Generative-Models | Comparison of Generative Models in Tensorflow | Machine Learning library

by kvmanohar22 Python Version: Current License: No License

X-Ray Key Features Code Snippets(1)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, Tensorflow, Generative adversarial networks applications. Generative-Models has no bugs, it has no vulnerabilities and it has low support. However Generative-Models build file is not available. You can download it from GitHub.

The different generative models considered here are Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). This experiment is accompanied by blog post at :

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Generative-Models has a low active ecosystem.

It has 97 star(s) with 35 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 715 days. There are no pull 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 0 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 0 security hotspots that need review.

License

Generative-Models 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.

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.

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

Generative-Models saves you 210 person hours of effort in developing the same functionality from scratch.

It has 516 lines of code, 26 functions and 4 files.

It has high 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.

2d convolution layer
leaky_relu
A fully connected layer
A fully connected linear layer
Convolution layer

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

MaxViT

pypi

Lines of Code : 18

License : No License

Copy

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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