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gen_12

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matthias@arch 2023-08-14 18:44:39 +02:00
parent 33d1945de2
commit 5895f39874
1 changed files with 11 additions and 13 deletions
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teng_ml/main.py
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@ -42,9 +42,9 @@ def test_interpol():
if __name__ == "__main__": if __name__ == "__main__":
# labels = LabelConverter(["foam_PDMS_white", "foam_PDMS_black", "foam_PDMS_TX100", "foam_PE", "antistatic_foil", "cardboard", "glass", "kapton", "bubble_wrap_PE", "fabric_PP", ]) labels = LabelConverter(["foam_PDMS_white", "foam_PDMS_black", "foam_PDMS_TX100", "foam_PE", "antistatic_foil", "cardboard", "glass", "kapton", "bubble_wrap_PE", "fabric_PP", ])
labels = LabelConverter(["foam_PDMS_white", "foam_PDMS_black", "foam_PDMS_TX100", "foam_PE", "kapton", "bubble_wrap_PE", "fabric_PP", ]) # labels = LabelConverter(["foam_PDMS_white", "foam_PDMS_black", "foam_PDMS_TX100", "foam_PE", "kapton", "bubble_wrap_PE", "fabric_PP", ])
models_dir = "/home/matth/Uni/TENG/teng_2/models_gen_8" # where to save models, settings and results models_dir = "/home/matth/Uni/TENG/teng_2/models_gen_12" # where to save models, settings and results
if not path.isdir(models_dir): if not path.isdir(models_dir):
makedirs(models_dir) makedirs(models_dir)
data_dir = "/home/matth/Uni/TENG/teng_2/sorted_data" data_dir = "/home/matth/Uni/TENG/teng_2/sorted_data"
@ -53,17 +53,17 @@ if __name__ == "__main__":
# gen_6 best options: no glass, cardboard and antistatic_foil, not bidirectional, lr=0.0007, no datasplitter, 2 layers n_hidden = 10 # gen_6 best options: no glass, cardboard and antistatic_foil, not bidirectional, lr=0.0007, no datasplitter, 2 layers n_hidden = 10
# Test with # Test with
num_layers = [ 2 ] num_layers = [ 2, 3 ]
hidden_size = [ 7, 11, 14 ] hidden_size = [ 21, 28 ]
bidirectional = [ False, True ] bidirectional = [ False, True ]
t_const_int = ConstantInterval(0.01) # TODO check if needed: data was taken at equal rate, but it isnt perfect -> maybe just ignore? t_const_int = ConstantInterval(0.01) # TODO check if needed: data was taken at equal rate, but it isnt perfect -> maybe just ignore?
t_norm = Normalize(-1, 1) t_norm = Normalize(-1, 1)
transforms = [[ ], [ t_norm ]] #, [ t_norm, t_const_int ]] transforms = [[ t_norm ]] #, [ t_norm, t_const_int ]]
batch_sizes = [ 4 ] batch_sizes = [ 4 ]
splitters = [ DataSplitter(50, drop_if_smaller_than=30), DataSplitter(100, drop_if_smaller_than=30) ] # smallest file has length 68 TODO: try with 0.5-1second snippets splitters = [ DataSplitter(50, drop_if_smaller_than=30) ] # smallest file has length 68 TODO: try with 0.5-1second snippets
num_epochs = [ 5 ] num_epochs = [ 80 ]
# (epoch, min_accuracy) # (epoch, min_accuracy)
training_cancel_points = [(10, 10), (20, 20), (40, 30)] training_cancel_points = [(15, 20), (40, 25)]
# training_cancel_points = [] # training_cancel_points = []
args = [num_layers, hidden_size, bidirectional, [None], [None], [None], transforms, splitters, num_epochs, batch_sizes] args = [num_layers, hidden_size, bidirectional, [None], [None], [None], transforms, splitters, num_epochs, batch_sizes]
@ -75,15 +75,13 @@ if __name__ == "__main__":
loss_func = nn.CrossEntropyLoss() loss_func = nn.CrossEntropyLoss()
optimizers = [ optimizers = [
lambda model: torch.optim.Adam(model.parameters(), lr=0.0005),
lambda model: torch.optim.Adam(model.parameters(), lr=0.0007), lambda model: torch.optim.Adam(model.parameters(), lr=0.0007),
# lambda model: torch.optim.Adam(model.parameters(), lr=0.008),
] ]
schedulers = [ schedulers = [
None, None,
# lambda optimizer, st: torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9), # lambda optimizer, st: torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9),
# lambda optimizer, st: torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.5), # lambda optimizer, st: torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.5),
lambda optimizer, st: torch.optim.lr_scheduler.StepLR(optimizer, step_size=st.num_epochs // 8, gamma=0.50, verbose=False), lambda optimizer, st: torch.optim.lr_scheduler.StepLR(optimizer, step_size=st.num_epochs // 8, gamma=0.60, verbose=False),
# lambda optimizer, st: torch.optim.lr_scheduler.StepLR(optimizer, step_size=st.num_epochs // 10, gamma=0.75, verbose=False), # lambda optimizer, st: torch.optim.lr_scheduler.StepLR(optimizer, step_size=st.num_epochs // 10, gamma=0.75, verbose=False),
] ]
@ -105,7 +103,7 @@ if __name__ == "__main__":
for s in range(len(schedulers)): for s in range(len(schedulers)):
for i in range(len(settings)): for i in range(len(settings)):
st = settings[i] st = settings[i]
train_set, test_set = load_datasets(data_dir, labels, exclude_n_object=None, voltage=None, transforms=st.transforms, split_function=st.splitter, train_to_test_ratio=0.7, random_state=80, num_workers=4) train_set, test_set = load_datasets(data_dir, labels, exclude_n_object=None, voltage=None, transforms=st.transforms, split_function=st.splitter, train_to_test_ratio=0.7, random_state=123, num_workers=4)
generator = torch.manual_seed(42) generator = torch.manual_seed(42)
train_loader = DataLoader(train_set, batch_size=st.batch_size, shuffle=True, generator=generator, collate_fn=PadSequences()) train_loader = DataLoader(train_set, batch_size=st.batch_size, shuffle=True, generator=generator, collate_fn=PadSequences())