keras之数据拼接

批量修改图片命，使其名为0.png  1.png 2.png 

path_name = r'D:\Jupyter Notebook\save_image\test' #原图片所在路径

i = 0
for item in os.listdir(path=path_name):
    new_item = str(i)+'.jpg'  #元图片均为.bmp格式
    os.rename(os.path.join(path_name,item), os.path.join(path_name,new_item))
    file_path = os.path.join(path_name, new_item)
    new_out = os.path.join(path_name, new_item)# print(new_out)
    print(new_out)
    out_path = os.path.splitext((new_out))[0] + '.png'
    print(out_path)
    Image.open(file_path).save(out_path)
    os.remove(os.path.join(path_name, new_item))
    i+=1

 

基于这句  keras.model.fit(x_train,y_train,epochs=5,batch_size=32)来看，这里的x_train,y_train是训练数据，拿二分类来说，这里的x_train包含两部分数据，一个是正样本，一个是负样本。

正样本数据导入：

import os
from PIL import Image
import numpy as np

def load_data():
    x_train_r = np.empty((36740, 64, 64, 3), dtype="float32")
    images = os.listdir('D:/GAN/GAN_1/extra_data/extra_data/images/')

    lens = len(images)
    for i in range(lens):
        img = Image.open('D:/GAN/GAN_1/extra_data/extra_data/images/' + images[i])
        arr = np.asarray(img, dtype="float32")
        x_train[i, :, :, :] = arr

    return x_train_r

x_train_r = load_data()
y_train_r = np.ones((36740,1))

负样本导入：

仿照上面同样方法：
x_train_f = load.data()


y_train_f = np.zeros((36740,1))

开始数据拼接：

x_train = np.concatenate((x_train_r, x_train_f))
y_train = np.concatenate((y_train_r, y_train_f))

然后就可以compile了