"nRs3": 10,
"nTs": 10,
"rcut": 3.18,
"acut": 3.18,
"zeta": 52.779232035094125,
"eta": 1.4954812022150898
}
estimator = ARMP(iterations=5283,
batch_size=37,
l1_reg=8.931599068573057e-06,
l2_reg=3.535679697949907e-05,
learning_rate=0.0008170485394812195,
representation_name='acsf',
representation_params=acsf_params,
tensorboard=True,
store_frequency=25,
hidden_layer_sizes=(15, 88))
estimator.set_properties(ene_isopent)
estimator.generate_representation(xyz_isopent, zs_isopent, method="fortran")
# Fitting the estimator and scoring it
estimator.fit(idx_train)
score = estimator.score(idx_test)
# Saving the model for later use
model_name = "md-nn"
estimator.save_nn(model_name)
print(score)
ene_true = np.array([0.5, 0.9, 1.0])
estimator = ARMP(iterations=10, l1_reg=0.0001, l2_reg=0.005, learning_rate=0.0005, representation='acsf',
representation_params={"radial_rs": np.arange(0, 10, 5), "angular_rs": np.arange(0, 10, 5),
"theta_s": np.arange(0, 3.14, 3)},
tensorboard=True, store_frequency=10
)
estimator.set_properties(ene_true)
estimator.generate_representation(xyz, zs)
idx = list(range(xyz.shape[0]))
estimator.fit(idx)
estimator.save_nn(save_dir="temp")
pred1 = estimator.predict(idx)
estimator.loaded_model = True
estimator.fit(idx)
pred2 = estimator.predict(idx)
estimator.session.close()
tf.reset_default_graph()
new_estimator = ARMP(iterations=10, l1_reg=0.0001, l2_reg=0.005, learning_rate=0.0005, representation='acsf',
representation_params={"radial_rs": np.arange(0, 10, 5), "angular_rs": np.arange(0, 10, 5),
"theta_s": np.arange(0, 3.14, 3)},
tensorboard=True, store_frequency=10