From fee2857f086c3771744af791e6ea666cf0b3c6af Mon Sep 17 00:00:00 2001 From: Tanqiu Liu Date: Mon, 18 Sep 2017 16:47:21 -0500 Subject: [PATCH] Delete train_CNN.py --- train_CNN.py | 74 ---------------------------------------------------- 1 file changed, 74 deletions(-) delete mode 100644 train_CNN.py diff --git a/train_CNN.py b/train_CNN.py deleted file mode 100644 index c24fecc..0000000 --- a/train_CNN.py +++ /dev/null @@ -1,74 +0,0 @@ -""" -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])