def func_sst(x, network, mini_batch_f, mini_batch_u, n):
c = 0.0 # The cost function itself, which must be calculated on the mini-package
mini_batch_size = len(mini_batch_f)
list_u = transform_to_matrix(
x, n) # Restored our list of trial basis matrices
for k in range(mini_batch_size):
fl = create_list_fl(mini_batch_f[k], n)
c = c + sst(interferometer(fl, list_u, n), mini_batch_u[k])
c = c / len(mini_batch_f)
return c # The function returns a real number
def derivative_func_sst(x, network, mini_batch_f, mini_batch_u, n):
list_u = transform_to_matrix(
x, n) # Restored our list of trial basis matrices
network.list_U = list_u # Updated values of trial base matrices (why?)
network.grad_sst(mini_batch_f,
mini_batch_u) # Calculation of stochastic gradient
list_of_grad = transform_to_1d_list(network.grad_U, n)
der = np.zeros_like(x)
for i in range(len(x)):
der[i] = list_of_grad[i]
return der
def trainer(file_name1,
file_name2,
file_name3,
n,
m,
mini_batch_size,
counts_of_epochs,
func,
derivative_func,
functional,
coeff,
noisy_f,
noisy_u,
network,
method='L-BFGS-B'):
fm, um = load_data(n, m, file_name2) # Got the whole sample
for u in um:
fm = fm + noisy_f * np.random.randn(n, n)
um = um + noisy_u * (np.random.randn(n, n) +
1j * np.random.randn(n, n))
um = polar_correct(um) # Unitarization of basis matrices
# network = Network(n, m, mini_batch_size, file_name3) # Created an object of class Network
if coeff is not None:
list_goal_u = load_goal_matrices(n, file_name1)
# Downloaded the list of correct unitary matrices to facilitate the search
list_u = get_list_noisy(list_goal_u, coeff, n)
network.list_U = list_u # Facilitating the search for a solution with large values of n
steps = []
results = []
cross_validation = []
norma = []
x0 = transform_to_1d_list(network.list_U,
n) # Initialized Optimization algorithm
list_goal_u = load_goal_matrices(n, file_name1)
if method == 'L-BFGS-B':
print('Turned on L-BFGS-B')
for i in range(counts_of_epochs):
mini_batch_f, mini_batch_u = create_mini_batch(
n, m, mini_batch_size, fm, um)
# Formed a mini-package for Learning at one step
steps.append(i)
results.append(func(x0, network, mini_batch_f, mini_batch_u, n))
f = get_random_phase(n)
cross_validation.append(
functional(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)))
norma.append(
norma_square(
interferometer(create_list_fl(mini_batch_f[0], n),
network.list_U, n), n))
res = minimize(func,
x0,
args=(network, mini_batch_f, mini_batch_u, n),
method='L-BFGS-B',
jac=derivative_func,
options={
'disp': False,
'maxiter': 1
}) # Optimization step 'BFGS'
network.list_U = transform_to_matrix(
res.x, n) # Updated the neural network
network.polar_correct()
x0 = res.x
f = get_random_phase(n)
print(
'Epoch: ', i + 1, ' Training set: ', results[i], ' Test set: ',
functional(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)))
if method == 'SGD':
print('Turned on SGD')
# rate_learning = 0.1 # The best
rate_learning = 0.2
for i in range(counts_of_epochs):
mini_batch_f, mini_batch_u = create_mini_batch(
n, m, mini_batch_size, fm, um)
# Formed a mini-package for Learning at one step
steps.append(i)
results.append(func(x0, network, mini_batch_f, mini_batch_u, n))
f = get_random_phase(n)
cross_validation.append(
functional(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)))
norma.append(
norma_square(
interferometer(create_list_fl(mini_batch_f[0], n),
network.list_U, n), n))
x0 = x0 - rate_learning * derivative_func(
x0, network, mini_batch_f, mini_batch_u, n)
# Optimization step 'SGD'
network.list_U = transform_to_matrix(
x0, n) # Updated the neural network
network.polar_correct()
# print(norma_square(network.list_U[0], network.N))
# print(x0, ' ', results[i])
f = get_random_phase(n)
print(
'epoch: ', i + 1, ' Training set: ', results[i], ' Test set: ',
functional(
interferometer(create_list_fl(f, n), network.list_U, n)))
# Cross validation
list_goal_u = load_goal_matrices(n, file_name1)
for i in range(10):
f = get_random_phase(n)
print(
frobenius_reduced(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)),
infidelity(interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)),
weak_reduced(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)),
sst(interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n)))
steps = np.array(steps)
results = np.array(results)
cross_validation = np.array(cross_validation)
norma = np.array(norma)
error = 0.0
for i in range(1000):
f = get_random_phase(n)
error = error + infidelity(
interferometer(create_list_fl(f, n), network.list_U, n),
interferometer(create_list_fl(f, n), list_goal_u, n))
error = error / 1000
return steps, results, cross_validation, norma, error