tensorboard

I am getting started with installation of Tensorflow on my RHEL 6.5 box. But it turns out that Tensorflow needs glibc >= 2.17 and the default glibc on rhel 6.5 is 2.12. I was wondering if anybody could help me with minimum/recommended system specifications for tensorflow? The TensorFlow requirements are listed here , but these do not recommend a particular operating system or glibc version. The best-supported operating systems are Ubuntu 14.04 64-bit, and Mac OS X 10.10 (Yosemite) and later. The current limiting factor is the set of supported operating systems for Bazel , which we use to make

When training an Object Detection DNN with Tensorflows Object Detection API it's Visualization Plattform Tensorboard plots a scalar named regularization_loss_1 What is this? I know what regularization is (to make the Network good at generalizing through various methods like dropout) But it is not clear to me what this displayed loss could be. Thanks! TL;DR : it's just the additional loss generated by the regularization function. Add that to the network's loss and optimize over the sum of the two . As you correctly state, regularization methods are used to help an optimization method to

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. 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

I am trying to output some summary scalars in an ML engine experiment at both train and eval time. tf.summary.scalar('loss', loss) is correctly outputting the summary scalars for both training and evaluation on the same plot in tensorboard. However, I am also trying to output other metrics at both train and eval time and they are only outputting at train time. The code immediately follows tf.summary.scalar('loss', loss) but does not appear to work. For example, the code as follows is only outputting for TRAIN, but not EVAL. The only difference is that these are using custom accuracy functions,

I build a seq2seq model using the seq2seq.py library provided with tensorflow. Before training anything I wanted to visualize the graph network of my untrained model in tensorboard, but it does not want to display this. Below a minimal example to reproduce my problem. Anybody an idea why this does not work? Can you only visualize a grap of a model after it has been trained? import tensorflow as tf import numpy as np from tensorflow.models.rnn import rnn_cell from tensorflow.models.rnn import seq2seq encoder_inputs = [] decoder_inputs = [] for i in xrange(350): encoder_inputs.append(tf