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DLIP
  • Introduction
  • Prerequisite
  • Image Processing Basics
    • Notes
      • Thresholding
      • Spatial Filtering
      • Masking with Bitwise Operation
      • Model n Calibration
    • Tutorial
      • Tutorial: Install OpenCV C++
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    • LAB
      • Lab Report Template
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      • LAB: Grayscale Image Segmentation
        • LAB: Grayscale Image Segmentation -Gear
        • LAB: Grayscale Image Segmentation - Bolt and Nut
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  • Deep Learning for Perception
    • Notes
      • Lane Detection with Deep Learning
      • Overview of Deep Learning
        • Object Detection
        • Deep Learning Basics: Introduction
        • Deep Learning State of the Art
        • CNN, Object Detection
      • Perceptron
      • Activation Function
      • Optimization
      • Convolution
      • CNN Overview
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      • LossFunction Regularization
      • Bias vs Variance
      • BottleNeck Unit
      • Object Detection
      • DL Techniques
        • Technical Strategy by A.Ng
    • Tutorial - PyTorch
      • Tutorial: Install PyTorch
      • Tutorial: Python Numpy
      • Tutorial: PyTorch Tutorial List
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        • Tutorial: Train Yolo v8 with custom dataset
          • Tutorial: Train Yolo v5 with custom dataset
        • Tutorial: Yolov5 in Pytorch (VS code)
        • Tutorial: Yolov3 in Keras
    • LAB
      • Assignment: CNN Classification
      • Assignment: Object Detection
      • LAB: CNN Object Detection 1
      • LAB: CNN Object Detection 2
      • LAB Grading Criteria
    • Tutorial- Keras
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  • DLIP Project
    • Report Template
    • DLIP 2021 Projects
      • Digital Door Lock Control with Face Recognition
      • People Counting with YOLOv4 and DeepSORT
      • Eye Blinking Detection Alarm
      • Helmet-Detection Using YOLO-V5
      • Mask Detection using YOLOv5
      • Parking Space Management
      • Vehicle, Pedestrian Detection with IR Image
      • Drum Playing Detection
      • Turtle neck measurement program using OpenPose
    • DLIP 2022 Projects
      • BakeryCashier
      • Virtual Mouse
      • Sudoku Program with Hand gesture
      • Exercise Posture Assistance System
      • People Counting Embedded System
      • Turtle neck measurement program using OpenPose
    • DLIP Past Projects
  • Installation Guide
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      • Installation Guide 2021
    • Anaconda
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      • CUDA 10.2
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    • Keras
      • Tutorial Keras
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    • PyTorch
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      • Autograd in PyTorch
      • Simple ConvNet
      • MNIST using LeNet
      • Train ConvNet using CIFAR10
  • Resources
    • Useful Resources
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On this page
  • Loss Functions
  • For Classification
  • Further Reading
  • Optimization
  • Gradient Descent
  • Further Reading

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  1. Deep Learning for Perception
  2. Notes

Optimization

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Last updated 3 years ago

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Loss Functions

A loss function calculates the error between the prediction from the ground truth. It averages all error from all datasets.

Logistic Loss and Multinomial Logistic Loss are other names for Cross-Entropy loss.

In a binary classification problem, where C′=2C′=2, the Cross Entropy Loss can be defined also as

For Classification

Softmax Loss

Softmax with Cross-Entropy Loss is often used. If we use this loss, we will train a CNN to output a probability over the C classes for each image. It is used for multi-class classification.

Note that The Softmax function cannot be applied independently to each s i , since it depends on all elements of s . For a given class s i , the Softmax function can be computed as:

Binary Cross-Entropy Loss

Also called Sigmoid Cross-Entropy loss. It is a Sigmoid activation plus a Cross-Entropy loss. Unlike Softmax loss it is independent for each vector component (class), meaning that the loss computed for every CNN output vector component is not affected by other component values. It is used for multi-label classification

Further Reading

Optimization

We want to get the model weights(W) to minimize the value of loss function for accurate prediction. How can we change the model parameters during training? Optimizer helps to move along the slope(gradient) for min or max point.

Gradient Descent

Minimize objective function J(w) by updating parameter(w) in opposite direction of gradient of J(w). Following the negative gradient of the Objective Function to find the minimum value of loss. It control the step size by learning rate n

Finding the derivative: 1) analytical 2) numerical approach. If possible, use analytical approach for faster and accurate gradient.

Examples of Optimizer include

  • SGD (Stochastic Gradient Descent)

Often SGD is refered to Mini-batch Gradient Descent

  • Adagrad

  • Momentum•Adam

Further Reading

Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names
CS231n Convolutional Neural Networks for Visual Recognition
CS231n Convolutional Neural Networks for Visual Recognition
LogoAn overview of gradient descent optimization algorithmsSebastian Ruder
Forward vs backward pass