Generative-Adversarial-Networks-Tensorflow

A Tensorflow Implementation of Generative Adversarial Networks as presented in the original paper by Goodfellow et. al. (https://arxiv.org/abs/1406.2661)

Introduction:

The code runs a toy GAN framework to try to generate samples from a known 1D gaussian distribution using uniform random noise. The algorithm used is the same as Algorithm 1 in the paper with the loss function of the generator changed to maximize log(D(G(z))) as suggested in the text of the paper.

Implementation Details:

Both the generator and the discriminator networks are 3 layer MLP's (Multi-layer perceptrons) with one input, one output and one hidden layer. The optimizer used to train the networks is Adam.

The code took ~4mins to run on my PC (CPU only: Core2Duo,1.3GHz)

Usage:

To use, run:

$ python gan.py [parameters]

Parameters:

'-mean' : Mean of target gaussian (float)

'-std' : Standard Deviation of Target Gaussian (float)

'-hneurons' : Number of hidden layer neurons to use (int)

'-epoch' : Number of epochs to train (int)

'-minbatch' : Size of batch to train (int)

'-sample' : Size of points to sample from true distribution (int)

Example Output:

Figure 1: Output of GAN after 1000 epochs on a mean = 5 sigma = 1 gaussian