Build a 2D convolutional neural network, part 4: Model overview

Key Takeaways

so here's the model that we build in this first pass example we go for just good enough to get the job done this isn't fancy this is definitely not pushing any limits of performance or challenging any of the leaderboard winners but it is enough to illustrate the operation and the concepts of a convolutional neural network so you can see we start with a layer that has our data feed it into a convolution block and then another block that finds a bias term often that's combined with the convolution block but in cottonwood those are two separate operations they don't lose anything from being separated and it lets us be more explicit about whether or not we're including it then a non-linear activation function which in this case is hyperbolic tangent then another convolution layer another bias layer another hyperbolic tangent and then a max pooling layer which shrinks that image down flattens it and then going through a linear layer so a standard dense neural network layer is a linear plus a bias plus a nonlinear activation function in this case we use logistic because it goes to gives you something between 0 and 1 which is a nice way to match then with the original data the original data goes through a one hot representation so instead of having a single label you have an array of size 10 one for each of the different classes and then depending on what label that is that element gets a one the rest of them are zeros the prediction then is also ten elements between zero and one that get compared to that one hot representation and those two get compared in the loss function which then turns around and back propagates that loss the prediction also gets fed to a hard maximum function which says hey of all of my guesses which one is the largest if i had to choose a single class for this example that's the class i would assign it to so that lets us then compare the predicted label to the actual label we'll go through in much more detail each of these pieces but here's the overall structure of the approach that we use