ClassificationCNN

• This neural network is intended for the parking lot app
• The current network that we will test is found from this website https://www.tensorflow.org/tutorials/images/deep_cnn
  • Origionally it classified images into the following categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck
    • Remarks:
      • it does not have a category that will do pickup trucks ( if that becomes a problem we will work on a different network)
      • automobiles do not overlap with trucks
  • We have discarded all categories except automobile and truck
    • We will either add a category called not car or output that the spot is empty when the system is not very confident there is a car
  • It also has a peak performance of 86% accuracy but that might improve in our case
  • The model follows the architecture described by Alex Krizhevsky, with a few differences in the top few layers.
  • The layers are currently (in order):
    • convolutional layer
    • max pooling layer
    • normalization layer
    • convolutional layer
    • normalization layer
    • max pooling layer
    • fully connected layer with rectified linear activation
    • fully connected layer with rectified linear activation
    • softmax linear transformation to produce logits
  • We will train several networks to see what works best for our project
  • One network will be :
    • Convolutional layer
    • max pooling layer
    • normalization layer
    • fully connected layer with rectified linear activation
    • softmax linear transformation to produce logits
  • Another will be:
    • convolutional layer
    • fully connected layer with rectified linear activation
    • softmax linear transformation to produce logits
  • Another will be:
    • convolutional layer
    • max pooling layer
    • normalization layer
    • convolutional layer
    • fully connected layer with rectified linear activation
    • softmax linear transformation to produce logits
• Things we will compare include:
• accuracy
• learning rate
• total loss