PsychoGAN

PsychoGAN is GAN made Tensorflow that generates 128 x 128 pixels images, a project made for fun

Model

Generator

The generator recieves noise, apply a dense linear layer and reshape to a conv2d compatible input, with then passes through:

A Conv2d_transpose (fillters = 128, kernel = 5, strides = 2)
A Batch Norm
A Relu Activation

A Conv2d_transpose (fillters = 64, kernel = 5, strides = 2)
A Batch Norm
A Relu Activation

A Conv2d_transpose (fillters = 3 (img channels), kernel = 5, strides = 1)
A Tanh Activation

Discriminator

The discriminator recieves images, classifying in real or fake. The model is as following:

A Conv2d (fillters = 64, kernel = 5, strides = 4)
A Batch Norm
A Leak Relu Activation (alpha = 0.2)

A Conv2d (fillters = 128, kernel = 5, strides = 2)
A Batch Norm
A Leak Relu Activation (alpha = 0.2)

A Flatten Layer
A Dense Layer (1 unit)
A Sigmoid Activation

Data

The data used to train was composed of images from random cubism arts. This way, the similarities in the training set is only the paiting style.

Training

In the discriminator, a learning rate of 0.0002 was used.
In the generator, a learning rate of 0.0001 was used.
In both, the optimizer used was Adam, with a beta1 of 0.5
Every step, the discriminator is updated one time and the generator was updated 2 times.

Real Images Discriminator Loss:

Fake Images Discriminator Loss:

Combined Discriminator Loss:

Generator Loss:

Results

After 20000 training steps, which took 2 hours in a GTX 980, the generated images were:

As you can see, the generator learned how to use colors and created a unique style.

Conclusion

This was a project made for fun, and possible no use, execpt share the code (explained) and show the results.