def test_stop_training_in_epoch(gpu):
qucumber.set_random_seed(SEED, cpu=True, gpu=gpu, quiet=True)
np.random.seed(SEED)
nn_state = PositiveWaveFunction(10, gpu=gpu)
data = torch.ones(100, 10)
callbacks = [
LambdaCallback(
on_epoch_end=lambda nn_state, ep: set_stop_training(nn_state))
]
nn_state.fit(data, callbacks=callbacks)
msg = "stop_training wasn't set!"
assert nn_state.stop_training, msg
def test_trainingpositive():
print("Positive WaveFunction")
print("---------------------")
train_samples_path = os.path.join(
__tests_location__,
"..",
"examples",
"Tutorial1_TrainPosRealWaveFunction",
"tfim1d_data.txt",
)
psi_path = os.path.join(
__tests_location__,
"..",
"examples",
"Tutorial1_TrainPosRealWaveFunction",
"tfim1d_psi.txt",
)
train_samples, target_psi = data.load_data(train_samples_path, psi_path)
nv = nh = train_samples.shape[-1]
fidelities = []
KLs = []
epochs = 5
batch_size = 100
num_chains = 200
CD = 10
lr = 0.1
log_every = 5
print("Training 10 times and checking fidelity and KL at 5 epochs...\n")
for i in range(10):
print("Iteration: ", i + 1)
nn_state = PositiveWaveFunction(num_visible=nv,
num_hidden=nh,
gpu=False)
space = nn_state.generate_hilbert_space(nv)
callbacks = [
MetricEvaluator(
log_every,
{
"Fidelity": ts.fidelity,
"KL": ts.KL
},
target_psi=target_psi,
space=space,
verbose=True,
)
]
initialize_posreal_params(nn_state)
nn_state.fit(
data=train_samples,
epochs=epochs,
pos_batch_size=batch_size,
neg_batch_size=num_chains,
k=CD,
lr=lr,
time=True,
progbar=False,
callbacks=callbacks,
)
fidelities.append(ts.fidelity(nn_state, target_psi, space))
KLs.append(ts.KL(nn_state, target_psi, space))
print("\nStatistics")
print("----------")
print(
"Fidelity: ",
np.average(fidelities),
"+/-",
np.std(fidelities) / np.sqrt(len(fidelities)),
"\n",
)
print("KL: ", np.average(KLs), "+/-",
np.std(KLs) / np.sqrt(len(KLs)), "\n")
assert abs(np.average(fidelities) - 0.85) < 0.02
assert abs(np.average(KLs) - 0.29) < 0.05
assert (np.std(fidelities) / np.sqrt(len(fidelities))) < 0.01
assert (np.std(KLs) / np.sqrt(len(KLs))) < 0.01
"Fidelity": ts.fidelity,
"KL": ts.KL,
"A_Ψrbm_0": psi_coefficient
},
target=true_psi,
verbose=True,
space=space,
A=1.0,
)
]
nn_state.fit(
train_data,
epochs=epochs,
pos_batch_size=pbs,
neg_batch_size=nbs,
lr=lr,
k=k,
callbacks=callbacks,
time=True,
)
final_state_vector = np.array(
nn_state.psi(space)[0] / nn_state.compute_normalization(space).sqrt_())
np.savetxt("phi_fourier_RBM.txt", final_state_vector, fmt='%1.5f')
print("Estado cuántico final:", final_state_vector)
plt.close()
fig1 = plt.bar(np.arange(np.power(2, nv)), np.power(final_state_vector.real,
2))
plt.xlabel("Probabilities")
plt.ylim(0, 0.01)