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)