我正在编写一个图像分类代码。使用ImageDataGenerator编写代码后，运行速度非常慢（1秒=1步）。我想将我自己的数据加载为np数组，但遇到了一些错误。

输出应该为：(15500 , 45 ,45,3)(4000, 1)(15500 , 45 ,45,3)(4000,1)

但我得到的是：

(15500 , 45 ,45,3)(4000, )(15500 , 45 ,45,3)(4000,)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
import glob
import cv2
import os
TEST_DATADIR = "C:/Users/TCSEAKIN/Desktop/Py/AI-hack/AI/Test"
TRAIN_DATADIR = "C:/Users/TCSEAKIN/Desktop/Py/AI-hack/AI/Training"
CATAGORIES = ["Armut", "Portakal", "Cilek", "Muz", "Portakal", "Elma_Kirmizi", "Elma_Yesil", "Mandalina"]
fruit_images = []
labels = []
for category in CATAGORIES :
    path = os.path.join(TRAIN_DATADIR, category)
    for img in os.listdir(path):
        image = cv2.imread(os.path.join(path,img), cv2.IMREAD_COLOR)
        image = cv2.resize(image, (45, 45))
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        fruit_images.append(image)
        labels.append(path)
fruit_images = np.array(fruit_images)
labels = np.array(labels)
validation_fruit_images = []
validation_labels = []
for category in CATAGORIES:
   path = os.path.join(TEST_DATADIR, category)
   for img in os.listdir(path):
        image = cv2.imread(os.path.join(path,img), cv2.IMREAD_COLOR)
        image = cv2.resize(image, (45, 45))
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        validation_fruit_images.append(image)
        validation_labels.append(path)
validation_fruit_images = np.array(validation_fruit_images)
validation_labels = np.array(validation_labels)
X_train, X_test = fruit_images, validation_fruit_images
Y_train, Y_test = labels, validation_labels
#将颜色值归一化到0到1之间
X_train = X_train/255
X_test = X_test/255
print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)

回答：

使用reshape函数：

labels = labels.reshape(-1, 1)
labels.shape