πŸ•ΈοΈ
Deep Learning
  • πŸ’«Deep Learning Notes
  • πŸ’ΌPractical Tools
  • πŸ’ŽConcepts of Neural Networks
    • 🌱Introduction
    • πŸ”ŽThe Problem in General
    • πŸ‘·β€β™€οΈ Implementation Notes
    • πŸ“šCommon Concepts
    • πŸ’₯Activation Functions
    • 🎈Practical Aspects
    • πŸ‘©β€πŸ”§ NN Regularization
    • ✨Optimization Algorithms
    • 🎨Softmax Regression
    • πŸƒβ€β™€οΈ Introduction to Tensorflow
    • πŸ‘©β€πŸ’» Python Code Snippets
  • πŸ™‹β€β™€οΈ Hello World of Deep Learning with Neural Networks
    • 🌱Introduction
    • 🌐CNNs In Browser
  • πŸšͺIntroduction to Computer Vision
    • 🌱Introduction
  • 🚩Concepts of Convolutional Neural Networks
    • 🌱Introduction
    • πŸ“ŒCommon Concepts
    • 🌟Advanced Concepts
    • πŸ‘€Visualization
    • πŸ‘΅Classic Networks
    • ✨Other Approaches
    • πŸ•ΈοΈCommon Applications
  • πŸ‘©β€πŸ’» Works and Notes on CNNs
    • 🌱Introduction
  • πŸ’„Popular Strategies of Deep Learning
    • 🌱Introduction
    • πŸš™Transfer Learning
    • πŸ“šOther Strategies
  • 🀑Image Augmentation
    • 🌱Introduction
  • πŸ€Έβ€β™€οΈ Notes on Applied Machine Learning
    • 🌱Introduction
    • πŸ‘©β€πŸ”§ Notes on Structuring Machine Learning Projects
    • πŸ‘©β€πŸ« Implementation Guidelines
  • πŸ•΅οΈβ€β™€οΈ Basics of Object Detection
    • 🌱Introduction
    • β­•Region-Based CNNs
    • 🀳SSD and YOLO
    • πŸ€–TensorFlow Object Detection API
    • 🐞Model Debugging
  • ➰Sequence Models In Deep Learning
    • 🌱Introduction
    • πŸ“šGeneral Concepts
    • πŸ”„Recurrent Neural Networks
    • 🌌Vanishing Gradients with RNNs
    • 🌚Word Representation
    • πŸ’¬Mixed Info On NLP
  • πŸ’¬NLP
    • 🌱Introduction
  • πŸ’¬Applied NLP
    • πŸ™ŒπŸ» Handling texts
    • 🧩Regex
  • πŸ‘€Quick Visual Info
  • πŸ“šPDFs that I found and recommend
Powered by GitBook
On this page
  • πŸ‘©β€πŸ’» Codes
  • βœ‹ RPS Dataset
  • πŸ› CNN Debugging
  • πŸ‘€ Visualization
  • πŸ‘·β€β™€οΈ Network Visualization Tool
  • 🧐 References

Was this helpful?

Export as PDF
  1. πŸ‘©β€πŸ’» Works and Notes on CNNs

Introduction

πŸ”¦ Convolutional Neural Networks Codes

This section will be filled by codes and notes gradually

πŸ‘©β€πŸ’» Codes

  1. πŸ‘Ά Basic CNNs

  2. πŸ‘€ CNN Visualization

  3. πŸ‘¨β€πŸ‘©β€πŸ‘§β€πŸ‘§ Human vs Horse Classifier with CNN

  4. 🐱 Dog vs Cat Classifier with CNN

  5. 🎨 Multi-Class Classification

  6. 🌐 Tensorflow.js based hand written digit recognizer

    1. Classifier.js

    2. MNISTData.js

    3. index.html

βœ‹ RPS Dataset

  • Rock Paper Scissors is an available dataset containing 2,892 images of diverse hands in Rock/Paper/Scissors poses.

  • Rock Paper Scissors contains images from a variety of different hands, from different races, ages and genders, posed into Rock / Paper or Scissors and labelled as such.

πŸ”Ž All of this data is posed against a white background. Each image is 300Γ—300 pixels in 24-bit color

πŸ› CNN Debugging

We can get info about our CNN by

model.summary()

And the output will be like:

Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_18 (Conv2D)           (None, 26, 26, 64)        640       
_________________________________________________________________
max_pooling2d_18 (MaxPooling (None, 13, 13, 64)        0         
_________________________________________________________________
conv2d_19 (Conv2D)           (None, 11, 11, 64)        36928     
_________________________________________________________________
max_pooling2d_19 (MaxPooling (None, 5, 5, 64)          0         
_________________________________________________________________
flatten_9 (Flatten)          (None, 1600)              0         
_________________________________________________________________
dense_14 (Dense)             (None, 128)               204928    
_________________________________________________________________
dense_15 (Dense)             (None, 10)                1290      
=================================================================

πŸ‘©β€πŸ’» For code in the notebook:

Here 🐾

  • πŸ”Ž The original dimensions of the images were 28x28 px

  • 1️⃣ 1st layer: The filter can not be applied on the pixels on the edges

    • The output of first layer has 26x26 px

  • 2️⃣ 2nd layer: After applying 2x2 max pooling the dimensions will be divided by 2

    • The output of this layer has 13x13 px

  • 3️⃣ 3rd layer: The filter can not be applied on the pixels on the edges

    • The output of this layer has 11x11 px

  • 4️⃣ 4th layer: After applying 2x2 max pooling the dimensions will be divided by 2

    • The output of this layer has 5x5 px

  • 5️⃣ 5th layer: The output of the previous layer will be flattened

    • This layer has 5x5x64=1600 units

  • 6️⃣ 6th layer: We set it to contain 128 units

  • 7️⃣ 7th layer: Since we have 10 categories it consists of 10 units

😡 😡

πŸ‘€ Visualization

The visualization of the output of each layer is available here πŸ”Ž

πŸ‘·β€β™€οΈ Network Visualization Tool

Netron ✨✨

🧐 References

  • Binary Cross-Entropy

  • RMSProp Explained

  • RMSProp in Tensorflow

  • Binary Classification

  • TensorFlow: an ML platform for solving impactful and challenging problems

  • Rock Paper Scissors Dataset

PreviousπŸ‘©β€πŸ’» Works and Notes on CNNsNextPopular Strategies of Deep Learning

Last updated 4 years ago

Was this helpful?

🌱