decay_rate为衰减指数 n_layers为模型总层数 depth为当前参数所在模型的层数 new_lr为当前参数的学习率 目前适配的任务有: 文本分类 文本匹配 ⚠️注意:使用Layer decay策略策略时,设置的学习率需要比正常学习率要大,例如不加Layer decay策略训练时学习率为5e-5,那么加上该策略学习率需要设置为1e-4。 文本分...
Add layer-wise learning rate schemes to Torch. At the moment, it works withnnandnngraphmodules. At the moment, the only supported optimization algorithm supported isoptimSGD implementation. Usage nnlradds the following methods tonn.Module:
blobs_lr: 1 # learning rate multiplier for the filters blobs_lr: 2 # learning rate multiplier for the biases weight_decay: 1 # weight decay multiplier for the filters weight_decay: 0 # weight decay multiplier for the biases convolution_param { num_output: 96 # learn 96 filters kernel_siz...
Deeply Supervised, Layer-wise Prediction-aware (DSLP) Transformer for Non-autoregressive Neural Machine Translation - chenyangh/DSLP
(AdaComp), which sorts the gradient values, selects the largest k items for transmission, and considers the influence of the decay effect of the gradient on model training. Chen27et al. proposed a sparse communication algorithm LAG, which adaptively calculates a threshold in each round of ...
We did not observe significant over-fitting present in layer- wise optimization (we discuss this phenomenon in more de- tails below) and in particular we found that even low values of weight decay/L2 weight regularization strength such as 10−6 could ...
The operation represents element-wise multiplication of vectors. From Figure 1, we can see that the output of a time-LSTM is used as the input of the time-LSTM at the same time step in the next layer and the recurrent input of the time-LSTM at the next time step in the same layer....
.iterations(parameters.getIterations()).learningRate(parameters.getLearningRate()).rmsDecay(0.95) .seed(parameters.getSeed()).regularization(true).l2(0.001).list(nLayers).pretrain(false).backprop(true);for(inti =0; i < nLayers; i++) ...
We introduce a layer-wise cosine annealing schedule for learning rates, progressively freezing the layers and shifting them to inference mode to save on computation.Figure 2: Layer Freezing Schedule.Key ResultsOur method achieved a reduction in training time by approximately 12.5% with only a 0.6%...
the initial learning rate was set to 0.01, which was reduced by a factor of 10 on epochs 300 and 350. The momentum and decay were set to 0.9 and\(10^{-6}\)for all models. The 5-fold cross validation accuracy and f-score are shown in the first two rows of Table1. Since texture...