DEEPz network flow diagram. The data are shown in grey and the networks in violet. The network architecture consists of an autoencoder with ten layers and 250 nodes in both the encoder and decoder. Each layer includes linear transformations followed by ReLU non-linearities, batch normalisation, and a 2% dropout, except for the last three layers. The autoencoder is fed galaxy flux ratios. The z_p network, which follows the same structure as the autoencoder, takes both the galaxy flux ratios and autoencoder features as input. It includes 1% dropout after all linear layers. This network is a MDN, representing the redshift distribution as a linear combination of 10 normaldistributions.