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

 by   wiseodd Python Version: Current License: Unlicense

kandi X-RAY | generative-models Summary

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.

            kandi-support Support

              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.

            kandi-Quality Quality

              generative-models has 0 bugs and 208 code smells.

            kandi-Security 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.

            kandi-License 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.

            kandi-Reuse 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

            copy iconCopy
            # Retrain dSprites
            python src/ --output_dir=$dir/ae  --variational=0
            python src/ --output_dir=$dir/vae --variational=1
            python src/ --output_dir=$dir/gt
            copy iconCopy
            cd Scripts
            cd ./Data
            #For the expert
            python --task disjoint --dataset mnist --n_tasks 1 --dir ../Archives
            #For the models to train
            python --task disjoint --dataset mnist --n_tasks 10 --dir  
            pypidot img3Lines of Code : 18dot img3no licencesLicense : No License
            copy iconCopy
            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


            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:



            Answered 2018-May-19 at 18:43

            Can be done by the following steps:

            Define variable scopes for discriminator and generator:



            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.




            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:

            For this task it might actually make sense to use a GAN: . 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!


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


            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.


            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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