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In contrast with CNN II and V, which have one more layer with 512 filters, these easy networks could produce equally good results on the training simulation models. However, Determine 9 reveals that for those networks skilled on blended dataset, the loss on 21cmFAST decreases faster and is smaller than that on zreion. For CNN III, it takes the network about 1500 epochs to supply correct prediction on zreion to the degree which is achieved on 21cmFAST at solely a thousand epochs.

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Because the validation dataset contains all the knowledge represented by these two models, the hope is that CNNs would carry out equally precisely on both of the datasets. For every batch-size of training, we use a half batch-dimension of data from 21cmFAST and the other half from zreion. As an alternative, we wrote a custom loss perform that operated on our mixed-pattern validation dataset. This calculation could possibly be implemented by writing customized callback capabilities, however in our testing such an approach considerably slowed down the training course of. Our custom loss function on the validation dataset operates on one batch-price every time. To additional visualize the conduct of the assorted CNN cases outlined above, we present the loss function values throughout training for 21cmFAST and zreion separately.

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Moreover, convolution layers usually have one or two orders of magnitude fewer parameters compared with dense layers (Li et al., 2019). The CNNs used in this work include a number of convolution layers adopted by several dense layers. This is a typical architecture used to regress on parameters from the pictures, and is similar to those used in Simonyan & Zisserman (2014), Gillet et al. Following widespread apply, we use 3×3333 imes 33 × 3 filters for each convolutional layers and pair them with 2×2222 imes 22 × 2 max pooling layers with a stride of 2, which is used to decrease the variety of parameters (Riesenhuber & Poggio, 1999). Between these convolutional and max pooling layers, batch normalization (Ioffe & Szegedy, 2015) is adopted to provide regularization.

The outcomes proven in Sec. The CNN –’s different performance on zreion and 21cmFAST implies that these two fashions has some particular features that are not common. 4 point out that though the snapshots generated by two reionization fashions are very comparable by eye, they are however ample differences that inhibit training a CNN that performs equally well on both models. In the best case, the CNN should not rely on the precise semi-numeric models, and each of them could seize the total complexity of 21 cm signal mapping so the networks trained on both certainly one of them might reproduce good results on the opposite. In other phrases, if the two fashions both precisely capture the same important particulars of reionization, the CNN’s dependence basically points out zreion or 21cmFAST or both of them are not common sufficient to include all the required characteristics of reionization.