Pytorch-基础入门之CNN

CNN卷积神经网络现在大家已经耳熟能详，在这里不做太多介绍。本次实验的模型使用两层卷积层。

这一块的话与上一篇逻辑斯蒂回归使用的是相同的数据集MNIST。

第一部分：构造模型

# Import Libraries
import torch
import torch.nn as nn
from torch.autograd import Variable

# Create CNN Model
class CNNModel(nn.Module):
    def __init__(self):
        super(CNNModel, self).__init__()
        
        # Convolution 1
        self.cnn1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=0)
        self.relu1 = nn.ReLU()
        
        # Max pool 1
        self.maxpool1 = nn.MaxPool2d(kernel_size=2)
     
        # Convolution 2
        self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=0)
        self.relu2 = nn.ReLU()
        
        # Max pool 2
        self.maxpool2 = nn.MaxPool2d(kernel_size=2)
        
        # Fully connected 1
        self.fc1 = nn.Linear(32 * 4 * 4, 10) 
    
    def forward(self, x):
        # Convolution 1
        out = self.cnn1(x)
        out = self.relu1(out)
        
        # Max pool 1
        out = self.maxpool1(out)
        
        # Convolution 2 
        out = self.cnn2(out)
        out = self.relu2(out)
        
        # Max pool 2 
        out = self.maxpool2(out)
        
        # flatten
        out = out.view(out.size(0), -1)

        # Linear function (readout)
        out = self.fc1(out)
        
        return out

# batch_size, epoch and iteration
batch_size = 100
n_iters = 2500
num_epochs = n_iters / (len(features_train) / batch_size)
num_epochs = int(num_epochs)

# Pytorch train and test sets
train = torch.utils.data.TensorDataset(featuresTrain,targetsTrain)
test = torch.utils.data.TensorDataset(featuresTest,targetsTest)

# data loader
train_loader = torch.utils.data.DataLoader(train, batch_size = batch_size, shuffle = False)
test_loader = torch.utils.data.DataLoader(test, batch_size = batch_size, shuffle = False)
    
# Create CNN
model = CNNModel()

# Cross Entropy Loss 
error = nn.CrossEntropyLoss()

# SGD Optimizer
learning_rate = 0.1
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

  第二部分：训练模型

# CNN model training
count = 0
loss_list = []
iteration_list = []
accuracy_list = []
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        
        train = Variable(images.view(100,1,28,28))
        labels = Variable(labels)
        
        # Clear gradients
        optimizer.zero_grad()
        
        # Forward propagation
        outputs = model(train)
        
        # Calculate softmax and ross entropy loss
        loss = error(outputs, labels)
        
        # Calculating gradients
        loss.backward()
        
        # Update parameters
        optimizer.step()
        
        count += 1
        
        if count % 50 == 0:
            # Calculate Accuracy         
            correct = 0
            total = 0
            # Iterate through test dataset
            for images, labels in test_loader:
                
                test = Variable(images.view(100,1,28,28))
                
                # Forward propagation
                outputs = model(test)
                
                # Get predictions from the maximum value
                predicted = torch.max(outputs.data, 1)[1]
                
                # Total number of labels
                total += len(labels)
                
                correct += (predicted == labels).sum()
            
            accuracy = 100 * correct / float(total)
            
            # store loss and iteration
            loss_list.append(loss.data)
            iteration_list.append(count)
            accuracy_list.append(accuracy)
        if count % 500 == 0:
            # Print Loss
            print('Iteration: {}  Loss: {}  Accuracy: {} %'.format(count, loss.data, accuracy))

结果：

Iteration: 500  Loss: 0.11975778639316559  Accuracy: 96 %
Iteration: 1000  Loss: 0.04979655146598816  Accuracy: 97 %
Iteration: 1500  Loss: 0.03124646656215191  Accuracy: 97 %
Iteration: 2000  Loss: 0.013534960336983204  Accuracy: 98 %

 第三部分：可视化展示

# visualization loss 
plt.plot(iteration_list,loss_list)
plt.xlabel("Number of iteration")
plt.ylabel("Loss")
plt.title("CNN: Loss vs Number of iteration")
plt.show()

# visualization accuracy 
plt.plot(iteration_list,accuracy_list,color = "red")
plt.xlabel("Number of iteration")
plt.ylabel("Accuracy")
plt.title("CNN: Accuracy vs Number of iteration")
plt.show()

 结果：