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description 🔦 Convolutional Neural Networks Codes

🌱 Introduction

This section will be filled by codes and notes gradually

👩‍💻 Codes

  1. 👶 Basic CNNs
  2. 👀 CNN Visualization
  3. 👨‍👩‍👧‍👧 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

✋ 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.

🔎 All of this data is posed against a white background. Each image is 300×300 pixels in 24-bit color

🐛 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      
=================================================================

👩‍💻 For code in the notebook:

Here 🐾

  • 🔎 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

😵 😵

👀 Visualization

The visualization of the output of each layer is available here 🔎

👷‍♀️ Network Visualization Tool

Netron ✨✨

🧐 References