Tutorial on creating your own GAN in Tensorflow
The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:
Generative adversarial networks (GANs) are one of the hottest topics in deep learning. From a high level, GANs are composed of two components, a generator and a discriminator. The discriminator has the task of determining whether a given image looks natural (ie, is an image from the dataset) or looks like it has been artificially created. The task of the generator is to create natural looking images that are similar to the original data distribution, images that look natural enough to fool the discriminator network.
The analogy used in the paper is that the generative model is like “a team of counterfeiters, trying to produce and use fake currency” while the discriminative model is like “the police, trying to detect the counterfeit currency”. The generator is trying to fool the discriminator while the discriminator is trying to not get fooled by the generator.
As the models train through alternating optimization, both methods are improved until a point where the “counterfeits are indistinguishable from the genuine articles”.
The tutorial is written in Python, with the Tensorflow library, so it would be good to have familiarity with Tensorflow before taking a look at this tutorial.
To install Anaconda, take a look at their website, which has some pretty great documentation.
If you want to install using pip, you'll need to update pip with the following code (Replace pip with pip3 if using Python 3).
On Linux/Mac OS: ~~~~ pip install -U pip setuptools ~~~~
On Windows: ~~~~ python -m pip install -U pip setuptools ~~~~
Next, you should be able to run the following. ~~~~ pip install jupyter ~~~~
For more resources on Jupyter Notebooks, check out the following: * Installation Documentation * Trying Jupyter just in your browser * Jupyter Docs * Video tutorial on Jupyter * Detailed tutorials on using different Python libraries in Jupyter Notebooks