"""
    损失函数
        # L1Loss:
        # MSELoss:
        # CrossEntropyLoss:

"""
import torch
import torchvision

from torch.nn import Conv2d, MaxPool2d, ReLU, Sigmoid, Linear, Flatten, Sequential, L1Loss
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch import nn

writer = SummaryWriter("logs/015")

dataset = torchvision.datasets.CIFAR10(root="./visionData", train=False, transform=torchvision.transforms.ToTensor(),
                                       download=True)
dataloader = DataLoader(dataset, batch_size=64)

# 输入（预测值）
inputs = torch.tensor([1, 2, 3], dtype=torch.float32)
# 目标（真实值）
targets = torch.tensor([1, 2, 5], dtype=torch.float32)

inputs = torch.reshape(inputs, (1, 1, 1, 3))
targets = torch.reshape(targets, (1, 1, 1, 3))

# 1 L1Loss
loss = L1Loss(reduction='sum')
result = loss(inputs, targets)
print(result)

# 2 MSELoss
loss_mse = nn.MSELoss()
result_mse = loss_mse(inputs, targets)
print(result_mse)

# 3 CrossEntropyLoss
x = torch.tensor([0.1, 0.2, 0.3])
x_re = torch.reshape(x, (1, 3))
y = torch.tensor([1])  # target
loss_cross = nn.CrossEntropyLoss()
result_cross = loss_cross(x_re, y)
print(result_cross)

writer.close()