README.md

MNIST Destroyer

My attempts at reaching 100% test accuracy on the MNIST dataset

Usage

To start traning any of the models run the following command

python3 main.py [mlp|conv|resnet50|resnet50_finetuned] 

The following parameters are avaibale for tuning throught the CMD using the options: learning_rate, batch_size and epochs

python3 main.py [mlp|conv|resnet50|resnet50_finetuned] --learning_rate=0.001 --batch_size=128 --epochs=10 

MLP implementation with numpy (mlp)

In this module, you will find a numpy implementation of a classic MLP with the following architecture:

Input -> 128 -> 64 -> 32 -> 10 relu relu relu softmax

Model implementation:

• Input normalization
• He (for ReLU) and Xavier (for Softmax) parameter initializations
• Forward and Backward propagation with Categorical Cross-Entropy Loss function
• ReLU and Softmax (for the last layer) activations
• Optimization (choose only one):
  • Mini-batch gradient descent
  • Momentum
  • Adam
• Learning rate decay
• Regularization:
  • L2
  • Dropout

CNN implementation with numpy

Work in Progress

CNN implementation with tensorflow (conv)

ConvNet with the following architecture:

Input -> Conv2D -> BatchNorm -> ReLU -> MaxPool -> Conv2D -> BatchNorm -> ReLU -> MaxPool -> Conv2D -> BatchNorm -> ReLU -> MaxPool -> Flatten -> Dense -> SoftMax

ResNet50 implemented with tensorflow (resnet50)

Tensorflow implementation of ResNet50:

Input -> Conv2D -> BatchNorm -> ReLU -> MaxPool -> ConvBlock -> 2xIdentityBlock -> ConvBlock -> 3xIdentityBlock -> ConvBlock -> 5xIdentityBlock -> ConvBlock -> 2xIdentityBlock -> AvgPool -> Flatten -> Softmax

ResNet50 pretrained on ImageNet dataset (resnet50_finetuned)

Fine-tuning a ResNet50 model pre-trained on the ImageNet dataset