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In contrast with CNN II and V, which have yet one more layer with 512 filters, these easy networks may produce equally good results on the coaching simulation fashions. But, Figure 9 exhibits that for those networks educated on mixed 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 accurate prediction on zreion to the degree which is achieved on 21cmFAST at only 1000 epochs.
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Because the validation dataset consists of all the knowledge represented by these two fashions, the hope is that CNNs would carry out equally accurately on each of the datasets. For each batch-size of coaching, we use a half batch-size of knowledge from 21cmFAST and the other half from zreion. As a substitute, we wrote a customized loss operate that operated on our combined-pattern validation dataset. This calculation may very well be carried out by writing custom callback functions, however in our testing such an approach considerably slowed down the coaching course of. Our custom loss operate on the validation dataset operates on one batch-worth each time. To further visualize the behavior of the various (read review) CNN cases outlined above, we show the loss perform values during coaching for 21cmFAST and zreion separately.
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Moreover, convolution layers sometimes have one or two orders of magnitude fewer parameters compared with dense layers (Li et al., 2019). The CNNs used on this work encompass a number of convolution layers adopted by a number of dense layers. This is a typical architecture used to regress on parameters from the photographs, and is much like the ones used in Simonyan & Zisserman (2014), Gillet et al. Following frequent apply, we use 3×3333 imes 33 × 3 filters for every 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 number of parameters (Riesenhuber & Poggio, 1999). Between these convolutional and max pooling layers, batch normalization (Ioffe & Szegedy, 2015) is adopted to provide regularization.
The results proven in Sec. The CNN’s different efficiency on zreion and 21cmFAST implies that these two fashions has some particular options that are not frequent. 4 indicate that although the snapshots generated by two reionization models are very similar by eye, they’re however ample differences that inhibit training a CNN that performs equally properly on both fashions. In the ideal case, the CNN – Read the Full Posting – should not depend on the particular semi-numeric models, and both of them might capture the full complexity of 21 cm signal mapping so the networks trained on either certainly one of them could reproduce good outcomes on the other. In other words, if the 2 fashions each accurately seize the identical important particulars of reionization, the CNN’s dependence basically points out zreion or 21cmFAST or each of them are not common sufficient to include all the required traits of reionization.