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https://github.com/yunjey/pytorch-tutorial.git
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84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
import torch
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import torch.nn as nn
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import torchvision.datasets as dsets
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import torchvision.transforms as transforms
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from torch.autograd import Variable
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# Hyper Parameters
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input_size = 784
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hidden_size = 500
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num_classes = 10
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num_epochs = 5
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batch_size = 100
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learning_rate = 0.001
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# MNIST Dataset
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train_dataset = dsets.MNIST(root='./data',
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train=True,
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transform=transforms.ToTensor(),
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download=True)
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test_dataset = dsets.MNIST(root='./data',
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train=False,
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transform=transforms.ToTensor())
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# Data Loader (Input Pipeline)
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train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
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batch_size=batch_size,
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shuffle=True)
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test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
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batch_size=batch_size,
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shuffle=False)
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# Neural Network Model (1 hidden layer)
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class Net(nn.Module):
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def __init__(self, input_size, hidden_size, num_classes):
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super(Net, self).__init__()
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self.fc1 = nn.Linear(input_size, hidden_size)
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self.relu = nn.ReLU()
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self.fc2 = nn.Linear(hidden_size, num_classes)
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def forward(self, x):
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out = self.fc1(x)
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out = self.relu(out)
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out = self.fc2(out)
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return out
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net = Net(input_size, hidden_size, num_classes)
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net.cuda()
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# Loss and Optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
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# Train the Model
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for epoch in range(num_epochs):
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for i, (images, labels) in enumerate(train_loader):
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# Convert torch tensor to Variable
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images = Variable(images.view(-1, 28*28)).cuda()
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labels = Variable(labels).cuda()
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# Forward + Backward + Optimize
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optimizer.zero_grad() # zero the gradient buffer
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outputs = net(images)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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if (i+1) % 100 == 0:
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print ('Epoch [%d/%d], Step [%d/%d], Loss: %.4f'
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%(epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.data[0]))
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# Test the Model
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correct = 0
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total = 0
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for images, labels in test_loader:
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images = Variable(images.view(-1, 28*28)).cuda()
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outputs = net(images)
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_, predicted = torch.max(outputs.data, 1)
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total += labels.size(0)
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correct += (predicted.cpu() == labels).sum()
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print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total)) |