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📜  TensorFlow与PyTorch

📅  最后修改于: 2021-01-11 10:58:02             🧑  作者: Mango

TensorFlow和PyTorch之间的区别

TensorFlow和PyTorch这两个框架都是使用Python语言开发的顶级机器学习库。这些是开源的神经网络库框架。 TensorFlow是用于执行各种任务所需的差分和数据流编程的软件库，但PyTorch基于Torch库。

为什么我们使用TensorFlow?

TensorFlow是一个用于机器学习应用程序的库框架。该框架是一个数学库，主要用于数值计算以应用来自图的数据。图的边缘可以表示多维数据数组，而节点则表示各种准确的表示形式。它教授神经网络有关数学符号，图像识别和局部微分的知识，并且完全能够在多个GPU和CPU上运行。它的体系结构是灵活的。

该框架可能还支持C＃，Haskell，Julia，Rust，Scala，Crystal和OCami 。

为什么我们使用PyTorch?

PyTorch是一个机器学习库，适用于诸如自然语言处理之类的应用。 Pytorch也适用于构建各种类型的应用程序。

该库框架具有两个基本功能：

该库的第一个功能是自动区分深度神经网络的训练和构建。

第二个特征是在高功率GPU加速的支持下的计算张量。

Pytorch具有三个操作模块。最佳模块，自动毕业模块和nn模块。每个模块都有其特定的功能和应用程序。

例如，最佳模块用于实现用于开发神经网络的各种算法。 nn模块用于定义所有复杂的低级神经网络。


TensorFlow与PyTorch的比较

Basic TensorFlow PyTorch
Library TensorFlow is a free software library, and this library is open source in nature. The PyTorch framework is an open-source machine learning library.
Origin This library is developed by the Google brain team based on the idea of a dataflow graph for building models. The library is developed by a Facebook artificial intelligence research group based on the torch.
Compatibility TensorFlow library is compatible with different coding languages like C, C++, Java. The PyTorch library is only for Python-based coding.
Feature This framework is used for teaching the machine about many computational methods. This framework is used to building a neural network and natural language processing.
APIs TensorFlow library has both low-level APIs and high-level APIs. The PyTorch library has low-level APIs that would focus on the working of array expression.
Ability It is famous for its fast computational ability across a few platforms. PyTorch is famous for its research purposes. It also assists in deep learning applications.
Speed The speed of TensorFlow is faster and provides high performance. The speed and performance of PyTorch are much similar to the TensorFlow.
Architecture The architecture of the TensorFlow is complex and would be a bit difficult to understand. The architecture of the Pytorch is pretty complicated, and it would be challenging for any beginner.
Debugging Ability The process of debugging in TensorFlow is complicated. Debugging abilities of Pytorch is better when it has compared to Keras and TensorFlow.
Capability TensorFlow is capable of handling large datasets, as the processing speed of the library is very fast. Pytorch can handle large datasets and high- performance tasks.
Size The size of the code of TensorFlow is small in format to increase accuracy. All codes for Pytorch consist of individual lines.
Projects Top TensorFlow projects are Magenta, Sonnet, Ludwig High PyTorch plans are CheXNet, PYRO, Horizon
Ramp-Up Time PyTorch is utilizing Numpy with the ability to make use of the Graphic card. TensorFlow has the dependency where the compiled code is run using the TensorFlow Execution Engine.

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