搭建小实战和Sequential使用

# 1.神经网络
# 把网络结构放在Sequential里面，好处就是代码写起来比较简介、易懂。
# 可以根据神经网络每层的尺寸，计算出神经网络中的参数。
import torch
import torchvision
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter


# 2.搭建神经网络
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 5, padding=2)
        self.maxpool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(32, 32, 5, padding=2)
        self.maxpool2 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(32, 64, 5, padding=2)
        self.maxpool3 = nn.MaxPool2d(2)
        self.flatten = nn.Flatten()
        self.linear1 = nn.Linear(1024, 64)
        self.linear2 = nn.Linear(64, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.maxpool3(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.linear2(x)
        return x


model = MyModel()
print(model)

# 3.神经网络输入数据
model1 = MyModel()
input = torch.ones((64, 3, 32, 32))
output = model1(input)
print(output.shape)


# 4.Sequential神经网络
class MyModule(nn.Module):
    def __init__(self):
        super(MyModule, self).__init__()
        self.model1 = Sequential(
            Conv2d(3, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):
        x = self.model1(x)
        return x


model_1 = MyModule()
input = torch.ones((64, 3, 32, 32))
output = model_1(input)
print(output.shape)

# 5.Tensorboard显示网络
dataset = torchvision.datasets.CIFAR10(root='D:\PyCharm\CIFAR10', train=False,
                                       transform=torchvision.transforms.ToTensor())
dataloader = DataLoader(dataset, batch_size=64, drop_last=True)


class MyModel_1(nn.Module):
    def __init__(self):
        super(MyModel_1, self).__init__()
        self.model2 = Sequential(
            Conv2d(3, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):
        x = self.model2(x)
        return x

model_2 = MyModel_1()
writer = SummaryWriter("logs14")

model_2 = MyModel_1()
input = torch.ones((64, 3, 32, 32))
output = model_2(input)
print(output.shape)

writer.add_graph(model_2, input)
writer.close()