Python Keras Advantages and Limitations

Keras is very useful for beginners starting with neural networks. It is a high-level framework that hides the backend computation and allows us to quickly build a neural network model. In this article, we will be learning the some of the advantages of keras and keras limitations.

advantages of keras and limitations of keras

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Advantages of Keras

1. User-Friendly and Fast Deployment

Keras is a user-friendly API and it is very easy to create neural network models with Keras. It is good for implementing deep learning algorithms and natural language processing. We can build a neural network model in just a few 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 is easy to understand the process and the flow. The functions and the parameters are very simple and therefore writing the code is simple. There is a good collection of keras functions to do data processing. Keras provides multiple layers including the support for Convolution and Recurrent Layers.

2. Quality Documentation and Large Community Support

Keras has one of the best documentations ever. Documentation introduces you to each function in a very organized and sequential way. The codes and the examples given are very useful to understand the behavior of the method.

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

3. Multiple Backend and Modularity

Keras provides multiple backend support, where Tensorflow, Theano and CNTK being the most common backends. We can choose any of these backends according to the needs for different projects.

We can also train the Keras model on one backend and test its results on other. It is very easy to change a backend in Keras, you just have to write the name of the backend in the configuration file.

4. Pretrained models

Keras provides some deep learning models with their pre-trained weights. We can use these models directly for making predictions or feature extraction.

These models have built-in weights, these weights are the results of training 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 train our model on a single GPU or use multiple GPUs. It provides built-in support for data parallelism. It can process a very large amount of data.

Limitations of Keras

Now let us see some 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 may want to perform some operations that Keras was not designed for.

It does not allow to modify much about its backend. Error logs are difficult to debug.

2. Need improvement in some features

Keras data-preprocessing tools are not that much satisfying when we compare it with other packages like scikit-learn. It is not so good to build some basic machine learning algorithms like clustering and PCM (principal component analysis). It does not have features of dynamic chart creation.

3. Slower than its backend

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


This article is a guideline to the advantages and disadvantages of working with Keras. This will give you clear picture when to use keras due to advantages of keras and when not to use it due to its limitations.

Hope you enjoyed the article. Do share your feedback in the comment section.

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