--- a
+++ b/architectures/neural_network_models.py
@@ -0,0 +1,121 @@
+'''
+About: Python script to define four MLP architectures for the classification problem.
+Author: Iman Kafian-Attari
+Date: 20.07.2021
+Licence: MIT
+version: 0.1
+=========================================================
+How to use:
+1. You do not need to interact with this script directly.
+=========================================================
+Notes:
+1. This script is called from the main script.
+2. It includes various MLP models for the classification problem.
+3. You can modify the existing MLP models or create new models, if you desire.
+4. If you created new models, do not forget to call them from the main script.
+=========================================================
+TODO for version O.2
+1. Add new neural network models.
+=========================================================
+'''
+
+print(__doc__)
+
+from tensorflow import keras
+from keras.models import Sequential
+from tensorflow.keras.callbacks import EarlyStopping
+from keras.layers import *
+from keras.optimizers import *
+from keras.losses import *
+from tensorflow.keras.losses import categorical_crossentropy
+
+
+# Defining the 1st architecture
+def neural_model1(num_input_nodes=281, num_label_nodes=4):
+    model = Sequential()
+    # Adding the first hidden layer with 5*node+5 neurons
+    model.add(Dense(num_input_nodes, activation='relu', input_shape=(num_input_nodes,)))
+    model.add(Dropout(0.2))
+    # Add the remaining hidden layers
+    model.add(BatchNormalization())
+    model.add(Dense(128, activation='relu'))
+    model.add(Dropout(0.2))
+    model.add(BatchNormalization())
+    model.add(Dense(64, activation='relu'))
+    model.add(Dropout(0.2))
+    model.add(BatchNormalization())
+    model.add(Dense(32, activation='relu'))
+    model.add(Dropout(0.2))
+    model.add(BatchNormalization())
+    model.add(Dense(16, activation='relu'))
+    model.add(Dropout(0.2))
+    model.add(BatchNormalization())
+    model.add(Dense(8, activation='relu'))
+    model.add(Dropout(0.2))
+    # Adding the output layer
+    # The activation for the output layer is always set to sigmoid function in this exercise session
+    model.add(Dense(num_label_nodes, activation='softmax'))
+
+    model.summary()
+
+    return model
+
+
+# Defining the 2nd architecture
+def neural_model2(num_input_nodes=281, num_label_nodes=4):
+
+    model = Sequential()
+    # Adding the first hidden layer with 5*node+5 neurons
+    model.add(Dense(num_input_nodes, activation='relu', input_shape=(num_input_nodes,)))
+    model.add(Dropout(0.2))
+    # Add the remaining hidden layers
+    model.add(BatchNormalization())
+    model.add(Dense(64, activation='relu'))
+    model.add(Dropout(0.2))
+    model.add(BatchNormalization())
+    model.add(Dense(16, activation='relu'))
+    model.add(Dropout(0.2))
+    # Adding the output layer
+    # The activation for the output layer is always set to sigmoid function in this exercise session
+    model.add(Dense(num_label_nodes, activation='softmax'))
+
+    model.summary()
+
+    return model
+
+
+# Defining the 3rd architecture
+def neural_model3(num_input_nodes=281, num_label_nodes=4):
+
+    model = Sequential()
+    # Adding the first hidden layer with 5*node+5 neurons
+    model.add(Dense(num_input_nodes, activation='relu', input_shape=(num_input_nodes,)))
+    model.add(Dropout(0.2))
+    # Add the remaining hidden layers
+    model.add(BatchNormalization())
+    model.add(Dense(32, activation='relu'))
+    model.add(Dropout(0.2))
+    # Adding the output layer
+    # The activation for the output layer is always set to sigmoid function in this exercise session
+    model.add(Dense(num_label_nodes, activation='softmax'))
+
+    model.summary()
+
+    return model
+
+
+# Defining the 4th architecture
+def neural_model4(num_input_nodes=281, num_label_nodes=4):
+
+    model = Sequential()
+    # Adding the first hidden layer with 5*node+5 neurons
+    model.add(Dense(num_input_nodes, activation='relu', input_shape=(num_input_nodes,)))
+    model.add(Dropout(0.2))
+    # Adding the output layer
+    # The activation for the output layer is always set to sigmoid function in this exercise session
+    model.add(Dense(num_label_nodes, activation='softmax'))
+
+    model.summary()
+
+    return model
+