實驗環境
win10 + anaconda + jupyter notebook
Pytorch1.1.0
Python3.7
gpu環境（可選）
MNIST數據集介紹
MNIST 包括6萬張28x28的訓練樣本，1萬張測試樣本，可以說是CV里的“Hello Word”。本文使用的CNN網絡將MNIST數據的識別率提高到了99%。下面我們就開始進行實戰。
導入包
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import torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom torchvision import datasets, transformstorch.__version__ |
定義超參數
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BATCH_SIZE=512EPOCHS=20DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
數據集
我們直接使用PyTorch中自帶的dataset，并使用DataLoader對訓練數據和測試數據分別進行讀取。如果下載過數據集這里download可選擇False
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train_loader = torch.utils.data.DataLoader( datasets.MNIST('data', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=BATCH_SIZE, shuffle=True)test_loader = torch.utils.data.DataLoader( datasets.MNIST('data', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=BATCH_SIZE, shuffle=True) |
定義網絡
該網絡包括兩個卷積層和兩個線性層，最后輸出10個維度，即代表0-9十個數字。
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class ConvNet(nn.Module): def __init__(self): super().__init__() self.conv1=nn.Conv2d(1,10,5) # input:(1,28,28) output:(10,24,24) self.conv2=nn.Conv2d(10,20,3) # input:(10,12,12) output:(20,10,10) self.fc1 = nn.Linear(20*10*10,500) self.fc2 = nn.Linear(500,10) def forward(self,x): in_size = x.size(0) out = self.conv1(x) out = F.relu(out) out = F.max_pool2d(out, 2, 2) out = self.conv2(out) out = F.relu(out) out = out.view(in_size,-1) out = self.fc1(out) out = F.relu(out) out = self.fc2(out) out = F.log_softmax(out,dim=1) return out |
實例化網絡
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model = ConvNet().to(DEVICE) # 將網絡移動到gpu上optimizer = optim.Adam(model.parameters()) # 使用Adam優化器 |
定義訓練函數
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def train(model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if(batch_idx+1)%30 == 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())) |
定義測試函數
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def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # 將一批的損失相加 pred = output.max(1, keepdim=True)[1] # 找到概率最大的下標 correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) |
開始訓練
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for epoch in range(1, EPOCHS + 1): train(model, DEVICE, train_loader, optimizer, epoch) test(model, DEVICE, test_loader) |
實驗結果
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Train Epoch: 1 [14848/60000 (25%)] Loss: 0.375058Train Epoch: 1 [30208/60000 (50%)] Loss: 0.255248Train Epoch: 1 [45568/60000 (75%)] Loss: 0.128060Test set: Average loss: 0.0992, Accuracy: 9690/10000 (97%)Train Epoch: 2 [14848/60000 (25%)] Loss: 0.093066Train Epoch: 2 [30208/60000 (50%)] Loss: 0.087888Train Epoch: 2 [45568/60000 (75%)] Loss: 0.068078Test set: Average loss: 0.0599, Accuracy: 9816/10000 (98%)Train Epoch: 3 [14848/60000 (25%)] Loss: 0.043926Train Epoch: 3 [30208/60000 (50%)] Loss: 0.037321Train Epoch: 3 [45568/60000 (75%)] Loss: 0.068404Test set: Average loss: 0.0416, Accuracy: 9859/10000 (99%)Train Epoch: 4 [14848/60000 (25%)] Loss: 0.031654Train Epoch: 4 [30208/60000 (50%)] Loss: 0.041341Train Epoch: 4 [45568/60000 (75%)] Loss: 0.036493Test set: Average loss: 0.0361, Accuracy: 9873/10000 (99%)Train Epoch: 5 [14848/60000 (25%)] Loss: 0.027688Train Epoch: 5 [30208/60000 (50%)] Loss: 0.019488Train Epoch: 5 [45568/60000 (75%)] Loss: 0.018023Test set: Average loss: 0.0344, Accuracy: 9875/10000 (99%)Train Epoch: 6 [14848/60000 (25%)] Loss: 0.024212Train Epoch: 6 [30208/60000 (50%)] Loss: 0.018689Train Epoch: 6 [45568/60000 (75%)] Loss: 0.040412Test set: Average loss: 0.0350, Accuracy: 9879/10000 (99%)Train Epoch: 7 [14848/60000 (25%)] Loss: 0.030426Train Epoch: 7 [30208/60000 (50%)] Loss: 0.026939Train Epoch: 7 [45568/60000 (75%)] Loss: 0.010722Test set: Average loss: 0.0287, Accuracy: 9892/10000 (99%)Train Epoch: 8 [14848/60000 (25%)] Loss: 0.021109Train Epoch: 8 [30208/60000 (50%)] Loss: 0.034845Train Epoch: 8 [45568/60000 (75%)] Loss: 0.011223Test set: Average loss: 0.0299, Accuracy: 9904/10000 (99%)Train Epoch: 9 [14848/60000 (25%)] Loss: 0.011391Train Epoch: 9 [30208/60000 (50%)] Loss: 0.008091Train Epoch: 9 [45568/60000 (75%)] Loss: 0.039870Test set: Average loss: 0.0341, Accuracy: 9890/10000 (99%)Train Epoch: 10 [14848/60000 (25%)] Loss: 0.026813Train Epoch: 10 [30208/60000 (50%)] Loss: 0.011159Train Epoch: 10 [45568/60000 (75%)] Loss: 0.024884Test set: Average loss: 0.0286, Accuracy: 9901/10000 (99%)Train Epoch: 11 [14848/60000 (25%)] Loss: 0.006420Train Epoch: 11 [30208/60000 (50%)] Loss: 0.003641Train Epoch: 11 [45568/60000 (75%)] Loss: 0.003402Test set: Average loss: 0.0377, Accuracy: 9894/10000 (99%)Train Epoch: 12 [14848/60000 (25%)] Loss: 0.006866Train Epoch: 12 [30208/60000 (50%)] Loss: 0.012617Train Epoch: 12 [45568/60000 (75%)] Loss: 0.008548Test set: Average loss: 0.0311, Accuracy: 9908/10000 (99%)Train Epoch: 13 [14848/60000 (25%)] Loss: 0.010539Train Epoch: 13 [30208/60000 (50%)] Loss: 0.002952Train Epoch: 13 [45568/60000 (75%)] Loss: 0.002313Test set: Average loss: 0.0293, Accuracy: 9905/10000 (99%)Train Epoch: 14 [14848/60000 (25%)] Loss: 0.002100Train Epoch: 14 [30208/60000 (50%)] Loss: 0.000779Train Epoch: 14 [45568/60000 (75%)] Loss: 0.005952Test set: Average loss: 0.0335, Accuracy: 9897/10000 (99%)Train Epoch: 15 [14848/60000 (25%)] Loss: 0.006053Train Epoch: 15 [30208/60000 (50%)] Loss: 0.002559Train Epoch: 15 [45568/60000 (75%)] Loss: 0.002555Test set: Average loss: 0.0357, Accuracy: 9894/10000 (99%)Train Epoch: 16 [14848/60000 (25%)] Loss: 0.000895Train Epoch: 16 [30208/60000 (50%)] Loss: 0.004923Train Epoch: 16 [45568/60000 (75%)] Loss: 0.002339Test set: Average loss: 0.0400, Accuracy: 9893/10000 (99%)Train Epoch: 17 [14848/60000 (25%)] Loss: 0.004136Train Epoch: 17 [30208/60000 (50%)] Loss: 0.000927Train Epoch: 17 [45568/60000 (75%)] Loss: 0.002084Test set: Average loss: 0.0353, Accuracy: 9895/10000 (99%)Train Epoch: 18 [14848/60000 (25%)] Loss: 0.004508Train Epoch: 18 [30208/60000 (50%)] Loss: 0.001272Train Epoch: 18 [45568/60000 (75%)] Loss: 0.000543Test set: Average loss: 0.0380, Accuracy: 9894/10000 (99%)Train Epoch: 19 [14848/60000 (25%)] Loss: 0.001699Train Epoch: 19 [30208/60000 (50%)] Loss: 0.000661Train Epoch: 19 [45568/60000 (75%)] Loss: 0.000275Test set: Average loss: 0.0339, Accuracy: 9905/10000 (99%)Train Epoch: 20 [14848/60000 (25%)] Loss: 0.000441Train Epoch: 20 [30208/60000 (50%)] Loss: 0.000695Train Epoch: 20 [45568/60000 (75%)] Loss: 0.000467Test set: Average loss: 0.0396, Accuracy: 9894/10000 (99%) |
總結
一個實際項目的工作流程：找到數據集，對數據做預處理，定義我們的模型，調整超參數，測試訓練，再通過訓練結果對超參數進行調整或者對模型進行調整。
以上這篇使用PyTorch實現MNIST手寫體識別代碼就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持服務器之家。
原文鏈接：https://blog.csdn.net/abcgkj/article/details/100884143
