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https://github.com/yunjey/pytorch-tutorial.git
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97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
import torch
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import torch.nn as nn
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import torchvision
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from torchvision import transforms
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from logger import Logger
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# Device configuration
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# MNIST dataset
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dataset = torchvision.datasets.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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# Data loader
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data_loader = torch.utils.data.DataLoader(dataset=dataset,
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batch_size=100,
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shuffle=True)
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# Fully connected neural network with one hidden layer
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class NeuralNet(nn.Module):
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def __init__(self, input_size=784, hidden_size=500, num_classes=10):
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super(NeuralNet, 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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model = NeuralNet().to(device)
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logger = Logger('./logs')
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# Loss and optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=0.00001)
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data_iter = iter(data_loader)
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iter_per_epoch = len(data_loader)
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total_step = 50000
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# Start training
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for step in range(total_step):
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# Reset the data_iter
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if (step+1) % iter_per_epoch == 0:
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data_iter = iter(data_loader)
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# Fetch images and labels
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images, labels = next(data_iter)
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images, labels = images.view(images.size(0), -1).to(device), labels.to(device)
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# Forward pass
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outputs = model(images)
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loss = criterion(outputs, labels)
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# Backward and optimize
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# Compute accuracy
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_, argmax = torch.max(outputs, 1)
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accuracy = (labels == argmax.squeeze()).float().mean()
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if (step+1) % 100 == 0:
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print ('Step [{}/{}], Loss: {:.4f}, Acc: {:.2f}'
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.format(step+1, total_step, loss.item(), accuracy.item()))
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# ================================================================== #
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# Tensorboard Logging #
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# ================================================================== #
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# 1. Log scalar values (scalar summary)
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info = { 'loss': loss.item(), 'accuracy': accuracy.item() }
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for tag, value in info.items():
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logger.scalar_summary(tag, value, step+1)
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# 2. Log values and gradients of the parameters (histogram summary)
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for tag, value in model.named_parameters():
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tag = tag.replace('.', '/')
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logger.histo_summary(tag, value.data.cpu().numpy(), step+1)
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logger.histo_summary(tag+'/grad', value.grad.data.cpu().numpy(), step+1)
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# 3. Log training images (image summary)
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info = { 'images': images.view(-1, 28, 28)[:10].cpu().numpy() }
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for tag, images in info.items():
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logger.image_summary(tag, images, step+1) |