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.
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=lambda x: abs(T.tanh(x)), sigmoid=T.nnet.sigmoid, softplus=T.nnet.softplus, sin=T.sin, cos=T.cos, sgn=T.sgn, sort=lambda x: T.sort(x, axis=1), abs=abs, log_abs=lambda x: T.log(abs(x) + eps), # this is awesome log_abs_p1=lambda x: T.log(abs(x) + 1), log_relu=lambda x: T.log(T.nnet.relu(x) + eps), log_square=lambda x: T.log(x**2 + eps), # just a scalar xlogx_abs=lambda x: T.xlogx.xlogx(abs(x) + eps), xlogx_abs_p1=lambda x: T.xlogx.xlogx(abs(x) + 1), xlogx_relu=lambda x: T.xlogx.xlogx(T.nnet.relu(x) + eps), xlogx_relu_p1=lambda x: T.xlogx.xlogx(T.nnet.relu(x) + 1), xlogx_square=lambda x: T.xlogx.xlogx(x**2 + eps), softmax=T.nnet.softmax, logsoftmax=T.nnet.logsoftmax, hard_sigmoid=T.nnet.hard_sigmoid, identity=lambda x: x, square=lambda x: x**2 )
See https://github.com/zxytim/neural-painter/blob/master/neural_painter.py for implementation. The code is based on theano.
Try it on your own
./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
- Npainter (Neural Painter in browser, using RNN, by rupeshs)
- 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)
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If you are interested or want to know more, send us your CV or anything you'd like to know to zxy at megvii dot com.