def gcmpm_one_class(para=None):
if para is None:
para = pm.parameters_gcmpm_one_class()
para['save_exp'] = save_exp_gcmpm_one_class(para)
if para['parallel'] is True:
par_pool = para['n_nodes']
else:
par_pool = None
if para['if_load'] and os.path.isfile(para['save_exp']):
a = bf.load_pr(os.path.join(para['data_path'], para['save_exp']), 'a')
else:
a = TNmachineLearning.MachineLearningMPS(para['d'], para['chi'], para['dataset'],
par_pool=par_pool)
a.images2vecs([para['class']], [100])
a.initialize_virtual_vecs_train()
a.update_virtual_vecs_train('all', 'all', 'both')
a.mps.correct_orthogonal_center(0, normalize=True)
a.mps.mps[0] /= np.linalg.norm(a.mps.mps[0].reshape(-1, ))
mps0 = a.mps.mps.copy()
for t in range(0, para['sweep_time']):
# from left to right
if para['if_print_detail']:
print('At the ' + str(t) + '-th sweep, from left to right')
for nt in range(0, a.length):
a.update_tensor_gradient(nt, para['step'])
if nt != a.length-1:
a.update_virtual_vecs_train('all', nt, 'left')
# from left to right
print('At the ' + str(t) + '-th sweep, from right to left')
for nt in range(a.length-1, -1, -1):
a.update_tensor_gradient(nt, para['step'])
if nt != 0:
a.update_virtual_vecs_train('all', nt, 'right')
if t > para['check_time0'] and ((t+1) % para['check_time'] == 0
or t+1 == para['sweep_time']):
fid = ln_fidelity_per_site(mps0, a.mps.mps)
if fid < (para['step'] * para['ratio_step_tol']):
print('After ' + str(t+1) + ' sweeps: fid = %g' % fid)
para['step'] *= para['step_ratio']
elif t+1 == para['sweep_time']:
print('After all ' + str(t+1) + ' sweeps finished, fid = %g. '
'Consider to increase the sweep times.' % fid)
else:
print('After ' + str(t+1) + ' sweeps, fid = %g.' % fid)
mps0 = a.mps.mps.copy()
if para['step'] < para['step_min']:
print('Now step = ' + str(para['step']) + ' is sufficiently small. Break the loop')
break
else:
print('Now step = ' + str(para['step']))
if para['if_save']:
save_pr(para['data_path'], para['save_exp'], [a, para], ['a', 'para'])
return a, para
def gtn_one_class(para=None, images=None, labels=None):
if 'to_black_and_white' not in para:
para['to_black_and_white'] = False
if para is None:
para = pm.parameters_gtn_one_class()
para['save_exp'] = save_exp_gtn_one_class(para)
if para['if_load'] and os.path.isfile(
os.path.join(para['data_path'], para['save_exp'])):
a = bf.load_pr(os.path.join(para['data_path'], para['save_exp']), 'a')
else:
a = TNmachineLearning.MachineLearningMPS(para['d'], para['chi'],
para['dataset'])
if para['dataset'] is 'custom':
a.input_data(copy.deepcopy(images), copy.deepcopy(labels))
else:
a.load_data()
if a.is_there_labels:
a.select_samples([para['class']])
if para['to_black_and_white']:
a.to_black_and_white()
if para['dct'] is True:
a.dct(shift=para['shift'], factor=para['factor'])
a.images2vecs(theta_max=para['theta'] * np.pi / 2)
a.initial_mps(center=0, ini_way='1')
a.initialize_virtual_vecs_train()
a.mps.correct_orthogonal_center(0)
a.update_tensor_gradient(0, para['step'])
nll0 = a.compute_nll()
step = copy.deepcopy(para['step'])
print('Iniitially, NLL = ' + str(nll0))
for t in range(0, para['sweep_time']):
# from left to right
if para['if_print_detail']:
print('At the ' + str(t + 1) + '-th sweep, from left to right')
t0 = time.time()
tt0 = time.clock()
for nt in range(0, a.length):
a.mps.correct_orthogonal_center(nt)
if nt != 0:
a.update_virtual_vecs_train(nt - 1, 'left')
a.update_tensor_gradient(nt, step)
# from right to left
if para['if_print_detail']:
print('At the ' + str(t + 1) + '-th sweep, from right to left')
for nt in range(a.length - 1, -1, -1):
a.mps.correct_orthogonal_center(nt)
if nt != a.length - 1:
a.update_virtual_vecs_train(nt + 1, 'right')
a.update_tensor_gradient(nt, step)
if para['if_print_detail']:
print('Wall time cost for one loop: %s' % (time.time() - t0))
print('CPU time cost for one loop: %s' % (time.clock() - tt0))
if t > (para['check_time0'] - 2) and (
(t + 1) % para['check_time'] == 0
or t + 1 == para['sweep_time']):
nll = a.compute_nll()
print('NLL = ' + str(nll))
# fid = fidelity_per_site(mps0, a.mps.mps)
fid = abs(nll - nll0) / nll0
if fid < (step * para['step_ratio']):
print('After ' + str(t + 1) + ' sweeps: fid = %g' % fid)
step *= para['step_ratio']
# mps0 = copy.deepcopy(a.mps.mps)
nll0 = nll
elif t + 1 == para['sweep_time']:
print('After all ' + str(t + 1) +
' sweeps finished, fid = %g. '
'Consider to increase the sweep times.' % fid)
else:
print('After ' + str(t + 1) + ' sweeps, fid = %g.' % fid)
# mps0 = copy.deepcopy(a.mps.mps)
nll0 = nll
if step < para['step_min']:
print('Now step = ' + str(step) +
' is sufficiently small. Break the loop')
break
else:
print('Now step = ' + str(step))
a.clear_before_save()
if para['if_save']:
save_pr(para['data_path'], para['save_exp'], [a, para],
['a', 'para'])
return a, para
def labeled_gtn(para):
if para is None:
para = pm.parameters_labeled_gtn()
para['save_exp'] = save_exp_labeled_gtn(para)
if para['parallel'] is True:
par_pool = para['n_nodes']
else:
par_pool = None
# Preparing testing dataset
b = TNmachineLearning.MachineLearningFeatureMap(
para['d'],
file_sample='t10k-images.idx3-ubyte',
file_label='t10k-labels.idx1-ubyte')
b.load_data()
b.select_samples(para['classes'])
b.add_labels_to_images()
b.images2vecs(para['theta'])
data_file = os.path.join(para['data_path'], para['save_exp'])
if para['if_load'] and os.path.isfile(data_file):
print('Data exist. Load directly.')
a = bf.load_pr(data_file, 'a')
else:
a = TNmachineLearning.MachineLearningMPS(para['d'],
para['chi'],
para['dataset'],
par_pool=par_pool)
a.load_data()
a.select_samples(para['classes'])
a.add_labels_to_images()
a.images2vecs(para['theta'] * np.pi / 2)
a.initial_mps()
a.mps.correct_orthogonal_center(0, normalize=True)
a.initialize_virtual_vecs_train()
a.update_virtual_vecs_train_all_tensors('both')
accuracy0 = 0
for t in range(0, para['sweep_time']):
# from left to right
for nt in range(0, a.length):
a.update_tensor_gradient(nt, para['step'])
if nt != a.length - 1:
a.update_virtual_vecs_train(nt, 'left')
# from left to right
for nt in range(a.length - 1, -1, -1):
a.update_tensor_gradient(nt, para['step'])
if nt != 0:
a.update_virtual_vecs_train(nt, 'right')
if t > para['check_time0'] and ((t + 1) % para['check_time'] == 0
or t + 1 == para['sweep_time']):
b.input_mps(a.mps)
accuracy = b.calculate_accuracy()
print('After the ' + str(t) +
'-th sweep, the testing accuracy = ' + str(accuracy))
if abs(accuracy - accuracy0) < (para['step'] *
para['ratio_step_tol']):
para['step'] *= para['step_ratio']
accuracy0 = accuracy
print('Converged. Reduce the gradient step to ' +
str(para['step']))
elif t + 1 == para['sweep_time']:
print('After all ' + str(t + 1) +
' sweeps finished, not converged. '
'Consider to increase the sweep times.')
else:
accuracy0 = accuracy
if para['step'] < para['step_min']:
print('Now step = ' + str(para['step']) +
' is sufficiently small. Break the loop')
break
else:
print('Now step = ' + str(para['step']))
a.clear_before_save()
if para['if_save']:
save_pr(para['data_path'], para['save_exp'], [a, para],
['a', 'para'])
accuracy = b.calculate_accuracy()
print('The final testing accuracy = ' + str(accuracy))
return a, para