Convolutional neural networks¶

Python notebook: https://github.com/daviskregers/data-science-recap/blob/main/34-keras-convolutional-networks.ipynb

CNN's: what are they for?¶

• When you have data that doesn't neatly align into columns
  • Images that you want to find features within
  • Machine translation
  • Sentence classification
  • Sentiment analysis
• They can find features that aren't in specific spot
  • Like a stop sign in a picture
  • Or words within a sentence
• They are "feature-location invariant"

How do they work?¶

• Inspired by the biology of the visual cortex
  • Local receptive fields are groups of neurons that only respond to a part of what your eyes see (subsampling)
  • They overlap each other to cover the entire field (convolutions)
  • They feed into a higher layers that identify increasingly complex images
    • Some receptive fields identify horizontal lines, lines at different angles, etc. (filters)
    • These would feed into a layer that identifies shapes
    • Which might feed into a layer that identifies objects
    • For color images, extra layers for red and blue

How do we know that's a stop sign?¶

• Individual local receptive fields scan the image looking for edges, and pick up the edges of the stop sign in a layer
• Those edges in turn get picked up by a higher level convolution that identifies the stop sign's shape (and letters too)
• This shape then gets matched against your pattern of what a stop sign looks like, also using the strong red signal coming from your red layers
• That information keeps getting processed upward until your foot hits the brake
• A CNN works the same way

CNNs with keras¶

• Source data must be of appropriate dimensions
  • ie with x length x colors channels
• Conv2D layer type does the actual convolution on a 2D image
  • Conv1D and Conv3D also available - doesn't have to be image data.
• MaxPooling2D layers can be used to reduce a 2d layer down by taking the minimum value in a given block
• Flatten layers will convert the 2D layer to a 1D layer for passing into a flat hidden layer of neurons
• Typical usage: Conv2D -> MaxPooling2D -> Dropout -> Flatten -> Dense -> Dropout -> Softmax

CNNs are hard¶

• Very resource-intensive (CPU, GPU and RAM)
• Lots of hyperparameters
  • Kernel sizes, many layers with different numbers of units, amount of pooling... in addition to the usual stuff like number of layers, choice of optimizer
• Getting the training data is often the hardest part. As well as storing and accessing it.

Specialized CNN architectures¶

• Defines specific arrangement of layers, padding, and hyperparameters
• LeNet-5
  • Good for handwriting recognition
• AlexNet
  • Image classification, deeper than LeNet
• GoogLeNet
  • Even deeper, buty with better performance
  • Introduces inception modules (groups of convolution layers)
• ResNet (Residual Network)
  • Even deeper - maintains performance via skip connections.