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import torch
def define_optimizer(model, lr, alpha):
# Define optimizer
optimizer = torch.optim.RMSprop(model.parameters(), lr=lr, alpha=alpha)
optimizer.zero_grad()
return optimizer
def tuple_of_tensors_to_tensor(tuple_of_tensors):
return torch.stack(list(tuple_of_tensors), dim=0)
def predict(model, inputs, notes, device):
outputs = model.forward(inputs, notes)
predicted = torch.sigmoid(outputs)
predicted = (predicted>0.5).float()
return outputs, predicted
def display_train(epoch, num_epochs, i, model, correct, total, loss, train_loader, valid_loader, device):
print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(train_loader)}], Train Loss: {loss.item():.4f}')
train_accuracy = correct/total
print(f'Epoch [{epoch+1}/{num_epochs}], Train Accuracy: {train_accuracy:.4f}')
valid_loss, valid_accuracy = eval_valid(model, valid_loader, epoch, num_epochs, device)
return train_accuracy, valid_accuracy, valid_loss
def eval_valid(model, valid_loader, epoch, num_epochs, device):
# Compute model train accuracy on test after all samples have been seen using test samples
model.eval()
with torch.no_grad():
correct = 0
total = 0
running_loss = 0
for inputs, labels, notes in valid_loader:
# Get images and labels from test loader
inputs = inputs.transpose(1,2).float().to(device)
labels = labels.float().to(device)
notes = notes.to(device)
# Forward pass and predict class using max
# outputs = model(inputs)
outputs, predicted = predict(model, inputs, notes, device) #torch.max(outputs.data, 1)
loss = torch.nn.functional.binary_cross_entropy_with_logits(outputs, labels)
running_loss += loss.item()*len(labels)
# Check if predicted class matches label and count numbler of correct predictions
total += labels.size(0)
#TODO: change acc criteria
# correct += torch.nn.functional.cosine_similarity(labels,predicted).sum().item() # (predicted == labels).sum().item()
values, indices = torch.max(outputs,dim=1)
correct += sum(1 for s, i in enumerate(indices)
if labels[s][i] == 1)
# Compute final accuracy and display
valid_accuracy = correct/total
validation_loss = running_loss/total
print(f'Epoch [{epoch+1}/{num_epochs}], Validation Accuracy: {valid_accuracy:.4f}, Validation Loss: {validation_loss:.4f}')
return validation_loss, valid_accuracy
def eval_test(model, test_loader, device):
# Compute model test accuracy on test after training
model.eval()
with torch.no_grad():
correct = 0
total = 0
for inputs, labels, notes in test_loader:
# Get images and labels from test loader
inputs = inputs.transpose(1,2).float().to(device)
labels = labels.float().to(device)
notes = notes.to(device)
# Forward pass and predict class using max
# outputs = model(inputs)
outputs, predicted = predict(model, inputs, notes, device)#torch.max(outputs.data, 1)
# Check if predicted class matches label and count numbler of correct predictions
total += labels.size(0)
#TODO: change acc criteria
# correct += torch.nn.functional.cosine_similarity(labels,predicted).sum().item() # (predicted == labels).sum().item()
values, indices = torch.max(outputs,dim=1)
correct += sum(1 for s, i in enumerate(indices)
if labels[s][i] == 1)
# Compute final accuracy and display
test_accuracy = correct/total
print(f'Ended Training, Test Accuracy: {test_accuracy:.4f}')
return test_accuracy |