kfac | An implementation of KFAC for TensorFlow | Data Visualization library

 by   tensorflow Python Version: 0.2.4 License: Apache-2.0

kandi X-RAY | kfac Summary

kandi X-RAY | kfac Summary

kfac is a Python library typically used in Analytics, Data Visualization, Tensorflow, Keras applications. kfac has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install kfac' or download it from GitHub, PyPI.

Please check KFAC docs for a detailed description with examples of how to use KFAC. Check the Keras KFAC docs for information on using KFAC with Keras.
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            kandi-support Support

              kfac has a low active ecosystem.
              It has 169 star(s) with 37 fork(s). There are 15 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 8 open issues and 23 have been closed. On average issues are closed in 177 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of kfac is 0.2.4

            kandi-Quality Quality

              kfac has 0 bugs and 0 code smells.

            kandi-Security Security

              kfac has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              kfac code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              kfac is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              kfac releases are not available. You will need to build from source code and install.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              kfac saves you 6015 person hours of effort in developing the same functionality from scratch.
              It has 12549 lines of code, 1171 functions and 52 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed kfac and discovered the below as its top functions. This is intended to give you an instant insight into kfac implemented functionality, and help decide if they suit your requirements.
            • Get layer collection
            • Add variables to the model
            • Returns a dict containing the given layers
            • Register a layer
            • Construct train quants
            • Create a training op
            • Multiply the inverse matrix
            • Convert a graph function to a function id
            • Return the inverse update op
            • Compute the eigenvalue of the covariance matrix
            • Trains MNIST
            • Train model
            • Apply gradients
            • Map a list of thunks
            • Instantiate the inverse graph
            • Train the model
            • Train a MNIST model
            • Perform a single training step
            • Register multiple layers
            • Load Keras model
            • Compute the covariance matrix
            • Creates a function that converts the covariance matrix and its inverse variables
            • Train MNIST
            • Extract convolution layer
            • Create a train op
            • Creates and returns a list of scoped variables
            • Generate the inverse update op
            • Compute the model loss function
            Get all kandi verified functions for this library.

            kfac Key Features

            No Key Features are available at this moment for kfac.

            kfac Examples and Code Snippets

            No Code Snippets are available at this moment for kfac.

            Community Discussions

            QUESTION

            How to speed up for loop?
            Asked 2021-Feb-26 at 06:19

            I am trying to speed up this for loop in python, where N is a large number around 12000. This loop takes over 20 seconds to run. The lines calculating cossum and sinsum seem to be the main culprits here. How can I optimise this so it runs faster?

            (I am pretty new to coding so a simple explanation would be very appreciated! Thanks)

            ...

            ANSWER

            Answered 2021-Feb-24 at 20:01

            You can remove all of the for loops in your code using vectorization in Numpy. I recommend reading Look Ma No For Loops as you start venturing down this path.

            I took your example and generated some junk data to stand in for your arrays and variables using random.

            Source https://stackoverflow.com/questions/66357503

            QUESTION

            pip search finds tensorflow, but pip install does not
            Asked 2020-Jan-23 at 06:55

            I am trying to build a Django app that would use Keras models to make recommendations. Right now I'm trying to use one custom container that would hold both Django and Keras. Here's the Dockerfile I've written.

            ...

            ANSWER

            Answered 2019-Jan-02 at 22:56

            It looks like tensorflow only publishes wheels (and only up to 3.6), and Alpine linux is not manylinux1-compatible due to its use of musl instead of glibc. Because of this, pip cannot find a suitable installation candidate and fails. Your best options are probably to build from source or change your base image.

            Source https://stackoverflow.com/questions/54014076

            QUESTION

            processing sketch throwing java.lang.AssertionError only when fill() is used
            Asked 2019-Jul-09 at 01:49

            The study project is about warping text along bezier curves(recursive polynomial form) on processing using the geomerative library to get shape from a .ttf type font file.(It needs a ttf file in the data directory to function.) currently, the sketch seems to throws errors when fill(any color); is used at the part where the code draws the character shapes and the bezier curve's length gets shorter than a certain length. If fill(); is not used, the sketch seems to function okay without any errors. The goal is to use the fill(); function to fill the characters without errors.

            I've tried; 1) getting rid of the beginContour(); and endContour(); because I thought it hasn't been written properly.(I thought it was wrong because the contours should only be drawn when the shape is the inner side of a letter but currently, it draws contours when it's not the first or last shape) But the sketch throws errors even when the contour function was not used (fill(); was used). 2) thought it had something to do with the length of the curve, so tried to add a if statement to the part where it draws the letters. So far, I've tried using the width of the RGroup generated from the initial font size and string in void setup(){}, and the length of the bezier curve. The condition examples within the if statement was as follows; -draw letters when the RGroup shape's width is smaller than the length of the curve -draw letters when the "indent"(a variable to calculate the position on the curve) value is smaller than the length of the curve. (this case made the sketch to draw letters only when the letters were placed within the curve, but the error still occurred) -draw letters when the "indent"(a variable to calculate the position on the curve) value is smaller than the width of the RGroup.

            I've failed to see where exactly the problem is occurring, so I'm sharing the entire code within the sketch, but I marked the spot where I presume the error is happening with "//*******".

            This study was based on the following link. The geomerative library documentation can be seen from the following link.

            ...

            ANSWER

            Answered 2019-Jul-09 at 01:49

            I don't think this would be a direct answer to my question, but it did stop the errors from occurring while using both fill() and the P2D renderer. The main problem, as pointed out by laancelot above, indeed seems to have been connected with stack overflow. So I approached the problem in two ways written below; Conclusion: The direct reason was a poorly expressed math formula.

            1) switching the RPoints inside a class. -I don't think this was the direct reason the errors were occurring, because at the stage where only this part of rewriting the code was done, the errors were still there. But maybe it was part of the problem. I'm not sure.

            2) rewriting the part where the code expresses the formula to evaluate bezier curves at a specific point. -Previously, the formula was made by using the explicit definition of a bezier curve with degree n. And, as a result, the formula had to be calculated(more like made) for every point in the RPoint points. As is mentioned on the wikipedia page about bezier curves, this way of computing is not recommended. -on the revised code, the formula used to warp text was expressed in the polynomial form. Thus, it was able to pre-calculate the coefficients of the polynomial before the RPoint points were iterated. This seems to have solved the problem.

            I'm still not really confident about what actually caused the problem and why it has been solved, and which part of the code I should show to explain this to others, so I'll share the entire code that has been rewritten. You need processing, the geomerative library, and a ttf type font file in a data folder to test the code. I have marked the place where the revised version of the formula is implicated. (It's still really messy....)

            Source https://stackoverflow.com/questions/56918998

            QUESTION

            How to extract 8 integers from a 256 vector using intel intrinsics?
            Asked 2017-Jun-30 at 22:48

            I'm trying to enhance the performance of my code by using the 256bit vector (Intel intrinsics - AVX).

            I have an I7 Gen.4 (Haswell architecture) processor supporting SSE1 to SSE4.2 and AVX/AVX2 Extensions.

            This is the code snippet that I'm trying to enhance:

            ...

            ANSWER

            Answered 2017-Jun-30 at 22:48

            A smart compiler could get table+factor into a register and use indexed addressing modes to get table+factor+k1fac6 as an address. Check the asm, and if the compiler doesn't do this for you, try changing the source to hand-hold the compiler:

            Source https://stackoverflow.com/questions/44847767

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

            Vulnerabilities

            No vulnerabilities reported

            Install kfac

            kfac is compatible with Python 2 and 3 and can be installed directly via pip,.

            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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            Install
          • PyPI

            pip install kfac

          • CLONE
          • HTTPS

            https://github.com/tensorflow/kfac.git

          • CLI

            gh repo clone tensorflow/kfac

          • sshUrl

            git@github.com:tensorflow/kfac.git

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