Keras Reviews & Product Details

keras is one of the prominent deep learning framework, it is easy to implement and provides great a amount of important functionalities which helps developer to achieve maximum accuracy Review collected by and hosted on G2.com.

There is nothing to dislike in keras except few things like it still haven't upgraded with the latest functionalities such as nlp and generative AI which are some important tools nowadays Review collected by and hosted on G2.com.

There are a lot of reasons to like Keras:

1. This open-source deep-learning library is designed to provide fast experimentation with deep neural networks.

2. Keras provides the flexibility to run on top of CNTK, TensorFlow, and Theano.

3. It is focused on being modular, user-friendly, readable, and extensible.

4. Keras provides the power to build deep neural networks using fewer lines of code, and this amazes me the most.

5. Since Keras was adopted and integrated into TensorFlow in mid-2017, we can leverage its power by deploying trained models to production thanks to the TensorFlow Serving framework.

6. Keras has excellent access to reusable code and tutorials, which makes it extremely suitable even for beginners.

7. Since Keras runs on top of TensorFlow, it can be equipped with single or multiple GPUs for faster computations. Review collected by and hosted on G2.com.

There are a few reasons for disliking Keras:

1. Keras is not very customizable on its own. While researching different algorithms or working on multi-dimensional matrices, we still need scikit-learn, OpenCV, or Tensorflow for performing such operations.

2. Sometimes the errors are difficult to debug since finding error logs is difficult.

For these reasons alone, Keras is still one of the most popular and favorite libraries for statisticians, data scientists, ML engineers, etc. Review collected by and hosted on G2.com.

I like the simplicity of building neural networks Review collected by and hosted on G2.com.

The difficulty of implementing my customized metric Review collected by and hosted on G2.com.

Best wrapper API available out there for Neural networks. You need not have to be an expert programmer, it does offer what you need to get the job done and it is an open source. Integrates well with tensor flow. Its native to python and I come up with python background, it does make my coding world a lot easier. Implementing a neural network would take hours of coding, but Keras has made it simpler with few lines of code and it is easily understandable. Review collected by and hosted on G2.com.

Understanding of the log traces to fix a problem takes time, as you would have to understand the way it is traced and written, that would take time because of limited documentation. As a python developer I find it to be easier to use, but it doesnt provide other language support, it could be a problem for a long term development. It doesnt offer a great backend support as it is limited. Review collected by and hosted on G2.com.

First of all Keras is a complete API for managing neural networks and is an open source tool. I find its API extremely convenient to use - definitely simpler to use than PyTorch Review collected by and hosted on G2.com.

It might get slow for some complicated use cases, so if you are aiming for speed and efficiency then probably PyTorch would be a better choice. Review collected by and hosted on G2.com.

easy and quick implementation of a variety of neural network models. simple and easy to learn with vast support from the Keras community and documentation. I like the most about Keras is the high-level framework and runs on top of TensorFlow with single or multiple GPUs for faster computations. availability of pre-trained models such as VGGNET, RESNET, etc. Review collected by and hosted on G2.com.

Preprocessing of the signals or images is still not widely used due to lack of customization. one needs to use additional tools such as Scikit-learn to do the proper preprocessing. Issues in the low-level backend can not be targeted and finding those error logs is difficult. Other than these issues Keras is widely famous in the AI field. Review collected by and hosted on G2.com.

It is easier to use and setup on most of the backend systems like tensor flow and pytorch. This provides a lot of operational freedom to developers to experiment. Review collected by and hosted on G2.com.

Some external integration is difficult to implement on the system and requires assistance from consultants. Initial setup on windows OS is also a bit challenging. Review collected by and hosted on G2.com.

Keras is amazing with its documentation and I've used it on Google collab. It worked super well, models were meeting expectations. Review collected by and hosted on G2.com.

Might not be that great as compared to alternatives, when it comes to speed, it's somewhat slow. Review collected by and hosted on G2.com.

Keras makes deep learning easy . Its easy to use and every code is thoroughly explained in the website Review collected by and hosted on G2.com.

Codes should be more easy to find.

apart from that there is no issue Review collected by and hosted on G2.com.

The most liked feature of Keras is it wraps the large chunks of codes in inbuilt functions, it is easy to write or implement the ANN compare to TensorFlow, Well documented. Review collected by and hosted on G2.com.

Overall Keras is good and has not many drawbacks as such, the only thing which can be improved in Keras is its performance on large numbers of epochs or iterations while training the model. Review collected by and hosted on G2.com.