diff --git a/train_CNN.py b/train_CNN.py
new file mode 100644
index 0000000..c24fecc
--- /dev/null
+++ b/train_CNN.py
@@ -0,0 +1,74 @@
+"""
+related to CNN_model 1-6
+"""
+
+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_model7.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='valid'))
+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='valid'))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+model.add(Flatten())
+model.add(Dense(1000))
+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])
diff --git a/train_CNN2.py b/train_CNN2.py
new file mode 100644
index 0000000..7fdf4c0
--- /dev/null
+++ b/train_CNN2.py
@@ -0,0 +1,74 @@
+"""
+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])