ReLU Layer

The convolution operation is interleaved with the pooling and ReLU operations. The ReLU activation is not very different from how it is applied in a traditional neural network. For each of the $L_q ×B_q ×d_q$ values in a layer, the ReLU activation function is applied to it to create $L_q×B_q×d_q$ thresholded values. These values are then passed on to the next layer. Therefore, applying the ReLU does not change the dimensions of a layer because it is a simple one-to one mapping of activation values. In traditional neural networks, the activation function is combined with a linear transformation with a matrix of weights to create the next layer of activations. Similarly, a ReLU typically follows a convolution operation (which is the rough equivalent of the linear transformation in traditional neural networks), and the ReLU layer is often not explicitly shown in pictorial illustrations of the convolution neural network architectures.

It is noteworthy that the use of the ReLU activation function is a recent evolution in neural network design. In the earlier years, saturating activation functions like sigmoid and tanh were used. However, it was shown  that the use of the ReLU has tremendous advantages over these activation functions both in terms of speed and accuracy. Increased speed is also connected to accuracy because it allows one to use deeper models and train them for a longer time. In recent years, the use of the ReLU activation function has replaced the other activation functions in convolutional neural network design .We will simply use the ReLU as the default activation function (unless otherwise mentioned).

Comments

Post a Comment

Popular posts from this blog

NEURAL NETWORKS AND DEEP LEARNING CST 395 CS 5TH SEMESTER HONORS COURSE NOTES - Dr Binu V P, 9847390760

About Me Syllabus Previous Year Question Papers CST 395 Neural Network and Deep Learning Module 1 ( Basics of Machine Learning) Overview of Machine Learning Machine Learning Algorithm Linear Regression Capacity, Overfitting and Underfitting Regularization Hyperparameters and Validation Sets Estimators, Bias , Variance and Consistency Challenges In Machine Learning Linear and Logistic Regression ( Python code) Performance Measures Differentiation of  sigmoid and cross entropy function Gradient Descent, Stochastic, Batch and mini batch gradient descent Regression with Gradient Descent Module-2 (Neural Networks) Introduction to Neural Networks Application of Neural Networks Basic Architecture- Single Layer Neural Network Power of Function Composition- Non Linear Activation XOR Activation Functions Choice of activation and loss functions Multi Layer Neural Network, Back Propagation Back Propagation- Example Implementation of a two layer network(XOR) with sigmoid activation function Practic
Read more

Syllabus CST 395 Neural Network and Deep Learning

Syllabus Module - 1 (Basics of Machine Learning ) Machine Learning basics - Learning algorithms - Supervised, Unsupervised, Reinforcement, overfitting, Underfitting, Hyper parameters and Validation sets, Estimators -Bias and Variance. Challenges in machine learning. Simple Linear Regression, Logistic Regression, Performance measures - Confusion matrix, Accuracy, Precision, Recall, Sensitivity, Specificity, Receiver Operating Characteristic curve( ROC), Area Under Curve(AUC). Module -2 (Neural Networks ) Introduction to neural networks -Single layer perceptrons, Multi Layer Perceptrons (MLPs), Representation Power of MLPs, Activation functions - Sigmoid, Tanh, ReLU, Softmax. Risk minimization, Loss function, Training MLPs with backpropagation, Practical issues in neural network training - The Problem of Overfitting, Vanishing and exploding gradient problems, Difficulties in convergence, Local and spurious Optima, Computational Challenges. Applications of neural networks. Module 3 (Deep
Read more

Introduction to neural networks

Image
Artificial neural networks are popular machine learning techniques that simulate the mechanism of learning in biological organisms. The human nervous system contains cells, which are referred to as neurons . The neurons are connected to one another with the use of axons and dendrites , and the connecting regions between axons and dendrites are referred to as synapses .The strengths of synaptic connections often change in response to external stimuli. This change is how learning takes place in living organisms. This biological mechanism is simulated in artificial neural networks, which contain computation units that are referred to as neurons . The computational units are connected to one another through weights, which serve the same role as the strengths of synaptic connections in biological organisms. Each input to a  neuron is scaled with a weight, which affects the function computed at that unit. The following figure shows both the biological and artificial neural networks. An artif
Read more