tinyml | Implement classic machine learning algorithms from scratch | Machine Learning library

by borgwang Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | tinyml Summary

tinyml is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. tinyml has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Implementation of classic machine learning algorithms with sklearn-style API.

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Quality

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Support

tinyml has a low active ecosystem.

It has 4 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

tinyml has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tinyml is current.

Quality

tinyml has 0 bugs and 0 code smells.

Security

tinyml has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tinyml code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tinyml does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

tinyml releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed tinyml and discovered the below as its top functions. This is intended to give you an instant insight into tinyml implemented functionality, and help decide if they suit your requirements.

Fit the model
Build the network
Step through gradients
Compute the tree
Calculates the best fit for each feature
Build a tree
Fit one time step
Calculate the inertia of the class
Perform the fit algorithm
Perform one - time clustering
Score function
Calculate the value of x
Predict the most common value for each sample
Predict the probability of each learner
Predict samples
Predict for each sample
Compute the mean distance between two points
Score function
Predict the covariance of the Gaussian distribution
Compute feature importances
Fit the model to the given data
Predict the probability for each class
Compute the feature importances
Calculates the center of the cluster
Fits the estimator
Calculate weights and dist

Get all kandi verified functions for this library.

tinyml Key Features

No Key Features are available at this moment for tinyml.

tinyml Examples and Code Snippets

No Code Snippets are available at this moment for tinyml.

Community Discussions

Trending Discussions on tinyml

Load Tensorflow Lite models in python

Building TF micro hello world: make: *** [tensorflow/lite/micro/examples/hello_world/Makefile.inc:34: test_hello_world_test] Error 1

Is the keras function Flatten() supported by TensorFlow Lite?

QUESTION

Load Tensorflow Lite models in python

Asked 2021-Jun-25 at 14:48

I'm working on a TinyML project using Tensorflow Lite with both quantized and float models. In my pipeline, I train my model with the tf.keras API and then convert the model to a TFLite model. Finally, I quantize the TFLite model to int8.
I can save and load the "normal" tensorflow model with the API model.save and tf.keras.model.load_model

Is it possible to do the same with the converted TFLite models? Going through the quantization process every time is quite time-consuming.

...

ANSWER

Answered 2021-Jun-25 at 14:48

You can use tflite interpreter to get inference from TFLite models directly in notebook.

Here is an example of a model for image classification. Let's say we have a tflite model as:

Source https://stackoverflow.com/questions/68132431

QUESTION

Building TF micro hello world: make: *** [tensorflow/lite/micro/examples/hello_world/Makefile.inc:34: test_hello_world_test] Error 1

Asked 2020-Aug-20 at 10:36

I am following the TinyML book by Pete Warden and Daniel Situnayake on how to deploy neural networks to microcontrollers with TFLite for microcontrollers. They closely follow the instructions at the end of this git repo.

To try and check for errors, they propose testing the code on the development machine(i.e my PC), but when running "make" I get some errors and it does not build.

When running $ git clone --depth 1 https://github.com/tensorflow/tensorflow.git and then $ make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test I get the following output:

...

ANSWER

Answered 2020-Aug-20 at 10:36

I still have to open an issue on github as I don't think this is the expected behavior but here is a workaround that allows you to test your TF micro code on your development machine.

First step is heading to the root of the git repo you just cloned. Then, instead of adding the test_ prefix to the target on make, just "make" it as a "normal target":

$ make -f tensorflow/lite/micro/tools/make/Makefile hello_world_test

Depending on the OS you are running, the executable(output) will be in different paths, but just change the windows_x86_64 to your corresponding folder. Now it is time to run the output:

$ tensorflow/lite/micro/tools/make/gen/windows_x86_64/bin/hello_world_test.exe

This returns, as expected:

Source https://stackoverflow.com/questions/63420827

QUESTION

Is the keras function Flatten() supported by TensorFlow Lite?

Asked 2020-Jul-01 at 00:22

I'm building my own CNN and I'm trying to put it on a Disco-f746ng according to the "TensorFlow Lite for microcontrollers" tutorials and the TinyML book. I know that the supported tensorflow-keras functions can be found here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/all_ops_resolver.cc But the Flatten() function seems not to be listed. That's irritating me because it is such a basic function, so I thought maybe it just has a different name in the all_ops_resolver. I'm using only functions that are listed there plus the Flatten() function. When I run a test with my own model, I always get a segmentation fault, no matter how much space I allocate. That's why I wanted to ask if the Flatten() function is supported by TensorFlow Lite?

That's my Python code for creating the CNN:

...

ANSWER

Answered 2020-Jul-01 at 00:22

Ok, I think I figured it out now. I had another problem that led to the segmentation faults, but I solved it now. Afterwards I was ready to check if Flatten() is supported. It works!

The CNN-model code above works when adding following Builtins to the micro-op-resolver:

Source https://stackoverflow.com/questions/62580548

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tinyml

You can download it from GitHub.
You can use tinyml like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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