# Shuffling the indices of the data and then selecting the first 9625 data points
idx = list(range(len(ene_isopent)))
shuffle(idx)
idx = idx[:7621]
# Appending the true energies to a list
predictions = [ene_isopent[idx]]
# Creating the estimator
acsf_params = {"nRs2":10, "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))
# Putting the data into the model
estimator.set_properties(ene_isopent)
estimator.generate_representation(xyz_isopent, zs_isopent, method="fortran")
estimator.load_nn("md-nn")
# Predicting the energies
predictions.append(estimator.predict(idx))
# Scoring the results
score = estimator.score(idx)
print(score)
# Saving the predictions to a npz file
np.savez("cross_pred_md_on_vr.npz", np.asarray(predictions))
"zeta": 220.127,
"eta": 30.8065
}
estimator = ARMP(iterations=200,
representation_name='acsf',
representation_params=acsf_params,
tensorboard=True,
store_frequency=10,
l1_reg=0.0001,
l2_reg=0.005,
learning_rate=0.0005)
estimator.set_properties(ene)
estimator.generate_representation(xyz, zs)
saved_dir = "saved_model"
estimator.load_nn(saved_dir)
idx = list(range(n_samples))
estimator.fit(idx)
pred_1 = estimator.predict(idx)
pred_2 = estimator.predict_from_xyz(xyz, zs)
# plt.scatter(pred_1, pred_2)
# plt.show()
# estimator.save_nn()
"zeta": 100.06564927139748,
"eta": 39.81824764370754
}
estimator = ARMP(iterations=2633,
batch_size=22,
l1_reg=1.46e-05,
l2_reg=0.0001,
learning_rate=0.0013,
representation_name='acsf',
representation_params=acsf_params,
tensorboard=True,
store_frequency=25,
hidden_layer_sizes=(185, ))
# Loading the model previously trained
estimator.load_nn("../trained_nn/vr-nn")
estimator.set_properties(ene_surface)
# Generating the representation
start = time.time()
estimator.generate_representation(xyz_surface, zs_surface, method="fortran")
end = time.time()
print("The time taken to generate the representations is %s s" %
(str(end - start)))
print("The shape of the representations is %s" %
(str(estimator.representation.shape)))
# Predicting the energies
idx = list(range(n_samples))
predictions = estimator.predict(idx)
'radial_cutoff':
5,
'angular_cutoff':
5,
'zeta':
17.8630,
'eta':
2.5148
}
# Generate estimator
estimator = ARMP(iterations=1,
l1_reg=0.0001,
l2_reg=0.005,
learning_rate=0.0005,
representation_name='acsf',
representation_params=acsf_params,
tensorboard=False,
store_frequency=10)
estimator.load_nn()
data_squal = h5py.File(
"/Volumes/Transcend/data_sets/CN_squalane/dft/squalane_cn_dft.hdf5", "r")
xyz_squal = np.array(data_squal.get("xyz")[:10])
zs_squal = np.array(data_squal.get("zs")[:10], dtype=np.int32)
ene_squal = np.array(data_squal.get("ene")[:10]) * 2625.50
pred1 = estimator.predict_from_xyz(xyz_squal, zs_squal)
print(pred1)
"rcut": 3.29,
"acut": 3.29,
"zeta": 100.06564927139748,
"eta": 39.81824764370754
}
estimator = ARMP(iterations=2633,
batch_size=22,
l1_reg=1.46e-05,
l2_reg=0.0001,
learning_rate=0.0013,
representation_name='acsf',
representation_params=acsf_params,
tensorboard=True,
store_frequency=25,
hidden_layer_sizes=(185, ))
# Input the data into the model
estimator.set_properties(ene_isopent)
estimator.generate_representation(xyz_isopent, zs_isopent, method="fortran")
estimator.load_nn("vr-nn")
# Predicting the energies
predictions.append(estimator.predict(idx))
# Scoring the results
score = estimator.score(idx)
print(score)
# Saving the results to a .npz file
np.savez("cross_pred_vr_on_md.npz", np.asarray(predictions))
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
)
new_estimator.set_properties(ene_true)
new_estimator.generate_representation(xyz, zs)
new_estimator.load_nn("temp")
pred3 = new_estimator.predict(idx)
new_estimator.fit(idx)
pred4 = new_estimator.predict(idx)
shutil.rmtree("temp")
# shutil.rmtree("tb")
# shutil.rmtree("tensorboard")
assert np.all(pred1 == pred3)
assert np.all(pred2 == pred4)