a session.sess = tf.Session()# Create a summary writer, add the 'graph' to the event file.writer = tf.summary.FileWriter
环境内的参数都保存到文件里,后续就可以用 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) file_writer = tf.summary.FileWriter...收集全部采集点.....with tf.Session() as sess: sess.run(tf.global_variables_initializer()) train_writer = tf.summary.FileWriter.../4/lr={},rl={},ru={}'.format(learning_rate, num_layers, lstm_size) #每一对参数写入一个文件 writer = tf.summary.FileWriter...tf.summary.histogram('softmax_w',softmax_w) 打包点: merged=tf.summary.merge_all() 设置读写文件: train_writer=tf.summary.FileWriter
init) xss=ss.run(cnt) for xc in range(3): ys2=ss.run(y2) print(ys2) xsum=tf.summary.FileWriter...config) as ss: ss.run(init) xss=ss.run([c],feed_dict={a:[7,2],b:[2,2]}) print(xss) xsum=tf.summary.FileWriter...) as ss: ss.run(init) xss=ss.run([c],feed_dict={a:a_data,b:b_data}) print(xss) xsum=tf.summary.FileWriter...allow_soft_placement=True) with tf.Session(config=config) as ss: xss=ss.run([m1,m2]) print(xss) xsum=tf.summary.FileWriter...allow_soft_placement=True) init=tf.initialize_all_variables() with tf.Session(config=config) as ss: xsum=tf.summary.FileWriter
train_writer = tf.summary.FileWriter('MNIST/logs/tf17/train') validation_writer = tf.summary.FileWriter...('MNIST/logs/tf17/validation') tf.summary.FileWriter构造summary文件写入器,接受一个log的目录作为保存文件的路径。...可以通过构造两个不同的文件写入器来达到; 绘制计算图 TensorBoard除了绘制动态数据,绘制静态的graph(计算图)更是easy,在构造“文件写入器”多添加一个参数sess.graph即可: train_writer = tf.summary.FileWriter...loss_scalar = tf.summary.scalar('loss', loss) merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter...( 'MNIST/logs/tf17/train', sess.graph) validation_writer = tf.summary.FileWriter(
xsum=tf.summary.FileWriter("."...x,name='output') sess=tf.Session() sess.run(tf.global_variables_initializer()) ans=sess.run(y) xsum=tf.summary.FileWriter...tf.float32,name='x') y=tf.placeholder(tf.float32,name='y') z=tf.add(x,y,name='z') ss=tf.Session() xsum=tf.summary.FileWriter
={xs:x_data,ys:y_data}) #计算需要写入的日志数据 writer.add_summary(result,i) #将日志数据写入文件 其中:writer = tf.summary.FileWriter...格式:tf.summaries.merge_all(key='summaries') 4、tf.summary.FileWriter 指定一个文件用来保存图。...accuracy',acc) #生成准确率标量图 merge_summary = tf.summary.merge_all() train_writer = tf.summary.FileWriter...(其他要显示的信息)]) train_writer = tf.summary.FileWriter(dir,sess.graph)#定义一个写入summary的目标文件,dir为写入文件地址 ....(其他要显示的信息)]) #这里的[]不可省 如果要在tensorboard中画多个数据图,需定义多个tf.summary.FileWriter并重复上述过程。
.: writer = tf.summary.FileWriter('....tf.train.import_meta_graph('criteo_80.meta') with tf.Session(graph=g) as sess: file_writer = tf.summary.FileWriter
train_writer = tf.summary.FileWriter('MNIST/logs/tf17/train')validation_writer = tf.summary.FileWriter...('MNIST/logs/tf17/validation') tf.summary.FileWriter构造summary文件写入器,接受一个log的目录作为保存文件的路径。...可以通过构造两个不同的文件写入器来达到; 绘制计算图 TensorBoard除了绘制动态数据,绘制静态的graph(计算图)更是easy,在构造“文件写入器”多添加一个参数sess.graph即可: train_writer = tf.summary.FileWriter...loss_scalar = tf.summary.scalar('loss', loss) merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter...( 'MNIST/logs/tf17/train', sess.graph) validation_writer = tf.summary.FileWriter(
另外除了保存模型以外,还有 tf.summary.FileWriter train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph
my-model') tf.nn.rnn_cell tf.nn.rnn_cell => tf.contrib.rnn tf.train.GradientDescentOptimizer() 梯度下降优化器 tf.summary.FileWriter...tf.trian.SummaryWriter => tf.summary.FileWriter Writes Summary protocol buffers to event files. tf.global_variables_initializer
www.tensorflow.org/api_guides/python/summary tf.summary.scalar tf.summary.histogram tf.summary.merge_all tf.summary.FileWriter...4. tf.summary.FileWriter 最后,为了将 summary data 写入磁盘,需要将 Summary protobuf 对象传递给 tf.summary.FileWriter。
格式:tf.summaries.merge_all(key='summaries')8、tf.summary.FileWriter指定一个文件用来保存图。...tf.summary.scalar('accuracy',acc) #生成准确率标量图 merge_summary = tf.summary.merge_all() train_writer = tf.summary.FileWriter...(其他要显示的信息)]) train_writer = tf.summary.FileWriter(dir,sess.graph)#定义一个写入summary的目标文件,dir为写入文件地址 .....
sess=tf.Session() result=sess.run(x) print(result) 运行数据的另一种方法是使用eval(),括号里面添加session部分,否则失效报错: (xsum=tf.summary.FileWriter...,sess.graph)只是一条额外的语句用于保存图) xsum=tf.summary.FileWriter("."
tf.constant(2) e = tf.constant(3) f = tf.multiply(d,e) g = tf.add(c,f) 现在,我们将创建一个tensorflow会话,我们将使用tf.summary.FileWriter...()将我们的图形结果写入称为事件文件的文件: with tf.Session() as sess: writer = tf.summary.FileWriter("logs", sess.graph...(d,e) with tf.name_scope("Result"): g = tf.add(c,f) with tf.Session() as sess: writer = tf.summary.FileWriter...tf.multiply(d,e) with tf.name_scope("Result"): g = tf.add(c,f) with tf.Session() as sess: writer = tf.summary.FileWriter
相加后的类型为") print(a_b) print("真正的结果为:") print(sess.run(a_b)) # 添加board记录文件 file_write = tf.summary.FileWriter...初始的权重为{}, 初始的偏置为{}".format(weight.eval(), bias.eval())) # 添加board记录文件 file_write = tf.summary.FileWriter..."初始的权重为{}, 初始的偏置为{}".format(weight.eval(), bias.eval())) # 添加board记录文件 file_write = tf.summary.FileWriter
汇总操作,逐一执行这些操作太麻烦, # 使用tf.summary.merge_all()直接获取所有汇总操作,以便后面执行 merged = tf.summary.merge_all() # 定义两个tf.summary.FileWriter...文件记录器再不同的子目录,分别用来存储训练和测试的日志数据 train_writer = tf.summary.FileWriter(log_dir + '/train', sess.graph) test_writer...= tf.summary.FileWriter(log_dir + '/test') # 同时,将Session计算图sess.graph加入训练过程,这样再TensorBoard的GRAPHS窗口中就能展示...train_writer.add_summary(summary, i) train_writer.close() test_writer.close() 三、TensorBoard的使用 1、找到代码中tf.summary.FileWriter
输出网络结构 with tf.Session() as sess: writer = tf.summary.FileWriter(your_dir, sess.graph) 命令行运行tensorboard...tf.summary.merge_all() init = tf.global_variable_initializer() with tf.Session() as sess: writer = tf.summary.FileWriter
执行结果 为了将上面程序可视化,我们需要下面一行程序将日志写入文件: writer = tf.summary.FileWriter([logdir], [graph]) [graph] 是运行程序所在的图...是存储日志文件的路径 import tensorflow as tf a = tf.constant(2) b = tf.constant(3) x = tf.add(a, b) writer = tf.summary.FileWriter.../graphs', tf.get_default_graph()) with tf.Session() as sess: # writer = tf.summary.FileWriter('.
参数说明: [1]key : 用于收集summaries的GraphKey,默认的为GraphKeys.SUMMARIES [2]scope:可选参数 8、tf.summary.FileWriter...merge_summary = tf.summary.merge_all() #定义一个写入summary的目标文件,dir为写入文件地址 (交叉熵、优化器等定义) train_writer = tf.summary.FileWriter...(其他要显示的信息)]) #定义一个写入summary的目标文件,dir为写入文件地址 (交叉熵、优化器等定义) train_writer = tf.summary.FileWriter(dir...(其他要显示的信息)]) #这里的[]不可省 如果要在tensorboard中画多个数据图,需定义多个tf.summary.FileWriter并重复上述过程。
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