add sunflower to neural network

app/neural_network.py

@@ -19,7 +19,7 @@ class NeuralNetwork:
         self.batch_size = 25
         self.img_width, self.img_height, self.img_num_channels = 25, 25, 3
         self.loss_function = sparse_categorical_crossentropy
-        self.no_classes = 7
+        self.no_classes = 8
         self.no_epochs = 40
         self.optimizer = Adam()
         self.verbosity = 1
@@ -28,7 +28,7 @@ class NeuralNetwork:
         self.input_shape = (self.img_width, self.img_height, self.img_num_channels)
 
         # labels
-        self.labels = ["cabbage", "carrot", "corn", "lettuce", "paprika", "potato", "tomato"]
+        self.labels = ["cabbage", "carrot", "corn", "lettuce", "paprika", "potato", "sunflower" , "tomato"]
 
     def init_model(self):
         if not self.model_dir_is_empty():
@@ -49,8 +49,18 @@ class NeuralNetwork:
             self.model.add(Dense(16, activation='relu'))
             self.model.add(Dense(self.no_classes, activation='softmax'))
 
+            self.model.compile(loss=self.loss_function,
+                               optimizer=self.optimizer,
+                               metrics=['accuracy'])
+
+            # Start training
+            self.model.fit(
+                self.train_datagen,
+                epochs=self.no_epochs,
+                shuffle=False)
+
         # Display a model summary
-        self.model.summary()
+        #self.model.summary()
 
     def load_images(self):
         # Create a generator
@@ -65,17 +75,6 @@ class NeuralNetwork:
             target_size=(25, 25),
             class_mode='sparse')
 
-    def train(self):
-        self.model.compile(loss=self.loss_function,
-                           optimizer=self.optimizer,
-                           metrics=['accuracy'])
-
-        # Start training
-        self.model.fit(
-            self.train_datagen,
-            epochs=self.no_epochs,
-            shuffle=False)
-
     def predict(self, field: BaseField) -> str:
         print(field.get_img_path())
         # corn_img_path = os.path.join(RESOURCE_DIR,'corn.png')
@@ -107,7 +106,7 @@ class NeuralNetwork:
         prediction = self.predict(field)
 
         # Saving model
-        if not self.model_dir_is_empty():
+        if self.model_dir_is_empty():
             save_model(self.model, MODEL_DIR)
 
         return prediction

config.py

@@ -34,7 +34,7 @@ MAP_DIR = os.path.join(BASE_DIR, 'maps')
 MAP_FILE_NAME = 'map'
 TRAINING_SET_DIR = os.path.join(RESOURCE_DIR, 'smaller_train')
 TEST_SET_DIR = os.path.join(RESOURCE_DIR, 'smaller_test')
-ADAPTED_IMG_DIR = os.path.join(RESOURCE_DIR, "adapted-images")
+ADAPTED_IMG_DIR = os.path.join(RESOURCE_DIR, "adapted_images")
 MODEL_DIR = os.path.join(RESOURCE_DIR, 'saved_model')
 
 # Picture format