量化是一个信息有损压缩的过程,如果训练过程中使用FP32,在模型推理时使用Post-training Quantization(PTQ)直接量化为INT8模型,模型精度会存在一定损失。而量化感知训练(Quantization-aware-training, QAT)在模型训练过程中就引入了伪量化(Fake-quantization)来模拟量化过程中带来的误差,通过这种方式能够进一步减少量化后模型...
),我们提出了三种domain aware training系统,包含了1)domain embedding系统,deep CORAL系统,和多任务学习系统。这些方法相互结合将domain knowledge应用到网络训练中去,并能够显著的提升远场环境下的关键词分类表现。试验结果显示,我们提出的方法能够在保持近场交谈语音中的性能的同时,极大的提升远场测试集上的结果。
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注:模型除了可以量化到int8之外,还可以量化到float16,int4等,只是在作者看来量化到int8之后,能保证压缩效果和准确率损失最优。 2,quantization aware training 论文:Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference quantization aware training技术来源于上面这篇论文,现在...
,同时使精度损失可以忽略不计 这次添加支持的第一个技术是向TensorFlowLite转换工具添加post-training模型量化(post-trainingquantization)。对于相关的...指南:https://www.tensorflow.org/performance/model_optimizationpost-trainingquantization的工作原理 在底层,我 ...
2.2. Decentralized Training 最近,理论上首次证明了去中心化算法可以优于中心化算法[20]。这两种算法共享相同的计算复杂度[20],但去中心化的算法享受更快的通信,因为通信负载分布在网络拓扑图上而不是集中在PS上。 尽管去中心化算法以前也有人研究过,但它的优点却隐藏了很久。先前的工作[10]表明,基于F是凸的假...
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original BERT repo here:https://github.com/google-research/bert. This requires invoking thecreate_pretraining_data.pyand therun_pretraining.pyscripts from the repo with additional instructions therein. This produces a new tensorflow checkpoint that can be used as the pre-trained checkpoint for UST...
Quantization-aware Training: Use quantization simulation to train the model further to improve accuracy 对于QAT部分还支持了RNN类的网络例如RNNs, LSTMs,GRUs 量化使用流程如下 在进行QAT时,单个节点的前向模拟 在进行QAT是,单个节点反向传播操作 量化...