【pytorch】简单的CNN神经网络

cnn神经网络主要由卷积层，池化层和全连接层构成，下面这段代码就是一个简单的cnn神经网络模型

#coding=utf-8
"""
一个简单的CNN网络模型
"""

#明天写

import torch
from torch import nn

class simpleCNN(nn.Module):
    def __init__(self):
        super(simpleCNN, self).__init__()
        #第一层
        layer1 = nn.Sequential()
        layer1.add_module("conv1", nn.Conv2d(3, 32, 3, 1, padding=1))
        layer1.add_module("relu1", nn.ReLU(True))
        layer1.add_module("pool1", nn.MaxPool2d(2, 2))
        self.layer1 = layer1
        #第二层
        layer2 = nn.Sequential()
        layer2.add_module("conv2", nn.Conv2d(32, 64, 3, 1, padding=1))
        layer2.add_module("relu2", nn.ReLU(True))
        layer2.add_module("pool2", nn.MaxPool2d(2, 2))
        self.layer2 = layer2
        #第三层
        layer3 = nn.Sequential()
        layer3.add_module("conv3", nn.Conv2d(64, 128, 3, 1, padding=1))
        layer3.add_module("relu3", nn.ReLU(True))
        layer3.add_module("pool3", nn.MaxPool2d(2, 2))
        self.layer3 = layer3
        #第四层，全连接层
        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("fc2", 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)

输出结果