neural-art | Recreate photos in the style of famous artists | Machine Learning library

by jayelm Python Version: Current License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | neural-art Summary

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

Recreate photos in the style of famous artists with neural networks

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neural-art has a low active ecosystem.

It has 86 star(s) with 24 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 802 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of neural-art is current.

Quality

neural-art has no bugs reported.

Security

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

License

neural-art 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

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

neural-art 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.

Top functions reviewed by kandi - BETA

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

Calculate the loss and gradient of the network .
Main function .
Set the style targets for each image .
Scrape artists .
Return the total number of pages .
Saves a picture to a directory .
Clean the painting URL .
Check if request object is bad .
Print a debug message .

Get all kandi verified functions for this library.

neural-art Key Features

No Key Features are available at this moment for neural-art.

neural-art Examples and Code Snippets

Print ASCII art .

python

Lines of Code : 32

License : Non-SPDX

Copy

def ascii(state, highlight, cycle=None):
    IP, IS, R0, R1, memory = state

    center = []
    for (i, s) in enumerate((IP, IS, R0, R1, *memory)):
        if i - 4 in highlight:
            s = "_" + str(s) + "_"
        center.append(str(s).center

Gets the cover art .

java

Lines of Code : 3

License : Permissive (MIT License)

Copy

public CoverArt getCoverArt() {
        return coverArt;
    }

Community Discussions

Trending Discussions on neural-art

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

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