• MNIST Digit Classifier
• • • • Implemented ML Model on The MNIST Database using different approaches.
  • 4. Siamese NN (with Triplet Loss)
  • • • Network Architechture
      • Cost
  • 3. Convolutional NN
  • • • This Network comprises of two Convolutional Layers followed by three Fully Connected Layers.
      • Network Architecture:
      • Learning Curve:
  • 2. Multi Layer NN
  • • • Uses 4 Linear Layers to train the model which takes 28*28 input features.
      • Network Architecture:
      • Learning Curve:
  • 1. Single Layer NN
  • • • Take input with input features = 28*28. Uses a Single Layer(also the output layer) to implement the model.
  • Others Branches

MNIST Digit Classifier

Implemented ML Model on The MNIST Database using different approaches.

4. Siamese NN (with Triplet Loss)

Implemented a convolutional network that learns to generate encodings of passed images such as to minimize the triplet loss function given by :

ℒ(A,P,N) = max( || f(A)-f(P) ||²) - || f(A)-f(N) ||² + 𝜶, 0)

where A is an anchor input, P is a positive input of the same class as A, N is a negative input of a different class from A, 𝜶 is a margin between positive and negative pairs, and f is an embedding.

Network Architechture

{
  "name": "Model",
  "arch": {
      "convnet1": {
          "conv1" : "Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2))",
          "conv2" : "Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))",
          "actv" : "ReLU()",
          "pool" : "MaxPool2d(kernel_size=3, stride=2)"
      },
      "convnet2": {
          "conv1" : "Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))",
          "conv2" : "Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))",
          "actv" : "ReLU()",
          "pool" : "MaxPool2d(kernel_size=2, stride=2)"
      },
      "convnet3": {
          "conv1" : "Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2))",
          "conv2" : "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))",
          "actv" : "ReLU()",
          "pool" : "MaxPool2d(kernel_size=3, stride=2)"
      },
      "FullyConnected": {
          "fc1": "Linear(in_features=4096, out_features=2048)",
          "fc2": "Linear(in_features=2048, out_features=512)",
          "fc3": "Linear(in_features=512, out_features=128)"
      } 
  },
  
  "training": {
    "images": "100 images each of classes 0, 1, 2 only",
    "optimizer": "Adam",
    "loss" : "Triplet Loss",
    "batch_size" : 10,
    "epochs" : 5
  },

  "results": {
    "Class 0": { "correct": 5804, "total": 5923,"acuracy": "97.99%" },
    "Class 1": { "correct": 6648, "total": 6742,"acuracy": "98.60%" },
    "Class 2": { "correct": 5830, "total": 5958,"acuracy": "97.85%" },
    "Class 3": { "correct": 5877, "total": 6131,"acuracy": "95.85%" },
    "Class 4": { "correct": 5830, "total": 5842,"acuracy": "99.79%" },
    "Class 5": { "correct": 5274, "total": 5421,"acuracy": "97.28%" },
    "Class 6": { "correct": 5908, "total": 5918,"acuracy": "99.83%" },
    "Class 7": { "correct": 5589, "total": 6265,"acuracy": "89.20%" },
    "Class 8": { "correct": 5777, "total": 5851,"acuracy": "98.73%" },
    "Class 9": { "correct": 5849, "total": 5949,"acuracy": "98.31%" }
  }
}

This Siamese Network was used to implement One Shot Learning which is a technique of learning representations from a single sample.
Images of classes 3 to 9 weren't used while training the model, i.e they were passed to the model for the first time while testing it.

Cost

---

3. Convolutional NN

This Network comprises of two Convolutional Layers followed by three Fully Connected Layers.

Network Architecture:

Network [
  Conv1    : [in_channels=1, out_channels=6, kernel_size=5, stride=1],
  MaxPool1 : [kernel_size=2, stride=2],
  Conv2    : [in_channels=6, out_channels=12, kernel_size=5, stride=1],
  MaxPool2 : [kernel_size=2, stride=2],
  FC1      : [in_features=192, out_features=120],
  FC2      : [in_features=120, out_features=60],
  Output   : [in_features=60, out_features=10],
]

Learning Curve:

  Batch size = 100
  Learning Rate = 0.002
  iterations = 50
  Accuracy on Train Set = 99.58 %
  Accuracy on Test  Set = 98.64 %

---

2. Multi Layer NN

Uses 4 Linear Layers to train the model which takes 28*28 input features.

Network Architecture:

Learning Curve:

  Learning Rate = 0.11
  Accuracy on Train Set = 98.272 %
  Accuracy on Test  Set = 98.259 %

---

1. Single Layer NN

Take input with input features = 28*28. Uses a Single Layer(also the output layer) to implement the model.

Learning Rate = 0.009  
Accuracy on Test Set = 94.181 %

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Others Branches

• My solutions to the Assignments of Coursera's course on Deep Learning Specialization
• My submission to the Kaggle's Digit Recognizer Competition