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.
Related
Previously, sess.graph was used as a handle to push things to tensorboard.
There is no current replacement AFAIK. Visualizing graphs is fundamental.
How can we viz graphs in tensorflow 2.0? Must be some hook into the functions.
Tensorboard works in 2.0. This example for keras and this without
I used Object Detection API from Tensorflow to train my model. The eval script also worked fine.
However, I want to get the coordinates of the bounding box in eval images. Is that possible to do so in eval script? How can I do it?
I used this script.
A few examples (how many is set by num_visualizations in the config file) are shown on TensorBoard.
If you want more than that, I suggest to export the model and use inference.
See the TF OD tutorial notebook for how to use an exported model for inference and visualizing results.
You can refer to the following link.
Although this does not have any relation with the eval.py script, it gets the job done. If you specify a directory consisting of the test images, you can get the bounding box coordinates of all the detected objects using the model you have trained.
I'm aware of Tensorboard and how awesome it is, but I think that simple console output with current graph summary is better (and faster) for prototyping purpose.
And also know that I can generate tensorboard graph after simply running session with last network node as shown here.
What I'm looking for is something similar to model.summary() from Keras.
In another words: how to iterate over tensorflow graph and print out only custom high end layer with their shapes and dtypes in the same order how all these layer where generated?
It's certainly possible. If you are using tf.keras wrapper to build you can easily visualize the graph, even before model.compile() method executes.
It's keras built-in functionality called plot_model().
*This method have dependency on graphviz and pydot libraries.
for pydot installation : pip install pydot
but for graphviz installation you have follow step in this page. And also probably you have to restart the machine because of there it create system environment variables.
for tutorial on how to use this method follow this link
To plot your model with shapes and dtypes before training you could use:
tf.keras.utils.plot_model(model, show_shapes=True, expand_nested=True, show_dtype=True)
where "model" is your built model. The output of a model could looks like this:
I am used tflearn
yet I want to use the tensorboard and its visualization
how can I use it?
how to get the session form tflearn?
for example for this example (Pannous speech_data) https://github.com/llSourcell/tensorflow_speech_recognition_demo/blob/master/demo.py
TFLearn supports a verbose level to automatically manage summaries. Setting it to 3 will enable visualization.
Set,
model = tflearn.DNN(net, tensorboard_verbose=3)
You can learn more about it in the Getting started guide.
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.