tensorboard应用学习

这个方法不错    思路比较清晰

http://blog.csdn.net/phdat101/article/details/52538061    ［重要］

要运行tensorboard应用直接在命令行输入 tensorboard --logdir  /home.....

截至62相关的测试代码：

# -*- coding: UTF-8 -*- '''Created on 2017年12月7日'''import pandas as pdfrom sklearn.model_selection import train_test_splitimport tensorflow as tfimport numpy as np#从CSV文件中读入数据data = pd.read_csv('train.csv')# data.info()#取部分特征字段用于分类，并将所有缺失的字段补充为0#对Sex字段进行正规化处理data['Sex'] = data['Sex'].apply(lambda s:1 if s == 'male' else 0)   #男为1，女为0data = data.fillna(0)  #缺失的字段全部补充为0dataset_X = data[['Sex','Age','Pclass','SibSp','Parch','Fare']]dataset_X = dataset_X.as_matrix()     #转换成矩阵# print dataset_X#两种分类分别是幸存和死亡，即'Survived'和'Deceased'data['Deceased'] = data['Survived'].apply( lambda s: int (not s))  #取非dataset_Y = data[['Deceased','Survived']]dataset_Y = dataset_Y.as_matrix()   #one-hot encoding'# print dataset_Y'#scikit-learn库中提供了用于切分数据集的工具函数train_test_split，随机打乱数据集后按比列拆分数据集#使用函数train_test_split将标记数据切分为训练数据集和验证数据集，其中验证数据集占20%X_train, X_validation, Y_train, Y_validation = train_test_split(dataset_X, dataset_Y, test_size = 0.2, random_state = 42 )#设置 random_state = 42之后，就是代表多次运行程序后得到的随机数都是一样的，若设不同的值或者不设则随机数就是不一样的# print Y_validation# print len(X_train)# a = [[1,2],[2,3],[3,4]]# print len(a)#接下来，使用TensorFlow构建计算图--------------------------------------------------------------------------------------------------------------------------------------#使用placeholder声明占位符#声明输入数据占位符#shape参数的第一个元素为none,表示可以同时放入任意条记录X = tf.placeholder(tf.float32, shape = [None, 6])Y = tf.placeholder(tf.float32, shape = [None, 2])#声明参数变量W = tf.Variable(tf.random_normal([6, 2]), name = 'weights')b = tf.Variable(tf.zeros([2]), name = 'bias')# use saver to save and restore modelsaver = tf.train.Saver()        #保存声明之前的所有变量#构造前向传播计算图y_pred = tf.nn.softmax(tf.matmul(X, W) + b)#声明代价函数cross_entropy = - tf.reduce_sum(Y*tf.log(y_pred+1e-10), reduction_indices = 1)  #reduction_indices指示按照哪个维度求和 *对应元素的乘积#这里表示把每行的2个值加起来# a = [1, 2]# b = [3, 4]# aa = np.array(a)# bb =np. array(b)# print aa# print bb# print aa*bbcost =  tf.reduce_mean(cross_entropy)     #代价函数使用了cross-entropytf.scalar_summary('cost',cost) #使用梯度下降算法最小化代价，系统自动构建反向传播部分的计算图train_op = tf.train.GradientDescentOptimizer(0.001).minimize(cost)   #此处learing rate设为0.01 且仅仅使用了梯度下降算法#计算图的声明完成-------------------至此计算图结束#构建训练迭代过程with tf.Session() as sess:   #session对象负责把运行环境打包    #初始化所有变量    tf.initialize_all_variables().run()    merged = tf.merge_all_summaries() #collect the tf.xxxxx_summary      writer = tf.train.SummaryWriter('/home/tensorBoardLog',sess.graph)           # maybe many writers to show different curvs in the same figure     #training loop    for epoch in range(100):        total_loss = 0.        for i in range(len(X_train)):   #len返回行数            #prepare feed data and run,这里相关于是使用随机梯度下降,对每一样本进来都进行参数更新            feed_dict = {X: [X_train[i]], Y:[Y_train[i]]}            summary, _, loss = sess.run([merged, train_op, cost], feed_dict = feed_dict)    #触发后端执行的入口,cost是tensor，不是优化算子，所以有值返回            total_loss += loss        writer.add_summary(summary, epoch)        print('Epoch: %04d, total loss = %-9f' % (epoch+1, total_loss))                            #用验证数据集合评估模型的表现    pred = sess.run(y_pred, feed_dict = {X: X_validation})    print pred    #argmax是找最大值的位置，后面的1指的是轴    correct = np.equal(np.argmax(pred, 1), np.argmax(Y_validation, 1))#     print correct#     cao = correct.astype(np.float32)#     print cao#astype指的是类型转换, boolen型换成float型，mean表示加起来，除以个数，这里是1 和 0， 所以可以    accuracy = np.mean(correct.astype(np.float32))    print ('Accuracy on validation set: %.9f' % accuracy)#     save_path = saver.save(sess, "model.ckpt" )      #保存训练好的参数# #再开一个session进行最后的测试# with tf.Session() as sess2:#     #predict on test data#     testdata = pd.read_csv('test.csv')#     testdata = testdata.fillna(0)#     testdata['Sex'] = testdata['Sex'].apply(lambda s: 1 if s == 'male' else 0)#     X_test = testdata[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare' ]]#     saver.restore(sess2,  "model.ckpt" )        #加载变量#     predictions = np.argmax(sess2.run(y_pred, feed_dict = {X: X_test}),1)#     print predictions#     submission = pd.DataFrame({#                  'PassengerId': testdata['PassengerId'],#                  'Survived': predictions#                                })#     submission.to_csv('Titanic-submission-miao.csv', index = False)