Dominic  Feeney

Dominic Feeney

1640418554

Tfmodel_parser: Repo for Parser Tensorflow(.pb) and Tflite(.tflite)

tfmodel_parser

.pb file is the format of tensorflow model

.tflite file is the format of tflite model, which usually used in mobile devices

before started

make sure you have installed these packages or tools

The best way to test is typing this code on your terminal

  • test flatbuffer
flatc --version 
# if you have installed flatbuffer correctly,you will see 
flatc version 2.0.0
  • test python packages ```python

python # or whatever version you use

import tensorflow
import rich
import bidict

start

read .pb file

with tf.io.gfile.GFile(file, 'rb') as f:
        graph_def = tf.compat.v1.GraphDef()
        graph_def.ParseFromString(f.read())
        tf.compat.v1.import_graph_def(graph_def)

        for node in graph_def.node:
            res.add(node.op) # get node attr from subgraph

read .tflite file

construct base file

  • find .fbs file you can get the default fbs file here
  • transfer fbs to python
flatc --python your.fbs

then you get a folder full of .py, move it to your project ; in this project , you may need the 'tflite' folder

using flatbuffer on your code

example : read op from tflite model

open fbs file , find 'root_type', here is model .

  • get model
 with open(file, 'rb') as f:
    buf = f.read()
    model_inner = tflite.Model.Model.GetRootAs(buf, 0)
  • get subgraph

In the table Model , Subgraphs is a vector containing a few subgraphs . So when we get subgraph, must set the index of Subgraph

subgraph = model_inner.Subgraphs(0) # 0 is the index
  • get op

Here we need all ops to save, we must traverse the vector of op

op_length = subgraph.OperatorsLength()
for index in range(op_length):
    temp_code = subgraph.Operators(index).OpcodeIndex()
    res.add(dic.inverse[temp_code])

Download Details:
Author: henry-tujia
Source Code: https://github.com/henry-tujia/tfmodel_parser
License: 

#tensorflow #python 

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Buddha Community

Tfmodel_parser: Repo for Parser Tensorflow(.pb) and Tflite(.tflite)
Dominic  Feeney

Dominic Feeney

1640418554

Tfmodel_parser: Repo for Parser Tensorflow(.pb) and Tflite(.tflite)

tfmodel_parser

.pb file is the format of tensorflow model

.tflite file is the format of tflite model, which usually used in mobile devices

before started

make sure you have installed these packages or tools

The best way to test is typing this code on your terminal

  • test flatbuffer
flatc --version 
# if you have installed flatbuffer correctly,you will see 
flatc version 2.0.0
  • test python packages ```python

python # or whatever version you use

import tensorflow
import rich
import bidict

start

read .pb file

with tf.io.gfile.GFile(file, 'rb') as f:
        graph_def = tf.compat.v1.GraphDef()
        graph_def.ParseFromString(f.read())
        tf.compat.v1.import_graph_def(graph_def)

        for node in graph_def.node:
            res.add(node.op) # get node attr from subgraph

read .tflite file

construct base file

  • find .fbs file you can get the default fbs file here
  • transfer fbs to python
flatc --python your.fbs

then you get a folder full of .py, move it to your project ; in this project , you may need the 'tflite' folder

using flatbuffer on your code

example : read op from tflite model

open fbs file , find 'root_type', here is model .

  • get model
 with open(file, 'rb') as f:
    buf = f.read()
    model_inner = tflite.Model.Model.GetRootAs(buf, 0)
  • get subgraph

In the table Model , Subgraphs is a vector containing a few subgraphs . So when we get subgraph, must set the index of Subgraph

subgraph = model_inner.Subgraphs(0) # 0 is the index
  • get op

Here we need all ops to save, we must traverse the vector of op

op_length = subgraph.OperatorsLength()
for index in range(op_length):
    temp_code = subgraph.Operators(index).OpcodeIndex()
    res.add(dic.inverse[temp_code])

Download Details:
Author: henry-tujia
Source Code: https://github.com/henry-tujia/tfmodel_parser
License: 

#tensorflow #python 

5 Steps to Passing the TensorFlow Developer Certificate

Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!

  1. Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.

2. Do the course questions in parallel in PyCharm.

#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing

Anda Lacacima

Anda Lacacima

1595857125

Tensorflow Lite (TFLite) with Golang (Go)

Tensorflow Lite commonly known as TFLite is used to generate and infer machine learning models on mobile and IoT(Edge) devices. TFLite made the on-device(offline) inference easier for multiple device architectures, such as Android, iOS, Raspberry pi and even backend servers. With TFLite you can build a lightweight server based inference application using any programming language with lightweight models, rather than using heavy Tensorflow models.

As developers, we can simply use existing optimized research models or convert existing Tensorflow models to TFLite. There are multiple ways of using TFLite in your mobile, IoT or server applications.

  • Implement the inference for different architecture(Android, iOS etc…) using the standard libraries, SDKs provided by TFLite.
  • Use the TFLite C API for inference along with platform independent programming language like Golang. And cross-compile for platforms like Android, iOS etc…

In this post I’m going to show case the implementation of TFLite inference application using platform independent language Golang and **cross-compiling **to a shared library. Which then can be consumed by Android, iOS etc…

First thanks to mattn who created the TFLite Go bindings and you can find the repo here. We will start the implementation of a simple Golang application for TFLite inference(You can find the example here). Here I’m using a simple text classifier which will classify to ‘Positive’ or ‘Negative’.

Here is the classifier.go, which has Go functions and are exported for use by C code.

#tensorflow #tflite #ios #golang #go

Avanya Shina

1603306920

TFLite Object Detection Android App Tutorial | TensorFlow Object Detection

In this video, we will show you how to Detect Tensorflow Objects in Android Apps using TFLite.

Clone this repository: https://github.com/tensorflow/examples

Subscribe : https://www.youtube.com/channel/UCgyQ4pSntDf9hw9Rv4hmNBA

#tensorflow #tflite #deep-learning

Uriah  Dietrich

Uriah Dietrich

1615946235

TensorFlow Estimators — TFLite and Model Generation

Building deep learning models for audio classification is pretty common and you will find numerous blogs and articles that describe how to build the standard audio classification models using Keras.
There are numerous use-cases associated with audio processing and deep learning, but the one that amazed me was audio separation library Spleeter — where they split the given audio into various tracks such as vocal, piano, drums, bass, and accompaniment. I was really baffled at the accuracy with which the library splits the tracks and I would give full credit to the authors for building such an amazing library.
One thing I observed while going through the library’s source code is that they have used Tensorflow’s estimator approach to build the model and not the Keras-based approach. That’s when I was intrigued to learn more about what Estimators are and their benefits.
This blog is to briefly introduce you to estimators, to build an audio classification model using estimators, and to generate TFLite models.

#tensorflow-estimator #heartbeat #audio-classification #tensorflow