neural-art-tf | A neural algorithm of Artistic style '' in tensorflow | Machine Learning library

by woodrush Python Version: Current License: No License

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kandi X-RAY | neural-art-tf Summary

neural-art-tf is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. neural-art-tf has no bugs, it has no vulnerabilities and it has low support. However neural-art-tf build file is not available. You can download it from GitHub.

"A neural algorithm of Artistic style" in tensorflow

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neural-art-tf has a low active ecosystem.

It has 541 star(s) with 121 fork(s). There are 49 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 2 have been closed. On average issues are closed in 3 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of neural-art-tf is current.

Quality

neural-art-tf has 0 bugs and 14 code smells.

Security

neural-art-tf has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

neural-art-tf code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

neural-art-tf 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.

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neural-art-tf releases are not available. You will need to build from source code and install.

neural-art-tf has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

neural-art-tf saves you 2205 person hours of effort in developing the same functionality from scratch.

It has 4827 lines of code, 27 functions and 6 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Setup the convolution layer
Adds a convolution layer
Create a pool of tensors
Add a variable
Return a unique name
Get the output of the current function
Setup the convolution pool

Get all kandi verified functions for this library.

neural-art-tf Key Features

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neural-art-tf Examples and Code Snippets

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Community Discussions

Trending Discussions on neural-art-tf

Can someone please explain the content loss function?

QUESTION

Can someone please explain the content loss function?

Asked 2019-Apr-08 at 11:42

I am currently getting familiar with TensorFlow and machine learning. I am doing some tutorials on style transfer and now I have a part of an example code that I somehow can not comprehend.

I think I get the main idea: there are three images, the content image, the style image and the mixed image. Let's just talk about the content loss first, because if I can understand that, I will also understand the style loss. So I have the content image and the mixed image (starting from some distribution with some noise), and the VGG16 model.

As far as I can understand, I should now feed the content image into the network to some layer, and see what is the output (feature map) of that layer for the content image input.

After that I also should feed the network with the mixed image to the same layer as before, and see what is the output (feature map) of that layer for the mixed image input.

I then should calculate the loss function from these two output, because I would like the mixed image to have a similar feature map to the content image.

My problem is that I do not understand how this is done in the example codes that I could find online.

The example code can be the following: http://gcucurull.github.io/tensorflow/style-transfer/2016/08/18/neural-art-tf/

But nearly all of the examples used the same approach.

The content loss is defined like this:

...

ANSWER

Answered 2019-Apr-08 at 11:42

The loss forces the networks to have similar activation on the layer you have chosen.

Let us call one convolutional map/pixel from target_out[layer] and corresponding map from cont_out . You want their difference to be as small as possible, i.e., the absolute value of their difference. For the sake of numerical stability, we use the square function instead of absolute value because it is a smooth function and more tolerant of small errors.

We thus get , which is: tf.square(tf.sub(target_out[layer], cont_out)).

Finally, we want to minimize the difference for each map and each example in the batch. This is why we sum all the difference into a single scalar using tf.reduce_sum.

Source https://stackoverflow.com/questions/55568573

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

Vulnerabilities

No vulnerabilities reported

Install neural-art-tf

You can download it from GitHub.
You can use neural-art-tf 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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