I am facing issues for convert TensorFlow to TensorFlow Lite. As per research first need to save the model in .pb and by using this file we can convert it into TensorFlow lite but facing an error.
Among the TF graph representations, exporting as a saved model is recommended. TFLiteConverter.from_saved_model API is more capable than the other conversion APIs. For example, signature def API is only available from the saved model API and there are better support of resource and variant types in the saved model API.
https://www.tensorflow.org/hub/exporting_tf2_saved_model
https://www.tensorflow.org/lite/convert
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I'm kinda new to TensorFlow and Keras, so please excuse any accidental stupidity, but I have an issue. I've been trying to load in models from the TensorFlow Detection Zoo, but haven't had much success.
I can't figure out how to read these saved_model folders (they contain a saved_model.pb file, and an assets and variables folder), so that they're accepted by Keras. Nor can I figure out a way to convert these models so that they may be loaded in. I've tried converting the SavedModel to ONNX, and then convert the ONNX-model to Keras, but that didn't work. Trying to load the original model as a saved_model, and then trying to to save this loaded model in another format gave me no success either.
Since you are new to Tensorflow (and I guess deep learning) I would suggest you stick with the API because the detection zoo models best interface with the object detection API. If you have already downloaded the model, you just need to export it using the exporter_main_v2.py script. This article explains it very well link.
I recently use Google AutoML service to create a model.
Its output seems to be in a saved model format. However,when I attempt to load it via tf.saved_model.load ,it display following error
Op type not registered 'TreeEnsembleSerialize' in binary ...
When I look up this op,I find that this op exists in tf.contrib.boosted_trees in Tensorflow 1.15,but since Tensorflow 2 removes tf.contrib,this op has be renamed to BoostedTreesSerializeEnsemble in tf.raw_ops.
My question is:Is there any way to duplicate the op and rename it to TreeEnsembleSerialize ,so the saved model could be loaded without errors.
Thanks.
There are no significant compatibility concerns for saved models.
TensorFlow 1.x saved_models work in TensorFlow 2.x.
TensorFlow 2.x
saved_models work in TensorFlow 1.x if all the ops are supported.
For more information visit Tensorflow doc
new Tensorflow 2.0 user. My project requires me to investigate the weights for the neural network i created in Tensorflow (super simple one). I think I know how to do it in the regular Tensorflow case. Namely I use the command model.save_weights(filename). I would like to repeat this effort for a .tflite model but I am having trouble. Instead of generating my own tensorflow lite model, I am using one of the many models which are provided online: https://www.tensorflow.org/lite/guide/hosted_model to avoid having to troubleshoot my use of the Tensorflow Lite converter. Any thoughts?
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 :)
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