Pytorch deep learning practice lesson 10 / assignment (basic CNN)

One 、1. Model structures,  

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F  #  use Relu function 
import torch.optim as optim  #  Optimizer optimization 

batch_size = 64
# transform Preprocessing , Transform image into image tensor 
'''ToTensor： Will a ’PIL Image‘or‘numpy.ndarray’ Turn into tensor Format ,PIL Image or numpy.ndarray Of shape by (H x W x C), The scope is [0, 255]
    Turn into shape by (C x H x W) The scope is [0.0, 1.0]'''

'''Normalize： Standardized functions , Use the mean and standard deviation to tensor Standardize 
   output[channel] = (input[channel] - mean[channel]) / std[channel]
'''
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])

# mnist The data set is 1*28*28 A single channel image of 
# ./ Represents the current directory   ../ Represents the parent directory   / Represents the root directory 
train_dataset = datasets.MNIST(root='../dataset/mnist',
                               train=True,
                               download=True,
                               transform=transform)  #  Training data set 
train_loader = DataLoader(train_dataset,
                          shuffle=True,
                          batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(test_dataset,
                         shuffle=False,
                         batch_size=batch_size)


class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc = torch.nn.Linear(320, 10)

    def forward(self, x):
        x = self.pooling(F.relu(self.conv1(x)))
        x = self.pooling(F.relu(self.conv2(x)))
        #  here x The dimension is 4 Of tensor, namely (batchsize,C,H,W),x.size(0) finger batchsize Value 
        # Flatten Layer is used to input “ Flatten ”, That is, the multidimensional input is unidimensional , It is often used in the transition from convolution layer to fully connected layer .
        x = x.view(x.size(0), -1) # -1 It's automatic calculation , by C*H*W
        x = self.fc(x)  # x Apply fully connected network 
        return x

model = Net()

# Migrate the model to GPU On 
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
#  Because the network model is already a little big , Therefore, a better optimization algorithm should be used in gradient descent , For example, with impulse （momentum）, To optimize the training process 


#  Encapsulate a loop into a function 
def train(epoch):
    running_loss = 0.0
    #  By function enumerate Return each batch of data data, And index index(batch_idx), because start=0 therefore index from 0 Start 
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, targets = data  #  Divide the data into images and corresponding labels 
        inputs, targets = inputs.to(device), targets.to(device)  #  Migrate to GPU On 
        optimizer.zero_grad()  #  Clear the historical loss gradient 
        # forward
        y_pred = model(inputs)  #  Input the training pictures into the network and get the output 
        # backward
        loss = criterion(y_pred, targets)
        loss.backward()
        # update
        optimizer.step()  #  Parameters are updated 
        running_loss += loss.item()
        if batch_idx % 300 == 299:  #  Every time 300 individual mini-batches Print once 
            print('[%d,%5d] loss：%.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0.0


def test():
    correct = 0  #  How much is correct 
    total = 0  #  How much is the total 
    with torch.no_grad():  #  The test doesn't count as a gradient 
        for data in test_loader:  #  from test_loader Get data 
            images, labels = data # Divide the data into images and corresponding labels 
            images, labels = images.to(device), labels.to(device) # Migrate to GPU On 
            outputs = model(images)  #  Get the data and make predictions 
            _, predicted = torch.max(outputs.data, dim=1)  #  Find the subscript of the maximum value along the first dimension , There are two return values , the reason being that 10 Come on , Return value 
            #  The return value is the maximum value of each row , The other is the subscript of the maximum （ Each sample is a line , Every line has 10 Quantity ）（ Line No 0 Dimensions , Column is number 1 Dimensions ）
            total += labels.size(0)  #  take size The first of a tuple 0 Elements （N,1）, After accumulation, the total number of test samples can be obtained 
            #  Inferred classification and label Whether it is equal or not , It's just 1, Fake is 0, After summing, take out the scalar , After the cycle is completed, the correct number of samples can be predicted 
            correct += (predicted == labels).sum().item()
    print("accuracy on test set:%d %% [%d/%d]" % (100 * correct / total, correct, total))


#  Training 
if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()  #  A round of training , Test round 

2. Result display

  Two 、1. Model structures, （ deepen ）

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F  #  use Relu function 
import torch.optim as optim  #  Optimizer optimization 

batch_size = 64
# transform Preprocessing , Transform image into image tensor 
'''ToTensor： Will a ’PIL Image‘or‘numpy.ndarray’ Turn into tensor Format ,PIL Image or numpy.ndarray Of shape by (H x W x C), The scope is [0, 255]
    Turn into shape by (C x H x W) The scope is [0.0, 1.0]'''

'''Normalize： Standardized functions , Use the mean and standard deviation to tensor Standardize 
   output[channel] = (input[channel] - mean[channel]) / std[channel]
'''
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])

# mnist The data set is 1*28*28 A single channel image of 
# ./ Represents the current directory   ../ Represents the parent directory   / Represents the root directory 
train_dataset = datasets.MNIST(root='../dataset/mnist',
                               train=True,
                               download=True,
                               transform=transform)  #  Training data set 
train_loader = DataLoader(train_dataset,
                          shuffle=True,
                          batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(test_dataset,
                         shuffle=False,
                         batch_size=batch_size)


class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=3)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=3, padding=1)
        self.conv3 = torch.nn.Conv2d(20, 32, kernel_size=3)
        self.pooling = torch.nn.MaxPool2d(2)
        self.relu = torch.nn.ReLU()
        self.fc1 = torch.nn.Linear(128, 64)
        self.fc2 = torch.nn.Linear(64, 32)
        self.fc3 = torch.nn.Linear(32, 10)

    def forward(self, x):
        # batch_size = x.size(0)
        x = self.pooling(self.relu(self.conv1(x)))
        x = self.pooling(self.relu(self.conv2(x)))
        x = self.pooling(self.relu(self.conv3(x)))
        #  here x The dimension is 4 Of tensor, namely (batchsize,C,H,W),x.size(0) finger batchsize Value 
        # Flatten Layer is used to input “ Flatten ”, That is, the multidimensional input is unidimensional , It is often used in the transition from convolution layer to fully connected layer .
        x = x.view(x.size(0), -1)
        x = self.fc1(x)  # x Apply fully connected network 
        x = self.fc2(x)
        x = self.fc3(x)
        return x

model = Net()

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
#  Because the network model is already a little big , Therefore, a better optimization algorithm should be used in gradient descent , For example, with impulse （momentum）, To optimize the training process 


#  Encapsulate a loop into a function 
def train(epoch):
    running_loss = 0.0
    #  By function enumerate Return each batch of data data, And index index(batch_idx), because start=0 therefore index from 0 Start 
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, targets = data  #  Divide the data into images and corresponding labels 
        inputs, targets = inputs.to(device), targets.to(device)  #  Migrate to GPU On 
        optimizer.zero_grad()  #  Clear the historical loss gradient 
        # forward
        y_pred = model(inputs)  #  Input the training pictures into the network and get the output 
        # backward
        loss = criterion(y_pred, targets)
        loss.backward()
        # update
        optimizer.step()  #  Parameters are updated 
        running_loss += loss.item()
        if batch_idx % 300 == 299:  #  Every time 300 individual mini-batches Print once 
            print('[%d,%5d] loss：%.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0.0


def test():
    correct = 0  #  How much is correct 
    total = 0  #  How much is the total 
    with torch.no_grad():  #  The test doesn't count as a gradient 
        for data in test_loader:  #  from test_loader Get data 
            images, labels = data # Divide the data into images and corresponding labels 
            images, labels = images.to(device), labels.to(device) # Migrate to GPU On 
            outputs = model(images)  #  Get the data and make predictions 
            _, predicted = torch.max(outputs.data, dim=1)  #  Find the subscript of the maximum value along the first dimension , There are two return values , the reason being that 10 Come on , Return value 
            #  The return value is the maximum value of each row , The other is the subscript of the maximum （ Each sample is a line , Every line has 10 Quantity ）（ Line No 0 Dimensions , Column is number 1 Dimensions ）
            total += labels.size(0)  #  take size The first of a tuple 0 Elements （N,1）, After accumulation, the total number of test samples can be obtained 
            #  Inferred classification and label Whether it is equal or not , It's just 1, Fake is 0, After summing, take out the scalar , After the cycle is completed, the correct number of samples can be predicted 
            correct += (predicted == labels).sum().item()
    print("accuracy on test set:%d %% [%d/%d]" % (100 * correct / total, correct, total))


#  Training 
if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()  #  A round of training , Test round 

2. Result display

reference

 《PyTorch Deep learning practice 》 Complete the collection _ Bili, Bili _bilibili