1 import numpy as np 2 import torch 3 import torch.nn as nn 4 import torch.nn.functional as F 5 import torch.optim as optim 6 from torchvision import datasets,transforms 7 print("PyTorch Version",torch.__version__) 8 9 class Net(nn.Module): 10 def __init__(self): 11 super(Net,self).__init__() 12 self.conv1=nn.Conv2d(1,20,5,1) 13 self.conv2=nn.Conv2d(20,50,5,1) 14 self.fc1=nn.Linear(4*4*50,500) 15 self.fc2=nn.Linear(500,10) 16 def forward(self,x): 17 #x=1*28*28 18 x=F.relu(self.conv1(x)) 19 x=F.max_pool2d(x,2,2) 20 x=F.relu(self.conv2(x)) 21 x=F.max_pool2d(x,2,2) 22 x=x.view(-1,4*4*50) 23 x=F.relu(self.fc1(x)) 24 x=self.fc2(x) 25 return F.log_softmax(x,dim=1) 26 27 28 def train(model, device, train_loader, optimizer, epoch): 29 model.train() 30 for idx, (data, target) in enumerate(train_loader): 31 data, target = data.to(device), target.to(device) 32 33 pred = model(data) 34 loss = F.nll_loss(pred, target) 35 36 # SGD 37 optimizer.zero_grad() 38 loss.backward() 39 optimizer.step() 40 41 if idx % 100 == 0: 42 print("Train Epoch:{},iteration:{},Loss:{}".format( 43 epoch, idx, loss.item())) 44 45 46 def test(model, device, test_loader): 47 model.eval() 48 total_loss = 0. 49 correct = 0. 50 with torch.no_grad(): 51 for idx, (data, target) in enumerate(test_loader): 52 data, target = data.to(device), target.to(device) 53 54 output = model(data) # batch_size*10 55 total_loss += F.nll_loss(output, target, reduction="sum").item() 56 pred = output.argmax(dim=1) # batch_zize*1 57 correct += pred.eq(target.view_as(pred)).sum().item() 58 total_loss /= len(test_loader.dataset) 59 acc = correct / len(test_loader.dataset) * 100. 60 print("Test loss:{},Accuracy:{}".format(total_loss, acc)) 61 device=torch.device("cuda" if torch.cuda.is_available() else "cpu") 62 batch_size=32 63 train_dataloader=torch.utils.data.DataLoader( 64 datasets.MNIST("./mnist_data",train=True,download=True, 65 transform=transforms.Compose([ 66 transforms.ToTensor(), 67 transforms.Normalize((0.1307,),(0.3081,)) 68 ])), 69 batch_size=batch_size,shuffle=True, 70 pin_memory=True 71 ) 72 test_dataloader=torch.utils.data.DataLoader( 73 datasets.MNIST("./mnist_data",train=False,download=True, 74 transform=transforms.Compose([ 75 transforms.ToTensor(), 76 transforms.Normalize((0.1307,),(0.3081,)) 77 ])), 78 batch_size=batch_size,shuffle=True, 79 pin_memory=True 80 ) 81 lr=0.01 82 momentum=0.5 83 model=Net().to(device) 84 optimizer=torch.optim.SGD(model.parameters(),lr=lr,momentum=momentum) 85 86 num_epochs=2 87 for epoch in range(num_epochs): 88 train(model,device,train_dataloader,optimizer,epoch) 89 test(model,device,test_dataloader) 90 torch.save(model.state_dict(),"mnist_cnn.pt")
1 PyTorch Version 1.5.0 2 Train Epoch:0,iteration:0,Loss:2.2912797927856445 3 Train Epoch:0,iteration:100,Loss:0.5194383859634399 4 Train Epoch:0,iteration:200,Loss:0.2786406874656677 5 Train Epoch:0,iteration:300,Loss:0.22770120203495026 6 Train Epoch:0,iteration:400,Loss:0.1970459222793579 7 Train Epoch:0,iteration:500,Loss:0.4494241774082184 8 Train Epoch:0,iteration:600,Loss:0.059495702385902405 9 Train Epoch:0,iteration:700,Loss:0.0606967955827713 10 Train Epoch:0,iteration:800,Loss:0.024992913007736206 11 Train Epoch:0,iteration:900,Loss:0.059543460607528687 12 Train Epoch:0,iteration:1000,Loss:0.052940040826797485 13 Train Epoch:0,iteration:1100,Loss:0.04461891949176788 14 Train Epoch:0,iteration:1200,Loss:0.07550729811191559 15 Train Epoch:0,iteration:1300,Loss:0.24627575278282166 16 Train Epoch:0,iteration:1400,Loss:0.15704390406608582 17 Train Epoch:0,iteration:1500,Loss:0.01860976219177246 18 Train Epoch:0,iteration:1600,Loss:0.01433052122592926 19 Train Epoch:0,iteration:1700,Loss:0.15008395910263062 20 Train Epoch:0,iteration:1800,Loss:0.015903979539871216 21 Test loss:0.07957145636081696,Accuracy:97.55 22 Train Epoch:1,iteration:0,Loss:0.03802196681499481 23 Train Epoch:1,iteration:100,Loss:0.03068508207798004 24 Train Epoch:1,iteration:200,Loss:0.010870471596717834 25 Train Epoch:1,iteration:300,Loss:0.01409807801246643 26 Train Epoch:1,iteration:400,Loss:0.0440949946641922 27 Train Epoch:1,iteration:500,Loss:0.028995990753173828 28 Train Epoch:1,iteration:600,Loss:0.01879104971885681 29 Train Epoch:1,iteration:700,Loss:0.013935379683971405 30 Train Epoch:1,iteration:800,Loss:0.021408677101135254 31 Train Epoch:1,iteration:900,Loss:0.2530629634857178 32 Train Epoch:1,iteration:1000,Loss:0.07138478755950928 33 Train Epoch:1,iteration:1100,Loss:0.05166466534137726 34 Train Epoch:1,iteration:1200,Loss:0.010311082005500793 35 Train Epoch:1,iteration:1300,Loss:0.06208721548318863 36 Train Epoch:1,iteration:1400,Loss:0.008724316954612732 37 Train Epoch:1,iteration:1500,Loss:0.035819828510284424 38 Train Epoch:1,iteration:1600,Loss:0.06683069467544556 39 Train Epoch:1,iteration:1700,Loss:0.0015392005443572998 40 Train Epoch:1,iteration:1800,Loss:0.10098540782928467 41 Test loss:0.04268560756444931,Accuracy:98.69
1 import numpy as np 2 import torch 3 import torch.nn as nn 4 import torch.nn.functional as F 5 import torch.optim as optim 6 from torchvision import datasets,transforms 7 print("PyTorch Version",torch.__version__) 8 9 class Net(nn.Module): 10 def __init__(self): 11 super(Net,self).__init__() 12 self.conv1=nn.Conv2d(1,20,5,1) 13 self.conv2=nn.Conv2d(20,50,5,1) 14 self.fc1=nn.Linear(4*4*50,500) 15 self.fc2=nn.Linear(500,10) 16 def forward(self,x): 17 #x=1*28*28 18 x=F.relu(self.conv1(x)) 19 x=F.max_pool2d(x,2,2) 20 x=F.relu(self.conv2(x)) 21 x=F.max_pool2d(x,2,2) 22 x=x.view(-1,4*4*50) 23 x=F.relu(self.fc1(x)) 24 x=self.fc2(x) 25 return F.log_softmax(x,dim=1) 26 27 28 def train(model, device, train_loader, optimizer, epoch): 29 model.train() 30 for idx, (data, target) in enumerate(train_loader): 31 data, target = data.to(device), target.to(device) 32 33 pred = model(data) 34 loss = F.nll_loss(pred, target) 35 36 # SGD 37 optimizer.zero_grad() 38 loss.backward() 39 optimizer.step() 40 41 if idx % 100 == 0: 42 print("Train Epoch:{},iteration:{},Loss:{}".format( 43 epoch, idx, loss.item())) 44 45 46 def test(model, device, test_loader): 47 model.eval() 48 total_loss = 0. 49 correct = 0. 50 with torch.no_grad(): 51 for idx, (data, target) in enumerate(test_loader): 52 data, target = data.to(device), target.to(device) 53 54 output = model(data) # batch_size*10 55 total_loss += F.nll_loss(output, target, reduction="sum").item() 56 pred = output.argmax(dim=1) # batch_zize*1 57 correct += pred.eq(target.view_as(pred)).sum().item() 58 total_loss /= len(test_loader.dataset) 59 acc = correct / len(test_loader.dataset) * 100. 60 print("Test loss:{},Accuracy:{}".format(total_loss, acc)) 61 device=torch.device("cuda" if torch.cuda.is_available() else "cpu") 62 batch_size=32 63 train_dataloader=torch.utils.data.DataLoader( 64 datasets.FashionMNIST("./fashion_mnist",train=True,download=True, 65 transform=transforms.Compose([ 66 transforms.ToTensor(), 67 transforms.Normalize((0.1307,),(0.3081,)) 68 ])), 69 batch_size=batch_size,shuffle=True, 70 pin_memory=True 71 ) 72 test_dataloader=torch.utils.data.DataLoader( 73 datasets.FashionMNIST("./fashion_mnist_data",train=False,download=True, 74 transform=transforms.Compose([ 75 transforms.ToTensor(), 76 transforms.Normalize((0.1307,),(0.3081,)) 77 ])), 78 batch_size=batch_size,shuffle=True, 79 pin_memory=True 80 ) 81 lr=0.01 82 momentum=0.5 83 model=Net().to(device) 84 optimizer=torch.optim.SGD(model.parameters(),lr=lr,momentum=momentum) 85 86 num_epochs=2 87 for epoch in range(num_epochs): 88 train(model,device,train_dataloader,optimizer,epoch) 89 test(model,device,test_dataloader) 90 torch.save(model.state_dict(),"fashion_mnist_cnn.pt")
1 PyTorch Version 1.5.0 2 Train Epoch:0,iteration:0,Loss:2.297414541244507 3 Train Epoch:0,iteration:100,Loss:1.0651390552520752 4 Train Epoch:0,iteration:200,Loss:0.7067466974258423 5 Train Epoch:0,iteration:300,Loss:0.7356132864952087 6 Train Epoch:0,iteration:400,Loss:0.8800692558288574 7 Train Epoch:0,iteration:500,Loss:0.47312575578689575 8 Train Epoch:0,iteration:600,Loss:0.3854885995388031 9 Train Epoch:0,iteration:700,Loss:0.4843595623970032 10 Train Epoch:0,iteration:800,Loss:0.5790951251983643 11 Train Epoch:0,iteration:900,Loss:0.9185382723808289 12 Train Epoch:0,iteration:1000,Loss:1.1618707180023193 13 Train Epoch:0,iteration:1100,Loss:0.35001927614212036 14 Train Epoch:0,iteration:1200,Loss:0.43829041719436646 15 Train Epoch:0,iteration:1300,Loss:0.2706330418586731 16 Train Epoch:0,iteration:1400,Loss:0.7168084383010864 17 Train Epoch:0,iteration:1500,Loss:0.741541862487793 18 Train Epoch:0,iteration:1600,Loss:0.29623979330062866 19 Train Epoch:0,iteration:1700,Loss:0.3909362256526947 20 Train Epoch:0,iteration:1800,Loss:0.4269924759864807 21 Test loss:0.42097568979263306,Accuracy:84.66 22 Train Epoch:1,iteration:0,Loss:0.3680488169193268 23 Train Epoch:1,iteration:100,Loss:0.4184131622314453 24 Train Epoch:1,iteration:200,Loss:0.3114379048347473 25 Train Epoch:1,iteration:300,Loss:0.2098887860774994 26 Train Epoch:1,iteration:400,Loss:0.5370076894760132 27 Train Epoch:1,iteration:500,Loss:0.30105966329574585 28 Train Epoch:1,iteration:600,Loss:0.7143597602844238 29 Train Epoch:1,iteration:700,Loss:0.14829662442207336 30 Train Epoch:1,iteration:800,Loss:0.2414536476135254 31 Train Epoch:1,iteration:900,Loss:0.4756997227668762 32 Train Epoch:1,iteration:1000,Loss:0.41830974817276 33 Train Epoch:1,iteration:1100,Loss:0.7150392532348633 34 Train Epoch:1,iteration:1200,Loss:0.1928122490644455 35 Train Epoch:1,iteration:1300,Loss:0.25824040174484253 36 Train Epoch:1,iteration:1400,Loss:0.3149867057800293 37 Train Epoch:1,iteration:1500,Loss:0.47235292196273804 38 Train Epoch:1,iteration:1600,Loss:0.12620753049850464 39 Train Epoch:1,iteration:1700,Loss:0.46117734909057617 40 Train Epoch:1,iteration:1800,Loss:0.30702289938926697 41 Test loss:0.3761528673410416,Accuracy:86.46000000000001