  During oscillations of cosmology inflation around the minimum of a cuspy
potential after inflation, the existence of extra high frequency gravitational
waves (HFGWs) (GHz) has been proven effectively recently. Based on the
electromagnetic resonance system for detecting such extra HFGWs, we adopt a new
data processing scheme to identify the corresponding GW signal, which is the
transverse perturbative photon fluxes (PPF). In order to overcome the problems
of low efficiency and high interference in traditional data processing methods,
we adopt deep learning to extract PPF and make some source parameters
estimation. Deep learning is able to provide an effective method to realize
classification and prediction tasks. Meanwhile, we also adopt anti-overfitting
technique and make adjustment of some hyperparameters in the course of study,
which improve the performance of classifier and predictor to a certain extent.
Here the convolutional neural network (CNN) is used to implement deep learning
process concretely. In this case, we investigate the classification accuracy
varying with the ratio between the number of positive and negative samples.
When such ratio exceeds to 0.11, the accuracy could reach up to 100%.
