这篇教程pytorch实现手写数字图片识别写得很实用，希望能帮到您。
本文实例为大家分享了pytorch实现手写数字图片识别的具体代码，供大家参考，具体内容如下 数据集：MNIST数据集，代码中会自动下载，不用自己手动下载。数据集很小，不需要GPU设备，可以很好的体会到pytorch的魅力。
模型+训练+预测程序： import torchfrom torch import nnfrom torch.nn import functional as Ffrom torch import optimimport torchvisionfrom matplotlib import pyplot as pltfrom utils import plot_image, plot_curve, one_hot# step1 load datasetbatch_size = 512train_loader = torch.utils.data.DataLoader( torchvision.datasets.MNIST('mnist_data', train=True, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( (0.1307,), (0.3081,) ) ])), batch_size=batch_size, shuffle=True)test_loader = torch.utils.data.DataLoader( torchvision.datasets.MNIST('mnist_data/', train=False, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( (0.1307,), (0.3081,) ) ])), batch_size=batch_size, shuffle=False)x , y = next(iter(train_loader))print(x.shape, y.shape, x.min(), x.max())plot_image(x, y, "image_sample")class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(28*28, 256) self.fc2 = nn.Linear(256, 64) self.fc3 = nn.Linear(64, 10) def forward(self, x): # x: [b, 1, 28, 28] # h1 = relu(xw1 + b1) x = F.relu(self.fc1(x)) # h2 = relu(h1w2 + b2) x = F.relu(self.fc2(x)) # h3 = h2w3 + b3 x = self.fc3(x) return xnet = Net()optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)train_loss = []for epoch in range(3): for batch_idx, (x, y) in enumerate(train_loader): #加载进来的图片是一个四维的tensor，x: [b, 1, 28, 28], y:[512] #但是我们网络的输入要是一个一维向量（也就是二维tensor)，所以要进行展平操作 x = x.view(x.size(0), 28*28) # [b, 10] out = net(x) y_onehot = one_hot(y) # loss = mse(out, y_onehot) loss = F.mse_loss(out, y_onehot) optimizer.zero_grad() loss.backward() # w' = w - lr*grad optimizer.step() train_loss.append(loss.item()) if batch_idx % 10 == 0: print(epoch, batch_idx, loss.item())plot_curve(train_loss) # we get optimal [w1, b1, w2, b2, w3, b3]total_correct = 0for x,y in test_loader: x = x.view(x.size(0), 28*28) out = net(x) # out: [b, 10] pred = out.argmax(dim=1) correct = pred.eq(y).sum().float().item() total_correct += correcttotal_num = len(test_loader.dataset)acc = total_correct/total_numprint("acc:", acc)x, y = next(iter(test_loader))out = net(x.view(x.size(0), 28*28))pred = out.argmax(dim=1)plot_image(x, pred, "test")主程序中调用的函数（注意命名为utils)： import torchfrom matplotlib import pyplot as pltdef plot_curve(data): fig = plt.figure() plt.plot(range(len(data)), data, color='blue') plt.legend(['value'], loc='upper right') plt.xlabel('step') plt.ylabel('value') plt.show()def plot_image(img, label, name): fig = plt.figure() for i in range(6): plt.subplot(2, 3, i + 1) plt.tight_layout() plt.imshow(img[i][0]*0.3081+0.1307, cmap='gray', interpolation='none') plt.title("{}: {}".format(name, label[i].item())) plt.xticks([]) plt.yticks([]) plt.show()def one_hot(label, depth=10): out = torch.zeros(label.size(0), depth) idx = torch.LongTensor(label).view(-1, 1) out.scatter_(dim=1, index=idx, value=1) return out打印出损失下降的曲线图： 
训练3个epoch之后，在测试集上的精度就可以89%左右，可见模型的准确度还是很不错的。
输出六张测试集的图片以及预测结果： 
六张图片的预测全部正确。 以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持51zixue.net。
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