def calculate_mode_connectivity(w1, w2, eval_loader, config):
net = load_model('{}/{}.pth'.format(config['exp_dir'], 'init')).to(DEVICE)
loss_history, acc_history, ts = [], [], []
for t in np.arange(0.0, 1.01, 0.025):
ts.append(t)
net = assign_weights(net, w1 + t*(w2-w1)).to(DEVICE)
metrics = eval_single_epoch(net, eval_loader)
loss_history.append(metrics['loss'])
acc_history.append(metrics['accuracy'])
return loss_history, acc_history, ts
def plot_loss_plane(w, eval_loader, path, w_labels, config):
u = w[2] - w[0]
dx = np.linalg.norm(u)
u /= dx
v = w[1] - w[0]
v -= np.dot(u, v) * u
dy = np.linalg.norm(v)
v /= dy
m = load_task_model_by_policy(0, 'init', config['exp_dir'])
m.eval()
coords = np.stack(get_xy(p, w[0], u, v) for p in w)
# print("coords", coords)
G = 15
margin = 0.2
alphas = np.linspace(0.0 - margin, 1.0 + margin, G)
betas = np.linspace(0.0 - margin, 1.0 + margin, G)
tr_loss = np.zeros((G, G))
grid = np.zeros((G, G, 2))
for i, alpha in enumerate(alphas):
for j, beta in enumerate(betas):
p = w[0] + alpha * dx * u + beta * dy * v
m = assign_weights(m, p).to(DEVICE)
err = eval_single_epoch(m, eval_loader)['loss']
c = get_xy(p, w[0], u, v)
#print(c)
grid[i, j] = [alpha * dx, beta * dy]
tr_loss[i, j] = err
contour = {'grid': grid, 'values': tr_loss, 'coords': coords}
save_np_arrays(contour, path=path)
plot_contour(grid,
tr_loss,
coords,
log_alpha=-5.0,
N=7,
path=path,
w_labels=w_labels,
dataset='mnist') #config['dataset'])
return contour