FaceTrain.py 文件源码

def read(self, nb_classes, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3):
        images, labels = extract_data('./train/')
        labels = np.reshape(labels, [-1])
        # numpy.reshape
        X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.3, random_state=random.randint(0, 100))
        X_valid, X_test, y_valid, y_test = train_test_split(images, labels, test_size=0.5, random_state=random.randint(0, 100))

        X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)
        X_valid = X_valid.reshape(X_valid.shape[0], img_rows, img_cols, 3)
        X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)
        input_shape = (img_rows, img_cols, 3)

        # the data, shuffled and split between train and test sets
        print('X_train shape:', X_train.shape)
        print(X_train.shape[0], 'train samples')
        print(X_valid.shape[0], 'valid samples')
        print(X_test.shape[0], 'test samples')

        # convert class vectors to binary class matrices
        Y_train = np_utils.to_categorical(y_train, nb_classes)
        Y_valid = np_utils.to_categorical(y_valid, nb_classes)
        Y_test = np_utils.to_categorical(y_test, nb_classes)

        X_train = X_train.astype('float32')
        X_valid = X_valid.astype('float32')
        X_test = X_test.astype('float32')
        X_train /= 255
        X_valid /= 255
        X_test /= 255

        self.X_train = X_train
        self.X_valid = X_valid
        self.X_test = X_test
        self.Y_train = Y_train
        self.Y_valid = Y_valid
        self.Y_test = Y_test