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

## kandi X-RAY | generative-models Summary

## kandi X-RAY | generative-models Summary

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

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### Top functions reviewed by kandi - BETA

- 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

## generative-models Key Features

## generative-models Examples and Code Snippets

```
# 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
#
```

```
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
```

```
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

QUESTION

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`

:

QUESTION

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 17:07I'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!

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

## Vulnerabilities

No vulnerabilities reported

## Install generative-models

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.

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