dtype=np.float32)
plt.figure() 
#draw a scatter plot
plt.scatter(x_train,y_train)
plt.xlabel('x_train')
#x轴名称
plt.ylabel('y_train')
#y轴名称
#显示图片
plt.show()

# Linear Regression Model
class LinearRegression(nn.Module):
    def __init__(self, input_size, output_size):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(input_size, output_size)  
    
    def forward(self, x):
        out = self.linear(x)
        return out

model = LinearRegression(input_size, output_size)

# Loss and Optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  

# Train the Model 
for epoch in range(num_epochs):
    # Convert numpy array to torch Variable
    inputs = Variable(torch.from_numpy(x_train))
    targets = Variable(torch.from_numpy(y_train))

    # Forward + Backward + Optimize
    optimizer.zero_grad()  
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()
    
    if (epoch+1) % 5 == 0:
        # 修改后
        print ('Epoch [%d/%d], Loss: %.4f' %(epoch+1, num_epochs, loss.item()))
        #修改前：
        #% (epoch + 1, num_epochs, loss.data[0]))
        
# Plot the graph



model.eval()
predicted = model(Variable(torch.from_numpy(x_train))).data.numpy()
plt.plot(x_train, y_train, 'ro')
plt.plot(x_train, predicted, label='predict')
plt.legend()
plt.show()

总结一下

1. 准备数据集
2. 定义模型
3. 定义损失函数和优化函数
4. 开始训练

optimizer.zero_grad() #梯度归零
loss.backward() #反向传播
optimizer.step() #更新参数

Basic steps to draw a scatter plot

1. Activate a drawing environment

plt.figure()
To create a graphfigure,Or activate an already existing shapefigure

2. 绘制散点图

plt.scatter()函数用于生成一个scatter散点图

3. x轴,y轴名称
4. 显示图片

模型构建

class LinearRegression(nn.Module): 
		def __init__(self, input_size, output_size):
        super(LinearRegression, self).__init__()

为什么要super（…）.init()
In order to be able to inherit the properties of the parent class after the subclass is initialized

损失函数

nn.MSELoss()

Check out this blogger！！！Common loss functions are introduced

优化函数

Check out this blogger！！！

Variable

Variable的介绍

（二）逻辑回归

import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable


# Hyper Parameters 
input_size = 784
num_classes = 10
num_epochs = 10
batch_size = 50
learning_rate = 0.001

# MNIST Dataset (Images and Labels)
train_dataset = dsets.MNIST(root='./data', 
                            train=True, 
                            transform=transforms.ToTensor(),
                            download=True)

test_dataset = dsets.MNIST(root='./data', 
                           train=False, 
                           transform=transforms.ToTensor())

# Dataset Loader (Input Pipline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
                                           batch_size=batch_size, 
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
                                          batch_size=batch_size, 
                                          shuffle=False)

# Model
class LogisticRegression(nn.Module):
    def __init__(self, input_size, num_classes):
        super(LogisticRegression, self).__init__()
        self.linear = nn.Linear(input_size, num_classes)
    
    def forward(self, x):
        out = self.linear(x)
        return out

model = LogisticRegression(input_size, num_classes)

# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
criterion = nn.CrossEntropyLoss()  
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  

# Training the Model
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = Variable(images.view(-1, 28*28))
        labels = Variable(labels)
        
        # Forward + Backward + Optimize
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        if (i+1) % 100 == 0:
            print ('Epoch: [%d/%d], Step: [%d/%d], Loss: %.4f' 
                   % (epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.item()))

# Test the Model
correct = 0
total = 0
for images, labels in test_loader:
    images = Variable(images.view(-1, 28*28))
    outputs = model(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels).sum()
    
print('Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total))

# Save the Model
torch.save(model.state_dict(), 'model.pkl')

The first download will download the file first and download it to the current file directorydata文件夹中

然后开始训练(大概2-3min)

Let's analyze the code

In fact, the logic is not much different from linear regression,It's nothing more than changing the loss function（replaced by cross entropy）和优化函数（Replaced with stochastic gradient descent）
transforms.ToTensor()：接受PIL Image或numpy.ndarray格式,修改通道顺序,Modify the data type and finally normalize（除255）

参考表述