In this Keras tutorial, you will learn about the Keras framework or API. It is used to develop and define Deep Learning Models. It is implemented on the freeware machine libraries such as Theano, TensorFlow, etc. Keras Tutorial provides a simple method to Develop Deep Learning Models.
|Keras tutorial - Table of Content|
Keras is a freeware deep learning framework of Python. It is developed by an artificial intelligence researcher whose name is “Francois Chollet”. It is a top-level neural network API developed in python. It supports both recurrent and convolutional networks and the amalgamation of both. Many Top companies, like Netflix, Google, Square, are presently using Keras tools. It is used to develop Deep Learning Models. It uses libraries of different programming languages like Python, C++, C, etc.
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The features of Keras are as follows:
The Advantages of Keras are as follows:
Keras is a framework of Deep Learning, so, let us study Deep Learning.
Deep Learning is considered a subdomain of Machine Learning. The main job of deep learning is studying the input layer by layer. Some of the fundamentals of Deep Learning are as follows:
The main method of deep learning is Artificial Neural networks. They are motivated by the human brain model. In the human brain, we will have interconnected neurons, in the same way in ANNs(Artificial Neural Networks), we will have interconnected nodes in the structure of hidden layers.
The nodes present in the inner layers will study the given input, and the input traverses through other hidden layers, and the output layer forecasts the output. The output layer may give the required output.
It is the easiest form of ANNs. It contains one input layer, multiple hidden layers, and lastly an output layer. In Multi-layer Perceptron, one hidden layer will process some part of the input, and it will transmit that to the other hidden layer. Each hidden layer contains single or multiple neurons. The final hidden layer transmits the data to the output layer. The output layer gives the required output.
It is a well-known Artificial Neural network. It is hugely used for the purpose of video and image recognition. It is founded on a mathematical concept called “convolution”. It is identical to the Multilayer Perceptron. The main layers of Convolution Neural Network are as follows:
It is used to detect the defects present in the other ANN Models. The main job of RNN is to save past data and decisions. This method is mainly used in image categorization. The RNN bidirectional is useful to anticipate the future according to the past.
The Existing Models of Keras are as follows:
The API of Keras is distributed into three primary categories:
In Keras, each ANN is indicated using Keras Models.
The above diagram represents the Keras Architecture.
Keras Models are of two types, they are:
Each Keras layer present in the Keras model depicts the respective layer present in the real neural network model. The essential Keras Layers are as follows:
Keras Provides some neural network functions; they are as follows:
Activations Module: It gives many activation functions like relu, softmax, etc.
Time Sequence Prediction through LSTM RNN
The sequence is a collection of values, and every value represents a specific instance of time. For example, if we are reading a sentence, we have to understand every word and the meaning of the word in a given perspective. In this scenario, values represent the words, the first value represents the first word, the second value represents the second word, and this order continues until the last word.
The features of the above model are as follows:
The Convolution Neural Network is depicted as follows:
The important features of this model are as follows:
At the time of development of the model, Evolution is a process to check the model is suitable for the problem and the respective data. To do the evolution process, Keras has a function. The three arguments of the function are as follows:
The final step in the Keras Model Development is “ Model Prediction”. To make Model Prediction, Keras gives a method called “predict” and it predicts the trained model.
|Objective||It is used for developing conventional Layers||It is used for developing model layers or calculation tasks.|
|Tools||It will use API tools like TFDBG||It will use Tensorboard Visualization tools|
|Difficulty||If you have knowledge of Python, we can use Python easily||For using TensorFlow, we need to learn the syntax of some TensorFlow Functions|
|Type||It is a High-Level Wrapper||It is a Low-level API|
|Community||It has many active communities||It has many active communities|
The Keras Applications is used for developing pre-trained models for the purpose of deep neural networks. Keras Models are used for fine-tuning, prediction, and feature extraction.
Trained Models contains two modules: Model weights and Model architecture. Model weights are big files. Therefore you have to download it, and the feature should be
Extracted from the ImageNet database. The famous Pre-trained Models are as follows:
Keras is considered as a framework used in deep learning to analyze the given input and develop the Deep Learning Models. It is built on libraries like Theano, Caffe, TensorFlow, Caffe, etc. It is more helpful in the image and video recognition process. As the requirement of machine learning is increasing, the demand for Keras framework and Deep Learning is also increasing. So, the professionals who are working with Machine learning must have knowledge of the Keras framework.
Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.