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📜  TensorFlow与Caffe

📅  最后修改于: 2021-01-11 11:00:55             🧑  作者: Mango

TensorFlow和Caffe之间的区别

TensorFlow是一个用于数值计算的基于python的开源软件库，它使使用数据流图的机器学习更容易访问和更快。 TensorFlow简化了获取数据流程图的过程。

Caffe是用于训练和运行神经网络模型的深度学习框架，视觉和学习中心可以对其进行开发。 TensorFlow简化了数据获取，预测功能，根据用户数据训练许多模型以及完善未来结果的过程。 Caffe设计时充分考虑了表达，速度和模块化。

TensorFlow与Caffe的比较

Basic TensorFlow Caffe
Definition TensorFlow is used in the field of research and server products as both have a different set of targeted users. Caffe is relevant for the production of edge deployment, where both structures have a different set of targeted users. Caffe desires for mobile phones and constrained platforms.
WLife Cycle management and API’s TensorFlow offers high- level API’s for model building so that we can experiment quickly with TensorFlow API. It has a suitable interface for python language (which is a choice of language for data scientists) in machine learning jobs. Caffe doesn’t have higher-level API due to which it will hard to experiment with Caffe, the configuration in a non-standard way with low-level APIs. The Caffe approach of middle-to-lower level API’s provides high-level support and limited deep setting. Caffe interface is more of C++, which means users need to perform more tasks manually, such as configuration file creation.
Easier Deployment TensorFlow is simple to deploy as users need to install the python-pip manager easily, whereas, in Caffe, we have to compile all source files. In Caffe, we don’t have straightforward methods to deploy. We need to compile each source code to implement it, which is a drawback.
GPU’s In TensorFlow, we use GPU by using the tf.device () in which all necessary adjustments can make without any documentation and further need for API changes. In TensorFlow, we able to run two copies of the model on two GPUs and a single model on two GPUs. In Caffe, there is no support of the python language. So all the training needs to be performed based on a C++ command-line interface. It supports a single layer of multi-GPU configuration, whereas TensorFlow supports multiple types of multi-GPU arrangements.
Multiple Machine support In TensorFlow, the configuration is straightforward for multi-node tasks by setting the tf. Device to arrangement some posts, to run. In Caffe, we need to use the MPI library for multi-node support, and it was initially used to break massive multi-node supercomputer applications.
Performance, the learning curve The TensorFlow framework has less performance than Caffee in the internal comparing of Facebook. It has a sharp learning curve, and it works well on sequences and images. It is the most-used deep learning library along with Keras. Caffe framework has a performance of 1 to 5 times more than TensorFlow in the internal benchmarking of Facebook. It works well for deep learning framework on images but not well on recurrent neural networks and sequence models.

结论

最后，我们希望对TensorFlow和Caffe框架有一个很好的了解。 Tensorflow框架是快速增长并被选为最常用的深度学习框架，最近，谷歌已对该框架进行了大量投资。 TensorFlow提供移动硬件支持，而低级API内核则提供了一个端到端的编程控制和高级API，与TensorFlow相比，这使得Caffe在这些领域的后退变得更加快速和强大。因此，TensorFlow在所有深度学习框架中均占主导地位。