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Readme Update und Smaller GN Training

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Steffen Illium
2022-02-02 13:11:35 +01:00
parent eb3b9b8958
commit 3fe4f49bca
2 changed files with 56 additions and 39 deletions
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30
experiments/meta_task_exp.py
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@ -117,8 +117,11 @@ def validate(checkpoint_path, ratio=0.1):
return acc
def new_train_storage_df():
return pd.DataFrame(columns=['Epoch', 'Batch', 'Metric', 'Score'])
def new_storage_df(identifier, weight_count):
if identifier == 'train':
return pd.DataFrame(columns=['Epoch', 'Batch', 'Metric', 'Score'])
elif identifier == 'weights':
return pd.DataFrame(columns=['Epoch', 'Weight', *(f'weight_{x}' for x in range(weight_count))])
def checkpoint_and_validate(model, out_path, epoch_n, final_model=False):
@ -163,8 +166,8 @@ def plot_training_result(path_to_dataframe):
if __name__ == '__main__':
self_train = False
training = False
self_train = True
training = True
plotting = True
particle_analysis = True
as_sparse_network_test = True
@ -172,9 +175,10 @@ if __name__ == '__main__':
data_path = Path('data')
data_path.mkdir(exist_ok=True, parents=True)
run_path = Path('output') / 'mnist_self_train_100_NEW_STYLE'
run_path = Path('output') / 'mn_st_smaller'
model_path = run_path / '0000_trained_model.zip'
df_store_path = run_path / 'train_store.csv'
weight_store_path = run_path / 'weight_store.csv'
if training:
utility_transforms = Compose([ToTensor(), ToFloat(), Resize((15, 15)), Flatten(start_dim=0)])
@ -186,11 +190,13 @@ if __name__ == '__main__':
interface = np.prod(dataset[0][0].shape)
metanet = MetaNet(interface, depth=5, width=6, out=10).to(DEVICE)
meta_weight_count = sum(p.numel() for p in next(metanet.particles).parameters())
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(metanet.parameters(), lr=0.008, momentum=0.9)
train_store = new_train_storage_df()
train_store = new_storage_df('train', None)
weight_store = new_storage_df('train', meta_weight_count)
for epoch in tqdm(range(EPOCH), desc='MetaNet Train - Epochs'):
is_validation_epoch = epoch % VALIDATION_FRQ == 0 if not debug else True
is_self_train_epoch = epoch % SELF_TRAIN_FRQ == 0 if not debug else True
@ -247,16 +253,24 @@ if __name__ == '__main__':
for key, value in dict(counter_dict).items():
step_log = dict(Epoch=int(epoch), Batch=BATCHSIZE, Metric=key, Score=value)
train_store.loc[train_store.shape[0]] = step_log
for particle in metanet.particles:
weight_log = (epoch, particle.name, *(x for y in particle.parameters() for x in y))
train_store.to_csv(df_store_path, mode='a', header=not df_store_path.exists())
# train_store = new_train_storage_df()
weight_store.to_csv(weight_store_path, mode='a', header=not weight_store_path.exists())
train_store = new_storage_df('train', None)
weight_store = new_storage_df('train', meta_weight_count)
metanet.eval()
accuracy = checkpoint_and_validate(metanet, run_path, EPOCH, final_model=True)
validation_log = dict(Epoch=EPOCH, Batch=BATCHSIZE,
Metric='Test Accuracy', Score=accuracy.item())
for particle in metanet.particles:
weight_log = (EPOCH, particle.name, *(x for y in particle.parameters() for x in y))
weight_store.loc[weight_store.shape[0]] = weight_log
train_store.loc[train_store.shape[0]] = validation_log
train_store.to_csv(df_store_path)
train_store.to_csv(df_store_path, mode='a', header=not df_store_path.exists())
weight_store.to_csv(weight_store_path, mode='a', header=not weight_store_path.exists())
if plotting:
plot_training_result(df_store_path)