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Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.
Which framework is better for us then?
This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.
What is Keras?
Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.
Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.
Major Applications of Keras
- The performance of Keras is smooth on both CPU and GPU.
- Keras provides modularity, flexibility to code, extensibility, and has an adaptation for innovation and research.
- The pythonic nature of Keras makes it easy to explore and debug the code.
What is Tensorflow?
Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.
Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.
Major applications of Tensorflow
- From mobiles to embedded devices and distributed servers Tensorflow runs on all the platforms.
- Tensorflow is the enterprise of solving real-world and real-time problems like image analysis, robotics, generating data, and NLP.
- Developers are implementing tools for translation languages and the detection of skin cancers using Tensorflow.
- Major projects using TensorFlow are Google translate, video detection, image recognition.
Keras Vs Tensorflow
Following points will help you to learn comparison between tensorflow and keras to find which one is more suitable for you.
1. Complexity
Keras allows the development of models without the worry of backend details. While in TensorFlow you have to deal with computation details in the form of tensors and graphs.
This feature of Keras provides more comfort and makes it less complex than TensorFlow.
2. Easy to Use API
Keras is a high-level API. Keras uses either Tensorflow, Theano, or CNTK as its backend engines.
Tensorflow provides both high and low-level APIs. Tensorflow is a math library that uses data flow programming for a wide variety of tasks.
If you are looking for a neural network tool that is easy to use and has simple syntax then you will find Keras more favorable.
3. Fast development
If you want to quickly deploy and test your deep learning models, choose Keras. Using Keras, you can create your models with very less lines of code and within a few minutes. Keras provides two APIs to write your neural network. These are:
- Model(functional API)
- Sequential
With these APIs, you can easily create any complex neural network.
4. Performance
Since Keras is not directly responsible for the backend computation, Keras is slower. Keras depends upon its backend engines for computation tasks. It provides an abstraction over its backend. To perform the underlying computations and training Keras calls its backend.
On the other hand, Tensorflow is a symbolic math library. Its complex architecture focuses on reducing cognitive load for computation. Hence, Tensorflow is fast and provides high performance.
5. Functionality and Flexibility
Tensorflow gives you more flexibility, more control, and advanced features for the creation of complex topologies. It provides more control over your network.
Therefore if you want to define your own cost function, metric, or layer Or, if you want to perform operations on input weights or gradients, choose TensorFlow.
6. Dataset
We prefer Keras if the size of the dataset is of relatively small or medium size. While if the dataset is large, we prefer TensorFlow because of fewer overheads. Also, TensorFlow provides more level of control, hence we have more options to handle large datasets.
Tensorflow provides more number of inbuilt datasets than Keras. It contains all the datasets that are available in Keras and tf.datasets module of TensorFlow contains a wide range of dataset and these are classified under the following headings:
Audio, Image, Image classification, object detection, question answering, structured, summarization, text, translate, and video.
The datasets in Keras are present under the Keras.datasets module.
7. Debug
Debugging the TensorFlow code is very difficult. In general, we perform de-bugging in TensorFlow debugger and done through the command line. We start by wrapping the TensorFlow session with,
tf_debug.LocalCLIDebugWrapperSession(session)
And then we execute the file with different necessary debug flags.
Keras is high level and does not deal with backend computation, therefore debugging is easy. We can also check the output from each layer in Keras using keras.backend.function().
8. Popularity
Keras has 48.7k stars on github and 18.4k fork on github. WhereasTensorflow has 146k stars and 81.7k forks on github.
Since both Keras and TensorFlow were released in 2015, it’s clear that TensorFlow has a larger developer community.
Other than the above factors, you should be aware that Tensorflow also provides support for Keras. Tensorflow provides tf.keras sub-module that allows you to drop Tensorflow code directly into Keras models. You can obtain features of both Keras and Tensorflow using tf.keras, i.e you can get the best of both worlds.
The below code describes how to use tf.keras to create your models:
import tensorflow as tf from tensorflow.keras import layers model= tf.keras.Sequential() model.add(layers.Dense(64,activation=’relu’)) model.add(layers.Dense(32,activation=’relu’)) model.add(layers.Dense(10))
Summary
This article explains different features to decide which framework, Keras Vs Tensorflow is more suitable for you. We talked about Ease to use, Fast development, Functionality and flexibility, and Performance factors of using Keras and Tensorflow. It also explains the tf.keras sub-module of Tensorflow that allows us to use all the specifications of Keras in Tensorflow.