使用PyTorch實現MNIST手寫體識別程式碼
阿新 • • 發佈:2020-01-19
實驗環境
win10 + anaconda + jupyter notebook
Pytorch1.1.0
Python3.7
gpu環境(可選)
MNIST資料集介紹
MNIST 包括6萬張28x28的訓練樣本,1萬張測試樣本,可以說是CV裡的“Hello Word”。本文使用的CNN網路將MNIST資料的識別率提高到了99%。下面我們就開始進行實戰。
匯入包
import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets,transforms torch.__version__
定義超引數
BATCH_SIZE=512 EPOCHS=20 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
資料集
我們直接使用PyTorch中自帶的dataset,並使用DataLoader對訓練資料和測試資料分別進行讀取。如果下載過資料集這裡download可選擇False
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,shuffle=True)
定義網路
該網路包括兩個卷積層和兩個線性層,最後輸出10個維度,即代表0-9十個數字。
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) 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
例項化網路
model = ConvNet().to(DEVICE) # 將網路移動到gpu上 optimizer = optim.Adam(model.parameters()) # 使用Adam優化器
定義訓練函式
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()))
定義測試函式
def test(model,test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data,target in test_loader: data,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
for epoch in range(1,EPOCHS + 1): train(model,DEVICE,epoch) test(model,test_loader)
實驗結果
Train Epoch: 1 [14848/60000 (25%)] Loss: 0.375058 Train Epoch: 1 [30208/60000 (50%)] Loss: 0.255248 Train Epoch: 1 [45568/60000 (75%)] Loss: 0.128060 Test set: Average loss: 0.0992,Accuracy: 9690/10000 (97%) Train Epoch: 2 [14848/60000 (25%)] Loss: 0.093066 Train Epoch: 2 [30208/60000 (50%)] Loss: 0.087888 Train Epoch: 2 [45568/60000 (75%)] Loss: 0.068078 Test set: Average loss: 0.0599,Accuracy: 9816/10000 (98%) Train Epoch: 3 [14848/60000 (25%)] Loss: 0.043926 Train Epoch: 3 [30208/60000 (50%)] Loss: 0.037321 Train Epoch: 3 [45568/60000 (75%)] Loss: 0.068404 Test set: Average loss: 0.0416,Accuracy: 9859/10000 (99%) Train Epoch: 4 [14848/60000 (25%)] Loss: 0.031654 Train Epoch: 4 [30208/60000 (50%)] Loss: 0.041341 Train Epoch: 4 [45568/60000 (75%)] Loss: 0.036493 Test set: Average loss: 0.0361,Accuracy: 9873/10000 (99%) Train Epoch: 5 [14848/60000 (25%)] Loss: 0.027688 Train Epoch: 5 [30208/60000 (50%)] Loss: 0.019488 Train Epoch: 5 [45568/60000 (75%)] Loss: 0.018023 Test set: Average loss: 0.0344,Accuracy: 9875/10000 (99%) Train Epoch: 6 [14848/60000 (25%)] Loss: 0.024212 Train Epoch: 6 [30208/60000 (50%)] Loss: 0.018689 Train Epoch: 6 [45568/60000 (75%)] Loss: 0.040412 Test set: Average loss: 0.0350,Accuracy: 9879/10000 (99%) Train Epoch: 7 [14848/60000 (25%)] Loss: 0.030426 Train Epoch: 7 [30208/60000 (50%)] Loss: 0.026939 Train Epoch: 7 [45568/60000 (75%)] Loss: 0.010722 Test set: Average loss: 0.0287,Accuracy: 9892/10000 (99%) Train Epoch: 8 [14848/60000 (25%)] Loss: 0.021109 Train Epoch: 8 [30208/60000 (50%)] Loss: 0.034845 Train Epoch: 8 [45568/60000 (75%)] Loss: 0.011223 Test set: Average loss: 0.0299,Accuracy: 9904/10000 (99%) Train Epoch: 9 [14848/60000 (25%)] Loss: 0.011391 Train Epoch: 9 [30208/60000 (50%)] Loss: 0.008091 Train Epoch: 9 [45568/60000 (75%)] Loss: 0.039870 Test set: Average loss: 0.0341,Accuracy: 9890/10000 (99%) Train Epoch: 10 [14848/60000 (25%)] Loss: 0.026813 Train Epoch: 10 [30208/60000 (50%)] Loss: 0.011159 Train Epoch: 10 [45568/60000 (75%)] Loss: 0.024884 Test set: Average loss: 0.0286,Accuracy: 9901/10000 (99%) Train Epoch: 11 [14848/60000 (25%)] Loss: 0.006420 Train Epoch: 11 [30208/60000 (50%)] Loss: 0.003641 Train Epoch: 11 [45568/60000 (75%)] Loss: 0.003402 Test set: Average loss: 0.0377,Accuracy: 9894/10000 (99%) Train Epoch: 12 [14848/60000 (25%)] Loss: 0.006866 Train Epoch: 12 [30208/60000 (50%)] Loss: 0.012617 Train Epoch: 12 [45568/60000 (75%)] Loss: 0.008548 Test set: Average loss: 0.0311,Accuracy: 9908/10000 (99%) Train Epoch: 13 [14848/60000 (25%)] Loss: 0.010539 Train Epoch: 13 [30208/60000 (50%)] Loss: 0.002952 Train Epoch: 13 [45568/60000 (75%)] Loss: 0.002313 Test set: Average loss: 0.0293,Accuracy: 9905/10000 (99%) Train Epoch: 14 [14848/60000 (25%)] Loss: 0.002100 Train Epoch: 14 [30208/60000 (50%)] Loss: 0.000779 Train Epoch: 14 [45568/60000 (75%)] Loss: 0.005952 Test set: Average loss: 0.0335,Accuracy: 9897/10000 (99%) Train Epoch: 15 [14848/60000 (25%)] Loss: 0.006053 Train Epoch: 15 [30208/60000 (50%)] Loss: 0.002559 Train Epoch: 15 [45568/60000 (75%)] Loss: 0.002555 Test set: Average loss: 0.0357,Accuracy: 9894/10000 (99%) Train Epoch: 16 [14848/60000 (25%)] Loss: 0.000895 Train Epoch: 16 [30208/60000 (50%)] Loss: 0.004923 Train Epoch: 16 [45568/60000 (75%)] Loss: 0.002339 Test set: Average loss: 0.0400,Accuracy: 9893/10000 (99%) Train Epoch: 17 [14848/60000 (25%)] Loss: 0.004136 Train Epoch: 17 [30208/60000 (50%)] Loss: 0.000927 Train Epoch: 17 [45568/60000 (75%)] Loss: 0.002084 Test set: Average loss: 0.0353,Accuracy: 9895/10000 (99%) Train Epoch: 18 [14848/60000 (25%)] Loss: 0.004508 Train Epoch: 18 [30208/60000 (50%)] Loss: 0.001272 Train Epoch: 18 [45568/60000 (75%)] Loss: 0.000543 Test set: Average loss: 0.0380,Accuracy: 9894/10000 (99%) Train Epoch: 19 [14848/60000 (25%)] Loss: 0.001699 Train Epoch: 19 [30208/60000 (50%)] Loss: 0.000661 Train Epoch: 19 [45568/60000 (75%)] Loss: 0.000275 Test set: Average loss: 0.0339,Accuracy: 9905/10000 (99%) Train Epoch: 20 [14848/60000 (25%)] Loss: 0.000441 Train Epoch: 20 [30208/60000 (50%)] Loss: 0.000695 Train Epoch: 20 [45568/60000 (75%)] Loss: 0.000467 Test set: Average loss: 0.0396,Accuracy: 9894/10000 (99%)
總結
一個實際專案的工作流程:找到資料集,對資料做預處理,定義我們的模型,調整超引數,測試訓練,再通過訓練結果對超引數進行調整或者對模型進行調整。
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