diff --git a/.idea/misc.xml b/.idea/misc.xml
index a2e120d..ba24381 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,4 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+  <component name="PyCharmProfessionalAdvertiser">
+    <option name="shown" value="true" />
+  </component>
 </project>
\ No newline at end of file
diff --git a/bin/Main/NeuralNetwork.py b/bin/Main/NeuralNetwork.py
new file mode 100644
index 0000000..0b11600
--- /dev/null
+++ b/bin/Main/NeuralNetwork.py
@@ -0,0 +1,40 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout
+from keras.preprocessing.image import ImageDataGenerator
+from keras.optimizers import Adam
+
+from sklearn.metrics import classification_report,confusion_matrix
+
+import tensorflow as tf
+
+import cv2
+import os
+
+import numpy as np
+
+
+def main():
+    labels = ['house', 'other']
+    img_size = 500
+
+    def get_data(data_dir):
+        data = []
+        for label in labels:
+            path = os.path.join(data_dir, label)
+            class_num = labels.index(label)
+            for img in os.listdir(path):
+                try:
+                    img_arr = cv2.imread(os.path.join(path, img))[..., ::-1]  # Convert BGR to RGB format
+                    resized_arr = cv2.resize(img_arr, (img_size, img_size))  # Reshaping images to preferred size
+                    data.append([resized_arr, class_num])
+                except Exception as e:
+                    print(e)
+        return np.array(data)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/bin/Main/temp.py b/bin/Main/temp.py
new file mode 100644
index 0000000..6593a17
--- /dev/null
+++ b/bin/Main/temp.py
@@ -0,0 +1,14 @@
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+fashion_mnist = keras.datasets.fashion_mnist
+(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()
+
+# 16777216 - because 24 bits color pixels = 2^24
+X_valid, X_train = X_train_full[:5000] / 16777216., X_train_full[5000:] / 16777216.
+y_valid, y_train = y_train_full[:5000], y_train_full[5000:]
+X_test = X_test / 16777216
+