- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客🍖 原作者：K同学啊这是我参加训练营的第一周，做的项目Pytorch实现mnist手写数字识别
这是用的卷积神经网络，每张图片大小为28乘28，一个batch为32。卷积层1，卷积层1的核函数为3乘3的矩阵，这样一张图片就经过卷积层1后就为26乘26大小，池化层1核函数为22的矩阵，2626的经过池化层1就变成了13乘13了。
运行截图
#引入库函数
import torchimport torch.nn as nnimport matplotlib.pyplot as pltimport torchvision#设置硬件设备，如果有GPU则使用，没有则使用cpu
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")device
#从torch下载数据集
train_ds = torchvision.datasets.MNIST('data', train=True, transform=torchvision.transforms.ToTensor(), # 将数据类型转化为Tensor download=True) test_ds = torchvision.datasets.MNIST('data', train=False, transform=torchvision.transforms.ToTensor(), # 将数据类型转化为Tensor download=True)#划分测试集、训练集
batch_size = 32train_dl = torch.utils.data.DataLoader(train_ds, batch_size=batch_size, shuffle=True)test_dl = torch.utils.data.DataLoader(test_ds, batch_size=batch_size)#设置超参数
learn_rate = 1e-2 # 学习率opt = torch.optim.SGD(model.parameters(),lr=learn_rate)#使用面向对象定义训练的过程
def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) # 训练集的大小，一共60000张图片 num_batches = len(dataloader) # 批次数目，1875（60000/32） train_loss, train_acc = 0, 0 # 初始化训练损失和正确率 for X, y in dataloader: # 获取图片及其标签 X, y = X.to(device), y.to(device) # 计算预测误差 pred = model(X) # 网络输出 loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失 # 反向传播 optimizer.zero_grad() # grad属性归零 loss.backward() # 反向传播 optimizer.step() # 每一步自动更新 # 记录acc与loss train_acc += (pred.argmax(1) == y).type(torch.float).sum().item() train_loss += loss.item() train_acc /= size train_loss /= num_batches return train_acc, train_loss#编写测试函数
size = len(dataloader.dataset) # 测试集的大小，一共10000张图片 num_batches = len(dataloader) # 批次数目，313（10000/32=312.5，向上取整） test_loss, test_acc = 0, 0 # 当不进行训练时，停止梯度更新，节省计算内存消耗 with torch.no_grad(): for imgs, target in dataloader: imgs, target = imgs.to(device), target.to(device) # 计算loss target_pred = model(imgs) loss = loss_fn(target_pred, target) test_loss += loss.item() test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item() test_acc /= size test_loss /= num_batches return test_acc, test_loss#训练5轮，将训练的参数带入写的面向对象的训练方法
epochs = 5train_loss = []train_acc = []test_loss = []test_acc = []for epoch in range(epochs): model.train() epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt) model.eval() epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn) train_acc.append(epoch_train_acc) train_loss.append(epoch_train_loss) test_acc.append(epoch_test_acc) test_loss.append(epoch_test_loss) template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}') print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))print('Done')#训练结果可视化
import matplotlib.pyplot as plt#隐藏警告import warningswarnings.filterwarnings("ignore") #忽略警告信息plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号plt.rcParams['figure.dpi'] = 100 #分辨率from datetime import datetimecurrent_time = datetime.now() # 获取当前时间epochs_range = range(epochs)plt.figure(figsize=(12, 3))plt.subplot(1, 2, 1)plt.plot(epochs_range, train_acc, label='Training Accuracy')plt.plot(epochs_range, test_acc, label='Test Accuracy')plt.legend(loc='lower right')plt.title('Training and Validation Accuracy')plt.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效plt.subplot(1, 2, 2)plt.plot(epochs_range, train_loss, label='Training Loss')plt.plot(epochs_range, test_loss, label='Test Loss')plt.legend(loc='upper right')plt.title('Training and Validation Loss')plt.show()#训练截图
