From 594bbaa3dd88c2db32e6a17668c61f200cec80b0 Mon Sep 17 00:00:00 2001 From: Steffen Illium Date: Tue, 8 Feb 2022 17:24:00 +0100 Subject: [PATCH] parameters for training --- experiments/meta_task_exp.py | 11 ++++++----- network.py | 2 +- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/experiments/meta_task_exp.py b/experiments/meta_task_exp.py index 507f183..0695ec3 100644 --- a/experiments/meta_task_exp.py +++ b/experiments/meta_task_exp.py @@ -41,7 +41,7 @@ from functionalities_test import test_for_fixpoints, FixTypes WORKER = 10 if not debug else 2 BATCHSIZE = 500 if not debug else 50 -EPOCH = 400 if not debug else 3 +EPOCH = 200 VALIDATION_FRQ = 5 if not debug else 1 SELF_TRAIN_FRQ = 1 if not debug else 1 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') @@ -194,15 +194,16 @@ def flat_for_store(parameters): if __name__ == '__main__': self_train = True - training = False + training = True plotting = True particle_analysis = True as_sparse_network_test = True + self_train_alpha = 100 data_path = Path('data') data_path.mkdir(exist_ok=True, parents=True) - run_path = Path('output') / 'mn_st_NoRes' + run_path = Path('output') / 'mn_st_200_8_alpha_100' model_path = run_path / '0000_trained_model.zip' df_store_path = run_path / 'train_store.csv' weight_store_path = run_path / 'weight_store.csv' @@ -216,7 +217,7 @@ if __name__ == '__main__': d = DataLoader(dataset, batch_size=BATCHSIZE, shuffle=True, drop_last=True, num_workers=WORKER) interface = np.prod(dataset[0][0].shape) - metanet = MetaNet(interface, depth=5, width=6, out=10, residual_skip=False).to(DEVICE) + metanet = MetaNet(interface, depth=5, width=6, out=10, residual_skip=True).to(DEVICE) meta_weight_count = sum(p.numel() for p in next(metanet.particles).parameters()) loss_fn = nn.CrossEntropyLoss() @@ -236,7 +237,7 @@ if __name__ == '__main__': if self_train and is_self_train_epoch: # Zero your gradients for every batch! optimizer.zero_grad() - self_train_loss = metanet.combined_self_train() + self_train_loss = metanet.combined_self_train() * self_train_alpha self_train_loss.backward() # Adjust learning weights optimizer.step() diff --git a/network.py b/network.py index a2d1bc8..5a2502a 100644 --- a/network.py +++ b/network.py @@ -296,7 +296,7 @@ class MetaCell(nn.Module): self.name = name self.interface = interface self.weight_interface = 5 - self.net_hidden_size = 4 + self.net_hidden_size = 8 self.net_ouput_size = 1 self.meta_weight_list = nn.ModuleList() self.meta_weight_list.extend(