卷积神经网络基本框架搭建

神经网络

卷积神经网络的实质就是矩阵乘法以及权值共享，我们利用pytorch进行开发。PyTorch的优点比较明显，原因是pytorch安装起来很方便，根据pip命令安装或者conda安装即可，不需要手动安装依赖库。第二个很大的优点便是简洁易用，下面就来介绍一下如何用pytorch搭建简单的卷积神经网络：

卷积层的输出满足这样的表达式：

网络框架：

import torch.nn as nn
import torch.optim
import torch.nn.functional as F
from sklearn.model_selection import train_test_split
import torch.utils
import numpy as np
from torch.utils.data import Dataset, DataLoader, TensorDataset
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=3, out_channels=10, kernel_size=3, stride=2)
        self.max_pool1 = nn.MaxPool1d(kernel_size=3, stride=2)
        self.conv2 = nn.Conv1d(10, 20, 3, 2)
        self.max_pool2 = nn.MaxPool1d(3, 2)
        self.conv3 = nn.Conv1d(20, 40, 3, 2)
        
        self.liner1 = nn.Linear(40 * 4, 120)
        self.liner2 = nn.Linear(120, 84)
        self.liner3 = nn.Linear(84, 4) 
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.max_pool1(x)
        x = F.relu(self.conv2(x))
        x = self.max_pool2(x)
        x = F.relu(self.conv3(x))
#        print(x.shape)
        x = x.view(-1, 40 * 4)
        x = F.relu(self.liner1(x))
        x = F.relu(self.liner2(x))
        x = self.liner3(x)
        return x
           

网络训练：

net = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for epoch in range(100):
    running_loss = 0
    for i, input_data in enumerate(x_train, 0):
        # print(input_data.shape)
        label = y_train[i]
        optimizer.zero_grad()
        
        outputs = net(input_data)
        loss = criterion(outputs, label)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %0.3f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0