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May 11, 2020 13:02
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| import sys | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| import torchvision.transforms as transforms | |
| from torchvision.datasets import MNIST | |
| from torch.utils.data import DataLoader | |
| import matplotlib.pyplot as plt | |
| device = 'cuda' | |
| class SimpleCNN(nn.Module): | |
| def __init__(self, num_channels=1, num_classes=10): | |
| super(SimpleCNN, self).__init__() | |
| self.conv1 = nn.Conv2d(num_channels, 32, 3, stride=1, padding=1) | |
| self.conv2 = nn.Conv2d(32, 32, 3, stride=1, padding=1) | |
| self.pool1 = nn.MaxPool2d(2) | |
| self.drop1 = nn.Dropout(0.25) | |
| self.fc1 = nn.Linear(14*14*32, 128) | |
| self.drop2 = nn.Dropout(0.5) | |
| self.fc2 = nn.Linear(128, num_classes) | |
| def forward(self, X): | |
| X = F.relu(self.conv1(X)) | |
| X = F.relu(self.conv2(X)) | |
| X = self.pool1(X) | |
| X = self.drop1(X) | |
| X = X.reshape(-1, 14*14*32) | |
| X = F.relu(self.fc1(X)) | |
| X = self.drop2(X) | |
| X = self.fc2(X) | |
| return X # logits | |
| def save_checkpoint(optimizer, model, epoch, filename): | |
| checkpoint_dict = { | |
| 'optimizer': optimizer.state_dict(), | |
| 'model': model.state_dict(), | |
| 'epoch': epoch | |
| } | |
| torch.save(checkpoint_dict, filename) | |
| def load_checkpoint(optimizer, model, filename): | |
| checkpoint_dict = torch.load(filename) | |
| epoch = checkpoint_dict['epoch'] | |
| model.load_state_dict(checkpoint_dict['model']) | |
| if optimizer is not None: | |
| optimizer.load_state_dict(checkpoint_dict['optimizer']) | |
| return epoch | |
| !mkdir -p checkpoints | |
| def train(optimizer, model, num_epochs=10, first_epoch=1): | |
| criterion = nn.CrossEntropyLoss() | |
| train_losses = [] | |
| valid_losses = [] | |
| for epoch in range(first_epoch, first_epoch + num_epochs): | |
| print('Epoch', epoch) | |
| # train phase | |
| model.train() | |
| # create a progress bar | |
| progress = ProgressMonitor(length=len(train_set)) | |
| train_loss = MovingAverage() | |
| for batch, targets in train_loader: | |
| # Move the training data to the GPU | |
| batch = batch.to(device) | |
| targets = targets.to(device) | |
| # clear previous gradient computation | |
| optimizer.zero_grad() | |
| # forward propagation | |
| predictions = model(batch) | |
| # calculate the loss | |
| loss = criterion(predictions, targets) | |
| # backpropagate to compute gradients | |
| loss.backward() | |
| # update model weights | |
| optimizer.step() | |
| # update average loss | |
| train_loss.update(loss) | |
| # update progress bar | |
| progress.update(batch.shape[0], train_loss) | |
| print('Training loss:', train_loss) | |
| train_losses.append(train_loss.value) | |
| # validation phase | |
| model.eval() | |
| valid_loss = RunningAverage() | |
| # keep track of predictions | |
| y_pred = [] | |
| # We don't need gradients for validation, so wrap in | |
| # no_grad to save memory | |
| with torch.no_grad(): | |
| for batch, targets in valid_loader: | |
| # Move the training batch to the GPU | |
| batch = batch.to(device) | |
| targets = targets.to(device) | |
| # forward propagation | |
| predictions = model(batch) | |
| # calculate the loss | |
| loss = criterion(predictions, targets) | |
| # update running loss value | |
| valid_loss.update(loss) | |
| # save predictions | |
| y_pred.extend(predictions.argmax(dim=1).cpu().numpy()) | |
| print('Validation loss:', valid_loss) | |
| valid_losses.append(valid_loss.value) | |
| # Calculate validation accuracy | |
| y_pred = torch.tensor(y_pred, dtype=torch.int64) | |
| accuracy = torch.mean((y_pred == valid_set.targets).float()) | |
| print('Validation accuracy: {:.4f}%'.format(float(accuracy) * 100)) | |
| # Save a checkpoint | |
| checkpoint_filename = 'checkpoints/mnist-{:03d}.pkl'.format(epoch) | |
| save_checkpoint(optimizer, model, epoch, checkpoint_filename) | |
| return train_losses, valid_losses, y_pred | |
| # transform for the training data | |
| train_transform = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.1307], [0.3081]) | |
| ]) | |
| # use the same transform for the validation data | |
| valid_transform = train_transform | |
| # load datasets, downloading if needed | |
| train_set = MNIST('./data/mnist', train=True, download=True, | |
| transform=train_transform) | |
| valid_set = MNIST('./data/mnist', train=False, download=True, | |
| transform=valid_transform) | |
| print(train_set.data.shape) | |
| print(valid_set.data.shape) | |
| train_loader = DataLoader(train_set, batch_size=256, num_workers=0, shuffle=True) | |
| valid_loader = DataLoader(valid_set, batch_size=512, num_workers=0, shuffle=False) | |
| model = SimpleCNN() | |
| model.to(device) | |
| optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, nesterov=True) | |
| train_losses, valid_losses, y_pred = train(optimizer, model, num_epochs=10) |
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