Difference between TensorFlow and Caffe
TensorFlow is an open-source python-based software library for numerical computation, which makes machine learning more accessible and faster using the data-flow graphs. TensorFlow eases the process of acquiring data-flow charts.
Caffe is a deep learning framework for training and running the neural network models, and vision and learning center develop it. TensorFlow relieves the process of acquiring data, predicting features, training many models based on the user data, and refining the future results. Caffe is designed with expression, speed, and modularity keep in mind.
Comparison between TensorFlow and Caffe
| 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. |
Conclusion
Finally, we hope that a good understanding of these frameworks TensorFlow and Caffe. Tensorflow framework is the fast-growing and voted as most-used deep learning frameworks, and recently, Google has invested heavily in the framework. TensorFlow provides mobile hardware support, and low-level API core gives one end-to-end programming control and high-level API's, which makes it fast and capable where Caffe backward in these areas compared to TensorFlow. So TensorFlow is more dominant in all deep learning frameworks.
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