def test_networks(self,
momenta,
nj,
models,
x_means,
x_stds,
y_means,
y_stds,
return_predictions=False):
try:
momenta = momenta.tolist()
except:
pass
if self.all_legs:
all_jets = False
nlegs = self.nlegs + 2
else:
all_jets = True
nlegs = self.nlegs
NN = Model(input_size=(nlegs) * 4,
momenta=momenta,
labels=nj,
all_jets=all_jets,
all_legs=self.all_legs,
model_datset=self.model_dataset,
high_precision=self.high_precision)
_, _, _, _, _, _, _, _ = NN.process_training_data()
for i in range(self.training_reruns):
print('Predicting on model {}'.format(i))
model_dir_new = self.model_base_dir + self.model_dir + '_{}/'.format(
i)
x_standard = NN.process_testing_data(moms=momenta,
x_mean=x_means[i],
x_std=x_stds[i],
y_mean=y_means[i],
y_std=y_stds[i])
pred = models[i].predict(x_standard)
y_pred = NN.destandardise_data(y_pred=pred.reshape(-1),
x_mean=x_means[i],
x_std=x_stds[i],
y_mean=y_means[i],
y_std=y_stds[i])
if not return_predictions:
np.save(model_dir_new + '/pred_{}'.format(len(momenta)),
y_pred)
else:
return y_pred
print('############### Finished ###############')
y_mean=y_mean,
y_std=y_std
)
test_momenta = [
[500., 0., 0., 500.],
[500., 0., 0., -500.],
[253.58419798, -239.58965912, 66.81985738, -49.36443422],
[373.92489886, 7.43568582, -321.18384469, 191.32558238],
[372.49090317, 232.1539733, 254.36398731, -141.96114816]
]
testing_X = NN.process_testing_data(moms=test_momenta)
pred_std = model.predict(testing_X)
pred_destd = NN.destandardise_data(y_pred=pred_std)
with open ('./single_test_arch.json', 'w') as fout:
fout.write(model.to_json())
model.save_weights(args.weights)
print ('Model JSON and weights save')
print ('Saving out sample data')
with open('./data/single_test_sample.dat', 'w') as fin:
fin.write("{}\n".format(len(testing_X[0])))
for idx, i in enumerate(testing_X[0]):
if idx == len(testing_X[0]) - 1:
fin.write(str(i) + "\n")
else:
fin.write(str(i) + " ")