MatthiasQuintern/teng-ml
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update parameters

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matthias@arch 2023-08-30 17:35:48 +02:00
parent 77b266929d
commit 570d6dbc25
1 changed files with 11 additions and 10 deletions
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teng_ml/main.py
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@ -42,9 +42,10 @@ 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", "antistatic_foil", "cardboard", "kapton", "bubble_wrap_PE", "fabric_PP" ])
models_dir = "/home/matth/Uni/TENG/teng_2/models_gen_12" # where to save models, settings and results # 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_15" # 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"
@ -53,18 +54,18 @@ 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, 3 ] num_layers = [ 4, 5 ]
hidden_size = [ 21, 28 ] hidden_size = [ 28, 36 ]
bidirectional = [ False, True ] bidirectional = [ 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_const_int ]]
batch_sizes = [ 4 ] batch_sizes = [ 4 ]
splitters = [ DataSplitter(50, 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 = [ 80 ] num_epochs = [ 80 ]
# (epoch, min_accuracy) # (epoch, min_accuracy)
training_cancel_points = [(15, 20), (40, 25)] # 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]
@ -81,7 +82,7 @@ if __name__ == "__main__":
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=0.60, 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),
] ]