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How to change ML python program to tflite so that we can use it in android studio

I am using nearest neighbour algorithm in the ML program. I am able to change program to .pb file but .pb file is not changing to tflite because of some error :-
RuntimeError: MetaGraphDef associated with tags {'serve'} could not be found in SavedModel, with available tags '[]'. To inspect available tag-sets in the SavedModel, please use the SavedModel CLI: saved_model_cli.
Please tell me how can I change .py to tflite.

Load custom trained spaCy model

I am trying to load a spaCy text classification model that I trained previously. After training, the model was saved into the en_textcat_demo-0.0.0.tar.gz file.
I want to use this model in a jupyter notebook, but when I do
import spacy
spacy.load("spacy_files/en_textcat_demo-0.0.0.tar.gz")
I get
OSError: [E053] Could not read meta.json from spacy_files/en_textcat_demo-0.0.0.tar.gz
What is the correct way to load my model here?

You need to either unzip the tar.gz file or install it with pip.
If you unzip it, that will result in a directory, and you can give the directory name as an argument to spaCy load.
If you use pip install, it will be put with your other libraries, and you can use the model name like you would with a pretrained spaCy model.

How to use an exported model from google colab in Pycharm

I have a LSTM Keras Tensorflow model trained and exported in .h5 (HDF5) format.
My local machine does not support keras tensorflow. I have tried installing. But does not work.
Therefore, i used google colabs and exported the model.
I would like to know, how i can use the exported model in pycharm
Edit : I just now installed tensorflow on my machine
Thanks in Advance

Found the answer :
I ve exported the model as follows
model.save('/content/drive/My Drive/Colab Notebooks/model.h5')
Then i downloaded the file and saved in the folder where my other codes are. I have installed tensorflow.
Next i load the code and predicted using the saved model as follows.
import keras
model=keras.models.load_model('/content/drive/My Drive/Colab Notebooks/model.h5')
model.predict(instace)

You still need keras and tensorflow to use the model.

The accepted answer is correct but it misses that you first need to mount the "/content/drive"
from google.colab import drive
drive.mount('/content/drive')
Then you can save the weights of the model:
model.save_weights('my_model_weights.h5')
..or even save the whole model :
model.save('my_model.h5')
Once done, disconnect your mounted point using:
drive.flush_and_unmount()

How to convert tflite model to quantized model without pb file

I'm trying to convert a tflite model to it's quantized version. (I try to convert the pose estimation module multi_person_mobilenet_v1_075_float.tflite hosted here) to it's quantized version.
I therefore installed the tflite_converter command line tool, recommended here. But the examples do not fit my case where I only have a *.tflite file and no corresponding frozen_graph.pb file.
Thus when I just call
tflite_convert --output_file multi_person_quant.tflite --saved_model_dir ./
from within the directory containing multi_person_mobilenet_v1_075_float.tflite, I get an error message:
IOError: SavedModel file does not exist at: .//{saved_model.pbtxt|saved_model.pb}
I guess I need a .pb file for whatever I want to do... Any idea how to generate it from the *.tflite file?
Any other advice would also be helpful.

How to generate HDF5/H5 file in Keras with existing Python project

I am trying to get HDF5/H5 file from existing project in keras.
this attention_ocr is related to OCR written in python. I would like to generate HDF5/H5 file so I can convert that with tensorflowjs_converter[ref] and will use in browser.
Reference:
How to import a TensorFlow SavedModel into TensorFlow.js
Importing a Keras model into TensorFlow.js
I am looking for installing keras environment and generating HDF5/H5 file.

Once your model is trained in keras, saving it as an HDF5 is simply one line:
my_model.save('my_filename.h5')