README.md
We use a neural network that takes a coordinate (x, y) and output the corresponding RGB value to generate an image.
With different random hyperparameters, the network generates images of large style variance.
Image may be NSFW.
Clik here to view.
Image may be NSFW.
Clik here to view.
The image is generated using a single-path feed-forward network with only fully-connected layers and batch-normalization, flavored with various nonlinearities and initialized with gaussian random weights.
The key to generate these intriguing images is the use of random nonlinearities from a large nonlinearity pool at each layer, especially those discontinuous functions (like sign) or functions with large gradient near some point (e.g, log(abs(x) + eps)).
Here are the nonlinearities we've used so far:
NONLIN_TABLE=dict(relu=T.nnet.relu,tanh=T.tanh,abs_tanh=lambdax: abs(T.tanh(x)),sigmoid=T.nnet.sigmoid,softplus=T.nnet.softplus,sin=T.sin,cos=T.cos,sgn=T.sgn,sort=lambdax: T.sort(x, axis=1),abs=abs,log_abs=lambdax: T.log(abs(x) + eps), # this is awesomelog_abs_p1=lambdax: T.log(abs(x) +1),log_relu=lambdax: T.log(T.nnet.relu(x) + eps),log_square=lambdax: T.log(x**2+ eps), # just a scalarxlogx_abs=lambdax: T.xlogx.xlogx(abs(x) + eps),xlogx_abs_p1=lambdax: T.xlogx.xlogx(abs(x) +1),xlogx_relu=lambdax: T.xlogx.xlogx(T.nnet.relu(x) + eps),xlogx_relu_p1=lambdax: T.xlogx.xlogx(T.nnet.relu(x) +1),xlogx_square=lambdax: T.xlogx.xlogx(x**2+ eps),softmax=T.nnet.softmax,logsoftmax=T.nnet.logsoftmax,hard_sigmoid=T.nnet.hard_sigmoid,identity=lambdax: x,square=lambdax: x**2
)See https://github.com/zxytim/neural-painter/blob/master/neural_painter.py for implementation. The code is based on theano.
./neural_painter.py --image_size 800x800 --hidden_size 100 --nr_hidden 4 --nonlin random_every_time --nr_channel 3 --output_nonlin identity --coord_bias --seed 42 --output 42.png- Wikipedia page for Compositional pattern-producing network
- CPPN in Tensorflow
- Image regression from karpathy:
- Generating Abstract Patterns with TensorFlow:
- High resolution MNIST generator (CPPN + GAN/VAE)
Clik here to view.
