help='explicit regularization penalty',
default=0)
parser.add_argument('--NTK_depth',
type=int,
default=3,
help='depth of Fully Connected ReLU NTK')
args = parser.parse_args()
d = args.input_dim
lamb = args.lamb
depth = args.NTK_depth
kmax = 30
degens = np.array([gegenbauer.degeneracy(d, k) for k in range(kmax)])
spectrum = compute_NTK_spectrum.get_effective_spectrum([depth],
kmax,
d,
ker='NTK')[0, :]
s = [i for i in spectrum if i > 0]
P = 50
P_teach = 300
P_vals = np.logspace(0.25, 3, num=15).astype('int')
num_repeats = 50
all_errs = np.zeros((len(P_vals), kmax))
all_mc = np.zeros(len(P_vals))
std_errs = np.zeros((len(P_vals), kmax))
std_MC = np.zeros(len(P_vals))
for i in range(len(P_vals)):
P = P_vals[i]
all_errs[i, :], all_mc[i], std_errs[i, :], std_MC[i] = generalization(
parser = argparse.ArgumentParser()
parser.add_argument('--input_dim', type=int, default= 30,
help='data input dimension')
parser.add_argument('--M', type=int,
help='number of hidden units', default = 500)
args = parser.parse_args()
d = args.input_dim
M = args.M
kmax = 25
degens = np.array( [gegenbauer.degeneracy(d,k) for k in range(kmax)] )
theory_spectrum = compute_NTK_spectrum.get_effective_spectrum([1], kmax, d, ker = 'NTK')[0,:]
ak2 = gegenbauer.calculate_activation_coeffs(kmax,d)**2
bk2 = gegenbauer.calculate_activation_coeffs(kmax,d,nonlinearity='step')**2
spec_emp = np.zeros(kmax)
for i in range(kmax-1):
if i==0:
spec_emp[i] = ak2[i] + bk2[i+1] * 1/(d)
elif i==1:
spec_emp[i] = ak2[i] + bk2[0] + bk2[i+1] * (i+1)/(2*i+d) + (i+d-3)/(2*i+d-4) * bk2[i-1]
else:
spec_emp[i] = ak2[i] + bk2[i+1] * (i+1)/(2*i+d) + bk2[i-1] * (i+d-3)/(2*i+d-4)
print(spec_emp)
plt.loglog(spec_emp, 'o', label = 'sample on sphere')