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Table 5 Hyper-parameters of the network and their possible values

From: A stack LSTM structure for decoding continuous force from local field potential signal of primary motor cortex (M1)

Hyper-parameter

Values

Layer 1 forward dropout

{0, 0.1, 0.2, 0.3, 0.4, 0.5}

Layer 1 backward dropout

{0, 0.1, 0.2, 0.3, 0.4, 0.5}

Layer 2 forward dropout

{0, 0.1, 0.2, 0.3, 0.4, 0.5}

Layer 2 backward dropout

{0, 0.1, 0.2, 0.3, 0.4, 0.5}

Regularization value

{0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}

Learning rate

{0.001, 0.0015, 0.002, …, 0.003}

Batch size

{5, 10, 15, 20,30}

Number of epoch

{30, 50, 70, 100, 120}