SofTeCode Blogs

One Place for all Tech News and Support

Python Keras Advantages and Disadvantages

3 min read

Keras is extremely useful for beginners starting with neural networks. it’s a high-level framework that hides the backend computation and allows us to quickly build a neural network model. during this article, we’ll be learning the a number of the benefits of keras and keras limitations.

image credit

advantages of keras and limitations of keras

Advantages of Keras

1. User-Friendly and Fast Deployment

Keras may be a user-friendly API and it’s very easy to make neural network models with Keras. it’s good for implementing deep learning algorithms and tongue processing. we will build a neural network model in only a couple of lines of code.

Let’s see an example:

from Keras.models import Sequential
from Keras.layers import Dense, Activation
model= Sequential()
model.add(Dense(64, activation=’relu’,input_dim=50))
model.add(Dense(28, activation=’relu’))

You can see, it’s easy to know the method and therefore the flow. The functions and thus the parameters are very simple and therefore writing the code is straightforward . there’s an honest collection of keras functions to try to to processing . Keras provides multiple layers including the support for Convolution and Recurrent Layers.

2. Quality Documentation and enormous Community Support

Keras has one among the simplest documentations ever. Documentation introduces you to every function during a very organized and sequential way. The codes and therefore the examples given are very useful to know the behavior of the tactic .

Keras also has great community support. There are many community codes on various open-source platforms. Many developers and Data Science enthusiasts prefer Keras for competing in Data Science challenges. So, we’ve a continuing stream of insightful competition codes in Keras.Many of the researchers publish their codes and tutorials to the overall public.

3. Multiple Backend and Modularity

Keras provides multiple backend support, where Tensorflow, Theano and CNTK being the foremost common backends. we will choose any of those backends consistent with the requirements for various projects.

We can also train the Keras model on one backend and test its results on other. it’s very easy to vary a backend in Keras, you only need to write the name of the backend within the configuration file.

4. Pretrained models

Keras provides some deep learning models with their pre-trained weights. we will use these models directly for creating predictions or feature extraction.

These models have built-in weights, these weights are the results of coaching the model on ImageNet dataset.

Some of the available models are:

  • Xception
  • VGG16
  • VGG19
  • ResNet, ResNetV2
  • InceptionV3
  • InceptionResNetV2
  • MobileNet
  • MobileNetV2
  • DenseNet
  • NASNet

5. Multiple GPU Support

Keras allows us to coach our model on one GPU or use multiple GPUs. It provides built-in support for data parallelism. It can process a really great deal of knowledge .
Limitations of Keras

Now allow us to see a number of the Cons of Keras:

1. Problems in low-level API

Sometimes you get low-level backend errors continuously and it becomes very irritating. These errors occur because we might want to perform some operations that Keras wasn’t designed for.

It doesn’t allow to switch much about its backend. Error logs are difficult to debug.

2. Need improvement in some features

Keras data-preprocessing tools aren’t that much satisfying once we compare it with other packages like scikit-learn. it’s not so good to create some basic machine learning algorithms like clustering and PCM (principal component analysis). It doesn’t have features of dynamic chart creation.

3. Slower than its backend

Sometimes it’s slow on GPU and takes longer time in computation compared with its backends. So we may need to sacrifice speed for its user-friendliness.

This article may be a guideline to the benefits and drawbacks of working with Keras. this may offer you clear picture when to use keras thanks to advantages of keras and when to not use it thanks to its limitations.

How useful was this post?

Click on a star to rate it!

Average rating 0 / 5. Vote count: 0

No votes so far! Be the first to rate this post.

Give your views

This site uses Akismet to reduce spam. Learn how your comment data is processed.