  Creating a database of 21cm brightness temperature signals from the Epoch of
Reionisation (EoR) for an array of reionisation histories is a complex and
computationally expensive task, given the range of astrophysical processes
involved and the possibly high-dimensional parameter space that is to be
probed. We utilise a specific type of neural network, a Progressively Growing
Generative Adversarial Network (PGGAN), to produce realistic tomography images
of the 21cm brightness temperature during the EoR, covering a continuous
three-dimensional parameter space that models varying X-ray emissivity, Lyman
band emissivity, and ratio between hard and soft X-rays. The GPU-trained
network generates new samples at a resolution of $\sim 3'$ in a second (on a
laptop CPU), and the resulting global 21cm signal, power spectrum, and pixel
distribution function agree well with those of the training data, taken from
the 21SSD catalogue \citep{Semelin2017}. Finally, we showcase how a trained
PGGAN can be leveraged for the converse task of inferring parameters from 21cm
tomography samples via Approximate Bayesian Computation.
