after some searching and asked a question here i found a way to save the entire model of keras inside one file which is example.h5 for later use i found an answer here
https://machinelearningmastery.com/save-load-keras-deep-learning-models/
my previous question is
keras save the model weights to one file
and now i have to change to the mode.h5 convert it to tflite for tensorflow lite then i can used inside the mobile app
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For deployment purposes, I need to save a deep learning model as a pickle file, but when doing so, even with the joblib function, I always get these errors.
I used to save my model using tf.keras (as a hdf5 file) , but with this method,I found that the test file needs tensorflow and keras packages and this is not really efficient when it comes to deployment..
So I have trained a posenet model for classifying different poses on the teachable machine learning website (link for the website). I want to use this trained model in a flutter app, and for that I need to convert the model into tflite format. I checked many online blogs which said that the website has an option of converting it to that format, but they have removed that feature. Thus, I wanted to know how can I convert this teachable machine model into tflite format?
I downloaded a pose model of my own from that site, and the zip appears to be a Tensorflow.JS model.
Now that we know the unzipped file is just a TF.js model, refer to a tutorial like this to convert the TFJS model back into a keras SavedModel, which can be then saved into a tflite model.
I have been searching for a method to do this for so long, and I can not find an answer. Most threads I found are of people wanting to do the opposite.
Backstory:
I am experimenting with some pre-trained models provided by the tensorflow/models repository. The models are saved as .pb frozen graphs. I want to fine-tune some of these models by changing the final layers to suit my application.
Hence, I want to load the models inside a jupyter notebook as a normal keras h5 model.
How can I do that?
do you have a better way to do so?
Thanks.
seems like all you would have to do is download the model files and store them in a directory. Call the directory for example c:\models. Then load the model.
model = tf.keras.models.load_model(r'c:\models')
model.summary() # prints out the model layers
# generate code to modify the model as you typically do for transfer learning
# compile the changed model
# train the model
# save the trained model as a .h5 file
dir=r'path to the directory you want to save the model to'
model_identifier= 'abcd.h5' # for abcd use whatever identification you want
save_path=os.path.join(dir, model_identifier)
model.save(save_path)
How do I convert a .h5 weight file to .pb file ? I trained keras pre-trained model and saved the file as something.h5 . How do i convert it to .pb file for using it for tensorflow serving ?
P.S: Don't degrade the question, I couldn't find any solution online. Do mention the reason why you degraded the question. Otherwise, help with solving the question.
import tensorflow as tf
model = tf.keras.models.load_model(keras_model_path)
tf.saved_model.save(model, saved_model_path)
Bingo.
Tensorflow explains that models can be saved in three file formats: .ckpt or .hdf5 or .pb. There's a lot of documentation so it would be nice to get a simpler comparison of when to use which file format.
Here's my current understanding:
ckpt
From https://www.tensorflow.org/guide/checkpoint:
Checkpoints capture the exact value of all parameters (tf.Variable
objects) used by a model. Checkpoints do not contain any description
of the computation defined by the model and thus are typically only
useful when source code that will use the saved parameter values is
available.
So it seems like you should use cpkt for checkpointing during training when you know that your source code will be the same. Why is it recommended though over .pb and .hdf5? Does it save space? Does it include data that the other file formats do not?
pb
Also from https://www.tensorflow.org/guide/checkpoint:
The SavedModel format on the other hand includes a serialized
description of the computation defined by the model in addition to the
parameter values (checkpoint). Models in this format are independent
of the source code that created the model. They are thus suitable for
deployment via TensorFlow Serving, TensorFlow Lite, TensorFlow.js, or
programs in other programming languages (the C, C++, Java, Go, Rust,
C# etc. TensorFlow APIs).
The SavedModel format is .pb plus some metadata. So you should save in .pb when you are deploying a model?
hdf5
Use when saving the model weights (matrix of numbers) only?
It seems you already know some of the differences, but just to add.
.ckpt
This is mainly used for resuming the training and also to allow users to customize savepoints and load to (ie. Highest Accuracy, Latest Trained Model, etc).
And also to create different models from different training checkpoints.
This only saves the weights of the variables or the graph therefore as you indicated you need to have full architectures and functions used.
.pb (Protobuffer)
This is the TensorFlow file format which saves everything about the Model including custom objects, this is the recommended file format to ensure maximum portability when using and exporting to different platforms (ie. Tensorflow Lite, Tensorflow Serving, etc.).
.h5 (HD5F)
This is the suggested saving format of Native Keras, which also saves everything about the model but when used in TensorFlow 2.1.0 (import tensorflow.keras) it will not save the custom objects automatically and will require additional steps to be performed.
You could read more about it in this link.