Understanding Tensorflow and Keras
Do I need to use Tensorflow or Keras for my deep learning project???
Frameworks are extremely important in deep learning.
Two of the most common frameworks used in deep learning are:
- Tensorflow
- Keras
What is Tensorflow?
Tensorflow is an open-source end-to-end framework developed by Google in order to accelerate machine learning and deep neural networks research.
It is a math library used for neural networks. All the machine learning and deep learning models such as CNN, RNN, Reinforcement learning, etc can be implemented with the help of TensorFlow.
Features of Tensorflow:
- Graph Visualizations:
Tensorflow can be used to visualize each and every part of a graph which is not possible using Numpy or Scikit libraries.
2. Easily Trainable:
Tensorflow is very flexible in its operability and since it is a low-level API we have more control over the code.
3. Great Community support:
Tensorflow has a really good community and since it's developed by Google, constant stability improvements are continuously provided.
4. Open Source:
One of the most important features is that Tensorflow is open source. This means that anyone can use it with an internet connection.
5. Easy to build models:
Tensorflow provides multiple levels of abstraction to build and train models.
6. Production anywhere:
Tensorflow allows us to train and deploy models easily platform-independent.
7. Powerful experimentation:
Tensorflow gives us control of features like the Keras API.
What is Keras?
Keras is a high-level library that is built on top of TensorFlow. Keras can be used to quickly build neural networks without worrying about the mathematical aspects.
Keras is easy to use for beginners as it's built with python…
Features of Keras:
- Prelabeled Datasets:
Keras provides many inbuilt datasets that can be imported and loaded directly.
2. Various layers and parameters:
Keras contains various implemented layers and parameters like loss functions, optimizers, etc. These layers can be used directly in our neural networks.
3. Various preprocessing methods:
Keras provides many methods to preprocess the data. These are extremely helpful as correct preprocessing will ensure that the neural network model functions properly on the data.
4. User-Friendly:
The interface that Keras provides is extremely simple and it is optimized for common use cases.
5. Modular:
The models can be created by connecting building blocks together with very few restrictions.
6. High-Level API:
It is a high-level API that uses TensorFlow as its back end. It is built on top of Tensorflow.
So Keras or Tensorflow??
Keras and Tensorflow are both great frameworks to use but there are a couple of differences between them.
Tensorflow is an open-source library for various tasks in machine learning whereas Keras is a neural network library.
Tensorflow provides both high and low-level APIs whereas Keras provides only a high-Level API.
Tensorflow is more flexible whereas Keras is simple and follows best practices to reduce the load for users.
Overall comparing Keras and Tensorflow is not accurate as Keras is a wrapper around Tensorflow and it depends on the task that is going to be performed. Generally, Tensorflow is used when working with large datasets and when high performance is needed.
Keras would be the best framework to start off with beginners since it is user-friendly. The models can be defined with the Keras interface and we can drop down into TensorFlow whenever a feature that is not present in Keras is needed. Therefore all the Tensorflow code can be placed into the Keras model.
Hope you found the article useful...
Happy Learning!!!
