Generative-Models | Implementation of some popular genrative models | 3D Printing library
kandi X-RAY | Generative-Models Summary
kandi X-RAY | Generative-Models Summary
Implementation of some popular genrative models
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Top functions reviewed by kandi - BETA
- Build the model
- Compute the sampler
- Transformer decoder
- Convolutional encoder
- Read an image
- Center crop
- Transform an image
- Read an image file
- Train the model
- Save the VE
- Load checkpoint from checkpoint_dir
- Save images
- Save images to disk
- Merge multiple images
- Transform images
- Loads checkpoint from checkpoint_dir
- Concatenate tensors
Generative-Models Key Features
Generative-Models Examples and Code Snippets
import pyotp
import time
base32secret = 'S3K3TPI5MYA2M67V'
print('Secret:', base32secret)
totp = pyotp.TOTP(base32secret)
print('OTP code:', totp.now())
time.sleep(30)
print('OTP code:', totp.now())
Secret: S3K3TPI5MYA2M67V
OTP code: 339838
OTP c
public void clientSideStreamingGetStatisticsOfStocks() throws InterruptedException {
logger.info("######START EXAMPLE######: ClientSideStreaming - getStatisticsOfStocks from a list of stocks");
final CountDownLatch finishLatc
public static ApplicationProtocolNegotiationHandler getClientAPNHandler(int maxContentLength, Http2SettingsHandler settingsHandler, Http2ClientResponseHandler responseHandler) {
final Http2FrameLogger logger = new Http2FrameLogger(INFO, Http2
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 18:43Can 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
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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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