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Artificial neural networks that simulate human achieves great successes. Fromthe perspective of simulating human memory method, we propose a stepped samplerbased on the "repeated input". We repeatedly inputted data to the LSTM modelstepwise in a batch. The stepped sampler is used to strengthen the ability offusing the temporal information in LSTM. We tested the stepped sampler on theLSTM built-in in PyTorch. Compared with the traditional sampler of PyTorch,such as sequential sampler, batch sampler, the training loss of the proposedstepped sampler converges faster in the training of the model, and the trainingloss after convergence is more stable. Meanwhile, it can maintain a higher testaccuracy. We quantified the algorithm of the stepped sampler. We assume that,the artificial neural networks have human-like characteristics, and humanlearning method could be used for machine learning.

A stepped sampling method for video detection using LSTM