6.nn.module

example

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

最简单的神经网络

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：Pytorch_learn 
@File    ：nn.module.py
@IDE     ：PyCharm 
@Author  ：咋
@Date    ：2023/7/2 16:19 
"""
import torch
from torch.nn import Module
import torch.nn as nn
import torch.nn.functional as F

class MyModule(Module):
    def __init__(self):
        super().__init__()
        # self.conv1 = nn.Conv2d(1, 20, 5)
        # self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        # x = F.relu(self.conv1(x))
        return x+1

x = torch.tensor(1) #创建一个tensor1

model = MyModule() # 实例化模型

output = model(x) # callback

print(output)
# print(modle.forward(x))

神经网络实战

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：Pytorch_learn 
@File    ：nn.module.py
@IDE     ：PyCharm 
@Author  ：咋
@Date    ：2023/7/2 16:19 
"""
import torch
from torch.nn import Module
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from torchvision import transforms
class MyModule(Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return x



image_path = "test.jpg"
image = Image.open(image_path).convert("L")
tran_tensor = transforms.ToTensor()
image_tensor = tran_tensor(image)


image_tensor = image_tensor.unsqueeze(0)
print(image_tensor.shape)
model = MyModule() # 实例化模型

output = model(image_tensor) # callback

print(output)
# print(modle.forward(x))