完美解决keras 读取多个hdf5文件进行训练的问题

 def load_dataset(path_name,data_path): images = [] labels = [] train_images = [] valid_images = [] train_labels = [] valid_labels = [] counter = 0 allpath = os.listdir(path_name) nb_classes = len(allpath) print("label_num: ",nb_classes) for child_dir in allpath: child_path = os.path.join(path_name, child_dir) for dir_image in os.listdir(child_path): if dir_image.endswith('.jpg-600'): img = cv2.imread(os.path.join(child_path, dir_image)) image = misc.imresize(img, (IMAGE_SIZE, IMAGE_SIZE), interp='bilinear') #resized_img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE)) images.append(image) labels.append(counter) 

 def split_dataset(images, labels): train_images, valid_images, train_labels, valid_labels = train_test_split(images,\ labels, test_size = 0.2, random_state = random.randint(0, 100)) #print(train_images.shape[0], 'train samples') #print(valid_images.shape[0], 'valid samples') return train_images, valid_images, train_labels ,valid_labels 

 images = np.array(images) t_images, v_images, t_labels ,v_labels = split_dataset(images, labels) for i in range(len(t_images)): train_images.append(t_images[i]) train_labels.append(t_labels[i]) for j in range(len(v_images)): valid_images.append(v_images[j]) valid_labels.append(v_labels[j]) if counter%50== 49: print( counter+1 , "is read to the memory!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!") images = [] labels = [] counter = counter + 1 print("train_images num: ", len(train_images), " ", "valid_images num: ",len(valid_images)) 

 if ((counter % 4316 == 4315) or (counter == nb_classes - 1)): print("start write images and labels data...................................................................") num = counter // 5000 dirs = data_path + "/" + "h5_" + str(num - 1) if not os.path.exists(dirs): os.makedirs(dirs) data2h5(dirs, t_images, v_images, t_labels ,v_labels) 

 def data2h5(dirs_path, train_images, valid_images, train_labels ,valid_labels): TRAIN_HDF5 = dirs_path + '/' + "train.hdf5" VAL_HDF5 = dirs_path + '/' + "val.hdf5" #shuffle state1 = np.random.get_state() np.random.shuffle(train_images) np.random.set_state(state1) np.random.shuffle(train_labels) state2 = np.random.get_state() np.random.shuffle(valid_images) np.random.set_state(state2) np.random.shuffle(valid_labels) datasets = [ ("train",train_images,train_labels,TRAIN_HDF5), ("val",valid_images,valid_labels,VAL_HDF5)] for (dType,images,labels,outputPath) in datasets: # HDF5 initial f = h5py.File(outputPath, "w") f.create_dataset("x_"+dType, data=images) f.create_dataset("y_"+dType, data=labels) #f.create_dataset("x_"+dType, data=images, compression="gzip", compression_opts=9) #f.create_dataset("y_"+dType, data=labels, compression="gzip", compression_opts=9) f.close() 

 def read_dataset(dirs): files = os.listdir(dirs) print(files) for file in files: path = dirs+'/' + file dataset = h5py.File(path, "r") file = file.split('.') set_x_orig = dataset["x_"+file[0]].shape[0] set_y_orig = dataset["y_"+file[0]].shape[0] print(set_x_orig) print(set_y_orig) 

 def generator(self, datagen, mode): passes=np.inf aug = ImageDataGenerator( featurewise_center = False, samplewise_center = False, featurewise_std_normalization = False, samplewise_std_normalization = False, zca_whitening = False, rotation_range = 20, width_shift_range = 0.2, height_shift_range = 0.2, horizontal_flip = True, vertical_flip = False) epochs = 0 # 默认是无限循环遍历 while epochs </div><p><strong>7、至此，就大功告成了</strong></p><p>完整的代码：</p><div class="gaodaimacode"><pre class="prettyprint linenums"> # -*- coding: utf-8 -*- """ Created on Mon Feb 12 20:46:12 2018 @author: william_yue """ import os import numpy as np import cv2 import random from scipy import misc import h5py from sklearn.model_selection import train_test_split from keras import backend as K K.clear_session() from keras.utils import np_utils IMAGE_SIZE = 128 # 加载数据集并按照交叉验证的原则划分数据集并进行相关预处理工作 def split_dataset(images, labels): # 导入了sklearn库的交叉验证模块，利用函数train_test_split()来划分训练集和验证集 # 划分出了20%的数据用于验证，80%用于训练模型 train_images, valid_images, train_labels, valid_labels = train_test_split(images,\ labels, test_size = 0.2, random_state = random.randint(0, 100)) return train_images, valid_images, train_labels ,valid_labels def data2h5(dirs_path, train_images, valid_images, train_labels ,valid_labels): #def data2h5(dirs_path, train_images, valid_images, test_images, train_labels ,valid_labels, test_labels): TRAIN_HDF5 = dirs_path + '/' + "train.hdf5" VAL_HDF5 = dirs_path + '/' + "val.hdf5" #采用标签与图片相同的顺序分别打乱训练集与验证集 state1 = np.random.get_state() np.random.shuffle(train_images) np.random.set_state(state1) np.random.shuffle(train_labels) state2 = np.random.get_state() np.random.shuffle(valid_images) np.random.set_state(state2) np.random.shuffle(valid_labels) datasets = [ ("train",train_images,train_labels,TRAIN_HDF5), ("val",valid_images,valid_labels,VAL_HDF5)] for (dType,images,labels,outputPath) in datasets: # 初始化HDF5写入 f = h5py.File(outputPath, "w") f.create_dataset("x_"+dType, data=images) f.create_dataset("y_"+dType, data=labels) #f.create_dataset("x_"+dType, data=images, compress<div style="color:transparent">来源gaodai.ma#com搞##代!^码@网</div>ion="gzip", compression_opts=9) #f.create_dataset("y_"+dType, data=labels, compression="gzip", compression_opts=9) f.close() def read_dataset(dirs): files = os.listdir(dirs) print(files) for file in files: path = dirs+'/' + file file_read = os.listdir(path) for i in file_read: path_read = os.path.join(path, i) dataset = h5py.File(path_read, "r") i = i.split('.') set_x_orig = dataset["x_"+i[0]].shape[0] set_y_orig = dataset["y_"+i[0]].shape[0] print(set_x_orig) print(set_y_orig) #循环读取每个标签集下的所有图片 def load_dataset(path_name,data_path): images = [] labels = [] train_images = [] valid_images = [] train_labels = [] valid_labels = [] counter = 0 allpath = os.listdir(path_name) nb_classes = len(allpath) print("label_num: ",nb_classes) for child_dir in allpath: child_path = os.path.join(path_name, child_dir) for dir_image in os.listdir(child_path): if dir_image.endswith('.jpg-600'): img = cv2.imread(os.path.join(child_path, dir_image)) image = misc.imresize(img, (IMAGE_SIZE, IMAGE_SIZE), interp='bilinear') #resized_img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE)) images.append(image) labels.append(counter) images = np.array(images) t_images, v_images, t_labels ,v_labels = split_dataset(images, labels) for i in range(len(t_images)): train_images.append(t_images[i]) train_labels.append(t_labels[i]) for j in range(len(v_images)): valid_images.append(v_images[j]) valid_labels.append(v_labels[j]) if counter%50== 49: print( counter+1 , "is read to the memory!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!") images = [] labels = [] if ((counter % 4316 == 4315) or (counter == nb_classes - 1)): print("train_images num: ", len(train_images), "  ", "valid_images num: ",len(valid_images)) print("start write images and labels data...................................................................") num = counter // 5000 dirs = data_path + "/" + "h5_" + str(num - 1) if not os.path.exists(dirs): os.makedirs(dirs) data2h5(dirs, train_images, valid_images, train_labels ,valid_labels) #read_dataset(dirs) print("File HDF5_%d "%num, " id done!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!") train_images = [] valid_images = [] train_labels = [] valid_labels = [] counter = counter + 1 print("All File HDF5 done!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!") read_dataset(data_path) #读取训练数据集的文件夹，把他们的名字返回给一个list def read_name_list(path_name): name_list = [] for child_dir in os.listdir(path_name): name_list.append(child_dir) return name_list if __name__ == '__main__': path = "data" data_path = "data_hdf5_half" if not os.path.exists(data_path): os.makedirs(data_path) load_dataset(path,data_path)