CNN

What is CNN?

• CNN(Convolution Neural Network)
• a class of deep neural networks, most commonly applied to analyzing visual imagery
• uses relatively little pre-processing compared to other image classification algorithms
  • the network learns to extract important features using filters
  • the number of parameters are lowered efficiently by the use of convolution layer and pooling layer, leading to reduced model complexity
• Design
  • A convolutional neural network consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of convolutional layers, RELU layer i.e. activation function, pooling layers, fully connected layers and normalization layers
    • Convolutional
    • Pooling
    • Fully connected
    • Weights

Return activations of nodes in CNN

• returns the activations of hidden nodes in CNN, using MNIST image
• trains a model with 2 convolution layers and visualizes the result of the 2 layers

CAM : Class Acitivation Map

• VGG16

  • VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes.

• CAM(Class Activation Map)

  • displays a heatmap on the image to show which part of the image was activated when classifying the image into a particular class

    

MxNet

• visualize the filters to see what aspects of the data each layer learns