generative-models | Collection of generative models, eg GAN, VAE in Pytorch and Tensorflow | Machine Learning library

by wiseodd Python Version: Current License: Unlicense

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | generative-models Summary

generative-models is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Generative adversarial networks applications. generative-models has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However generative-models build file is not available. You can download it from GitHub.

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine.

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generative-models has a medium active ecosystem.

It has 7071 star(s) with 2039 fork(s). There are 302 watchers for this library.

It had no major release in the last 6 months.

There are 16 open issues and 49 have been closed. On average issues are closed in 37 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of generative-models is current.

Quality

generative-models has 0 bugs and 208 code smells.

Security

generative-models has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

generative-models code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

generative-models is licensed under the Unlicense License. This license is Permissive.

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

Reuse

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

generative-models has no build file. You will be need to create the build yourself to build the component from source.

generative-models saves you 2132 person hours of effort in developing the same functionality from scratch.

It has 4674 lines of code, 216 functions and 50 files.

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

Top functions reviewed by kandi - BETA

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

Infer the model
Sigmoid function
Plot a PNG image
Computes the derivative of the Hessian
Calculates the Sigmoid of x
Computes the D_B
Calculate G_AB
Calculate the GBA
Generate a random sample of z - likelihood
Sample from X
Sample from training data
Computes the Hessian derivative of 2
Transformer encoder
Compute the Hessian
Generate a random sample of size c
Sigmoid transformation
Reset grad
Computes the derivative of the derivative between two inputs
Computes the tensorflow residuals
Compute the probability for a given tensor
Generate a tensorflow function
Compute the discriminator

Get all kandi verified functions for this library.

generative-models Key Features

No Key Features are available at this moment for generative-models.

generative-models Examples and Code Snippets

Evaluating the Interpretability of Generative Models by Interactive Reconstruction,Reproducing Results,Re-training Models

HTML

Lines of Code : 20

License : Permissive (MIT)

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# Retrain dSprites
dir=./quiz/public/models/dsprites
python src/train_dsprites.py --output_dir=$dir/ae  --variational=0
python src/train_dsprites.py --output_dir=$dir/vae --variational=1
python src/train_dsprites_supervised.py --output_dir=$dir/gt

#

Generative Models from the perspective of Continual Learning,Run experiments

Python

Lines of Code : 18

License : Permissive (MIT)

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cd Scripts
./generate_test.sh
./test_todo.sh

cd ./Data
#For the expert
python main_data.py --task disjoint --dataset mnist --n_tasks 1 --dir ../Archives
#For the models to train
python main_data.py --task disjoint --dataset mnist --n_tasks 10 --dir

MaxViT

pypi

Lines of Code : 18

License : No License

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import torch
from vit_pytorch.max_vit import MaxViT

v = MaxViT(
    num_classes = 1000,
    dim_conv_stem = 64,               # dimension of the convolutional stem, would default to dimension of first layer if not specified
    dim = 96,

Community Discussions

Trending Discussions on generative-models

How to collect trainable variables of the generator and discriminator? (Tensorflow)

Conditional Variational Autoencoder for cocktail recipe generation

QUESTION

How to collect trainable variables of the generator and discriminator? (Tensorflow)

Asked 2018-May-19 at 19:57

I would like to implement Generative Adversarial Networks following this tutorial

Unfortunately I have no idea how to apply this part in my project:

...

ANSWER

Answered 2018-May-19 at 18:43

Can be done by the following steps:

Define variable scopes for discriminator and generator:

Source https://stackoverflow.com/questions/50425850

QUESTION

Conditional Variational Autoencoder for cocktail recipe generation

Asked 2018-May-14 at 17:07

I want to use a conditional variational autoencoder to generate cocktail recipes. I modified the code from this repo so it can read my own data. The input is an array of all the possible ingredients, so most of the entries have the value 0. If an ingredient is present, it gets a value which is the amount normalized by 250 ml. The last index is what is 'left over' to make sure a cocktail always adds op to 1.

Example:

...

ANSWER

Answered 2018-May-14 at 17:07

I'm not sure you want to use probabilities here. It seems you're doing a regression to some specific values. Hence, it would make sense to not use a softmax, and use a simple mean-squared-error loss.

Note that if certain values are always biased in your loss, you can just use an extra weight on your loss, or use some abstraction (e.g. Keras's class_weight).

For this task you could consider using Keras, especially for this task it would make sense. There is an example checked into master: https://github.com/keras-team/keras/blob/master/examples/variational_autoencoder.py

For this task it might actually make sense to use a GAN: https://github.com/keras-team/keras/blob/master/examples/mnist_acgan.py . You'll let it distinguish between a random cocktail and a 'real' cocktail. It will learn to distinguish between the two, and in the process, it will train the weights to be able to create a generator that will generate cocktails for you!

Source https://stackoverflow.com/questions/50235368

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

Vulnerabilities

No vulnerabilities reported

Install generative-models

You can download it from GitHub.
You can use generative-models 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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