/Densenet_SVHN

A tensorflow implementation of dense net using Street view house number(SVHN) dataset

Primary LanguagePython

Densenet_SVHN

A tensorflow implementation of dense net on Street view house number(SVHN) dataset.

Model

A brief description of the Model is provided below.

The files I have modified significantly are:

  1. preprocess.py
  2. train.py
  3. models.py

High level view

Input layer -> Block 1 -> Transition 1 -> Block 2 -> Transition 2 -> Block 3 -> Batch Normalization -> Relu -> Global average pooling -> Fully connected layer

  • Block consists of 4 Dense layers.

  • 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(stride=2)

Hyper parameters and other essential attributes

  • Input dimension = [100, 32, 32, 1] (Trained using batches of 100 images)
  • Ouput dimension = [10]
  • epoch = 800

Preprocessing

  • Balanced subsampling on training dataset.
  • Converting SVHN images from RGB to grayscale.
  • The training and validation data are stored in HDF5 binary data format.