（接上文）
增量学习的过程，就是读取磁盘上原有的训练结果，并在此基础上继续训练。
这里用到的是 tensorFlow的saver，用于存取训练结果。
整个代码与前文的首次训练很像，只是把init()换为读取：
替换前：
sess = tf.Session(graph=graph)sess.run(init)# Very important
替换后：
sess = tf.Session(graph=graph)check_point_path = 'saved_model/' # 保存好模型的文件路径ckpt = tf.train.get_checkpoint_state(checkpoint_dir=check_point_path)saver.restore(sess,ckpt.model_checkpoint_path)
可以看到，训练的代价已经基本维持当前值
runfile('./gn_next_training.py', wdir='./')INFO:tensorflow:Restoring parameters from saved_model/model.ckptINFO:tensorflow:Restoring parameters from saved_model/model.ckpt0 1024 0.002105550 2048 0.001450820 3072 0.001203480 4096 0.001413150 5120 0.0040 6144 0.001966140 7168 0.001545350 8192 0.001565750 9216 0.001367180 10240 0.001699930 11264 0.00186610 12288 0.001281511 13312 0.001086381 14336 0.0009872831 15360 0.0009077511 16384 0.001039141 17408 0.001445841 18432 0.001515181 19456 0.00108151 20480 0.001179911 21504 0.001242971 22528 0.001298191 23552 0.001485721 24576 0.00107887
最后，附加完整的增量学习代码
# -*- coding: utf-8 -*-"""Created on Sun Nov 26 15:24:50 @author: goldenhawking"""from __future__ import print_functionimport tensorflow as tfimport numpy as npimport configparserimport reimport matplotlib.pyplot as mpltrainning_task_file = 'train_task.cfg'trainning_input_file = 'train_input.txt'model_path = './saved_model/'#读取配置config = configparser.ConfigParser()config.read(trainning_task_file)n= int(config['network']['input_nodes'])# input vector sizeK= int(config['network']['output_nodes'])# output vector sizelam = float(config['network']['lambda'])#隐层规模 用逗号分开,类似 ”16,16,13“ hidden_layer_size = config['network']['hidden_layer_size'] #分离字符reobj = pile('[\s,\"]')ls_array = reobj.split(hidden_layer_size);ls_array = [item for item in filter(lambda x:x != '', ls_array)] #删空白#隐层个数hidden_layer_elems = len(ls_array);#转为整形，并计入输出层 ns_array = []for idx in range(0,hidden_layer_elems) :ns_array.append(int(ls_array[idx]))#Output is the last layer, append to lastns_array.append(K)#总层数（含有输出层）total_layer_size = len(ns_array)#--------------------------------------------------------------#create graphgraph = tf.Graph()with graph.as_default():with tf.name_scope('network'):with tf.name_scope('input'):s = [n]a = [tf.placeholder(tf.float32,[None,s[0]],name="in")]W = []b = []z = []punish = tf.constant(0.0)for idx in range(0,total_layer_size) :with tf.name_scope('layer'+str(idx+1)):s.append(int(ns_array[idx]))W.append(tf.Variable(tf.random_uniform([s[idx],s[idx+1]],0,1),name='W'+str(idx+1)))b.append(tf.Variable(tf.random_uniform([1],0,1),name='b'+str(idx+1)))z.append(tf.matmul(a[idx],W[idx]) + b[idx]*tf.ones([1,s[idx+1]],name='z'+str(idx+1)))a.append(tf.nn.tanh(z[idx],name='a'+str(idx+1)))with tf.name_scope('regular'):punish = punish + tf.reduce_sum(W[idx]**2) * lam#--------------------------------------------------------------with tf.name_scope('loss'):y_ = tf.placeholder(tf.float32,[None,K],name="tr_out")loss = tf.reduce_mean(tf.square(a[total_layer_size]-y_),name="loss") + punishwith tf.name_scope('trainning'):optimizer = tf.train.AdamOptimizer(name="opt")train = optimizer.minimize(loss,name="train")init = tf.global_variables_initializer()#save graph to Disksaver = tf.train.Saver()#--------------------------------------------------------------### create tensorflow structure end ###sess = tf.Session(graph=graph)check_point_path = 'saved_model/' # 保存好模型的文件路径ckpt = tf.train.get_checkpoint_state(checkpoint_dir=check_point_path)saver.restore(sess,ckpt.model_checkpoint_path)#writer = tf.summary.FileWriter("./netdemo/")#writer.add_graph(sess.graph)#writer.close();file_deal_times = int(config['performance']['file_deal_times'])trunk = int(config['performance']['trunk'])train_step= int(config['performance']['train_step'])iterate_times = int(config['performance']['iterate_times'])#trainningx_data = np.zeros([trunk,n]).astype(np.float32)#read n features and K outputsy_data = np.zeros([trunk,K]).astype(np.float32)total_red = 0plot_x = []plot_y = []for rc in range(file_deal_times):with open(trainning_input_file, 'rt') as ftr:while 1:lines = ftr.readlines()if not lines:#reach end of file, run trainning for tail items if there is some.if (total_red>0):for step in range(iterate_times):sess.run(train,feed_dict={a[0]:x_data[0:min(total_red,trunk)+1],y_:y_data[0:min(total_red,trunk)+1]})breakline_count = len(lines)for lct in range(line_count):x_arr = reobj.split(lines[lct]);x_arr = [item for item in filter(lambda x:x != '', x_arr)] #remove null stringsfor idx in range(n) :x_data[total_red % trunk,idx] = float(x_arr[idx])for idx in range(K) : y_data[total_red % trunk,idx] = float(x_arr[idx+n]) total_red = total_red + 1#the trainning set run trainningif (total_red % train_step == 0):#trainningfor step in range(iterate_times):sess.run(train,feed_dict={a[0]:x_data[0:min(total_red,trunk)+1],y_:y_data[0:min(total_red,trunk)+1]})#print losslss = sess.run(loss,feed_dict={a[0]:x_data[0:min(total_red,trunk)+1],y_:y_data[0:min(total_red,trunk)+1]})print(rc,total_red,lss)plot_x.append(total_red)plot_y.append(lss)if (lss<0.0001):break;mpl.plot(plot_x,plot_y)#saving# 保存，这次就可以成功了saver.save(sess,model_path+'/model.ckpt')
在下一篇文章，我们将介绍进行测试集测试、分类应用的代码。
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