Artificial Inteligence
  • Preface
  • Introduction
  • Machine Learning
    • Linear Algebra
    • Supervised Learning
      • Neural Networks
      • Linear Classification
      • Loss Function
      • Model Optimization
      • Backpropagation
      • Feature Scaling
      • Model Initialization
      • Recurrent Neural Networks
        • Machine Translation Using RNN
    • Deep Learning
      • Convolution
      • Convolutional Neural Networks
      • Fully Connected Layer
      • Relu Layer
      • Dropout Layer
      • Convolution Layer
        • Making faster
      • Pooling Layer
      • Batch Norm layer
      • Model Solver
      • Object Localization and Detection
      • Single Shot Detectors
        • Yolo
        • SSD
      • Image Segmentation
      • GoogleNet
      • Residual Net
      • Deep Learning Libraries
    • Unsupervised Learning
      • Principal Component Analysis
      • Generative Models
    • Distributed Learning
    • Methodology for usage
      • Imbalanced/Missing Datasets
  • Artificial Intelligence
    • OpenAI Gym
    • Tree Search
    • Markov Decision process
    • Reinforcement Learning
      • Q_Learning_Simple
      • Deep Q Learning
      • Deep Reinforcement Learning
    • Natural Language Processing
      • Word2Vec
  • Appendix
    • Statistics and Probability
      • Probability
        • Markov Chains
        • Random Walk
    • Lua and Torch
    • Tensorflow
      • Multi Layer Perceptron MNIST
      • Convolution Neural Network MNIST
      • SkFlow
    • PyTorch
      • Transfer Learning
      • DataLoader and DataSets
      • Visualizing Results
Powered by GitBook
On this page
  • Introduction
  • Forward propagation
  • Python forward propagation
  • Matlab forward propagation
  • Backward propagation
  • Python backward propagation
  • Next Chapter

Was this helpful?

  1. Machine Learning
  2. Deep Learning

Relu Layer

PreviousFully Connected LayerNextDropout Layer

Last updated 5 years ago

Was this helpful?

Introduction

We will start this chapter explaining how to implement in Python/Matlab the ReLU layer.

From the picture above, observe that all positive elements remain unchanged while the negatives become zero. Also the spatial information and depth are the same.

Thinking about neural networks, it's just a new type of Activation function, but with the following features:

  1. Easy to compute (forward/backward propagation)

  2. Suffer much less from vanishing gradient on deep models

  3. A bad point is that they can irreversibly die if you use a big learning rate

Forward propagation

Change all negative elements to zero while retaining the value of the positive elements. No spatial/depth information is changed.

Python forward propagation

Matlab forward propagation

Backward propagation

Python backward propagation

Next Chapter

Next chapter we will learn about Dropout layers

In simple words, the ReLU layer will apply the function f(x)=max(0,x)f(x)=max(0,x)f(x)=max(0,x) in all elements on a input tensor, without changing it's spatial or depth information.

Basically we're just applying the max(0,x) function to every X=[x1,x2,x3]X=[x_1,x_2,x_3]X=[x1​,x2​,x3​] input element. From the back-propagation chapter we can notice that the gradient dx will be zero if the element xnx_nxn​is negative or doutndout_ndoutn​ if the element is positive.