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