代码 python实现手写数字识别（小白入门）
项目结构
1.py
需要下载joblib包
import numpy as np from sklearn.linear_model import LogisticRegression import os import joblib #数据预处理 trainData = np.loadtxt(open('digits_training.csv', 'r'), delimiter=",",skiprows=1)#装载数据 MTrain, NTrain = np.shape(trainData) #行列数 print("训练集：",MTrain,NTrain) xTrain = trainData[:,1:NTrain] xTrain_col_avg = np.mean(xTrain, axis=0) #对各列求均值 xTrain =(xTrain- xTrain_col_avg)/255 #归一化 yTrain = trainData[:,0] '''=================================''' #训练模型 model = LogisticRegression(solver='lbfgs', multi_class='multinomial', max_iter=500) model.fit(xTrain, yTrain) print("训练完毕") '''=================================''' #测试模型 testData = np.loadtxt(open('digits_testing.csv', 'r'), delimiter=",",skiprows=1) MTest,NTest = np.shape(testData) print("测试集：",MTest,NTest) xTest = testData[:,1:NTest] xTest = (xTest-xTrain_col_avg) /255 # 使用训练数据的列均值进行处理 yTest = testData[:,0] yPredict = model.predict(xTest) errors = np.count_nonzero(yTest - yPredict) #返回非零项个数 print("预测完毕。错误：", errors, "条") print("测试数据正确率:", (MTest - errors) / MTest) '''=================================''' #保存模型 # 创建文件目录 dirs = 'testModel' if not os.path.exists(dirs): os.makedirs(dirs) joblib.dump(model, dirs+'/model.pkl') print("模型已保存")
结果1
2.py
需要下载cv2包
在test下放置几张数字图片
import cv2 import numpy as np import joblib map=cv2.imread(r"test/img1.png") GrayImage = cv2.cvtColor(map, cv2.COLOR_BGR2GRAY) # 获取图片的宽和高 width, height = map.shape[:2][::-1] ret,thresh2=cv2.threshold(GrayImage,width,height,cv2.THRESH_BINARY_INV) Image=cv2.resize(thresh2,(28,28)) img_array = np.asarray(Image) z=img_array.reshape(1,-1) '''================================================''' model = joblib.load('testModel'+'/model.pkl') yPredict = model.predict(z) print(yPredict) y=str(yPredict) cv2.putText(map,y, (10,20), cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,0,255), 2, cv2.LINE_AA) cv2.imshow("map",map) cv2.waitKey(0)
结果2
代码 用PyTorch实现MNIST手写数字识别(非常详细)
pytorch.c
import torch import torchvision from torch.utils.data import DataLoader import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import matplotlib.pyplot as plt n_epochs = 3 batch_size_train = 64 batch_size_test = 1000 learning_rate = 0.01 momentum = 0.5 log_interval = 10 random_seed = 1 torch.manual_seed(random_seed) train_loader = torch.utils.data.DataLoader( torchvision.datasets.MNIST('./data/', train=True, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( (0.1307,), (0.3081,)) ])), batch_size=batch_size_train, shuffle=True) test_loader = torch.utils.data.DataLoader( torchvision.datasets.MNIST('./data/', train=False, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( (0.1307,), (0.3081,)) ])), batch_size=batch_size_test, shuffle=True) examples = enumerate(test_loader) batch_idx, (example_data, example_targets) = next(examples) # print(example_targets) # print(example_data.shape) fig = plt.figure() for i in range(6): plt.subplot(2, 3, i + 1) plt.tight_layout() plt.imshow(example_data[i][0], cmap='gray', interpolation='none') plt.title("Ground Truth: {}".format(example_targets[i])) plt.xticks([]) plt.yticks([]) plt.show() class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=1) network = Net() optimizer = optim.SGD(network.parameters(), lr=learning_rate, momentum=momentum) train_losses = [] train_counter = [] test_losses = [] test_counter = [i * len(train_loader.dataset) for i in range(n_epochs + 1)] def train(epoch): network.train() for batch_idx, (data, target) in enumerate(train_loader): optimizer.zero_grad() output = network(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) train_losses.append(loss.item()) train_counter.append((batch_idx * 64) + ((epoch - 1) * len(train_loader.dataset))) torch.save(network.state_dict(), './model.pth') torch.save(optimizer.state_dict(), './optimizer.pth') def test(): network.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: output = network(data) test_loss += F.nll_loss(output, target, reduction='sum').item() pred = output.data.max(1, keepdim=True)[1] correct += pred.eq(target.data.view_as(pred)).sum() test_loss /= len(test_loader.dataset) test_losses.append(test_loss) print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) train(1) test() # 不加这个，后面画图就会报错：x and y must be the same size for epoch in range(1, n_epochs + 1): train(epoch) test() fig = plt.figure() plt.plot(train_counter, train_losses, color='blue') plt.scatter(test_counter, test_losses, color='red') plt.legend(['Train Loss', 'Test Loss'], loc='upper right') plt.xlabel('number of training examples seen') plt.ylabel('negative log likelihood loss') examples = enumerate(test_loader) batch_idx, (example_data, example_targets) = next(examples) with torch.no_grad(): output = network(example_data) fig = plt.figure() for i in range(6): plt.subplot(2, 3, i + 1) plt.tight_layout() plt.imshow(example_data[i][0], cmap='gray', interpolation='none') plt.title("Prediction: {}".format(output.data.max(1, keepdim=True)[1][i].item())) plt.xticks([]) plt.yticks([]) plt.show() # ----------------------------------------------------------- # continued_network = Net() continued_optimizer = optim.SGD(network.parameters(), lr=learning_rate, momentum=momentum) network_state_dict = torch.load('model.pth') continued_network.load_state_dict(network_state_dict) optimizer_state_dict = torch.load('optimizer.pth') continued_optimizer.load_state_dict(optimizer_state_dict) # 注意不要注释前面的“for epoch in range(1, n_epochs + 1):”部分， # 不然报错：x and y must be the same size # 为什么是“4”开始呢，因为n_epochs=3，上面用了[1, n_epochs + 1) for i in range(4, 9): test_counter.append(i * len(train_loader.dataset)) train(i) test() fig = plt.figure() plt.plot(train_counter, train_losses, color='blue') plt.scatter(test_counter, test_losses, color='red') plt.legend(['Train Loss', 'Test Loss'], loc='upper right') plt.xlabel('number of training examples seen') plt.ylabel('negative log likelihood loss') plt.show()
运行结果
运行之后项目结构
代码 自己
import torch import torchvision from torch.utils.data import DataLoader import torch.nn as nn import torch.nn.functional as F import torch.optim as optim # import matplotlib.pyplot as plt num_epochs = 6 batch_size = 100 learning_rate = 0.1 momentum = 0.5 log_interval = 10 random_seed = 1 torch.manual_seed(random_seed) input_size=28*28 num_classes=10 train_dataset= torchvision.datasets.MNIST('./data/', train=True, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( (0.1307,), (0.3081,)) ])) test_dataset=torchvision.datasets.MNIST('./data/', train=False, download=True,transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( (0.1307,), (0.3081,)) ])) 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=False) class NeuralNet(nn.Module): def __init__(self,input_size,hidden_size,num_classes): super(NeuralNet, self).__init__() self.fc1=nn.Linear(input_size,hidden_size[0]) self.fc2=nn.Linear(hidden_size[0],hidden_size[1]) self.fc3=nn.Linear(hidden_size[1],num_classes) self.relu = nn.ReLU() def forward(self, x): out = self.fc1(x) out = self.relu(out) out = self.fc2(out) out = self.relu(out) out = self.fc3(out) return out model = NeuralNet(input_size, [256, 64], num_classes) criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=learning_rate) for epoch in range(num_epochs): for i,(images,labels) in enumerate(train_loader): images=images.reshape(-1,28*28) outputs=model(images) labels_onehot = F.one_hot(labels) loss=criterion(outputs,labels_onehot.float()) optimizer.zero_grad() loss.backward() optimizer.step() if i % 600 == 0: print("Epoch :{} \t Loss:{:.6f}".format(epoch, loss.item())) torch.save(model,'model_total.ckpt') # torch.save(net.state_dict(),'model_para.ckpt') #------------------------------------------------------------------- model=torch.load('model_total.ckpt') ''' net=NeuralNet(input_size,[500,100],num_classes) net.load_state_dict(torch.load('model_para.ckpt')) ''' def acc(labels,outputs): _,predicted=torch.max(outputs.data,1) num=len(labels) right=(predicted==labels).sum().item() return num,right with torch.no_grad(): correct,total=0,0 for images,labels in test_loader: images=images.reshape(-1,28*28) outputs=model(images) num,right=acc(labels,outputs) correct=correct+right total=total+num print('Accuracy of the network on the 10000 test images:{}%'.format(100*correct/total))
结果
Epoch :0 Loss:2.294961 Epoch :1 Loss:0.086749 Epoch :2 Loss:0.101823 Epoch :3 Loss:0.045709 Epoch :4 Loss:0.053201 Epoch :5 Loss:0.032638 Accuracy of the network on the 10000 test images:98.0%
