Category: Tutorials

In this chapter, we will understand the various aspects of neural network training which can be implemented using TensorFlow framework. Following are the ten recommendations, which can be evaluated − Back Propagation Back propagation is a simple method to compute partial derivatives, which includes the basic form of composition best suitable for neural nets. Stochastic…

TensorFlow includes a special feature of image recognition and these images are stored in a specific folder. With relatively same images, it will be easy to implement this logic for security purposes. The folder structure of image recognition code implementation is as shown below − The dataset_image includes the related images, which need to be…

A partial differential equation (PDE) is a differential equation, which involves partial derivatives with unknown function of several independent variables. With reference to partial differential equations, we will focus on creating new graphs. Let us assume there is a pond with dimension 500*500 square − N = 500 Now, we will compute partial differential equation…

Gradient descent optimization is considered to be an important concept in data science. Consider the steps shown below to understand the implementation of gradient descent optimization − Step 1 Include necessary modules and declaration of x and y variables through which we are going to define the gradient descent optimization. Step 2 Initialize the necessary…

In this chapter, we will learn about the XOR implementation using TensorFlow. Before starting with XOR implementation in TensorFlow, let us see the XOR table values. This will help us understand encryption and decryption process. A B A XOR B 0 0 0 0 1 1 1 0 1 1 1 0 XOR Cipher encryption method is…

Optimizers are the extended class, which include added information to train a specific model. The optimizer class is initialized with given parameters but it is important to remember that no Tensor is needed. The optimizers are used for improving speed and performance for training a specific model. The basic optimizer of TensorFlow is − This…

In this chapter, we will be focus on the network we will have to learn from known set of points called x and f(x). A single hidden layer will build this simple network. The code for the explanation of hidden layers of perceptron is as shown below − Output Following is the representation of function…

Multi-Layer perceptron defines the most complicated architecture of artificial neural networks. It is substantially formed from multiple layers of perceptron. The diagrammatic representation of multi-layer perceptron learning is as shown below − MLP networks are usually used for supervised learning format. A typical learning algorithm for MLP networks is also called back propagation’s algorithm. Now,…

Here, we will focus on MetaGraph formation in TensorFlow. This will help us understand export module in TensorFlow. The MetaGraph contains the basic information, which is required to train, perform evaluation, or run inference on a previously trained graph. Following is the code snippet for the same − One of the typical usage model for…

This chapter will focus on how to get started with distributed TensorFlow. The aim is to help developers understand the basic distributed TF concepts that are reoccurring, such as TF servers. We will use the Jupyter Notebook for evaluating distributed TensorFlow. The implementation of distributed computing with TensorFlow is mentioned below − Step 1 − Import…