chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import torch
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import torch.nn as nn
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import torchvision
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import torchvision.transforms as transforms
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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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# Hyper-parameters
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sequence_length = 28
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input_size = 28
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hidden_size = 128
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num_layers = 2
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num_classes = 10
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batch_size = 100
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num_epochs = 2
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learning_rate = 0.003
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# MNIST dataset
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train_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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test_dataset = torchvision.datasets.MNIST(root='../../data/',
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train=False,
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transform=transforms.ToTensor())
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# Data loader
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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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# Bidirectional recurrent neural network (many-to-one)
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class BiRNN(nn.Module):
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def __init__(self, input_size, hidden_size, num_layers, num_classes):
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super(BiRNN, self).__init__()
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
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self.fc = nn.Linear(hidden_size*2, num_classes) # 2 for bidirection
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def forward(self, x):
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# Set initial states
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h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device) # 2 for bidirection
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c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
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# Forward propagate LSTM
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out, _ = self.lstm(x, (h0, c0)) # out: tensor of shape (batch_size, seq_length, hidden_size*2)
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# Decode the hidden state of the last time step
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out = self.fc(out[:, -1, :])
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return out
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model = BiRNN(input_size, hidden_size, num_layers, num_classes).to(device)
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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=learning_rate)
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# Train the model
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total_step = len(train_loader)
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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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images = images.reshape(-1, sequence_length, input_size).to(device)
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labels = 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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if (i+1) % 100 == 0:
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print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
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.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
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# Test the model
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with torch.no_grad():
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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 = images.reshape(-1, sequence_length, input_size).to(device)
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labels = labels.to(device)
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outputs = model(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 == labels).sum().item()
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print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
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# Save the model checkpoint
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torch.save(model.state_dict(), 'model.ckpt')
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import torch
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import torch.nn as nn
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import torchvision
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import torchvision.transforms as transforms
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# Device configuration
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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# Hyper parameters
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num_epochs = 5
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num_classes = 10
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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 = 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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test_dataset = torchvision.datasets.MNIST(root='../../data/',
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train=False,
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transform=transforms.ToTensor())
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# Data loader
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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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# Convolutional neural network (two convolutional layers)
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class ConvNet(nn.Module):
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def __init__(self, num_classes=10):
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super(ConvNet, self).__init__()
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self.layer1 = nn.Sequential(
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nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
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nn.BatchNorm2d(16),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2))
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self.layer2 = nn.Sequential(
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nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
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nn.BatchNorm2d(32),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2))
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self.fc = nn.Linear(7*7*32, num_classes)
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def forward(self, x):
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out = self.layer1(x)
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out = self.layer2(out)
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out = out.reshape(out.size(0), -1)
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out = self.fc(out)
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return out
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model = ConvNet(num_classes).to(device)
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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=learning_rate)
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# Train the model
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total_step = len(train_loader)
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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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images = images.to(device)
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labels = 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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if (i+1) % 100 == 0:
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print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
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.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
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# Test the model
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model.eval() # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
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with torch.no_grad():
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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 = images.to(device)
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labels = labels.to(device)
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outputs = model(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 == labels).sum().item()
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print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
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# Save the model checkpoint
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torch.save(model.state_dict(), 'model.ckpt')
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# ---------------------------------------------------------------------------- #
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# An implementation of https://arxiv.org/pdf/1512.03385.pdf #
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# See section 4.2 for the model architecture on CIFAR-10 #
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# Some part of the code was referenced from below #
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# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py #
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# ---------------------------------------------------------------------------- #
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import torch
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import torch.nn as nn
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import torchvision
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import torchvision.transforms as transforms
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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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# Hyper-parameters
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num_epochs = 80
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batch_size = 100
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learning_rate = 0.001
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# Image preprocessing modules
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transform = transforms.Compose([
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transforms.Pad(4),
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transforms.RandomHorizontalFlip(),
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transforms.RandomCrop(32),
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transforms.ToTensor()])
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# CIFAR-10 dataset
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train_dataset = torchvision.datasets.CIFAR10(root='../../data/',
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train=True,
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transform=transform,
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download=True)
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test_dataset = torchvision.datasets.CIFAR10(root='../../data/',
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train=False,
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transform=transforms.ToTensor())
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# Data loader
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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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# 3x3 convolution
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def conv3x3(in_channels, out_channels, stride=1):
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return nn.Conv2d(in_channels, out_channels, kernel_size=3,
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stride=stride, padding=1, bias=False)
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# Residual block
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class ResidualBlock(nn.Module):
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def __init__(self, in_channels, out_channels, stride=1, downsample=None):
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super(ResidualBlock, self).__init__()
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self.conv1 = conv3x3(in_channels, out_channels, stride)
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self.bn1 = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(out_channels, out_channels)
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self.bn2 = nn.BatchNorm2d(out_channels)
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self.downsample = downsample
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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# ResNet
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class ResNet(nn.Module):
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def __init__(self, block, layers, num_classes=10):
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super(ResNet, self).__init__()
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self.in_channels = 16
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self.conv = conv3x3(3, 16)
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self.bn = nn.BatchNorm2d(16)
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self.relu = nn.ReLU(inplace=True)
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self.layer1 = self.make_layer(block, 16, layers[0])
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self.layer2 = self.make_layer(block, 32, layers[1], 2)
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self.layer3 = self.make_layer(block, 64, layers[2], 2)
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self.avg_pool = nn.AvgPool2d(8)
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self.fc = nn.Linear(64, num_classes)
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def make_layer(self, block, out_channels, blocks, stride=1):
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downsample = None
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if (stride != 1) or (self.in_channels != out_channels):
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downsample = nn.Sequential(
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conv3x3(self.in_channels, out_channels, stride=stride),
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nn.BatchNorm2d(out_channels))
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layers = []
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layers.append(block(self.in_channels, out_channels, stride, downsample))
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self.in_channels = out_channels
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for i in range(1, blocks):
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layers.append(block(out_channels, out_channels))
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return nn.Sequential(*layers)
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def forward(self, x):
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out = self.conv(x)
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out = self.bn(out)
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out = self.relu(out)
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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out = self.avg_pool(out)
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out = out.view(out.size(0), -1)
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out = self.fc(out)
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return out
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model = ResNet(ResidualBlock, [2, 2, 2]).to(device)
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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=learning_rate)
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# For updating learning rate
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def update_lr(optimizer, lr):
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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# Train the model
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total_step = len(train_loader)
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curr_lr = learning_rate
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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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images = images.to(device)
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labels = 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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if (i+1) % 100 == 0:
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print ("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}"
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.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
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# Decay learning rate
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if (epoch+1) % 20 == 0:
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curr_lr /= 3
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update_lr(optimizer, curr_lr)
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# Test the model
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model.eval()
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with torch.no_grad():
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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 = images.to(device)
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labels = labels.to(device)
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outputs = model(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 == labels).sum().item()
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print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))
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# Save the model checkpoint
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torch.save(model.state_dict(), 'resnet.ckpt')
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@@ -0,0 +1,46 @@
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import torch
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import os
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class Dictionary(object):
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def __init__(self):
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self.word2idx = {}
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self.idx2word = {}
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self.idx = 0
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def add_word(self, word):
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if not word in self.word2idx:
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self.word2idx[word] = self.idx
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self.idx2word[self.idx] = word
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self.idx += 1
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def __len__(self):
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return len(self.word2idx)
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class Corpus(object):
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def __init__(self):
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self.dictionary = Dictionary()
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def get_data(self, path, batch_size=20):
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# Add words to the dictionary
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with open(path, 'r') as f:
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tokens = 0
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for line in f:
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words = line.split() + ['<eos>']
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tokens += len(words)
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for word in words:
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self.dictionary.add_word(word)
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# Tokenize the file content
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ids = torch.LongTensor(tokens)
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token = 0
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with open(path, 'r') as f:
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for line in f:
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words = line.split() + ['<eos>']
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for word in words:
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ids[token] = self.dictionary.word2idx[word]
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token += 1
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num_batches = ids.size(0) // batch_size
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ids = ids[:num_batches*batch_size]
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return ids.view(batch_size, -1)
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@@ -0,0 +1,120 @@
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# Some part of the code was referenced from below.
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# https://github.com/pytorch/examples/tree/master/word_language_model
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import torch
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import torch.nn as nn
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import numpy as np
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from torch.nn.utils import clip_grad_norm_
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from data_utils import Dictionary, Corpus
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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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# Hyper-parameters
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embed_size = 128
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hidden_size = 1024
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num_layers = 1
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num_epochs = 5
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num_samples = 1000 # number of words to be sampled
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batch_size = 20
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seq_length = 30
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learning_rate = 0.002
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# Load "Penn Treebank" dataset
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corpus = Corpus()
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ids = corpus.get_data('data/train.txt', batch_size)
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vocab_size = len(corpus.dictionary)
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num_batches = ids.size(1) // seq_length
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# RNN based language model
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class RNNLM(nn.Module):
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def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
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super(RNNLM, self).__init__()
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self.embed = nn.Embedding(vocab_size, embed_size)
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self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
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self.linear = nn.Linear(hidden_size, vocab_size)
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def forward(self, x, h):
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# Embed word ids to vectors
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x = self.embed(x)
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# Forward propagate LSTM
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out, (h, c) = self.lstm(x, h)
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# Reshape output to (batch_size*sequence_length, hidden_size)
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out = out.reshape(out.size(0)*out.size(1), out.size(2))
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# Decode hidden states of all time steps
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out = self.linear(out)
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return out, (h, c)
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model = RNNLM(vocab_size, embed_size, hidden_size, num_layers).to(device)
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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=learning_rate)
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# Truncated backpropagation
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def detach(states):
|
||||
return [state.detach() for state in states]
|
||||
|
||||
# Train the model
|
||||
for epoch in range(num_epochs):
|
||||
# Set initial hidden and cell states
|
||||
states = (torch.zeros(num_layers, batch_size, hidden_size).to(device),
|
||||
torch.zeros(num_layers, batch_size, hidden_size).to(device))
|
||||
|
||||
for i in range(0, ids.size(1) - seq_length, seq_length):
|
||||
# Get mini-batch inputs and targets
|
||||
inputs = ids[:, i:i+seq_length].to(device)
|
||||
targets = ids[:, (i+1):(i+1)+seq_length].to(device)
|
||||
|
||||
# Forward pass
|
||||
states = detach(states)
|
||||
outputs, states = model(inputs, states)
|
||||
loss = criterion(outputs, targets.reshape(-1))
|
||||
|
||||
# Backward and optimize
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
clip_grad_norm_(model.parameters(), 0.5)
|
||||
optimizer.step()
|
||||
|
||||
step = (i+1) // seq_length
|
||||
if step % 100 == 0:
|
||||
print ('Epoch [{}/{}], Step[{}/{}], Loss: {:.4f}, Perplexity: {:5.2f}'
|
||||
.format(epoch+1, num_epochs, step, num_batches, loss.item(), np.exp(loss.item())))
|
||||
|
||||
# Test the model
|
||||
with torch.no_grad():
|
||||
with open('sample.txt', 'w') as f:
|
||||
# Set intial hidden ane cell states
|
||||
state = (torch.zeros(num_layers, 1, hidden_size).to(device),
|
||||
torch.zeros(num_layers, 1, hidden_size).to(device))
|
||||
|
||||
# Select one word id randomly
|
||||
prob = torch.ones(vocab_size)
|
||||
input = torch.multinomial(prob, num_samples=1).unsqueeze(1).to(device)
|
||||
|
||||
for i in range(num_samples):
|
||||
# Forward propagate RNN
|
||||
output, state = model(input, state)
|
||||
|
||||
# Sample a word id
|
||||
prob = output.exp()
|
||||
word_id = torch.multinomial(prob, num_samples=1).item()
|
||||
|
||||
# Fill input with sampled word id for the next time step
|
||||
input.fill_(word_id)
|
||||
|
||||
# File write
|
||||
word = corpus.dictionary.idx2word[word_id]
|
||||
word = '\n' if word == '<eos>' else word + ' '
|
||||
f.write(word)
|
||||
|
||||
if (i+1) % 100 == 0:
|
||||
print('Sampled [{}/{}] words and save to {}'.format(i+1, num_samples, 'sample.txt'))
|
||||
|
||||
# Save the model checkpoints
|
||||
torch.save(model.state_dict(), 'model.ckpt')
|
||||
@@ -0,0 +1,103 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torchvision
|
||||
import torchvision.transforms as transforms
|
||||
|
||||
|
||||
# Device configuration
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
# Hyper-parameters
|
||||
sequence_length = 28
|
||||
input_size = 28
|
||||
hidden_size = 128
|
||||
num_layers = 2
|
||||
num_classes = 10
|
||||
batch_size = 100
|
||||
num_epochs = 2
|
||||
learning_rate = 0.01
|
||||
|
||||
# MNIST dataset
|
||||
train_dataset = torchvision.datasets.MNIST(root='../../data/',
|
||||
train=True,
|
||||
transform=transforms.ToTensor(),
|
||||
download=True)
|
||||
|
||||
test_dataset = torchvision.datasets.MNIST(root='../../data/',
|
||||
train=False,
|
||||
transform=transforms.ToTensor())
|
||||
|
||||
# Data loader
|
||||
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
|
||||
batch_size=batch_size,
|
||||
shuffle=True)
|
||||
|
||||
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
|
||||
batch_size=batch_size,
|
||||
shuffle=False)
|
||||
|
||||
# Recurrent neural network (many-to-one)
|
||||
class RNN(nn.Module):
|
||||
def __init__(self, input_size, hidden_size, num_layers, num_classes):
|
||||
super(RNN, self).__init__()
|
||||
self.hidden_size = hidden_size
|
||||
self.num_layers = num_layers
|
||||
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
|
||||
self.fc = nn.Linear(hidden_size, num_classes)
|
||||
|
||||
def forward(self, x):
|
||||
# Set initial hidden and cell states
|
||||
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
|
||||
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
|
||||
|
||||
# Forward propagate LSTM
|
||||
out, _ = self.lstm(x, (h0, c0)) # out: tensor of shape (batch_size, seq_length, hidden_size)
|
||||
|
||||
# Decode the hidden state of the last time step
|
||||
out = self.fc(out[:, -1, :])
|
||||
return out
|
||||
|
||||
model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
|
||||
|
||||
|
||||
# Loss and optimizer
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
|
||||
|
||||
# Train the model
|
||||
total_step = len(train_loader)
|
||||
for epoch in range(num_epochs):
|
||||
for i, (images, labels) in enumerate(train_loader):
|
||||
images = images.reshape(-1, sequence_length, input_size).to(device)
|
||||
labels = labels.to(device)
|
||||
|
||||
# Forward pass
|
||||
outputs = model(images)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# Backward and optimize
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
if (i+1) % 100 == 0:
|
||||
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
|
||||
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
|
||||
|
||||
# Test the model
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
correct = 0
|
||||
total = 0
|
||||
for images, labels in test_loader:
|
||||
images = images.reshape(-1, sequence_length, input_size).to(device)
|
||||
labels = labels.to(device)
|
||||
outputs = model(images)
|
||||
_, predicted = torch.max(outputs.data, 1)
|
||||
total += labels.size(0)
|
||||
correct += (predicted == labels).sum().item()
|
||||
|
||||
print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
|
||||
|
||||
# Save the model checkpoint
|
||||
torch.save(model.state_dict(), 'model.ckpt')
|
||||
Reference in New Issue
Block a user