subject = 1 # layers = int(sys.argv[1]) lr_decay = 0.99 lr = 1E-4 print("Training models with {} layers".format(layers)) width = 64 lstm_width = 49 for layers in [1, 2, 3]: for val_set in range(1, 8): # Load the data set dataset_options = load_data.make_default_options(train_bs=1, train_sl=2048, val_bs=10, ar=False, val_set=val_set) dataset_options["subject"] = subject train_loader, val_loader, test_loader, scaling_factors = load_data.load_dataset( dataset="gait_prediction_stairs", dataset_options=dataset_options) nu = train_loader.nu ny = train_loader.ny # Options for the solver # solver_options = nlsdp.make_stochastic_nlsdp_options(max_epochs=max_epochs, lr=5.0E-4, mu0=100, lr_decay=0.98) solver_options = nlsdp.make_stochastic_nlsdp_options( max_epochs=max_epochs, lr=lr,
eps = 1E-2 ar = False max_epochs = 500 patience = 20 layers = int(sys.argv[1]) # layers = 3 print("Training models with {} layers".format(layers)) width = 200 for subject in range(1, 50): for val_set in range(0, 20): # Load the data set dataset_options = load_data.make_default_options(train_bs=40, train_sl=200, val_set=val_set, test_sl=2000) dataset_options["gain"] = 1.4 train_loader, val_loader, test_loader = load_data.load_dataset( dataset="chen", dataset_options=dataset_options) nu = train_loader.nu ny = train_loader.ny # Options for the solver solver_options = nlsdp.make_stochastic_nlsdp_options( max_epochs=max_epochs, lr=0.1E-4, mu0=2000, lr_decay=0.96, patience=10)