keras数据自动生成器，继承keras.utils.Sequence，结合fit_generator实现节约内存训练

#coding=utf-8
'''
Created on 2018-7-10

'''
import keras
import math
import os
import cv2
import numpy as np
from keras.models import Sequential
from keras.layers import Dense


class DataGenerator(keras.utils.Sequence):
    
    def __init__(self, datas, batch_size=1, shuffle=True):
        self.batch_size = batch_size
        self.datas = datas
        self.indexes = np.arange(len(self.datas))
        self.shuffle = shuffle

    def __len__(self):
        #计算每一个epoch的迭代次数
        return math.ceil(len(self.datas) / float(self.batch_size))

    def __getitem__(self, index):
        #生成每个batch数据，这里就根据自己对数据的读取方式进行发挥了
        # 生成batch_size个索引
        batch_indexs = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
        # 根据索引获取datas集合中的数据
        batch_datas = [self.datas[k] for k in batch_indexs]

        # 生成数据
        X, y = self.data_generation(batch_datas)

        return X, y

    def on_epoch_end(self):
        #在每一次epoch结束是否需要进行一次随机，重新随机一下index
        if self.shuffle == True:
            np.random.shuffle(self.indexes)

    def data_generation(self, batch_datas):
        images = []
        labels = []

        # 生成数据
        for i, data in enumerate(batch_datas):
            #x_train数据
            image = cv2.imread(data)
            image = list(image)
            images.append(image)
            #y_train数据 
            right = data.rfind("\\",0)
            left = data.rfind("\\",0,right)+1
            class_name = data[left:right]
            if class_name=="dog":
                labels.append([0,1])
            else: 
                labels.append([1,0])
        #如果为多输出模型，Y的格式要变一下，外层list格式包裹numpy格式是list[numpy_out1,numpy_out2,numpy_out3]
        return np.array(images), np.array(labels)
    
# 读取样本名称，然后根据样本名称去读取数据
class_num = 0
train_datas = []  
for file in os.listdir("D:/xxx"):
    file_path = os.path.join("D:/xxx", file)
    if os.path.isdir(file_path):
        class_num = class_num + 1
        for sub_file in os.listdir(file_path):
            train_datas.append(os.path.join(file_path, sub_file))

# 数据生成器
training_generator = DataGenerator(train_datas)

#构建网络
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=784))
model.add(Dense(units=2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])
model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit_generator(training_generator, epochs=50,max_queue_size=10,workers=1)