diff --git a/train_CNN2.py b/train_CNN2.py
deleted file mode 100644
index 7fdf4c0..0000000
--- a/train_CNN2.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""
-related to CNN_detect 7,8
-"""
-
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Flatten, Activation
-from keras.layers import Convolution2D, MaxPooling2D
-from keras.utils import np_utils
-from util import *
-
-
-
-BATCH_SIZE = 100
-NB_CLASSES = 2
-NB_EPOCH = 5
-img_rows, img_cols = (50, 50)
-INPUT_SHAPE = (img_rows, img_cols, 1)
-MODEL_SAVE_PATH = './CNN_model9.h5'
-
-print("loading data...")
-X, y = load_data(['./data/positive_data3.csv', './data/negative_not_empty4.csv'])
-train_size = int(0.9 * X.shape[0])
-X_train = X[0:train_size, :]
-y_train = y[0:train_size]
-X_test = X[train_size:, :]
-y_test = y[train_size:]
-
-X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
-X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
-X_train = X_train.transpose(0, 2, 3, 1)
-X_test = X_test.transpose(0, 2, 3, 1)
-
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 65535.
-X_test /= 65535.
-print('X_train shape:', X_train.shape)
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
-Y_train = np_utils.to_categorical(y_train, NB_CLASSES)
-Y_test = np_utils.to_categorical(y_test, NB_CLASSES)
-
-
-# define the CNN
-model = Sequential()
-model.add(Convolution2D(nb_filter=16, nb_row=3, nb_col=3, border_mode='valid', input_shape=INPUT_SHAPE))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3, border_mode='same'))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3, border_mode='same'))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Dropout(0.25))
-model.add(Flatten())
-model.add(Dense(32))
-model.add(Activation('relu'))
-model.add(Dropout(0.5))
-model.add(Dense(NB_CLASSES))
-model.add(Activation('softmax'))
-
-model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
-
-model.fit(X_train, Y_train, batch_size=BATCH_SIZE, nb_epoch=NB_EPOCH, shuffle=True,
-          verbose=1, validation_split=0.1)
-
-model.save(MODEL_SAVE_PATH)
-"""
-model = load_model(MODEL_SAVE_PATH)
-"""
-score = model.evaluate(X_test, Y_test, verbose=0)
-print('Test score:', score[0])
-print('Test accuracy:', score[1])