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卷积网络中的输入和层与传统神经网络有些区别#xff0c;需重新设计#xff0c;训练模块基本一致 import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets,transforms
impor…构建卷积神经网络
卷积网络中的输入和层与传统神经网络有些区别需重新设计训练模块基本一致 import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets,transforms
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline 首先读取数据 分别构建训练集和测试集验证集DataLoader来迭代取数据 # 定义超参数
input_size 28 #图像的总尺寸28*28
num_classes 10 #标签的种类数
num_epochs 3 #训练的总循环周期
batch_size 64 #一个撮批次的大小64张图片# 训练集
train_dataset datasets.MNIST(root./data, trainTrue, transformtransforms.ToTensor(), downloadTrue) # 测试集
test_dataset datasets.MNIST(root./data, trainFalse, transformtransforms.ToTensor())# 构建batch数据
train_loader torch.utils.data.DataLoader(datasettrain_dataset, batch_sizebatch_size, shuffleTrue)
test_loader torch.utils.data.DataLoader(datasettest_dataset, batch_sizebatch_size, shuffleTrue) 卷积网络模块构建 一般卷积层relu层池化层可以写成一个套餐注意卷积最后结果还是一个特征图需要把图转换成向量才能做分类或者回归任务 class CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.conv1 nn.Sequential( # 输入大小 (1, 28, 28)nn.Conv2d(in_channels1, # 灰度图out_channels16, # 要得到几多少个特征图kernel_size5, # 卷积核大小stride1, # 步长padding2, # 如果希望卷积后大小跟原来一样需要设置padding(kernel_size-1)/2 if stride1), # 输出的特征图为 (16, 28, 28)nn.ReLU(), # relu层nn.MaxPool2d(kernel_size2), # 进行池化操作2x2 区域, 输出结果为 (16, 14, 14))self.conv2 nn.Sequential( # 下一个套餐的输入 (16, 14, 14)nn.Conv2d(16, 32, 5, 1, 2), # 输出 (32, 14, 14)nn.ReLU(), # relu层nn.Conv2d(32, 32, 5, 1, 2),nn.ReLU(),nn.MaxPool2d(2), # 输出 (32, 7, 7))self.conv3 nn.Sequential( # 下一个套餐的输入 (16, 14, 14)nn.Conv2d(32, 64, 5, 1, 2), # 输出 (32, 14, 14)nn.ReLU(), # 输出 (32, 7, 7))self.out nn.Linear(64 * 7 * 7, 10) # 全连接层得到的结果def forward(self, x):x self.conv1(x)x self.conv2(x)x self.conv3(x)x x.view(x.size(0), -1) # flatten操作结果为(batch_size, 32 * 7 * 7)output self.out(x)return output 准确率作为评估标准 def accuracy(predictions, labels):pred torch.max(predictions.data, 1)[1] rights pred.eq(labels.data.view_as(pred)).sum() return rights, len(labels) 训练网络模型 # 实例化
net CNN()
#损失函数
criterion nn.CrossEntropyLoss()
#优化器
optimizer optim.Adam(net.parameters(), lr0.001) #定义优化器普通的随机梯度下降算法#开始训练循环
for epoch in range(num_epochs):#当前epoch的结果保存下来train_rights [] for batch_idx, (data, target) in enumerate(train_loader): #针对容器中的每一个批进行循环net.train() output net(data) loss criterion(output, target) optimizer.zero_grad() loss.backward() optimizer.step() right accuracy(output, target) train_rights.append(right) if batch_idx % 100 0: net.eval() val_rights [] for (data, target) in test_loader:output net(data) right accuracy(output, target) val_rights.append(right)#准确率计算train_r (sum([tup[0] for tup in train_rights]), sum([tup[1] for tup in train_rights]))val_r (sum([tup[0] for tup in val_rights]), sum([tup[1] for tup in val_rights]))print(当前epoch: {} [{}/{} ({:.0f}%)]\t损失: {:.6f}\t训练集准确率: {:.2f}%\t测试集正确率: {:.2f}%.format(epoch, batch_idx * batch_size, len(train_loader.dataset),100. * batch_idx / len(train_loader), loss.data, 100. * train_r[0].numpy() / train_r[1], 100. * val_r[0].numpy() / val_r[1])) 当前epoch: 0 [0/60000 (0%)] 损失: 2.300918 训练集准确率: 10.94% 测试集正确率: 10.10%
当前epoch: 0 [6400/60000 (11%)] 损失: 0.204191 训练集准确率: 78.06% 测试集正确率: 93.31%
当前epoch: 0 [12800/60000 (21%)] 损失: 0.039503 训练集准确率: 86.51% 测试集正确率: 96.69%
当前epoch: 0 [19200/60000 (32%)] 损失: 0.057866 训练集准确率: 89.93% 测试集正确率: 97.54%
当前epoch: 0 [25600/60000 (43%)] 损失: 0.069566 训练集准确率: 91.68% 测试集正确率: 97.68%
当前epoch: 0 [32000/60000 (53%)] 损失: 0.228793 训练集准确率: 92.85% 测试集正确率: 98.18%
当前epoch: 0 [38400/60000 (64%)] 损失: 0.111003 训练集准确率: 93.72% 测试集正确率: 98.16%
当前epoch: 0 [44800/60000 (75%)] 损失: 0.110226 训练集准确率: 94.28% 测试集正确率: 98.44%
当前epoch: 0 [51200/60000 (85%)] 损失: 0.014538 训练集准确率: 94.78% 测试集正确率: 98.60%
当前epoch: 0 [57600/60000 (96%)] 损失: 0.051019 训练集准确率: 95.14% 测试集正确率: 98.45%
当前epoch: 1 [0/60000 (0%)] 损失: 0.036383 训练集准确率: 98.44% 测试集正确率: 98.68%
当前epoch: 1 [6400/60000 (11%)] 损失: 0.088116 训练集准确率: 98.50% 测试集正确率: 98.37%
当前epoch: 1 [12800/60000 (21%)] 损失: 0.120306 训练集准确率: 98.59% 测试集正确率: 98.97%
当前epoch: 1 [19200/60000 (32%)] 损失: 0.030676 训练集准确率: 98.63% 测试集正确率: 98.83%
当前epoch: 1 [25600/60000 (43%)] 损失: 0.068475 训练集准确率: 98.59% 测试集正确率: 98.87%
当前epoch: 1 [32000/60000 (53%)] 损失: 0.033244 训练集准确率: 98.62% 测试集正确率: 99.03%
当前epoch: 1 [38400/60000 (64%)] 损失: 0.024162 训练集准确率: 98.67% 测试集正确率: 98.81%
当前epoch: 1 [44800/60000 (75%)] 损失: 0.006713 训练集准确率: 98.69% 测试集正确率: 98.17%
当前epoch: 1 [51200/60000 (85%)] 损失: 0.009284 训练集准确率: 98.69% 测试集正确率: 98.97%
当前epoch: 1 [57600/60000 (96%)] 损失: 0.036536 训练集准确率: 98.68% 测试集正确率: 98.97%
当前epoch: 2 [0/60000 (0%)] 损失: 0.125235 训练集准确率: 98.44% 测试集正确率: 98.73%
当前epoch: 2 [6400/60000 (11%)] 损失: 0.028075 训练集准确率: 99.13% 测试集正确率: 99.17%
当前epoch: 2 [12800/60000 (21%)] 损失: 0.029663 训练集准确率: 99.26% 测试集正确率: 98.39%
当前epoch: 2 [19200/60000 (32%)] 损失: 0.073855 训练集准确率: 99.20% 测试集正确率: 98.81%
当前epoch: 2 [25600/60000 (43%)] 损失: 0.018130 训练集准确率: 99.16% 测试集正确率: 99.09%
当前epoch: 2 [32000/60000 (53%)] 损失: 0.006968 训练集准确率: 99.15% 测试集正确率: 99.11%
