tensorboard

I'm working on a segmentation problem in Keras and I want to display segmentation results at the end of every training epoch. I want something similar to Tensorflow: How to Display Custom Images in Tensorboard (e.g. Matplotlib Plots) , but using Keras. I know that Keras has the TensorBoard callback but it seems limited for this purpose. I know this would break the Keras backend abstraction, but I'm interested in using TensorFlow backend anyway. Is it possible to achieve that with Keras + TensorFlow? Fábio Perez So, the following solution works well for me: import tensorflow as tf def make

How can you write a python script to read Tensorboard log files, extracting the loss and accuracy and other numerical data, without launching the GUI tensorboard --logdir=... ? user1501961 You can use TensorBoard's Python classes or script to extract the data: How can I export data from TensorBoard? If you'd like to export data to visualize elsewhere (e.g. iPython Notebook), that's possible too. You can directly depend on the underlying classes that TensorBoard uses for loading data: python/summary/event_accumulator.py (for loading data from a single run) or python/summary/event_multiplexer.py

The Image Dashboard section of the Tensorboard ReadMe says: Since the image dashboard supports arbitrary pngs, you can use this to embed custom visualizations (e.g. matplotlib scatterplots) into TensorBoard. I see how a pyplot image could be written to file, read back in as a tensor, and then used with tf.image_summary() to write it to TensorBoard, but this statement from the readme suggests there is a more direct way. Is there? If so, is there any further documentation and/or examples of how to do this efficiently? It is quite easy to do if you have the image in a memory buffer. Below, I show

I want to have a visual of confusion matrix in tensorboard. To do this, I am modifying Evaluation example of Tensorflow Slim: https://github.com/tensorflow/models/blob/master/slim/eval_image_classifier.py In this example code, Accuracy already provided but it is not possible to add "confusion matrix" metric directly because it is not streaming. What is difference between streaming metrics and non-streaming ones? Therefore, I tried to add it like this: c_matrix = slim.metrics.confusion_matrix(predictions, labels) #These operations needed for image summary c_matrix = tf.cast(c_matrix, uint8) c

I have a TensorFlow model, and one part of this model evaluates the accuracy. The accuracy is just another node in the tensorflow graph, that takes in logits and labels . When I want to plot the training accuracy, this is simple: I have something like: tf.scalar_summary("Training Accuracy", accuracy) tf.scalar_summary("SomethingElse", foo) summary_op = tf.merge_all_summaries() writer = tf.train.SummaryWriter('/me/mydir/', graph=sess.graph) Then, during my training loop, I have something like: for n in xrange(1000): ... summary, ..., ... = sess.run([summary_op, ..., ...], feed_dict) writer.add

Is there a way to plot both the training losses and validation losses on the same graph? It's easy to have two separate scalar summaries for each of them individually, but this puts them on separate graphs. If both are displayed in the same graph it's much easier to see the gap between them and whether or not they have begin to diverge due to overfitting. Is there a built in way to do this? If not, a work around way? Thank you much! The work-around I have been doing is to use two SummaryWriter with different log dir for training set and cross-validation set respectively. And you will see

TensorBoard seems to have a feature to display multiple different runs and toggle them. How can I make multiple runs show up here and how can assign a name to them to differentiate them? In addition to TensorBoard scanning subdirectories (so you can pass a directory containing the directories with your runs), you can also pass multiple directories to TensorBoard explicitly and give custom names (example taken from the --help output): tensorboard --logdir=name1:/path/to/logs/1,name2:/path/to/logs/2 More information can be found at the TensorBoard documentation . Maarten I found the answer to my