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July 28, 2019 10:11
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Training CNN
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| import torch | |
| from torchvision import datasets, transforms | |
| import matplotlib.pyplot as plt | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.optim as optim | |
| import logging | |
| import numpy as np | |
| # Define transformers | |
| transform_train = transforms.Compose( | |
| [ | |
| transforms.RandomCrop(28, padding=4), | |
| transforms.ToTensor(), | |
| # transforms.Normalize((mean,), (std,)), | |
| ] | |
| ) | |
| transform_valid = transforms.Compose( | |
| [ | |
| transforms.ToTensor(), | |
| # transforms.Normalize((mean,), (std,)), | |
| ] | |
| ) | |
| # Fixed Params | |
| root = "" | |
| batch_size = 128 | |
| test_batch_size = 128 | |
| lr = 1e-3 | |
| EPOCHS = 200 | |
| # Prepare loaders | |
| train = datasets.EMNIST( | |
| root, split="balanced", train=True, download=True, transform=transform_train | |
| ) | |
| test = datasets.EMNIST( | |
| root, split="balanced", train=False, download=True, transform=transform_valid | |
| ) | |
| train_loader = torch.utils.data.DataLoader( | |
| train, batch_size=batch_size, shuffle=True, num_workers=2, drop_last=True | |
| ) | |
| test_loader = torch.utils.data.DataLoader( | |
| test, batch_size=test_batch_size, shuffle=False, num_workers=2, drop_last=True | |
| ) | |
| # Define model | |
| def init_weights(m): | |
| if type(m) == nn.Linear: | |
| torch.nn.init.xavier_uniform_(m.weight) | |
| m.bias.data.fill_(0.01) | |
| class Net(nn.Module): | |
| def __init__(self): | |
| super(Net, self).__init__() | |
| self.conv1 = nn.Conv2d(28, 64, (5, 5), padding=2) | |
| self.conv1_bn = nn.BatchNorm2d(64) | |
| self.conv2 = nn.Conv2d(64, 128, 2, padding=2) | |
| self.fc1 = nn.Linear(2048, 1024) | |
| self.dropout = nn.Dropout(0.3) | |
| self.fc2 = nn.Linear(1024, 512) | |
| self.bn = nn.BatchNorm1d(1) | |
| self.fc3 = nn.Linear(512, 128) | |
| self.fc4 = nn.Linear(128, 47) | |
| def forward(self, x): | |
| x = F.relu(self.conv1(x)) | |
| x = F.max_pool2d(x, 2, 2) | |
| x = self.conv1_bn(x) | |
| x = F.relu(self.conv2(x)) | |
| x = F.max_pool2d(x, 2, 2) | |
| x = x.view(-1, 2048) | |
| x = F.relu(self.fc1(x)) | |
| x = self.dropout(x) | |
| x = self.fc2(x) | |
| x = x.view(-1, 1, 512) | |
| x = self.bn(x) | |
| x = x.view(-1, 512) | |
| x = self.fc3(x) | |
| x = self.fc4(x) | |
| return F.log_softmax(x, dim=1) | |
| # | |
| return x | |
| net = Net() | |
| net.apply(init_weights) | |
| def count_parameters(model): | |
| return sum(p.numel() for p in model.parameters() if p.requires_grad) | |
| print(count_parameters(net)) | |
| logging.basicConfig(filename="cnn2.log", level=logging.DEBUG) | |
| logging.info(net) | |
| logging.info("Number of parameters: {}".format(count_parameters(net))) | |
| # Training | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| def one_hot(x, K): | |
| return np.array(x[:, None] == np.arange(K)[None, :], dtype=int) | |
| def inf_generator(iterable): | |
| """Allows training with DataLoaders in a single infinite loop: | |
| for i, (x, y) in enumerate(inf_generator(train_loader)): | |
| """ | |
| iterator = iterable.__iter__() | |
| while True: | |
| try: | |
| yield iterator.__next__() | |
| except StopIteration: | |
| iterator = iterable.__iter__() | |
| def accuracy(model, dataset_loader): | |
| total_correct = 0 | |
| for x, y in dataset_loader: | |
| x = x.view(-1, 28, 28, 1) | |
| x = torch.transpose(x, 1, 2) | |
| x = x.to(device) | |
| y = one_hot(np.array(y.numpy()), 47) | |
| target_class = np.argmax(y, axis=1) | |
| predicted_class = np.argmax(model(x).cpu().detach().numpy(), axis=1) | |
| total_correct += np.sum(predicted_class == target_class) | |
| return total_correct / len(dataset_loader.dataset) | |
| data_gen = inf_generator(train_loader) | |
| batches_per_epoch = len(train_loader) | |
| optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9) | |
| criterion = nn.CrossEntropyLoss().to(device) | |
| best_acc = 0 | |
| net.to(device) | |
| for itr in range(EPOCHS * batches_per_epoch): | |
| optimizer.zero_grad() | |
| x, y = data_gen.__next__() | |
| x = x.view(-1, 28, 28, 1) | |
| x = torch.transpose(x, 1, 2) | |
| x = x.to(device) | |
| y = y.to(device) | |
| logits = net(x) | |
| loss = criterion(logits, y) | |
| loss.backward() | |
| optimizer.step() | |
| if itr % batches_per_epoch == 0: | |
| with torch.no_grad(): | |
| train_acc = accuracy(net, train_loader) | |
| val_acc = accuracy(net, test_loader) | |
| if val_acc > best_acc: | |
| torch.save({"state_dict": net.state_dict()}, "cnn2.pth") | |
| best_acc = val_acc | |
| logging.info( | |
| "Epoch {:04d}" | |
| "Train Acc {:.4f} | Test Acc {:.4f}".format( | |
| itr // batches_per_epoch, train_acc, val_acc | |
| ) | |
| ) | |
| print( | |
| "Epoch {:04d}" | |
| "Train Acc {:.4f} | Test Acc {:.4f}".format( | |
| itr // batches_per_epoch, train_acc, val_acc | |
| ) | |
| ) |
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