Update KI-KNN

KI-KNN

@@ -23,30 +23,25 @@ def train_test_data_split(X, y):
     return X_train, X_test, y_train, y_test
 
 # creating a model
-
 def create_and_compile_model():
- print("Creating a Model")
- from keras.models import Sequential
- from keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten,
-BatchNormalization
- model = models.Sequential()
- model.add(Conv2D(32, kernel_size=(3, 3), activation="relu",
-input_shape=(constants.N_FEATURE, constants.FEATURE_MAX_LEN,
-constants.CHANNELS)))
- model.add(Conv2D(32, kernel_size=(3, 3), activation="relu"))
- model.add(MaxPooling2D(pool_size=(2,2)))
- model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
- model.add(MaxPooling2D(pool_size=(2,2)))
- model.add(Dropout(0.5))
- model.add(Flatten())
- model.add(Dense(128, activation="relu"))
- model.add(Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation='softmax'))
- print("Compiling a Model")
- optimizer = keras.optimizers.RMSprop()
- model.compile(optimizer=optimizer, loss=constants.LOSS_FUNCTION_SPARSE,
-metrics=[constants.ACCURACY_METRICS])
- print(model.summary())
- return model
+    print("Creating a Model")
+    from keras.models import Sequential
+    from keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten, BatchNormalization
+    model = models.Sequential()
+    model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=(constants.N_FEATURE, constants.FEATURE_MAX_LEN, constants.CHANNELS)))
+    model.add(Conv2D(32, kernel_size=(3, 3), activation="relu"))
+    model.add(MaxPooling2D(pool_size=(2,2)))
+    model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
+    model.add(MaxPooling2D(pool_size=(2,2)))
+    model.add(Dropout(0.5))
+    model.add(Flatten())
+    model.add(Dense(128, activation="relu"))
+    model.add(Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation='softmax'))
+    print("Compiling a Model")
+    optimizer = keras.optimizers.RMSprop()
+    model.compile(optimizer=optimizer, loss=constants.LOSS_FUNCTION_SPARSE, metrics=[constants.ACCURACY_METRICS])
+    print(model.summary())
+    return model
 
     def preprocessing_csv_data():
     print("Reading Features... ")