Using the MNIST set to experiment with GANs using LSTM's
Model
Following a generic generative adversarial network, the model consists two networks trained in parallel, and sharing weights.
The pink portion of the model is the generator and the orange-brown portion is the discriminator. For purposes of clarity the image is
split into quadrants here, but in other experiments the attempt was to split the image into pixels in an attempt to create a
generator that could create digits pixel by pixel using long range memory. Up to now the best results have occurred with splitting
the image into 16 sections, beyond that the model fails.
Generator
Discriminator
Experiments
TIMESTEP MODEL
Variable
Value
timesteps
4
lstm_layers_RNN_g
6
lstm_layers_RNN_d
2
hidden_size_RNN_g
600
hidden_size_RNN_d
400
lr
1e-4
iterations
> 2.5e6
SAMPLES
0
1
2
3
4
5
6
7
8
9
TIMESTEP MODEL
Variable
Value
timesteps
16
lstm_layers_RNN_g
6
lstm_layers_RNN_d
2
hidden_size_RNN_g
600
hidden_size_RNN_d
400
lr
2e-4:GEN/1e-4:DISC
iterations
> 5e5
SAMPLES
0
1
2
3
4
5
6
7
8
9
About
This repository contains the source for the paper "S-LSTM-GAN: Shared recurrent neural networks with adversarial training"