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import torch
import torch.nn as nn
import torch.nn.functional as F
class Base(nn.Module):
def training_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
return loss
def validation_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
acc = accuracy(out, labels) # Calculate accuracy
return {'val_loss': loss.detach(), 'val_acc': acc}
def validation_epoch_end(self, outputs):
batch_losses = [x['val_loss'] for x in outputs]
epoch_loss = torch.stack(batch_losses).mean() # Combine losses
batch_accs = [x['val_acc'] for x in outputs]
epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies
return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
def epoch_end(self, epoch, result):
print("Epoch [{}], train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}".format(
epoch, result['train_loss'], result['val_loss'], result['val_acc']))
# print(f'Epoch: {epoch} | Train_loss: {result['train_loss']} | Val_loss:{result['val_loss']} | Val_acc: {result['val_acc']}')
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
import torch.nn as nn
import torch
class TumorDetectionModel(Base):
def __init__(self):
super(TumorDetectionModel, self).__init__()
# Define the network layers
self.network = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), stride=1, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2)),
nn.Dropout(0.25),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2)),
nn.Dropout(0.25),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2)),
nn.Dropout(0.25),
nn. Flatten()
)
# Define the classifier layers
self.classifier = nn.Sequential(
nn.Linear(in_features=128 * 28 * 28, out_features=128),
nn.BatchNorm1d(128),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(in_features=128, out_features=4)
)
def forward(self, x):
# Pass the input through the network
x = self.network(x)
# Pass the output of the network through the classifier
x = self.classifier(x)
return x
tumor_model = TumorDetectionModel() |