sq_RHOB = np.sqrt(deno_log_RHOB)
# # DATASET FINAL # #
dataset_final = pd.DataFrame(
list(
zip(deno_log_GR, deno_log_NPHI, logh_NPHI, ilogh_NPHI, sq_NPHI,
deno_log_RHOB, logh_RHOB, ilogh_RHOB, sq_RHOB)))
dataset_final = dataset_final.values
# # # # # # # CNN MODEL BUILDING & TRAINING DATA # # # # # #
# FEATURE SCALING
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
dataset_final = sc.fit_Transform(dataset_final)
dataset_final = np.expand_dims(dataset_final, axis=2)
# TARGET ENCODING --> ONE-HOT ENCODING
from sklearn.preprocessing import OneHotEncoder
onehotencoder = OneHotEncoder()
target = dataset.iloc[:, -1].values
target = np.reshape(target, (1200, 1))
target_ = onehotencoder.fit_Transform(target).toarray()
# print(np.shape(target))
# SHUFFLE THE DATASET
from sklearn.utils import shuffle
dataset_final, target_ = shuffle(dataset_final, target_)
# # # # # # # BUILD AND TRAIN CNN MODEL # # # # # # #
