Implements dense net on MNIST dataset

A brief description of the Model is provided below.

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

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