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  • 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。

    展开全文
  • WML Training

    2020-12-09 14:33:17
    <div><p>Branch: Training <h4>Documentation <ul><li>[x] Model <code>/README.md</code> in the root directory includes a <em>training</em> section ...
  • training problems

    2020-12-08 19:35:22
    <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?...
  • Trainingdata

    2020-12-08 20:46:02
    s too waste time to collect training data with manually operate the car, so do you mind to give your training data or upload the training data?</p><p>该提问来源于开源项目:multunus/autonomous-rc...
  • <div><p>hello , in the process of Training and Validation,I come across an problem,as the epoch goes,the L1 and EPE become larger and larger,that's Mathematical Divergent which confuse me . I don&...
  • <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...
  • <p>Im trying to continue training from last saved model after break the training. At the first i started training and saved first epoch in checkpoints and stopped it. Afterall i set load_pretrain =...
  • <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 ...
  • 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:

    1. Weight Adjustment

    在执行Per-tensor量化时,由于Weights或Activation的数值分布存在奇异性,例如存在个别数值较大的outliers,导致宽泛的分布区间对量化(如MAX方法)不友好,产生较大的量化精度损失。Weight Adjustment通过在相邻的[Weight-tensor, Weight-tensor]或[Activation-tensor, Weight-tensor]之间,执行均衡调整、等价变换(确保变换后推理精度不变),使得调整之后的数值分布对量化更为友好。

    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。

    展开全文
  • 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 ...
  • <p>I am working with training dataset QGIS 3.10, but the training often is inconsistent with the downloaded data. The vector which the documentation refer sometimes have the same name but the field ...
  • American Accent Training

    2016-08-13 05:14:21
    American Accent Training
  • Resume LM Training

    2021-01-08 14:20:08
    <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 ...
  • m just looking for some feedback on your experience with variability in trained model quality between training sessions. <p>I'm progressively trying to retrace some important steps: 1. validating ...
  • <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>
  • linux training

    2008-10-08 10:03:51
    linux training ppt linux training ppt linux training ppt
  • Crew Training Settings

    2020-12-09 10:43:40
    It looks like my changes will cause the training expiration blocks to be removed from saves. So even after going back to old DLLs, all trainings would still lack expiration dates. <p>hashtag DELURK ...
  • Mixed precision training

    2020-11-26 05:07:16
    <div><p>Any plans to introduce mixed precision training as an analog to opt_level O2 in Nvidia/Apex? I'm training GPT-2 model right now. It's not training well with XLA_USE_BF16=1. I can ...
  • <p>Can you give an example on using the training conf. of the following training strategy? (1) Apply the two-stage training strategy or the one-stage training strategy: <p>Two-stage training: <p>First...
  • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift TLDR; The authors introduce Batch Normalization, a technique to normalize unit activations to zero mean and...

    Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

    作者介绍了批次归一化,这是一种将单元激活归一化为网络中零均值和单元方差的技术。作者表明,在前馈和卷积网络中,批量归一化可加快训练速度并提高准确性。 BN还可以充当正则化器,从而减少对Dropout的需求。使用批标准化网络的集成,作者可以在ILSVRC上达到最先进的水平。

    关键点

    • 网络训练很复杂,因为随着较低层中的参数的变化,输入到较高层的值也会发生变化:内部协变量平移。解决方案:在网络内进行标准化。
    • BN:将输入归一化为非线性,以使均值和单位方差为零。然后,每单位添加两个附加参数(缩放和偏差)以保持网络的可表达性。统计数据是按小批量计算的。
    • 网络参数增加,但幅度不大:每单位2个参数已应用批量归一化。
    • 适用于完全连接和卷积的图层。作者没有尝试RNN。
    • 添加BN时进行更改:提高学习率,删除/减少辍学率和l2正则化,加速学习率衰减,更彻底地整理训练示例。
    展开全文
  • 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。

    展开全文
  • developer android 官网 training 离线PDF
  • <div><p>Our research shows that more plugin users are interested in the Basic SEO training than would like to follow the copywriting training. We should replace the SEO copywriting link in step 11 of ...
  • <p>training-I5_hf_RGR: Training with training/test data at: training-I5_hf_RGR: DATA_DIR: /mnt/data/gesture-detect training-I5_hf_RGR: MODEL_DIR: /job/model-code training-I5_hf_RGR: TRAINING_JOB: ...
  • Cloudera_Administrator_Training.pdf
  • training/webapp镜像

    2018-10-10 23:11:44
    training/webapp镜像,方面一些内网用户无法直接pull镜像时下载: 使用方法: docker load -i training-webapp.tar.gz
  • In detail, after some epoches of training, resnet50 and se-resnext dygraph training accuracy improve as expected, but validation accuracy is much lower than training accuracy. <p>We do not understand...
  • Android Training 中文版

    2017-07-26 16:09:38
    Android Training 中文版
  • Training: Regex (Training, Regex) 题目描述 Your objective in this challenge is to learn the regex syntax. Regular Expressions are a powerful tool in your way to master programming, so you should be ...

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