[人工智能] pytorch 深度学习实践第10讲卷积神经网络(基础篇)

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[人工智能]pytorch 深度学习实践第10讲卷积神经网络(基础篇)_代码

pytorch学习视频——B站视频链接：《PyTorch深度学习实践》完结合集_哔哩哔哩_bilibili

视频中所涉及的代码如下：

basic_cnn.py 一个简单的卷积层
padding.py 卷积层参数——padding填充
stride.py 参数——步长
maxpooling.py 下采样——最大池化
cnn.py 一个简单的卷积神经网络
cnn_job.py 作业——修改网络结构

1. basic_cnn.py 一个简单的卷积层

? 示范卷积层所做的工作

import torch
in_channels, out_channels = 5, 10
width, height = 100, 100
kernel_size = 3
batch_size = 1

# 生成一个服从正态分布的随机数 4维张量
input = torch.randn(batch_size,
                    in_channels,
                    width,
                    height)
conv_layer = torch.nn.Conv2d(in_channels,
                             out_channels,
                             kernel_size=kernel_size)

output = conv_layer(input)

print(input.shape)
print(output.shape)
print(conv_layer.weight.shape)

# 输出结果：
# torch.Size([1, 5, 100, 100])
# torch.Size([1, 10, 98, 98])
# torch.Size([10, 5, 3, 3])

print(output)

2. padding.py 卷积层参数——padding填充

在原图像周围填充0，使输出与输入图像的高度和宽度一样

import torch

input = [3, 4, 6, 5, 7,
         2, 4, 6, 8, 2,
         1, 6, 7, 8, 4,
         9, 7, 4, 6, 2,
         3, 7, 5, 4, 1]
#               (1, 1, 5, 5)——>(batch, channel, width, height)
input = torch.Tensor(input).view(1, 1, 5, 5)
conv_layer = torch.nn.Conv2d(1, 1, kernel_size=3, padding=1, bias=False)

#                (1, 1, 3, 3)——>(output, input, kernel_width, kernel_height)
kernel = torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9]).view(1, 1, 3, 3)
# 卷积层权重初始化
conv_layer.weight.data = kernel.data

output = conv_layer(input)
print(output)

3. stride.py 参数——步长

卷积核移动的速度

import torch

input = [3, 4, 6, 5, 7,
         2, 4, 6, 8, 2,
         1, 6, 7, 8, 4,
         9, 7, 4, 6, 2,
         3, 7, 5, 4, 1]
#               (1, 1, 5, 5)——>(batch, channel, width, height)
input = torch.Tensor(input).view(1, 1, 5, 5)
conv_layer = torch.nn.Conv2d(1, 1, kernel_size=3, stride=2, bias=False)

#                (1, 1, 3, 3)——>(output, input, kernel_width, kernel_height)
kernel = torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9]).view(1, 1, 3, 3)
# 卷积层权重初始化
conv_layer.weight.data = kernel.data

output = conv_layer(input)
print(output)

4. maxpooling.py 下采样——最大池化

取一个池化窗口中权重最大的值保存下来

import torch

input = [3, 4, 6, 5,
         2, 4, 6, 8,
         1, 6, 7, 8,
         9, 7, 4, 6]

input = torch.Tensor(input).view(1, 1, 4, 4)
#                                  步长stride也默认等于2
maxpooling_layer = torch.nn.MaxPool2d(kernel_size=2)
output = maxpooling_layer(input)
print(output)

5. cnn.py 一个简单的卷积神经网络

使用的mnist数据集进行训练和测试，与第9将中的代码一样，只需要更换模型定义

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt

batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307, ), (0.3081, ))
])

train_dataset = datasets.MNIST(root='../dataset/mnist',
                               train=True,
                               download=True,
                               # 指定数据用transform来处理
                               transform=transform)
train_loader = DataLoader(dataset=train_dataset,
                          batch_size=batch_size,
                          shuffle=True)

test_dataset = datasets.MNIST(root='../dataset/mnist',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(dataset=test_dataset,
                         batch_size=batch_size,
                         shuffle=False)


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的第0维就是batch_size
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size, -1)
        x = self.fc(x)
        return x


model = Net()
# 将模型迁移到GPU上运行，cuda:0表示第0块显卡
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(torch.cuda.is_available())
model.to(device)

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)


# 将训练和测试过程分别封装在两个函数当中
def train(epoch):
    running_loss = 0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        # 将要计算的张量也迁移到GPU上——输入和输出
        inputs, target = inputs.to(device), target.to(device)
        optimizer.zero_grad()

        # 前馈 反馈 更新
        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0

accuracy = []
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            # 测试中的张量也迁移到GPU上
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, dim=1)
            total += labels.size(0)
            # ？？怎么比较的相等
            # print(predicted)
            # print(labels)
            # print('predicted == labels', predicted == labels)
            # 两个张量比较，得出的是其中相等的元素的个数（即一个批次中预测正确的个数）
            correct += (predicted == labels).sum().item()
            # print('correct______', correct)
    print('Accuracy on test  set: %d %%' % (100 * correct / total))
    accuracy.append(100 * correct / total)


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()
    print(accuracy)
    plt.plot(range(10), accuracy)
    plt.xlabel("epoch")
    plt.ylabel("Accuracy")
    plt.show()

结果：

6. cnn_job.py 作业——修改网络结构

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt

batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307, ), (0.3081, ))
])

train_dataset = datasets.MNIST(root='../dataset/mnist',
                               train=True,
                               download=True,
                               # 指定数据用transform来处理
                               transform=transform)
train_loader = DataLoader(dataset=train_dataset,
                          batch_size=batch_size,
                          shuffle=True)

test_dataset = datasets.MNIST(root='../dataset/mnist',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(dataset=test_dataset,
                         batch_size=batch_size,
                         shuffle=False)


class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # 模型修改如下（随便改的）
        self.conv1 = torch.nn.Conv2d(1, 16, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(16, 32, kernel_size=5)
        self.conv3 = torch.nn.Conv2d(32, 64, kernel_size=3)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc1 = torch.nn.Linear(64, 32)
        self.fc2 = torch.nn.Linear(32, 10)

    def forward(self, x):
        # x的第0维就是batch_size
        batch_size = x.size(0)
        x = self.pooling(F.relu((self.conv1(x))))
        x = self.pooling(F.relu((self.conv2(x))))
        x = self.pooling(F.relu((self.conv3(x))))
        x = x.view(batch_size, -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x


model = Net()
# 将模型迁移到GPU上运行，cuda:0表示第0块显卡
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(torch.cuda.is_available())
model.to(device)

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)


# 将训练和测试过程分别封装在两个函数当中
def train(epoch):
    running_loss = 0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        # 将要计算的张量也迁移到GPU上——输入和输出
        inputs, target = inputs.to(device), target.to(device)
        optimizer.zero_grad()

        # 前馈 反馈 更新
        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0

accuracy = []
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            # 测试中的张量也迁移到GPU上
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, dim=1)
            total += labels.size(0)
            # ？？怎么比较的相等
            # print(predicted)
            # print(labels)
            # print('predicted == labels', predicted == labels)
            # 两个张量比较，得出的是其中相等的元素的个数（即一个批次中预测正确的个数）
            correct += (predicted == labels).sum().item()
            # print('correct______', correct)
    print('Accuracy on test  set: %d %%' % (100 * correct / total))
    accuracy.append(100 * correct / total)


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()
    print(accuracy)
    plt.plot(range(10), accuracy)
    plt.xlabel("epoch")
    plt.ylabel("Accuracy")
    plt.show()