ml Morgan

# ML & PyTorch

## Hello World: Linear Regression

Based on this tutorial. ######### Loading and normalizing CIFAR10 ######### 12345678901234567890123456789012345678901234567890123456789012345678901234567890 import torch import torchvision import torchvision.transforms as transforms transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')  a ######### Define a Linear Regression Model ######### import torch.nn as nn import torch.nn.functional as F class LinearRegressionModel(nn.Module): def __init__(self): super(LinearRegressionModel, self).__init__() self.linearModel = nn.Linear(3 * 32 * 32, 120) def forward(self, x): return self.linearModel(x.view(-1, 16 * 5 * 5)) linearModel = LinearRegressionModel()  a ######### Define a Loss function and optimizer ########## import torch.optim as optim criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)  a ########## Run test first to get baseline ########## net.eval() # Makes net more efficient by telling it it can ignore backpro running_accuracy = 0 for i, (inputs, labels) in enumerate(testloader, 0): outputs = net(inputs).cpu().detach().numpy() labels = labels.cpu().detach().numpy() running_accuracy += (outputs.argmax(1) == labels).mean() running_accuracy /= TEST_BATCH_COUNT print("accuracy", running_accuracy)  a ########## Train the network ########## net.train() for epoch in range(2): # loop over the dataset multiple times training_count = 0 training_loss = 0 training_accuracy = 0 for i, (inputs, labels) in enumerate(trainloader, 0): inputs = inputs.cuda() labels = labels.cuda() optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # print statistics training_count += 1 training_loss += loss.item() training_accuracy += (outputs.cpu().detach().numpy().argmax(1) == labels.cpu().detach().numpy()).mean() print('loss: %.3f accuracy: %.3f' % (running_loss / training_count, running_accuracy / training_count))  a ########## Run test again at the end ########## net.eval() # Makes net more efficient by telling it it can ignore backpro running_accuracy = 0 for i, (inputs, labels) in enumerate(testloader, 0): if HAS_GPU: inputs = inputs.cuda() outputs = net(inputs).cpu().detach().numpy() labels = labels.detach().numpy() running_accuracy += (outputs.argmax(1) == labels).mean() running_accuracy /= TEST_BATCH_COUNT print("accuracy", running_accuracy)  a