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卷积神经网络中nn.Conv2d()和nn.MaxPool2d()以及卷积神经网络实现minist数据集分类

卷积神经网络中nn.Conv2d()和nn.MaxPool2d()

卷积神经网络之Pythorch实现:

nn.Conv2d()就是PyTorch中的卷积模块

参数列表

参数 作用
in_channels 输入数据体的深度
out_channels 输出数 据体的深度
kernel_size 滤波器(卷积核)的大小 注1
stride 滑动的步长
padding 零填充的圈数 注2
bias 是否启用偏置,默认是True,代表启用
groups 输出数据体深度上和输入数 据体深度上的联系 注3
dilation 卷积对于输入数据体的空间间隔 注4

注:1. 可以使用一 个数字来表示高和宽相同的卷积核,比如 kernel_size=3,也可以使用 不同的数字来表示高和宽不同的卷积核,比如 kernel_size=(3, 2);

  1. padding=0表示四周不进行零填充,而 padding=1表示四周进行1个像素点的零填充;

  2. groups表示输出数据体深度上和输入数 据体深度上的联系,默认 groups=1,也就是所有的输出和输入都是相 关联的,如果 groups=2,这表示输入的深度被分割成两份,输出的深 度也被分割成两份,它们之间分别对应起来,所以要求输出和输入都 必须要能被 groups整除。

  3. 默认dilation=1详情见 nn.Conv2d()中dilation参数的作用或者CSDN

nn.MaxPool2d()表示网络中的最大值池化

参数列表:

参数 作用
kernel_size 与上面nn.Conv2d()相同
stride 与上面nn.Conv2d()相同
padding 与上面nn.Conv2d()相同
dilation 与上面nn.Conv2d()相同
return_indices 表示是否返回最大值所处的下标,默认 return_indices=False
ceil_mode 表示使用一些方格代替层结构,默认 ceil_mode=False

注:一般不会去设置return_indicesceil_mode参数

import torch.nn as nn


class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        layer1 = nn.Sequential()
        # 把一个三通道的照片RGB三个使用32组卷积核卷积,每组三个卷积核,组内卷积后相加得出32组输出
        layer1.add_module('conv1', nn.Conv2d(3, 32, (3, 3), (1, 1), padding=1))
        layer1.add_module('relu1', nn.ReLU(True))
        layer1.add_module('pool1', nn.MaxPool2d(2, 2))
        self.layer1 = layer1

        layer2 = nn.Sequential()
        layer2.add_module('conv2', nn.Conv2d(32, 64, (3, 3), (1, 1), padding=1))
        layer2.add_module('relu2', nn.ReLU(True))
        layer2.add_module('pool2', nn.MaxPool2d(2, 2))
        self.layer2 = layer2

        layer3 = nn.Sequential()
        layer3.add_module('conv3', nn.Conv2d(64, 128, (3, 3), (1, 1), padding=1))
        layer3.add_module('relu3', nn.ReLU(True))
        layer3.add_module('pool3', nn.MaxPool2d(2, 2))
        self.layer3 = layer3

        layer4 = nn.Sequential()
        layer4.add_module('fc1', nn.Linear(2048, 512))
        layer4.add_module('fc_relu1', nn.ReLU(True))
        layer4.add_module('fc2', nn.Linear(512, 64))
        layer4.add_module('fc_relu2', nn.ReLU(True))
        layer4.add_module('f3', nn.Linear(64, 10))
        self.layer4 = layer4

    def forward(self, x):
        conv1 = self.layer1(x)
        conv2 = self.layer2(conv1)
        conv3 = self.layer3(conv2)
        fc_input = conv3.view(conv3.size(0), -1)
        fc_out = self.layer4(fc_input)
        return fc_out

model = SimpleCNN()
print(model)

输出

SimpleCNN(
  (layer1): Sequential(
    (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (relu1): ReLU(inplace=True)
    (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (layer2): Sequential(
    (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))
    (relu2): ReLU(inplace=True)
    (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (layer3): Sequential(
    (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (relu3): ReLU(inplace=True)
    (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (layer4): Sequential(
    (fc1): Linear(in_features=2048, out_features=512, bias=True)
    (fc_relu1): ReLU(inplace=True)
    (fc2): Linear(in_features=512, out_features=64, bias=True)
    (fc_relu2): ReLU(inplace=True)
    (f3): Linear(in_features=64, out_features=10, bias=True)
  )
)

提取模型的层级结构

提取层级结构可以使用以下几个nn.Model的属性,第一个是children()属性,它会返回下一级模块的迭代器,在上面这个模型中,它会返回在self.layer1,self.layer2,self.layer4上的迭代器而不会返回它们内部的东西;modules()
会返回模型中所有的模块的迭代器,这样它就能访问到最内层,比如self.layer1.conv1这个模块;还有一个与它们相对应的是name_children()属性以及named_modules(),这两个不仅会返回模块的迭代器,还会返回网络层的名字。

提取出model中的前两层

nn.Sequential(*list(model.children())[:2])

输出:

Sequential(
  (0): Sequential(
    (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (relu1): ReLU(inplace=True)
    (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (1): Sequential(
    (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (relu2): ReLU(inplace=True)
    (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
)

提取出model中的所有卷积层

conv_model = nn.Sequential()
for layer in model.named_modules():
    if isinstance(layer[1], nn.Conv2d):
        conv_model.add_module(layer[0].split('.')[1] ,layer[1])
print(conv_model)

输出:

Sequential(
  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)

提取网络参数并对其初始化

nn.Moudel里面有两个特别重要的关于参数的属性,分别是named_parameters()parameters()。前者会输出网络层的名字和参数的迭代器,后者会给出一个网络的全部参数的迭代器。

for param in model.named_parameters():
    print(param[0])
    # print(param[1])

输出:

layer1.conv1.weight
layer1.conv1.bias
layer2.conv2.weight
layer2.conv2.bias
layer3.conv3.weight
layer3.conv3.bias
layer4.fc1.weight
layer4.fc1.bias
layer4.fc2.weight
layer4.fc2.bias
layer4.f3.weight
layer4.f3.bias

主流神经网络案例分析

案例:使用卷积神经网络实现对Minist数据集的预测

import matplotlib.pyplot as plt
import torch.utils.data
import torchvision.datasets
import os
import torch.nn as nn
from torchvision import transforms


class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=(3, 3)),
            nn.BatchNorm2d(16),
            nn.ReLU(inplace=True),
        )

        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=(3, 3)),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )

        self.layer3 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=(3, 3)),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )

        self.layer4 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=(3, 3)),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.fc = nn.Sequential(
            nn.Linear(128 * 4 * 4, 1024),
            nn.ReLU(inplace=True),
            nn.Linear(1024, 128),
            nn.Linear(128, 10)
        )

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x



os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

data_tf = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize([0.5], [0.5])]
)

train_dataset = torchvision.datasets.MNIST(root='F:/机器学习/pytorch/书/data/mnist', train=True,
                                           transform=data_tf, download=True)

test_dataset = torchvision.datasets.MNIST(root='F:/机器学习/pytorch/书/data/mnist', train=False,
                                          transform=data_tf, download=True)

batch_size = 100
train_loader = torch.utils.data.DataLoader(
    dataset=train_dataset, batch_size=batch_size
)

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

model = CNN()
model = model.cuda()
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.Adam(model.parameters())

# 节约时间,三次够了
iter_step = 3
loss1 = []
loss2 = []
for step in range(iter_step):
    loss1_count = 0
    loss2_count = 0
    for images, labels in train_loader:
        images = images.cuda()
        labels = labels.cuda()
        images = images.reshape(-1, 1, 28, 28)
        output = model(images)
        pred = output.squeeze()

        optimizer.zero_grad()
        loss = criterion(pred, labels)
        loss.backward()
        optimizer.step()

        _, pred = torch.max(pred, 1)

        loss1_count += int(torch.sum(pred == labels)) / 100
# 测试
    else:
        test_loss = 0
        accuracy = 0
        with torch.no_grad():
            for images, labels in test_loader:
                images = images.cuda()
                labels = labels.cuda()
                pred = model(images.reshape(-1, 1, 28, 28))
                _, pred = torch.max(pred, 1)
                loss2_count += int(torch.sum(pred == labels)) / 100

    loss1.append(loss1_count / len(train_loader))
    loss2.append(loss2_count / len(test_loader))

    print(f'第{step}次训练:训练准确率:{loss1[len(loss1)-1]},测试准确率:{loss2[len(loss2)-1]}')

plt.plot(loss1, label='Training loss')
plt.plot(loss2, label='Validation loss')
plt.legend()

输出:

第0次训练:训练准确率:0.9646166666666718,测试准确率:0.9868999999999996
第1次训练:训练准确率:0.9865833333333389,测试准确率:0.9908999999999998
第2次训练:训练准确率:0.9917000000000039,测试准确率:0.9879999999999994
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posted @ 2021-12-03 23:21  [X_O]  阅读(1309)  评论(0编辑  收藏  举报