I am very new to machine learning. I have a python file with a very simple TensorFlow model that I need to deploy on Android using Google's ML Kit (which would be creating tflite file). I absolutely don't understand what should be the structure of my python file and Google's documentation doesn't make it any easier. Maybe someone has a good example of converting CUSTOM MODEL WRITTEN FROM SCRATCH and then using it in Java. I need to pass a string from Android's text field and get a predicted answer.
You need to first train your model on whatever the dataset you have. The layers in the model must comply with the supported layers by the TFLite library. Here is a list of layers that are supported and unsupported.
Once you have trained it, based on how you saved it (Let's say using kerasmodel.save). Convert it to TFLite following this tutorial or other tutorials on this page.
Now you can use this .tflite model in Android studio. For this you can follow this good tutorial.
Related
I'm trying to convert TFLite Face Mesh model to MLModel (Apple).
TFLite model description:
https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view
TFLite actual .tflite file:
https://github.com/google/mediapipe/blob/master/mediapipe/models/face_landmark.tflite
Looking at CoreMLTools provided by Apple (https://coremltools.readme.io/docs/introductory-quickstart) seems like it's possible, but all the samples codes demonstrate conversation from Keras and not from TFLite (although it's clearly supported):
How does one convert TFLite model to MLModel model?
As far as I know, there is no direct conversion from TFLite to Core ML. Someone could create such a converter but apparently no one has.
Two options:
Do it yourself. There is a Python API to read the TFLite file (flatbuffers) and an API to write Core ML files (NeuralNetworkBuilder in coremltools). Go through the layers of the TFLite model one-by-one, and add them to the NeuralNetworkBuilder, then save as a .mlmodel file.
Let TFLite do this for you. When you use the CoreMLDelegate in TFLite, it actually performs the model conversion on-the-fly and saves a .mlmodel file (or the compiled version, .mlmodelc). Then it uses Core ML to run this model. You can write some code to load the model with TFLite using the CoreMLDelegate, then grab the .mlmodel file that this created from the app bundle and use that.
I trained a model in Google Cloud ML and saved it as a saved model format. I've attached the directory for the saved model below.
https://drive.google.com/drive/folders/18ivhz3dqdkvSQY-dZ32TRWGGW5JIjJJ1?usp=sharing
I am trying to load the model into R using the following code but it is returning <tensorflow.python.training.tracking.tracking.AutoTrackable> with an object size of 552 bytes, definetly not correct. If anyone can properly load the model, I would love to know how you did it. It should also be able to be loaded into python I assume, that could work too. The model was trained on GPU, not sure which tensorflow version. Thank you very much!
library(keras)
list.files("/path/to/inceptdual400OG")
og400<-load_model_tf("/path/to/inceptdual400OG")
Since the shared model is not available anymore (it says that is in the trash folder)and it is not specified in the question I can't tell which framework you used to save the model on first place. I will suggest trying the Keras load function or the Tensorflow load function depending on which type of saved file model you have.
Bear in mind modify this argument as "compile = FALSE" if you have the model already compiled.
Remember to import the latest libraries if you trained your model with tf>=2.0 because of dependencies incompatibilities {Tensorflow, Keras} and rsconnect::appDependencies() output would be worth checking.
I have a deep learning model trained in matlab and it is available in .mat format, How can i use this file for prediction in python environment?
I tried scipy.io.loadmat(filename.mat) but getting some errors
One option for you might be to export the model from MATLAB to ONYX. From there, you should be able to use the result in any deep learning framework that supports ONYX, or move from from the ONYX representation to something supported in your framework of choice.
https://www.mathworks.com/help/deeplearning/ref/exportonnxnetwork.html
I built my first covnet using the process described in this colab. Now I would like to run the model on Googles shiny new edge tpu.
But according to the Model Requirments described here, I need to use quantization-aware training (post-training quantization is not supported). to be able to convert the model into a format that I can use on the EdgeTPU.
How do I modify the example colab to do this quantization-aware training thing?
well because the keras API does not support quantization in the current edition you are left with 3 options:
wait for keras to have the required functionality
rewrite your model with a different API that has this functionality
find a different TPU that does not require you to quantize your data
either way though the solution is not great though.
I'm currently developing a prediction model using Tensorflow and my model works well for a customer, so I'm tring to make it as a real product.
My model needs to be retrained using customer's input as time passes, and it should be deployed on customers infrastructure. (Not a SaaS or cloud.) Moreover, I'd like to protect my codes and models.
From my understanding of Tensorflow, trained model can be exported as protobuf, freezed and kept nodes that are required by prediction. freeze_graph.py at Tensorflow repo, I tried it and I successfully ran my prediction model using Golang + libtensorflow.so runtime. (Or, I could use Tensorflow Serving & C++)
If I can train my model on our company's infra, I could say "Okay, let's get some beers". However, my model has to be trained on the customer's infra, and without python code, it seems like I cannot train my model.
https://www.tensorflow.org/versions/r0.12/how_tos/language_bindings/index.html
At this time, support for gradients, functions and control flow operations ("if" and "while") is not available in languages other than Python. This will be updated when the C API provides necessary support.
Is there any workaround deploying TF app without exposing python code or model? Thanks in advance.
You can still use Python with a pre-trained model, without exposing all the code you needed to build it in the first place. As an example of this, have a look at the Inception retraining code, which loads a pretrained GraphDef and then retrains a new top layer:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py