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Table 1 Hyper-parameter values of Tree-LSTM

From: Querying knowledge graphs in natural language

Parameter Value
Input dimensions 300 × 1
LSTM memory dimensions 150 × 1
Epochs 15
Mini batch size 25
Learning rate 1 × \(10^{-2}\)
Weight decay (Regularization) 2.25 × \(10^{-3}\)
Dropout 0.2
Loss function Kullback-Leibler divergence loss
Optimizer Adagrad optimizer
Learning rate scheduler Stepwise learning rate decay
Step learning rate step size Once every 2 epochs
Step learning rate decay 0.25