Update main.py

main.py

@@ -63,10 +63,8 @@ def extract_features(trainingDataDir, trainingDataSubDirs):
 def preprocessing_csv_data():
     print("Reading Features... ")
     data = pd.read_csv(constants.FEATURES_CSV_NAME)
-    data.head()
     # Dropping unnecessary columns (Column Filename is dropped)
     data = data.drop(['filename'], axis=1)
-    data.head()
     return data
 
 
@@ -109,10 +107,11 @@ def create_and_compile_model(X, hidden_layer_dimensions):
 
 
 def train_and_save_model(model, X_train, y_train, X_test, y_test):
-    logdir = constants.LOG_DIR_PATH
-    tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
+    #logdir = constants.LOG_DIR_PATH
+    #tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
     print("Start Training...")
-    history = model.fit(X_train, y_train, epochs=35, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])
+    #history = model.fit(X_train, y_train, epochs=35, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])
+    history = model.fit(X_train, y_train, epochs=35, validation_data=(X_test, y_test))
     # Saving the trained model to avoid re-training
     #model.save(constants.TRAINED_MODEL)
     return history
@@ -155,14 +154,10 @@ def plot_model_loss(history):
     plt.legend(['Train', 'Test'], loc='upper right')
     plt.show()
 
-def construct_and_apply_network(hidden_layer_dimensions):
-    data = preprocessing_csv_data()
-    target_labels, encoder = encode_labels(data)
-    X = normalize_data(data)
+def construct_and_apply_network(hidden_layer_dimensions, data, target_labels, encoder, X):
     X_train, X_test, y_train, y_test = train_test_data_split(X, target_labels)
     model = create_and_compile_model(X, hidden_layer_dimensions)
     history = train_and_save_model(model, X_train, y_train, X_test, y_test)
-    history
     predict(model, X_test, y_test)
     accuracy = model_predict(model, X_test, y_test)
     #plot_model_accuracy(history)
@@ -224,6 +219,10 @@ if __name__ == "__main__":
     else:
         extract_features(trainingDataDir, trainingDataSubDirs)
 
+    data = preprocessing_csv_data()
+    target_labels, encoder = encode_labels(data)
+    X = normalize_data(data)
+
     max_accuracy = 0
     neurons_increment_by = 8
     start_neuron_value = 8
@@ -242,7 +241,7 @@ if __name__ == "__main__":
             row_counter += 1
             hidden_layer_dimensions[i] = j
             start = time.time()
-            new_accuracy = construct_and_apply_network(hidden_layer_dimensions)
+            new_accuracy = construct_and_apply_network(hidden_layer_dimensions, data, target_labels, encoder, X)
             end = time.time()
             elapsed_time = end - start
             sheet.cell(row=(row_counter), column=1).value = hidden_layer_dimensions.__str__()