def run_preliminary(args, device):
"""
Function executes preliminary experiments
:param args: command line arguments
:param device: device to be used
"""
subs_train = [
1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 15000,
20000, 30000, 40000, 50000, 60000
]
subs_test = [1000, 1500, 2000, 3000, 4000, 5000, 7000, 8000, 9000, 10000]
epochs = [2, 5, 10, 15, 17, 20, 22, 25, 27, 30]
train_loader, test_loader = nnvis.data_load()
model = prep.get_net(device, train_loader, test_loader, args.epochs)
model.load_state_dict(torch.load(nnvis.init_state))
nnvis.pre_train_subset(model, device, subs_train, args.epochs, test_loader)
nnvis.pre_test_subset(model, device, subs_test)
nnvis.pre_epochs(model, device, epochs)
nnvis.plot_impact(subs_train,
nnvis.train_subs_loss,
nnvis.train_subs_acc,
xlabel="Size of training dataset")
nnvis.plot_impact(epochs,
nnvis.epochs_loss,
nnvis.epochs_acc,
annotate=False,
xlabel="Number of epochs")
nnvis.plot_box(subs_test, show=False, xlabel="Size of test subset")
def run_rand_dirs(args):
use_cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
train_loader, test_loader = nnvis.data_load()
model = prep.get_net(device, train_loader, test_loader, args.epochs)
dirs = nnvis.get_directions(model, device)
nnvis.calc_loss(model, test_loader, dirs, device)
nnvis.surface3d_rand_dirs()
nnvis.surface_heatmap_rand_dirs()
def run_pca_surface(args, device):
"""
Runs the visualization of loss surface around trained model.
:param args: experiment configuration
:param device: device to be used
"""
train_loader, test_loader = nnvis.data_load()
model = prep.get_net(device, train_loader, test_loader, args.epochs)
examine = nnvis.Examinator2D(model, device)
data = examine.get_loss_grid(test_loader)
nnvis.contour_path(data["path_2d"], data["loss_grid"], data["coords"], data["pcvariances"])
nnvis.surface_contour(data["loss_grid"], data["coords"])
def run_layer(args, device):
"""
Function setups and executes experiment of interpolation of parameters
:param args: experiment configuration
:param device: device to be used
"""
alpha = np.linspace(0, 1, args.alpha_steps)
train_loader, test_loader = nnvis.data_load()
model = prep.get_net(device, train_loader, test_loader, args.epochs)
interpolate = nnvis.Linear(model, device, alpha, nnvis.final_state,
nnvis.init_state)
interpolate.layers_linear(test_loader, args.layer)
def run_complete(args, device):
"""
Function runs a linear path experiment over all parameters of the model
:param args: CLI arguments with experiment configuration
:param device: device to be used
"""
alpha = np.linspace(0, 1, args.alpha_steps)
train_loader, test_loader = nnvis.data_load()
model = prep.get_net(device, train_loader, test_loader, args.epochs)
interpolate = nnvis.Linear(model, device, alpha, nnvis.final_state,
nnvis.init_state)
interpolate.interpolate_all_linear(test_loader)
def run_individual(args, device):
"""
Runs quadratic interpolation examination on the level of parameters.
:param args: experiment configuration
:param device: device to be used
"""
alpha = np.linspace(args.alpha_start, args.alpha_end, args.alpha_steps)
train_loader, test_loader = nnvis.data_load()
model = prep.get_net(device, train_loader, test_loader, args.epochs)
interpolate = nnvis.Quadratic(model, device, alpha, nnvis.final_state, nnvis.init_state)
interpolate.individual_param_quadratic(test_loader, args.layer, args.idxs)
def run_individual(args, device):
"""
Function executes experiment with individual parameter
:param args: command line arguments
:param device: device to be used
"""
alpha = np.linspace(args.alpha_start, args.alpha_end,
args.alpha_steps) # setup interpolation coefficient
train_loader, test_loader = nnvis.data_load() # setup data loaders
model = prep.get_net(device, train_loader, test_loader,
args.epochs) # setup model
interpolate = nnvis.Linear(model, device, alpha, nnvis.final_state,
nnvis.init_state) # get interpolator instance
interpolate.individual_param_linear(test_loader, args.layer,
args.idxs) # execute the experiment
def run_complete(args, device):
"""
Runs quadratic interpolation examination over all parameters of the model.
:param args: experiment configuration
:param device: device to be used
"""
alpha = np.linspace(0, 1, args.alpha_steps)
train_loader, test_loader = nnvis.data_load()
model = prep.get_net(device, train_loader, test_loader, args.epochs)
interpolate = nnvis.Quadratic(model, device, alpha, nnvis.final_state, nnvis.init_state)
interpolate_l = nnvis.Linear(model, device, alpha, nnvis.final_state, nnvis.init_state)
interpolate_l.interpolate_all_linear(test_loader)
interpolate.interpolate_all_quadratic(test_loader)
nnvis.plot_lin_quad_real(alpha)