ylg | CVPR 2020 ] Official Implementation | Machine Learning library

by giannisdaras Python Version: Current License: GPL-3.0

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

kandi X-RAY | ylg Summary

ylg is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. ylg has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

[CVPR 2020] Official Implementation: "Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models".

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ylg has a low active ecosystem.

It has 112 star(s) with 14 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of ylg is current.

Quality

ylg has 0 bugs and 0 code smells.

Security

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

ylg code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ylg is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

ylg 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.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Creates training and validation inputs
Returns a function that preprocesses the dataset record records
Provide a dataset
Makes a random tensor
Run train and write images
Writes the image to disk
Log performance statistics
Predict and write images to disk
Get x and y coordinates
Get the list of nearest neighbors
Visualize attention
Calculate saliency weights
Samples a nonlocal block
1x1d convolutional convolution layer
Get real activations
Get activations from a dataset
Inject weights into model_vars
Assign two lists
Provides training data
Run training
Run continuous evaluation
Get a 2D grid mask
Computes the discriminator loss
Returns a list of the lookups and fast weights
Interpolate between two points
Generate a repetitive mask for the given nL

Get all kandi verified functions for this library.

ylg Key Features

No Key Features are available at this moment for ylg.

ylg Examples and Code Snippets

No Code Snippets are available at this moment for ylg.

Community Discussions

Trending Discussions on ylg

How to describe a discrete parameter using GEKKO?

GEKKO Infeasible system of ODE equations of a fed-batch Bioreactor

QUESTION

How to describe a discrete parameter using GEKKO?

Asked 2019-Dec-19 at 06:36

I have a model with 10 equations that describe a fed-batch bioreactor. Basically, every 24h "food" (Glucose and other components) is added to the system. My problem is that this feeding procedure is currently being modeled as the flow-rate (L/H) over two time steps (delta_T), instead of a single discrete food addition (delta_T = 0).

This is what the glucose equation looks like:

...

ANSWER

Answered 2019-Dec-19 at 06:36

Your strategy to feed glucose as a pulse is a good method to have a discontinuous input. The problem with a discrete jump in glucose concentration is that there is a glucose derivative term as equation 4: m.Equation(G.dt() == e4). If the dG/dt term changes over a very short amount of time then the derivative term gets very large.

One strategy to deal with the large derivatives at discrete points is to use m.options.NODES=2 to avoid problems with the additional internal nodes with orthogonal collocation on finite elements. With no internal nodes, you may need to increase the number of time points to maintain accuracy for the integration. This allows a very short-duration impulse input of glucose to the batch reactor such as 3.6 seconds for the addition.

feed_small_delta_t = 0.001 # 3.6 seconds

The index for the feed input is off by one so Fi[i+1] should be where the impulse is applied, not Fi[i].

Source https://stackoverflow.com/questions/59399823

QUESTION

GEKKO Infeasible system of ODE equations of a fed-batch Bioreactor

Asked 2019-Dec-10 at 23:16

I am new to GEKKO and also to modeling bioreactors, so I might be missing something obvious.

I have a system of 10 ODEs that describe a fed-batch bioreactor. All constants are given. The picture below shows the expected behavior of this model (extracted from a paper). However, the only feasible solution I found is when Viable Cells Density (XV) = 0, and stays 0 for all time t, or if time T is really small (<20). If a lower boundary >= 0 or initial value is set to XV and t > 20, the system becomes infeasible.

Equations and constants were checked multiple times. I tried giving initial values to my variables, but it didn't work either. I can only think of two problems: I am not initiating variables properly, or I am not using GEKKO properly. Any ideas? Thanks!!

...

ANSWER

Answered 2019-Dec-10 at 23:16

In the end it is not a programming problem, but a problem reading the equations and correctly translating them.

mu and Kd are not dynamical variables, they are ordinary functions of the state vector (which then only has dimension 8). For such intermediate variables Gekko has the construction function m.Intermediate

Source https://stackoverflow.com/questions/59259514

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

Vulnerabilities

No vulnerabilities reported

Install ylg

We recommend installing YLG using an Anaconda virtual environment. For installing Anaconda refer to the official docs.

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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