universalised NN class; build series-type NN

.gitignore

@@ -144,4 +144,5 @@ cython_debug/
 # local sandbox
 sandbox/
 /algorithms/learn/decision_tree/decistion_tree.png
-/resources/data/neural_network/train
+/resources/data/neural_network/specificity/train
+/resources/data/neural_network/series/train/

algorithms/learn/neural_network/neural_network.py

@@ -3,14 +3,23 @@ import joblib
 import pathlib
 import tensorflow as tf
 
+from enum import Enum
 from tensorflow import keras
 from tensorflow.keras import layers
 from tensorflow.keras.models import Sequential
 
 
+# int value is a number of classes
+class NNType(Enum):
+    SERIES = "series", 4,
+    SPECIFICITY = "specificity", 3
+
+
 class NeuralNetwork:
-    def __init__(self, saved_model_path=None, classes_path=None, img_height=180, img_width=180):
+    def __init__(self, network_type: NNType, saved_model_path=None, classes_path=None, img_height=180, img_width=180):
-        self.training_data_dir = pathlib.Path(r"../../../resources/data/neural_network/train")
+        self.type = network_type
+        self.training_data_dir = pathlib.Path(fr"../../../resources/data/neural_network/{self.type.value[0]}/train")
+
         self.img_height = img_height
         self.img_width = img_width
 
@@ -20,7 +29,9 @@ class NeuralNetwork:
             self.model = None
             self.class_names = None
 
-    def build(self, num_classes=3, epochs=15, batch_size=32):
+    def build(self, epochs=15, batch_size=32):
+        num_classes = self.type.value[1]
+
         train_ds = tf.keras.preprocessing.image_dataset_from_directory(
             self.training_data_dir,
             validation_split=0.2,
@@ -84,8 +95,8 @@ class NeuralNetwork:
         self.model = model
 
     def save(self):
-        self.model.save("saved_model.h5")
+        self.model.save(fr"{self.type.value[0]}/saved_model.h5")
-        joblib.dump(self.class_names, "saved_model_classes.joblib")
+        joblib.dump(self.class_names, fr"{self.type.value[0]}/saved_model_classes.joblib")
 
     def load(self, model_path, classes_path):
         self.model = tf.keras.models.load_model(model_path)
@@ -112,13 +123,13 @@ if __name__ == "__main__":
     # Building and saving a new model:
 
     # (requires a valid training set in resources/data/neural_network/train)
-    # neural_network = NeuralNetwork()
+    # neural_network = NeuralNetwork(NNType.SERIES)
     # neural_network.build(epochs=10)
     # neural_network.save()
 
     # Loading a model from file:
-    neural_network = NeuralNetwork("saved_model.h5", "saved_model_classes.joblib")
+    neural_network = NeuralNetwork(NNType.SERIES, r"series/saved_model.h5", r"series/saved_model_classes.joblib")
 
     # Test
-    image = r"../../../resources/data/neural_network/disarm/ships/0e91bae301a5f5b136c2a0d20ef97cf6.jpg"
+    image = r"../../../resources/data/neural_network/series/disarm/W/5.jpg"
     neural_network.get_answer(image)