I've been trying to use tensorflow.js, but I need the model in the SavedModel format. So far, I only have the Frozen Graph, as I used Tensorflow for Poets Codelab.
How can I convert the Frozen Graph into SavedModel?
I've been using the latest Python version and Tensorflow 1.8
The SavedModel is really just a wrapper around a frozen graph that provides assets and the serving signature. For a code implementation, see this answer.
Related
I trained a model using yolov5, Then exported it to TensorFlow saved_model format, the result was a yolo5s.pt file. As far as I know yolov5 uses PyTorch, I prefer TensorFlow. Now I want to build a model in TensorFlow using the saved_model file, how can I do it?
It will be preferable if the solution is in google colab, I didn't included my code because I don't have any ideas how to start.
For.pb SavedModel : model.save("my_model") default saves to .pb
For .tf SavedModel : model.save("my_model",save_format='.tf')
I would like to know the difference between these two formats. Are they both SavedModel? Are they both the same ? Which is better? Both are TensorFlow extension?
See the documentation of tf.keras.Model.save. save_format can have one of two values:
tf (default in TensorFlow 2.x) means TensorFlow format, a SavedModel protocol buffers file.
h5 (default in TensorFlow 1.x) means the HDF5 Keras format, defined back when Keras was completely independent of TensorFlow and aimed to support multiple backends without being tied to anyone in particular.
In TensorFlow 2.x you should not ever need h5, unless you want to produce a file compatible with older versions or something like that. SavedModel is also more integrated into the TensorFlow ecosystem, for example if you want to use it with TensorFlow Serving.
I have a Java application that use my old tensorflow models. I used to convert the .h5 weights and .json model into a frozen graph in .pb.
I used a similar code than in this github https://github.com/amir-abdi/keras_to_tensorflow.
But this code but it's not compatible with tf 2.0 model.
I couldn't find any other resources.
Is it even possible?
Thank you :)
I have a frozen inference graph(frozen_inference_graph.pb) and a checkpoint (model.ckpt.data-00000-of-00001, model.ckpt.index), how to deploy these to Tensorflow serving? serving need SavedModel format, how to convert to it?
I study Tensorflow and found Deeplab v3+ provide PASCAL VOC 2012 model, I run train, eval, visualization on my local PC, but I don't know how to deploy it on serving.
Have you tried export_inference_graph.py?
Prepares an object detection tensorflow graph for inference using model
configuration and a trained checkpoint. Outputs inference
graph, associated checkpoint files, a frozen inference graph and a
SavedModel
Basically I am trying to use google's pre trained Speaker-id model for speaker detection. But this being a TensorFlow Lite model, I can't use it on my Linux pc. For that, I am trying to find a converter back to its frozen graph model.
Any help on this converter or any direct way to use tensorflow Lite pretrained models on desktop itself, will be appreciated.
You can use the converter which generates tflite models to convert it back to a .pb file if that is what you're searching for.
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md