loss_history = []
optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=reg)
scheduler = ReduceLROnPlateau(optimizer,
patience=adaptive_lr_patience,
cooldown=2,
verbose=1,
min_lr=1e-5 * lr,
factor=adaptive_lr_factor)
for epoch in range(1, num_epochs + 1):
print("Begin epoch {}/{}".format(epoch, num_epochs))
epoch_losses, epoch_f2 = train_epoch(
train_loader,
model,
loss_fn,
optimizer,
dtype,
sigmoid_threshold=sigmoid_threshold,
print_every=20)
scheduler.step(np.mean(epoch_losses), epoch)
## f2 score for validation dataset
f2_acc = validate_epoch(model,
val_loader,
dtype,
sigmoid_threshold=sigmoid_threshold)
## store results
train_acc_history += epoch_f2
val_acc_history.append(f2_acc)
loss_history += epoch_losses
## overwrite the model .pkl file every epoch
torch.save(model.state_dict(), save_model_path)
loss_fn = nn.MultiLabelSoftMarginLoss().type(dtype)
optimizer = optim.Adam(model.parameters(), lr=lr)
scheduler = ReduceLROnPlateau(optimizer,
patience=1,
factor=0.5,
min_lr=0.01 * lr)
acc_history = []
loss_history = []
## don't load model params from file - instead retrain the model
if not from_pickle:
for epoch in range(num_epochs):
print("Begin epoch {}/{}".format(epoch + 1, num_epochs))
epoch_loss = train_epoch(train_loader,
model,
loss_fn,
optimizer,
dtype,
print_every=10)
scheduler.step(epoch_loss[-1], epoch)
## f2 score for validation dataset
acc = validate_epoch(model, val_loader, dtype)
acc_history.append(acc)
loss_history += epoch_loss
print("END epoch {}/{}: F2 score = {:.02f}".format(
epoch + 1, num_epochs, acc))
## serialize model data and save as .pkl file
torch.save(model.state_dict(), save_model_path)
print("model saved as {}".format(os.path.abspath(save_model_path)))
## save loss and accuracy as .mat file
savemat(save_mat_path, {
"acc": acc_history,
optimizer_1 = optim.Adam(model.fc.parameters(),
lr=lr_1,
weight_decay=reg_1)
train_acc_history_1 = []
val_acc_history_1 = []
loss_history_1 = []
print("training {} fully connected layer".format(model_names[i]))
for epoch in range(num_epochs_1):
print("Begin {} epoch {}/{}".format(model_names[i], epoch + 1,
num_epochs_1))
epoch_losses, epoch_f2 = train_epoch(
train_loaders[i],
model,
loss_fn,
optimizer_1,
dtype,
print_every=20,
sigmoid_threshold=sigmoid_threshold)
## f2 score for validation dataset
f2_acc = validate_epoch(model,
val_loaders[i],
dtype,
sigmoid_threshold=sigmoid_threshold)
## store results
train_acc_history_1 += epoch_f2
val_acc_history_1.append(f2_acc)
loss_history_1 += epoch_losses
print("END {} epoch {}/{}: Val F2 score = {:.02f}".format(
model_names[i], epoch + 1, num_epochs_1, f2_acc))
for param in model.fc.parameters():
param.requires_grad = True
optimizer_1 = optim.Adam(model.fc.parameters(),
lr=lr_1,
weight_decay=reg_1)
train_acc_history_1 = []
val_acc_history_1 = []
loss_history_1 = []
print("training final fully connected layer")
for epoch in range(num_epochs_1):
print("Begin epoch {}/{}".format(epoch + 1, num_epochs_1))
epoch_losses, epoch_f2 = train_epoch(train_loader,
model,
loss_fn,
optimizer_1,
dtype,
print_every=10)
## f2 score for validation dataset
f2_acc = validate_epoch(model, val_loader, dtype)
## store results
train_acc_history_1 += epoch_f2
val_acc_history_1.append(f2_acc)
loss_history_1 += epoch_losses
print("END epoch {}/{}: validation F2 score = {:.02f}".format(
epoch + 1, num_epochs_1, f2_acc))
## now finetue the whole param set to our data
for param in model.parameters():
param.requires_grad = True