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parameters for training

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Steffen Illium 2022-02-08 17:24:00 +01:00
parent d4c25872c6
commit 594bbaa3dd
2 changed files with 7 additions and 6 deletions
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11
experiments/meta_task_exp.py
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@ -41,7 +41,7 @@ from functionalities_test import test_for_fixpoints, FixTypes
WORKER = 10 if not debug else 2 WORKER = 10 if not debug else 2
BATCHSIZE = 500 if not debug else 50 BATCHSIZE = 500 if not debug else 50
EPOCH = 400 if not debug else 3 EPOCH = 200
VALIDATION_FRQ = 5 if not debug else 1 VALIDATION_FRQ = 5 if not debug else 1
SELF_TRAIN_FRQ = 1 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') DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@ -194,15 +194,16 @@ def flat_for_store(parameters):
if __name__ == '__main__': if __name__ == '__main__':
self_train = True self_train = True
training = False training = True
plotting = True plotting = True
particle_analysis = True particle_analysis = True
as_sparse_network_test = True as_sparse_network_test = True
self_train_alpha = 100
data_path = Path('data') data_path = Path('data')
data_path.mkdir(exist_ok=True, parents=True) 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' model_path = run_path / '0000_trained_model.zip'
df_store_path = run_path / 'train_store.csv' df_store_path = run_path / 'train_store.csv'
weight_store_path = run_path / 'weight_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) d = DataLoader(dataset, batch_size=BATCHSIZE, shuffle=True, drop_last=True, num_workers=WORKER)
interface = np.prod(dataset[0][0].shape) 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()) meta_weight_count = sum(p.numel() for p in next(metanet.particles).parameters())
loss_fn = nn.CrossEntropyLoss() loss_fn = nn.CrossEntropyLoss()
@ -236,7 +237,7 @@ if __name__ == '__main__':
if self_train and is_self_train_epoch: if self_train and is_self_train_epoch:
# Zero your gradients for every batch! # Zero your gradients for every batch!
optimizer.zero_grad() optimizer.zero_grad()
self_train_loss = metanet.combined_self_train() self_train_loss = metanet.combined_self_train() * self_train_alpha
self_train_loss.backward() self_train_loss.backward()
# Adjust learning weights # Adjust learning weights
optimizer.step() optimizer.step()

2
network.py
View File

@ -296,7 +296,7 @@ class MetaCell(nn.Module):
self.name = name self.name = name
self.interface = interface self.interface = interface
self.weight_interface = 5 self.weight_interface = 5
self.net_hidden_size = 4 self.net_hidden_size = 8
self.net_ouput_size = 1 self.net_ouput_size = 1
self.meta_weight_list = nn.ModuleList() self.meta_weight_list = nn.ModuleList()
self.meta_weight_list.extend( self.meta_weight_list.extend(