I'm able to display the tf graph itself in Jupyter using code from here:
Simple way to visualize a TensorFlow graph in Jupyter?
But I'm interested in more full featured tensorboard functionality, like visualizing training progress. I'm running in Azure Notebooks, where I'm not able to run a tensorboard server.
Related
I am currently training a cycle gan on horse2zebra dataset using colab.
It seems that the logs are being written in a .txt file.The original repository for cyclegan uses visdom to visualize error logs.But since colab doesn't support visdom,so I can't use it to visualize the logs.
With that in mind, can I visualize the same log.txt files using tensorboard? And if yes then how shall i go about it
is it the same as
tensorboard --log_dir path to log.txt
I am importing a pretrained mobilenet's model mobilenet_v1_0.25_128_frozen.pb into my tensorflow environment. Once imported, I want to be able to save a snapshot of the model architecture in the form of .png. I know that there is a way to do this in keras with tf.keras.utils.plot_model(model, to_file="model.png"). Is there a way to do this in tensorflow session without using Tensorboard. In case, you recommend using tensorboard, I don't want to separately run tensorboard. I want a way to save the model architecture inside the tensorflow session without starting tensorboard.
I'm testing out this object detection implementation on a small subset of the DOTA dataset using Google Colab. The training is going fine, but the the images in Tensorboard are washed out and beige. Could there be an issue with the images when they converted to a tfrecord, or is there some issue with Tensorboard/ Colab compatibility? I'm using tensorflow-gpu = 1.13.1 & tensorboard 1.13.1. See below screenshot for commands I used to open tensorboard and the issues with the images.
There was in fact a problem with image normalization caused bu user error editing code.
I defined a tensorflow network structure, and then I wanted to print it just like pytorch.
Does tensorflow have a corresponding function?
Or how should I achieve it?
You can visualize your Tensorflow network using Tensorboard.
https://www.tensorflow.org/guide/summaries_and_tensorboardTensorboard details
Above link given complete details of how to do it.
In Theano, I can use pydotprint to generate a nice graph of my model. Very useful for debugging, and for presenting too. Is there an equivalent for TensorFlow?
As #JHafdahl points out, TensorBoard provides graph visualization for TensorFlow graphs, which includes support for summarizing complex nested subgraphs.
To visualize a graph, build a TensorFlow graph as normal, then add the following statements to your Python program:
writer = tf.train.SummaryWriter("/path/to/logs", tf.get_default_graph().as_graph_def())
writer.flush()
Then, in a separate terminal, run TensorBoard to visualize your graph:
$ tensorboard --logdir=/path/to/logs --port 6006
Finally, connect to TensorBoard by opening http://localhost:6006 in your web browser. Clicking on the "Graph" tab will show the visualization of your graph; see the graph visualization tutorial for more details.
Look into Tensorboard, which ships with Tensorflow. I use it to track the performance of my models and make sure they are converging.