134 lines
No EOL
5.3 KiB
Python
134 lines
No EOL
5.3 KiB
Python
import torch
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import torch.nn as nn
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import torch.optim as optim
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from torchvision import datasets, transforms
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from torch.utils.data import DataLoader
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import matplotlib.pyplot as plt
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import os
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class CNN:
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def __init__(self, device=None):
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self.device = device or torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.model = self._build_model().to(self.device)
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self.criterion = nn.CrossEntropyLoss() # cross entropy is used for loss from torch
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self.optimizer = optim.Adam(self.model.parameters(), lr=0.001) # adam is used for optimization from torch
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def _build_model(self):
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(1, 32, 3, padding=1) # convolutional layer 1
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self.conv2 = nn.Conv2d(32, 64, 3, padding=1) # convolutional layer 2
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self.fc1 = nn.Linear(64 * 7 * 7, 256) # hidden layer with 256 units
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self.fc2 = nn.Linear(256, 10) # output layer
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self.relu = nn.ReLU() # ReLU activation from torch
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self.pool = nn.MaxPool2d(2, 2) # pooling from torch
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def forward(self, x):
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# forwards pass through the network
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x = self.relu(self.conv1(x))
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x = self.pool(x)
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x = self.relu(self.conv2(x))
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x = self.pool(x)
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x = x.view(x.size(0), -1)
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x = self.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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return Net()
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def fit(self, train_loader, val_loader, epochs=10):
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self.train_losses = []
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self.val_accuracies = []
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for epoch in range(1, epochs + 1):
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self.model.train()
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epoch_loss = 0.0
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for images, labels in train_loader:
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images, labels = images.to(self.device), labels.to(self.device)
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self.optimizer.zero_grad()
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outputs = self.model(images)
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loss = self.criterion(outputs, labels)
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loss.backward()
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self.optimizer.step()
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epoch_loss += loss.item() # updating the epoch loss
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self.train_losses.append(epoch_loss)
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val_acc = self.evaluate(val_loader)
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self.val_accuracies.append(val_acc)
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print(f"Epoch {epoch:02d} | Training Loss: {epoch_loss:.4f} | Validation Accuracy: {val_acc:.4f}")
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self.plot_graph(self.train_losses, self.val_accuracies)
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return self.val_accuracies[-1]
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def evaluate(self, loader):
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# measures the accuracy of your model on a given dataset
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self.model.eval()
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correct = 0
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total = 0
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with torch.no_grad():
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for images, labels in loader:
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images, labels = images.to(self.device), labels.to(self.device)
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outputs = self.model(images)
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preds = torch.argmax(outputs, dim=1)
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correct += (preds == labels).sum().item()
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total += labels.size(0)
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return correct / total
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def plot_graph(self, train_losses, val_accuracies):
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if not os.path.exists('results'):
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os.makedirs('results') # creates results director
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fig, ax1 = plt.subplots() # initializes the plot
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ax1.set_xlabel('Epochs')
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ax1.set_ylabel('Training Loss', color='tab:blue')
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ax1.plot(range(1, len(train_losses)+1), train_losses, color='tab:blue', label='Training Loss')
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ax1.tick_params(axis='y', labelcolor='tab:blue') # defines loss subplot
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ax2 = ax1.twinx()
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ax2.set_ylabel('Validation Accuracy', color='tab:orange')
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ax2.plot(range(1, len(val_accuracies)+1), val_accuracies, color='tab:orange', label='Validation Accuracy')
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ax2.tick_params(axis='y', labelcolor='tab:orange') # defines accuracy subplot
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plt.title('CNN Training Loss and Validation Accuracy over Epochs')
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result_path = 'results/experiment-6-convolutional-neural-network.png' # defines the file name
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fig.savefig(result_path)
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print(f"Graph saved to: {result_path}")
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def predict(self, loader):
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# returns the predicted class labels for all samples in the loader, instead of summarizing them as accuracy.
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self.model.eval()
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all_preds = []
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with torch.no_grad():
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for images, _ in loader:
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images = images.to(self.device)
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outputs = self.model(images)
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preds = torch.argmax(outputs, dim=1)
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all_preds.append(preds.cpu())
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return torch.cat(all_preds)
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# pre-processing the images
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.5,), (0.5,))
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])
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# acquiring the FashionMNIST dataset
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train_set = datasets.FashionMNIST(root='.', train=True, download=True, transform=transform)
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test_set = datasets.FashionMNIST(root='.', train=False, download=True, transform=transform)
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# splits data using dataloader with 256 batches
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train_loader = DataLoader(train_set, batch_size=256, shuffle=True)
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test_loader = DataLoader(test_set, batch_size=256, shuffle=False)
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# CNN initialization and training the model
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cnn = CNN()
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cnn.fit(train_loader, test_loader, epochs=10)
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# tests the model
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test_acc = cnn.evaluate(test_loader)
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print(f"\nFinal test accuracy: {test_acc:.4f}") |