Is there any way to convert data-00000-of-00001 to Tensorflow Lite model?
The file structure is like this
|-semantic_model.data-00000-of-00001
|-semantic_model.index
|-semantic_model.meta
Using TensorFlow Version: 1.15
The following 2 steps will convert it to a .tflite model.
1. Generate a TensorFlow Model for Inference (a frozen graph .pb file) using the answer posted here
What you currently have is model checkpoint (a TensorFlow 1 model saved in 3 files: .data..., .meta and .index. This model can be further trained if needed). You need to convert this to a frozen graph (a TensorFlow 1 model saved in a single .pb file. This model cannot be trained further and is optimized for inference/prediction).
2. Generate a TensorFlow lite model ( .tflite file)
A. Initialize the TFLiteConverter: The .from_frozen_graph API can be defined this way and the attributes which can be added are here. To find the names of these arrays, visualize the .pb file in Netron
converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
graph_def_file='....path/to/frozen_graph.pb',
input_arrays=...,
output_arrays=....,
input_shapes={'...' : [_, _,....]}
)
B. Optional: Perform the simplest optimization known as post-training dynamic range quantization. You can refer to the same document for other types of optimizations/quantization methods.
converter.optimizations = [tf.lite.Optimize.DEFAULT]
C. Convert it to a .tflite file and save it
tflite_model = converter.convert()
tflite_model_size = open('model.tflite', 'wb').write(tflite_model)
print('TFLite Model is %d bytes' % tflite_model_size)
Related
I've used tensor flow lite to make my own custom android.tflite model as described in this tflite collab demo.
I was going to test my object detection model using tflite_support, but this module is not Windows compatible.
Can I use my .tflite model with "regular" tensorflow? If so, how do I load the .tflite model? Following this example, we use tf.saved_model.load(model_dir), which I believe will not load our android.tflite file.
Is there a function for loading a .tflite file? Or while I'm making my model with tensorflow_model_maker, to I need to specify a different format for the model?
Additionally, I see that tflite has a way to convert normal tensorflow models to tflite models, but I don't see that it has a means of doing the reverse:
tf.lite.TFLiteConverter(
funcs, trackable_obj=None
)
I was following this tutorial, but when I wanted to convert the .h5 file into .pb I found out that I can't use checkpoints.
https://www.thepythoncode.com/article/skin-cancer-detection-using-tensorflow-in-python
Please explain this to me
Use model.save() to save the model in .pb format. For more information read Tensorflow SavedModel format.
import tensorflow as tf
#Load .h5 saved model
pre_model = tf.keras.models.load_model("final_model.h5")
#Converts .pb format
pre_model.save("saved_model/my_model")
I am pretty new to Deep Learning, I have a custom dataset which is quite large, how do I convert the .h5 model to a .tflite model and also how do I generate all the labels without doing it manually?
From Tensorflow documentation
Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) # path to the SavedModel directory
tflite_model = converter.convert()
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 would like to convert the weights in tensorflow model into a hdf5 so that i can use it as a pre-train model for some other network. How would i do it ?