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.
Related
I am new to tensorflow lite development and would like to know a guide to understand the code of inference process of any neural network in tflite.
How can I proceed?
Thanks in advance
I have tried to follow the inference flow for a dense neural network.
You can start from here to know what APIs does what and follow from there.
Some high level points
To load TF Lite file you use TfLite Interpreter.
A TFLite graph consists of a list of subgraphs (basically each subgraph can be viewed as a function).
Each subgraph should have operations in execution order and calling Invoke will trigger them in the provided order.
I would like to train a MLP(Multi Layer Perceptron) with MNIST dataset. I use a validation set so I can save the weights of the best model. Then I want to load these weights back into the same architecture and use them to initialize and train with another dataset. I would like to know if this is possible with Tensorflow 1.x or 2.x. Right now I am trying to write a custom function to do it but it is getting complicated. I am using tf 1.x.
I suggest you take a look at tensorflow's documentation, here a link of a tutorial to save your weights and load them afterwards:
https://www.tensorflow.org/tutorials/keras/save_and_load
I have a problem where I need to record function trace for a neural network model.
I need to create a task graph of a neural network model written in tensorflow. I think adding logs in a tensorflow code first can solve my problem. However it is very time consuming. What can be the best way to solve this. Does tensorflow provide any facility to accomplish this task Please help.
You can use the tensorboard. I strongly recommend it. Because you can find the very detail of your model.
writer = tf.summary.FileWriter(your-dir, sess.graph)
And open it in the Chrome.
I have trained DNNClassifier using Python (conda tensorflow installation). The trained model needs to be used for evaluation using C_API. Is there a way to load both graph and weights of the trained model using C_API?
There is a way to load h5 and any data for C_API. Maybe some googling could help. I've found this article to be helpful.
And for DNNClassifier on C_API I think you should Implement it manually using pure Tensor Array on C_API. cmiimw
I would like to run the googlenet with tensorflow. Is there any multi-GPU version available that can be run with tensorflow?
You can train, evaluate and fine-tune an Inception v3 model. See links there-in for pointers.
http://googleresearch.blogspot.com/2016/03/train-your-own-image-classifier-with.html
Can you also please check out: https://github.com/tensorflow/models/tree/master/inception? (The image is from the URL.)