frugally-deep | Header-only library for using Keras models | Machine Learning library

by Dobiasd C++ Version: v0.15.20-p0 License: MIT

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

kandi X-RAY | frugally-deep Summary

frugally-deep is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. frugally-deep has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Would you like to build/train a model using Keras/Python? And would you like to run the prediction (forward pass) on your model in C++ without linking your application against TensorFlow? Then frugally-deep is exactly for you.

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Support

frugally-deep has a medium active ecosystem.

It has 942 star(s) with 218 fork(s). There are 49 watchers for this library.

It had no major release in the last 12 months.

There are 8 open issues and 282 have been closed. On average issues are closed in 39 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of frugally-deep is v0.15.20-p0

Quality

frugally-deep has no bugs reported.

Security

frugally-deep has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

frugally-deep is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

frugally-deep releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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frugally-deep Key Features

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frugally-deep Examples and Code Snippets

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Community Discussions

Trending Discussions on frugally-deep

How to rename the layers of a Keras model without corrupting the structure?

Where can I find the algorithm behind model.predict?

Conversion of a sequential model to a functional model with Keras 2.2.0

QUESTION

How to rename the layers of a Keras model without corrupting the structure?

Asked 2020-Aug-14 at 14:26

For some library functionality I'm trying to rename the layers (including the input layers) of a given model.

The following minimal example shows the error I run into with my current approach (using TensorFlow 2.3):

...

ANSWER

Answered 2020-Aug-14 at 14:26

Problem: Keras serializes the network by traversing layer._inbound_nodes and comparing against model._network_nodes; when setting layer._name, latter persists original names.

Solution: rename _network_nodes accordingly. Working function at bottom, with example below:

Source https://stackoverflow.com/questions/63373692

QUESTION

Where can I find the algorithm behind model.predict?

Asked 2019-Aug-06 at 18:13

I would like to implement the code for model.predict (https://keras.io/models/model/) in C++. But I am unable to find the exact logic (equations, formula) used in prediction?

For C++, I implemented the source code here: https://github.com/Dobiasd/frugally-deep but unfortunately could not find the equation behind the predict function. (Frugally deep exports the model as a .json file and does the prediction using the predict function).

Would there be any resources that I could refer to find the equations for model.predict?

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ANSWER

Answered 2019-Aug-06 at 15:57

Looking at the repo, it appears you're looking for this.

Source https://stackoverflow.com/questions/57379730

QUESTION

Conversion of a sequential model to a functional model with Keras 2.2.0

Asked 2018-Jul-03 at 14:20

Up to Keras version 2.1.6 one was able to "convert" a sequential model to a functional model by accessing the underlying model.model. Since version 2.2.0 this is no longer possible.

Can it still be done in some other way?

(In case you wonder why I would like to do something like this, I'm maintaining a library that relies on this conversion. :wink:)

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ANSWER

Answered 2018-Jun-19 at 21:20

I can't test this solution right now since I don't have Keras 2.2.0 installed, but I think it should work. Let's assume your sequential model is stored in seqmodel:

Source https://stackoverflow.com/questions/50837715

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

Vulnerabilities

No vulnerabilities reported

Install frugally-deep

A **C14**-compatible compiler: Compilers from these versions on are fine: GCC 4.9, Clang 3.7 (libc 3.7) and Visual C++ 2015. Python 3.7 or higher. Guides for different ways to install frugally-deep can be found in [INSTALL.md](INSTALL.md).
A **C14**-compatible compiler: Compilers from these versions on are fine: GCC 4.9, Clang 3.7 (libc 3.7) and Visual C++ 2015
Python 3.7 or higher
TensorFlow 2.5.0

Support

Layer types typically used in image recognition/generation are supported, making many popular model architectures possible (see [Performance section](#performance)).

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