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Microsoft认知工具包(CNTK)-简介 (Microsoft Cognitive Toolkit (CNTK) - Introduction)

In this chapter, we will learn what is CNTK, its features, difference between its version 1.0 and 2.0 and important highlights of version 2.7.

在本章中，我们将学习什么是CNTK，其功能，1.0和2.0版之间的区别以及2.7版的重要亮点。

什么是Microsoft认知工具包(CNTK)？ (What is Microsoft Cognitive Toolkit (CNTK)?)

Microsoft Cognitive Toolkit (CNTK), formerly known as Computational Network Toolkit, is a free, easy-to-use, open-source, commercial-grade toolkit that enables us to train deep learning algorithms to learn like the human brain. It enables us to create some popular deep learning systems like feed-forward neural network time series prediction systems and Convolutional neural network (CNN) image classifiers.

Microsoft Cognitive Toolkit(CNTK)，以前称为Computational Network Toolkit，是一种免费，易于使用的开源商业级工具包，使我们能够训练深度学习算法来像人脑一样学习。 它使我们能够创建一些流行的深度学习系统，例如前馈神经网络时间序列预测系统和卷积神经网络(CNN)图像分类器 。

For optimal performance, its framework functions are written in C++. Although we can call its function using C++, but the most commonly used approach for the same is to use a Python program.

为了获得最佳性能，其框架功能使用C ++编写。 尽管我们可以使用C ++调用其函数，但是最常用的方法是使用Python程序。

CNTK的功能 (CNTK’s Features)

Following are some of the features and capabilities offered in the latest version of Microsoft CNTK:

以下是最新版本的Microsoft CNTK中提供的一些功能：

内置组件 (Built-in components)

• CNTK has highly optimised built-in components that can handle multi-dimensional dense or sparse data from Python, C++ or BrainScript.

  CNTK具有高度优化的内置组件，可以处理来自Python，C ++或BrainScript的多维密集或稀疏数据。

• We can implement CNN, FNN, RNN, Batch Normalisation and Sequence-to-Sequence with attention.

  我们可以注意实现CNN，FNN，RNN，批处理规范化和序列到序列。

• It provides us the functionality to add new user-defined core-components on the GPU from Python.

  它为我们提供了从Python在GPU上添加新的用户定义的核心组件的功能。

• It also provides automatic hyperparameter tuning.

  它还提供自动超参数调整。

• We can implement Reinforcement learning, Generative Adversarial Networks (GANs), Supervised as well as Unsupervised learning.

  我们可以实施强化学习，生成对抗网络(GAN)，监督学习和无监督学习。

• For massive datasets, CNTK has built-in optimised readers.

  对于海量数据集，CNTK具有内置的优化读取器。

有效利用资源 (Usage of resources efficiently)

• CNTK provides us parallelism with high accuracy on multiple GPUs/machines via 1-bit SGD.

  CNTK通过1位SGD在多个GPU /机器上为我们提供了高精度的并行性。

• To fit the largest models in GPU memory, it provides memory sharing and other built-in methods.

  为了适合GPU内存中最大的模型，它提供了内存共享和其他内置方法。

轻松表达我们自己的网络 (Express our own networks easily)

• CNTK has full APIs for defining your own network, learners, readers, training and evaluation from Python, C++, and BrainScript.

  CNTK具有完整的API，用于定义您自己的网络，学习者，读者，Python，C ++和BrainScript的培训和评估。

• Using CNTK, we can easily evaluate models with Python, C++, C# or BrainScript.

  使用CNTK，我们可以轻松地使用Python，C ++，C＃或BrainScript评估模型。

• It provides both high-level as well as low-level APIs.

  它提供了高级和低级API。

• Based on our data, it can automatically shape the inference.

  根据我们的数据，它可以自动塑造推理。

• It has fully optimised symbolic Recurrent Neural Network (RNN) loops.

  它具有完全优化的符号递归神经网络(RNN)循环。

测量模型性能 (Measuring model performance)

• CNTK provides various components to measure the performance of neural networks you build.

  CNTK提供了各种组件来衡量您构建的神经网络的性能。

• Generates log data from your model and the associated optimiser, which we can use to monitor the training process.

  从您的模型和关联的优化器生成日志数据，我们可以使用它们来监视训练过程。

1.0版与2.0版 (Version 1.0 vs Version 2.0)

Following table compares CNTK Version 1.0 and 2.0:

下表比较了CNTK版本1.0和2.0：

2.7版的重要亮点 (Important Highlights of Version 2.7)

Version 2.7 is the last main released version of Microsoft Cognitive Toolkit. It has full support for ONNX 1.4.1. Following are some important highlights of this last released version of CNTK.

2.7版是Microsoft Cognitive Toolkit的最后一个主要发行版本。 它完全支持ONNX 1.4.1。 以下是该CNTK的最新发行版的一些重要亮点。

• Full support for ONNX 1.4.1.

  完全支持ONNX 1.4.1。

• Support for CUDA 10 for both Windows and Linux systems.

  Windows和Linux系统均支持CUDA 10。

• It supports advance Recurrent Neural Networks (RNN) loop in ONNX export.

  它支持ONNX导出中的高级递归神经网络(RNN)循环。

• It can export more than 2GB models in ONNX format.

  它可以以ONNX格式导出2GB以上的模型。

• It supports FP16 in BrainScript scripting language’s training action.

  它在BrainScript脚本语言的训练操作中支持FP16。

翻译自: https://www.tutorialspoint.com/microsoft_cognitive_toolkit/microsoft_cognitive_toolkit_introduction.htm

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