/densenet

Implements dense net on MNIST dataset

Primary LanguagePython

Densenet

Implements dense net on MNIST dataset

Model

A brief description of the Model is provided below.

High Level View

Input layer -> Block 1 -> Batch Normalization -> Relu -> Global average pooling -> Fully connected layer

  • Block 1 consists of 4 alternating combinations of dense layer and transition layer

  • Dense layer is made of the following sequence:

    1. Batch Normalization
    2. Relu
    3. Convolutional 2d layer
    4. Concatination of the previous layers output to the previous element(Convolutional 2d layer)

  • Transition Layer is made of the following sequence:

    1. Batch Normalization
    2. Relu
    3. Convolutional 2d layer
    4. Average Pooling

Hyper parameters and other essential attributes

  • Input dimension = [100, 28, 28, 1] (Trained using batches of 100 images)
  • Ouput dimension = [10]
  • epoch = 3000