"""
    在神经网络中加入 损失值。
"""
import torchvision
from torch import nn

from torch.nn import Conv2d, MaxPool2d, ReLU, Sigmoid, Linear, Flatten, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter("logs/016")

dataset = torchvision.datasets.CIFAR10(root="./visionData", train=False, transform=torchvision.transforms.ToTensor(),
                                       download=True)
dataloader = DataLoader(dataset, batch_size=1)


# Sequential。使代码更加简洁
class MyNnSe(nn.Module):
    def __init__(self):
        super(MyNnSe, 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

loss = nn.CrossEntropyLoss()
mySeNn = MyNnSe()
for data in dataloader:
    imgs, tagets = data
    outputs = mySeNn(imgs)
    result_loss = loss(outputs, tagets)

    # 计算梯度，用于优化器（optim）反向调节。（调节网络结构中的卷积权重）
    result_loss.backward()

    print(result_loss)


writer.close()