loss_name = loss.__class__.__name__
print(f"Loss: {loss_name}\n")
for noise_value in noise_values:
# RUN Experiments
name = f'CNN_{loss_name}_{tp_noise}_{noise_value}'
print(f"Training {name} with noise of type {tp_noise} and probability {noise_value}...")
# data preparation
dataset = FashionMnistHandler(data_dir, False)
dataset.load()
train_loader, val_loader, test_loader = dataset.get_noisy_loaders(p_noise=noise_value,
type_noise=tp_noise,
val_size=1 / 6,
train_batch_size=batch_size,
val_batch_size=128,
test_batch_size=128)
# model, optimizer, summary
model = CNNModel()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
summ = Summary(name, type_noise=tp_noise, noise_rate=noise_value)
solver = Solver(name, PROJECT_DIR, batch_model_dir, batch_summaries_dir, model,
optimizer, loss, summ, train_loader, val_loader, test_loader)
solver.pretrain()
solver.train(loss)
print(f"Completed training...")
