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', train=True, transform=transforms.ToTensor(), download=True) # 测试集 test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor()) # 构建batch数据 train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)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_channels=1, # 灰度图 out_channels=16, # 要得到几多少个特征图 kernel_size=5, # 卷积核大小 stride=1, # 步长 padding=2, # 如果希望卷积后大小跟原来一样，需要设置padding=(kernel_size-1)/2 if stride=1 ), # 输出的特征图为 (16, 28, 28) nn.ReLU(), # relu层 nn.MaxPool2d(kernel_size=2), # 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(), lr=0.001) #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%