tensorflow使用tf.data.Dataset 处理大型数据集

系统管理员 2021-12-17 05:03 526阅读 1赞

最近深度学习用到的数据集比较大，如果一次性将数据集读入内存，那服务器是顶不住的，所以需要分批进行读取，这里就用到了tf.data.Dataset构建数据集，先看一个博文，入入门：

https://www.jianshu.com/p/f580f4fc2ba0

概括一下，tf.data.Dataset主要有几个部分最重要：

1.  构建生成器函数
2.  使用tf.data.Dataset的from\_generator函数，通过指定数据类型，数据的shape等参数，构建一个Dataset
3.  指定batch\_size
4.  使用make\_one\_shot\_iterator()函数，构建一个iterator
5.  使用上面构建的迭代器开始get\_next() 。（必须要有这个get\_next()，迭代器才会工作）

## **一.构建生成器** ##

生成器的要点是要在while True中加入yield，yield的功能有点类似return，有yield才能起到迭代的作用。  
我的数据是一个\[6047, 6000, 1\]的文本数据，我每次迭代返回的shape为\[1,6000,1\]，要注意的是返回的shape要和构建Dataset时的shape一致，下面会说到。代码如下：

def gen():				
    		train=pd.read_csv('/home/chenqiren/PycharmProjects/code/test/formal/small_sample/train2.csv', header=None)
            train.fillna(0, inplace = True)
            label_encoder = LabelEncoder().fit(train[6000])
            label = label_encoder.transform(train[6000])  
            train = train.drop([6000], axis=1) 
            scaler = StandardScaler().fit(train.values)   #train.values中的值是csv文件中的那些值，     这步标准化可以保留
            scaled_train = scaler.transform(train.values)
            #print(scaled_train)
            #拆分训练集和测试集--------------
            sss=StratifiedShuffleSplit(test_size=0.1, random_state=23)
            for train_index, valid_index in sss.split(scaled_train, label):   #需要的是数组，train.values得到的是数组
                X_train, X_valid=scaled_train[train_index], scaled_train[valid_index]  #https://www.cnblogs.com/Allen-rg/p/9453949.html
                y_train, y_valid=label[train_index], label[valid_index]
            X_train_r=np.zeros((len(X_train), 6000, 1))   #先构建一个框架出来，下面再赋值
            X_train_r[:,: ,0]=X_train[:,0:6000]     
        
            X_valid_r=np.zeros((len(X_valid), 6000, 1))
            X_valid_r[:,: ,0]=X_valid[:,0:6000]
        
            y_train=np_utils.to_categorical(y_train, 3)
            y_valid=np_utils.to_categorical(y_valid, 3)
            
            leng=len(X_train_r)
            index=0
            while True:
                x_train_batch=X_train_r[index, :, 0:1]
                y_train_batch=y_train[index, :]
                yield (x_train_batch, y_train_batch)
                index=index+1
                if index>leng:
                    break

代码中while True上面的部分是标准化数据的代码，可以不用看，只需要看 while True中的代码即可。x\_train\_batch, y\_train\_batch都只是一行的数据，这里是一行一行数据迭代。

## 二.使用tf.data.Dataset包装生成器 ##

data=tf.data.Dataset.from_generator(gen_1, (tf.float32, tf.float32), (tf.TensorShape([6000,1]), tf.TensorShape([3])))
    data=data.batch(128)
    iterator=data.make_one_shot_iterator()

这里的tf.TensorShape(\[6000,1\]) 和 tf.TensorShape(\[3\])中的shape要和上面生成器yield返回的数据的shape一致。  
data=data.batch(128)是设置batchsize，这里设为128，在运行时，因为我们yield的是一行的数据\[1, 6000, 1\]，所以将会循环yield够128次，得到\[128, 6000, 1\]，即一个batch，才会开始训练。  
iterator=data.make\_one\_shot\_iterator()是构建迭代器，one\_shot迭代器人如其名，意思就是数据输出一次后就丢弃了。

## 三.获取生成器返回的数据 ##

x, y=iterator.get_next()
    x_batch, y_batch=sess.run([x,y])

注意要有get\_next()，迭代器才能开始工作。  
第二行是run第一行代码。获取训练数据和训练标签。

**这里做个关于yield的小笔记：**  
**上一次迭代，yield返回了值，然后get\_next()开启了下一次迭代，此时，程序是从yield处开始运行的，也就是说，如果yield后面还有程序，那就会运行yield后面的程序。一直运行的是while True中的程序，没有运行while True外面的程序。**

下面是我写的总的代码。可以不用看。

import os
    import keras
    import numpy as np
    import pandas as pd
    from sklearn.preprocessing import LabelEncoder
    from sklearn.preprocessing import StandardScaler
    from sklearn.model_selection import StratifiedShuffleSplit
    from sklearn.model_selection import train_test_split
    from keras.models import Sequential, Model
    from keras.layers import Dense, Activation, Flatten, Conv1D, Dropout, MaxPooling1D, GlobalAveragePooling1D
    from keras.layers import GlobalAveragePooling2D,BatchNormalization, UpSampling1D, RepeatVector,Reshape
    from keras.layers.core import Lambda
    from keras.optimizers import SGD, Adam, Adadelta
    from keras.utils import np_utils
    from keras.applications.inception_resnet_v2 import InceptionResNetV2
    from keras.backend import conv3d,reshape, shape, categorical_crossentropy, mean, square
    from keras.applications.vgg16 import VGG16
    from keras.layers import Input,LSTM
    from keras import regularizers
    from keras.utils import multi_gpu_model
    import tensorflow as tf
    import keras.backend.tensorflow_backend as KTF
    os.environ["CUDA_VISIBLE_DEVICES"]="2" 
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    session = tf.Session(config=config)
    keep_prob = tf.placeholder("float")
    # 设置session
    KTF.set_session(session )
    
    #-----生成训练数据-----------------------------------------------
    def gen_1():
        train=pd.read_csv('/home/chenqiren/PycharmProjects/code/test/formal/small_sample/train2.csv', header=None)
        train.fillna(0, inplace = True)
        label_encoder = LabelEncoder().fit(train[6000])
        label = label_encoder.transform(train[6000])  
        train = train.drop([6000], axis=1) 
        scaler = StandardScaler().fit(train.values)   #train.values中的值是csv文件中的那些值，     这步标准化可以保留
        scaled_train = scaler.transform(train.values)
        #print(scaled_train)
        #拆分训练集和测试集--------------
        sss=StratifiedShuffleSplit(test_size=0.1, random_state=23)
        for train_index, valid_index in sss.split(scaled_train, label):   #需要的是数组，train.values得到的是数组
            X_train, X_valid=scaled_train[train_index], scaled_train[valid_index]  #https://www.cnblogs.com/Allen-rg/p/9453949.html
            y_train, y_valid=label[train_index], label[valid_index]
        X_train_r=np.zeros((len(X_train), 6000, 1))   #先构建一个框架出来，下面再赋值
        #开始赋值
        #https://stackoverflow.com/questions/43290202/python-typeerror-unhashable-type-slice-for-encoding-categorical-data
        X_train_r[:,: ,0]=X_train[:,0:6000]     
    
        X_valid_r=np.zeros((len(X_valid), 6000, 1))
        X_valid_r[:,: ,0]=X_valid[:,0:6000]
    
        y_train=np_utils.to_categorical(y_train, 3)
        y_valid=np_utils.to_categorical(y_valid, 3)
        
        leng=len(X_train_r)
        index=0
        while True:
            x_train_batch=X_train_r[index, :, 0:1]
            y_train_batch=y_train[index, :]
            yield (x_train_batch, y_train_batch)
            index=index+1
            if index>leng:
                break
            
    #----生成测试数据--------------------------------------
    def gen_2():
        train=pd.read_csv('/home/chenqiren/PycharmProjects/code/test/formal/small_sample/train2.csv', header=None)
        train.fillna(0, inplace = True)
        label_encoder = LabelEncoder().fit(train[6000])
        label = label_encoder.transform(train[6000])  
        train = train.drop([6000], axis=1) 
        scaler = StandardScaler().fit(train.values)   #train.values中的值是csv文件中的那些值，     这步标准化可以保留
        scaled_train = scaler.transform(train.values)
        #print(scaled_train)
        #拆分训练集和测试集--------------
        sss=StratifiedShuffleSplit(test_size=0.1, random_state=23)
        for train_index, valid_index in sss.split(scaled_train, label):   #需要的是数组，train.values得到的是数组
            X_train, X_valid=scaled_train[train_index], scaled_train[valid_index]  #https://www.cnblogs.com/Allen-rg/p/9453949.html
            y_train, y_valid=label[train_index], label[valid_index]
        X_train_r=np.zeros((len(X_train), 6000, 1))   #先构建一个框架出来，下面再赋值
        #开始赋值
        #https://stackoverflow.com/questions/43290202/python-typeerror-unhashable-type-slice-for-encoding-categorical-data
        X_train_r[:,: ,0]=X_train[:,0:6000]     
    
        X_valid_r=np.zeros((len(X_valid), 6000, 1))
        X_valid_r[:,: ,0]=X_valid[:,0:6000]
    
        y_train=np_utils.to_categorical(y_train, 3)
        y_valid=np_utils.to_categorical(y_valid, 3)
        
        leng=len(X_valid_r)
        index=0
        while True:
            x_test_batch=X_valid_r[index, :, 0:1]
            y_test_batch=y_valid[index, :]
            yield (x_test_batch, y_test_batch)
            index=index+1
            if index>leng:
                break
            
    #---------------------------------------------------------------------
            
    def custom_mean_squared_error(y_true, y_pred):
        return mean(square(y_pred - y_true))
    def custom_categorical_crossentropy(y_true, y_pred):
        return categorical_crossentropy(y_true, y_pred)
    
    def loss_func(y_loss, x_loss):
        return categorical_crossentropy + 0.05 * mean_squared_error
    
    #建立模型
    with tf.device('/cpu:0'):
        inputs1=tf.placeholder(tf.float32, shape=(None,6000,1))
    
        x1=LSTM(128, return_sequences=True)(inputs1)
        encoded=LSTM(64 ,return_sequences=True)(x1)
        print('encoded shape:',shape(encoded))
    
        #decode
        x1=LSTM(128, return_sequences=True)(encoded)
        decoded=LSTM(1, return_sequences=True,name='decode')(x1)
        #classify
        labels=tf.placeholder(tf.float32, shape=(None,3))
        x2=Conv1D(20,kernel_size=50, strides=2, activation='relu' )(encoded)  #步数论文中未提及,第一层
        x2=MaxPooling1D(pool_size=2, strides=1)(x2)
        x2=Conv1D(20,kernel_size=50, strides=2, activation='relu')(x2)   #第二层
        x2=MaxPooling1D(pool_size=2, strides=1)(x2)
        x2=Dropout(0.25)(x2)
        x2=Conv1D(24,kernel_size=30, strides=2, activation='relu')(x2)   #第三层
        x2=MaxPooling1D(pool_size=2, strides=1)(x2)
        x2=Dropout(0.25)(x2)
        x2=Conv1D(24,kernel_size=30, strides=2, activation='relu')(x2)   #第四层
        x2=MaxPooling1D(pool_size=2, strides=1)(x2)
        x2=Dropout(0.25)(x2)
        x2=Conv1D(24,kernel_size=10, strides=2, activation='relu')(x2)  #第五层
        x2=MaxPooling1D(pool_size=2, strides=1)(x2)
        x2=Dropout(0.25)(x2)
    
        x2=Dense(192)(x2) #第一个全连接层
        x2=Dense(192)(x2)  #第二个全连接层
        x2=Flatten()(x2)
        x2=Dense(3,activation='softmax', name='classify')(x2)
    
        def get_accuracy(x2, labels):
            current = tf.cast(tf.equal(tf.argmax(x2, 1), tf.argmax(labels, 1)), 'float')
            accuracy = tf.reduce_mean(current)
            return accuracy
        #实例化获取准确率函数
        getAccuracy = get_accuracy(x2, labels)
        #定义损失函数
        all_loss=tf.reduce_mean(categorical_crossentropy(x2 , labels) + tf.convert_to_tensor(0.5)*square(decoded-inputs1))
        train_option=tf.train.AdamOptimizer(0.01).minimize(all_loss)
        #-----------------------------------------
        #生成训练数据
        data=tf.data.Dataset.from_generator(gen_1, (tf.float32, tf.float32), (tf.TensorShape([6000,1]), tf.TensorShape([3])))
        data=data.batch(128)
        iterator=data.make_one_shot_iterator()
        
        #生成测试数据
        data2=tf.data.Dataset.from_generator(gen_2, (tf.float32, tf.float32), (tf.TensorShape([6000,1]), tf.TensorShape([3])))
        data2=data2.batch(128)
        iterator2=data2.make_one_shot_iterator()
        #-----------------------------------------
        with tf.Session() as sess:
            init=tf.global_variables_initializer()
            sess.run(init)
            i=-1
            
            for k in range(20):
                #-----------------------------------------
                x, y=iterator.get_next()
                x_batch, y_batch=sess.run([x,y])
                print('batch shape:',x_batch.shape, y_batch.shape)
                #-----------------------------------------
                if k%2==0:
                    print('第',k,'轮')
                    x3=sess.run(x2, feed_dict={inputs1:x_batch, labels:y_batch })
                    dc=sess.run(decoded, feed_dict={inputs1:x_batch})
                    accuracy=sess.run(getAccuracy, feed_dict={x2:x3, labels:y_batch, keep_prob: 1.0})
                    loss=sess.run(all_loss, feed_dict={x2:x3, labels:y_batch, inputs1:x_batch, decoded:dc})
                    print("step(s): %d ----- accuracy: %g -----loss: %g" % (i, accuracy, loss))
                    sess.run(train_option, feed_dict={inputs1:x_batch, labels:y_batch, keep_prob: 0.5})
            x, y=iterator2.get_next()
            x_test_batch, y_test_batch=sess.run([x,y])
            print('batch shape:',x_test_batch.shape, y_test_batch.shape)
            x_test=sess.run(x2, feed_dict={inputs1:x_test_batch, labels:y_test_batch })
            print ("test accuracy %f"%getAccuracy.eval(feed_dict={x2:x_test, labels:y_test_batch, keep_prob: 1.0}))