问题标签 [cntk]

我刚刚安装了 microsoft/cntk docker 映像，我想知道如何查看 Jupyter notebook 界面（以便在 /cntk/Tutorials 中执行 docker 映像中包含的教程）？

对于另一个 docker 映像，我只需执行以下命令：

docker run -d -p 8888:8888 -v /home/name/notebooks:/home/ds/notebooks dataquestio/python3-starter

然后我打开浏览器并转到 localhost:8888

谢谢

问题

我是一名学生，.NET 开发人员，我会感谢 CNTK 重量级贡献者和设计师的建议。我正在阅读文档和教程，实际的源代码令人印象深刻。

在 Visual Studio 2015 中使用的最佳 api 是什么，以便我可以进入 CNTK 中的功能，看看微软是如何编写多层感知器的？通常，对于 .NET 项目，我会编写一个我理解的示例应用程序，然后在调试器上浏览源代码。这是我学习的最好方法。我的问题是针对 Visual Studio 2015，但我愿意走出自己的舒适区，了解最佳实践是否是在不同的 IDE 甚至操作系统上执行此操作。

我计划按原样使用该工具，但我想尽可能熟悉该工具，因为我想开始在 Caffe、TensorFlow 等上使用它，因为我是一名 .NET 开发人员。

尝试

我已经阅读了有关 CTNK 的教程，发现自己经常使用大脑脚本，但我经常在命令行上调用它。我知道 Visual Studio 2015 中有一个 python api，我想知道这是否是使用 CNTK 访问后端的首选方法？

请不要对此投反对票。我已经阅读了提供的文档，我觉得在 GitHub 存储库上问这个问题是不合适的，这是我觉得问这个问题的唯一其他场所。如果有其他渠道或论坛可以向 CNTK 提出一般性问题，请告诉我，我会这样做，但如果没有，我感谢您的指导和对认真学习者的友好帮助。

尝试运行 python 脚本时出现以下错误：

Traceback (most recent call last): File "A1_GenerateInputROIs.py", line 5, in <module> import PARAMETERS File "C:\local\CNTK-2-0-beta9-0-Windows-64bit-CPU-Only\cntk\Examples\Image\Detection\FastRCNN\PARAMETERS.py", line 2, in <module> from cntk_helpers import * File "C:\local\CNTK-2-0-beta9-0-Windows-64bit-CPU-Only\cntk\Examples\Image\Detection\FastRCNN\cntk_helpers.py", line 7, in <module> from fastRCNN.nms import nms as nmsPython File "C:\local\CNTK-2-0-beta9-0-Windows-64bit-CPU-Only\cntk\Examples\Image\Detection\FastRCNN\fastRCNN\__init__.py", line 7, in <module> from .imdb import imdb File "C:\local\CNTK-2-0-beta9-0-Windows-64bit-CPU-Only\cntk\Examples\Image\Detection\FastRCNN\fastRCNN\imdb.py", line 19, in <module> from .utils3_win64.cython_bbox import bbox_overlaps ImportError: DLL load failed: The specified module could not be found.

我正在关注https://github.com/Microsoft/CNTK/wiki/Object-Detection-using-Fast-R-CNN上的教程。当我尝试在 ubuntu 系统上运行脚本时，我也遇到了类似的错误。

你认为问题出在哪里？

我正在探索的一个假设如下：

因为在教程中，它说：

本教程代码假设您使用的是 64 位版本的 Python 3.4，因为 utils_win64 下的 Fast R-CNN DLL 文件是为此版本预构建的

由于我使用的是 python 3.5 而不是 python 3.4，我开始认为这可能是原因。我会随时通知你我的发现。

谢谢

trainer.restore_from_checkpoint在 CNTK中调用时出现以下异常。

'This' 函数与从检查点恢复的函数不等价（同构）。

我的恢复代码在下面。这些与本文档中提到的创建训练器和保存方法trainer.dnn的结构相同。trainer.save_checkpoint("trainer.dnn")

每次我在我的python环境中导入cntk时，我都会收到“DLL加载失败：找不到指定的模块”

我的环境设置正确，我什至在编写代码时自动完成了cntk，但是每当我运行项目时，它总是在第一行失败。

我完全无法解决这个问题，因为我已经按照 CNTK 教程的每一步操作，并且这个问题的github 票仍然是开放的，这绝对没有帮助。

我的 python 路径是 C:\Anaconda3\envs\cntk-py35\Lib\site-packages;F:\cntkInstall\envs\cntk-py34\Lib\site-packages

出于绝望，我刚刚开始添加任何与路径相关的远程python，看看我是否可以让它工作。依然没有。请帮忙！

编辑：这是我得到的错误：

With a model like this, how can one access the trained parameters like weight and bias of each layer?

Thanks.

在大纪元期间在某种程度上，但是一旦大纪元结束，我就会看到这个关于复制空矩阵的警告。通常是什么导致此警告？

01/23/2017 13:06:49: Epoch[ 1 of 50]-Minibatch[691301-691400]: ce = 0.06757763 * 9404; errs = 1.595% * 9404; time = 14.3775s; samplesPerSecond = 654.1 01/23/2017 13:07:04: Epoch[ 1 of 50]-Minibatch[691401-691500]: ce = 0.08411693 * 9784; errs = 1.962% * 9784; time = 15.1554s; samplesPerSecond = 645.6 01/23/2017 13:07:18: Epoch[ 1 of 50]-Minibatch[691501-691600]: ce = 0.07443892 * 9847; errs = 1.696% * 9847; time = 14.1284s; samplesPerSecond = 697.0 WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. 01/23/2017 13:07:33: Epoch[ 1 of 50]-Minibatch[691601-691700]: ce = 0.07692308 * 9815; errs = 1.854% * 9815; time = 14.4867s; samplesPerSecond = 677.5 WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. 01/23/2017 13:07:48: Epoch[ 1 of 50]-Minibatch[691701-691800]: ce = 0.08028341 * 9809; errs = 1.906% * 9809; time = 14.7772s; samplesPerSecond = 663.8 01/23/2017 13:08:03: Epoch[ 1 of 50]-Minibatch[691801-691900]: ce = 0.09192892 * 10073; errs = 2.214% * 10073; time = 14.8481s; samplesPerSecond = 678.4 01/23/2017 13:08:17: Epoch[ 1 of 50]-Minibatch[691901-692000]: ce = 0.07414725 * 9616; errs = 1.841% * 9616; time = 14.9059s; samplesPerSecond = 645.1 01/23/2017 13:08:32: Finished Epoch[ 1 of 50]: [Training] ce = 0.08177092 * 67573150; errs = 1.962% * 67573150; totalSamplesSeen = 67573150; learningRatePerSample = 0.0020000001; epochTime=104968s WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times. WARNING: The same matrix with dim [0, 0] has been transferred between different devices for 20 times.

我想计算梯度值（每个示例或小批量），并将权重直接修改为任何值（因此我可以用任何方法控制梯度下降，而不仅仅是提供的 sgd / 学习率计划）。我正在使用python接口。

我想用负采样实现 CBOW word2vec。我通读了 CNTK 的文档，但找不到可以将句子作为输入和输出2*k + 1单词的阅读器（当前单词和当前单词k左右的上下文单词）。另外，我想在python中实现这个。

是否有在 python 中创建自定义 cntk 文本阅读器的指南？

在 CNTK 中，它有 relu、hardmax、softmax、sigmoid 和所有好东西，但我正在构建一个基于回归的算法，最后一层需要预测 2 个或更多回归输出。所以我需要 n 个节点，并且激活只是一次线性激活。我看到我可以将激活设置为无，这实际上是正确的吗？