• ## Training

千次阅读 2019-10-23 23:38:49
github链接 之前的教程，您应该有了一个常用模型，已经熟悉了数据读取接口。 现在可以自由创建优化器，写训练逻辑，用Pytorch很容易做到这些，而且能很容易让使用者看清训练逻辑。 同时，我们也提供标准训练器，是最...
github链接
之前的教程，您应该有了一个常用模型，已经熟悉了数据读取接口。
现在可以自由创建优化器，写训练逻辑，用Pytorch很容易做到这些，而且能很容易让使用者看清训练逻辑。
同时，我们也提供标准训练器，是最简单的hook系统，帮助简化训练流程。
可以使用SimpleTrainer().train()做单损失，单优化器和单数据源训练。或者，也可以使用更多标准操作的DefaultTrainer().train()来优化训练。


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• 在这里，整理发布了APQP TRAINING，只为方便大家用于学习、参考，喜欢APQP TRAINING的朋友赶快...该文档为APQP TRAINING，是一份很不错的参考资料，具有较高参考价值，感兴趣的可以下载看看
• <div><p>Is it possible to do incremental training? I build training sets that have between 10-20K training examples and training takes a long time. Would like to be able to add a new training example ...
• <div><p>Branch: Training <h4>Documentation <ul><li>[x] Model <code>/README.md</code> in the root directory includes a <em>training</em> section ...
• <div><p>i use your source training code to training from scratch, but i found when the loss decreased to 2, it start to fluctuate between 3 and 7.so it that reflects that the training is overfitting?...
• atf_training 1 6 7 8 9 10 11 12
• <p>I am aware that the authors have explicitly confirmed no support in for training. I am just looking out for others who are also looking to do the training on lf-net. Anyone, who can share their ...
• <div><p>How create training samples from adversarially perturbed original training sampels? <p>To be simple, suppose I had 100 training images and wanted to use Deep Fool and FGSM to perturb these ...
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• 资料库标题：#helloci_training
• <div><p>Tactical blind spots are one of the most common causes of game losses for lc0, yet different training runs appear to have largely distinct blind spots. A prime example is the capture-promotion...
• Windchill training windchill
• <div><p>For the classification models, I want to plot my model training losses by epoch to compare how different models train. Why is _create_training_progress_scores tied to the "evaluate during ...
• <div><p>I am training FCOS based on FCOS_MS_X_101_64x4d_2x model. The estimated time required is approximately 4-5 days. Is there any tricks I can use to speed up the training please? My training ...
• The training games are played with training specific settings: -Nb playouts (1600) -Noise (for exploration) <p>1) Are there any other training specific settings than these two? 2) Can we have more ...
• 基于Pre-trained模型，采用Post-training量化策略，能够在一定程度上弥补量化精度损失，并且避免了相对耗时的quantization-ware training或re-training过程。 WA与BC "Data-Free Quantization through Weight ...
基于Pre-trained模型，采用Post-training量化策略，能够在一定程度上弥补量化精度损失，并且避免了相对耗时的quantization-ware training或re-training过程。

WA与BC

"Data-Free Quantization through Weight Equalization and Bias Correction" 这篇文章提出了两种post-training策略，包括Weight Adjustment (WA)与Bias Correction (BC)。

Paper地址：

具体的WA策略如下所示，均衡调整通常在W1的output channel与W2的input channel之间进行：

调整系数计算如下，相邻tensors按channel均衡调整之后，分布范围将达到相一致的水平：

2. Bias Correction

Per-tensor或Per-channel量化的误差，直接体现在Conv2D等计算节点的输出产生了误差项：

沿channel c的误差项，可按前置BN层的参数予以估计：

将估计获得的误差项补偿回Bias，可提升一定的量化精度。

基于BN层的调整策略
"A Quantization-Friendly Separable Convolution for MobileNets" 这篇文章2提出了基于BN层的调整策略，即将BN层中趋于零的Variance替换为剩余Variance的均值，以消除对应通道输出的奇异性，从而获得对量化更为友好的Activation数值分布。

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• <p>When I try to train a 20GB training data file using pytorch RNNLM I always run into OOM issue where the training process is being killed by OS. For Network training there is option to use the ...
• Android Training请好学的朋友一起探讨
• <div><p>May I pause training and continue training next time,due to the limitation I could use for training. Thank you !</p><p>该提问来源于开源项目：zju3dv/clean-pvnet</p></div>
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• ## DDR Training

千次阅读 2019-10-17 22:47:16
DDR Training Motivation:As the clock frequency runs higher, the width of the data eye becomes narrower to sample data (channel signal integrity and jitter contribute to data eyereduction). DDR trainin...
DDR Training Motivation:As the clock frequency runs higher, the width of the data eye becomes narrower to sample data (channel signal integrity and jitter contribute to data eyereduction).
DDR training is introduced to remove static skew/noise so that the data eye is kept wider for better data sampling.
DDR Training动机：
随着时钟频率升高，数据眼的宽度变得更窄以采样数据（通道信号完整性和抖动有助于减少数据眼）。
引入了DDR Training以消除静态偏斜/噪声，从而使数据眼保持更大的范围，以进行更好的数据采样。


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