Category: Tutorials

ResNet is a pre-trained model. It is trained using ImageNet. ResNet model weights pre-trained on ImageNet. It has the following syntax − Here, Let us understand the model by writing a simple example − Step 1: import the modules Let us load the necessary modules as specified below − Step 2: Select an input Let us choose an…

Keras applications module is used to provide pre-trained model for deep neural networks. Keras models are used for prediction, feature extraction and fine tuning. This chapter explains about Keras applications in detail. Pre-trained models Trained model consists of two parts model Architecture and model Weights. Model weights are large file so we have to download…

In this chapter, let us write a simple Long Short Term Memory (LSTM) based RNN to do sequence analysis. A sequence is a set of values where each value corresponds to a particular instance of time. Let us consider a simple example of reading a sentence. Reading and understanding a sentence involves reading the word…

In this chapter, let us write a simple MPL based ANN to do regression prediction. Till now, we have only done the classification based prediction. Now, we will try to predict the next possible value by analyzing the previous (continuous) values and its influencing factors. The Regression MPL can be represented as below − The…

Let us modify the model from MPL to Convolution Neural Network (CNN) for our earlier digit identification problem. CNN can be represented as below − The core features of the model are as follows − Step 1 − Import the modules Let us import the necessary modules. Step 2 − Load data Let us import the mnist…

This chapter deals with the model evaluation and model prediction in Keras. Let us begin by understanding the model evaluation. Model Evaluation Evaluation is a process during development of the model to check whether the model is best fit for the given problem and corresponding data. Keras model provides a function, evaluate which does the…

Previously, we studied the basics of how to create model using Sequential and Functional API. This chapter explains about how to compile the model. The compilation is the final step in creating a model. Once the compilation is done, we can move on to training phase. Let us learn few concepts required to better understand…

As learned earlier, Keras model represents the actual neural network model. Keras provides a two mode to create the model, simple and easy to use Sequential API as well as more flexible and advanced Functional API. Let us learn now to create model using both Sequential and Functional API in this chapter. Sequential The core idea of Sequential API is simply arranging the Keras…

Keras allows to create our own customized layer. Once a new layer is created, it can be used in any model without any restriction. Let us learn how to create new layer in this chapter. Keras provides a base layer class, Layer which can sub-classed to create our own customized layer. Let us create a simple layer…

As learned earlier, Keras layers are the primary building block of Keras models. Each layer receives input information, do some computation and finally output the transformed information. The output of one layer will flow into the next layer as its input. Let us learn complete details about layers in this chapter. Introduction A Keras layer…