带有numpy数组的张量板 [英] tensorboard with numpy array

问题描述

有人可以举一个例子来说明如何使用tensorboard可视化numpy数组值吗?

这里有一个相关的问题，我真的不明白. 张量板记录非张量(numpy)信息(AUC)

例如， 如果我有

for i in range(100):
    foo = np.random.rand(3,2)

如何使用Tensorboard跟踪100次迭代的foo分布?有人可以提供代码示例吗? 谢谢.

解决方案

对于简单值(标量)，可以使用此配方

summary_writer = tf.train.SummaryWriter(FLAGS.logdir)
summary = tf.Summary()
summary.value.add(tag=tagname, simple_value=value)
summary_writer.add_summary(summary, global_step)
summary_writer.flush()

就使用数组而言，也许您可​​以在序列中添加6个值，即

for value in foo:
  summary.value.add(tag=tagname, simple_value=value)

Can someone give a example on how to use tensorboard visualize numpy array value?

There is a related question here, I don't really get it. Tensorboard logging non-tensor (numpy) information (AUC)

For example, If I have

for i in range(100):
    foo = np.random.rand(3,2)

How can I keep tracking the distribution of foo using tensorboard for 100 iterations? Can someone give a code example? Thanks.

解决方案

For simple values (scalar), you can use this recipe

summary_writer = tf.train.SummaryWriter(FLAGS.logdir)
summary = tf.Summary()
summary.value.add(tag=tagname, simple_value=value)
summary_writer.add_summary(summary, global_step)
summary_writer.flush()

As far as using array, perhaps you can add 6 values in a sequence, ie

for value in foo:
  summary.value.add(tag=tagname, simple_value=value)

这篇关于带有numpy数组的张量板的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！