Change the Network Shape
Explore how changing the number of hidden nodes in a neural network impacts learning capacity, training time, and accuracy. This lesson helps you understand the role of network shape in optimizing model performance and guides you through evaluating trade-offs between accuracy and runtime for effective machine learning development.
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Structure of the neural network
One thing we haven’t yet tried, and perhaps we should have earlier, is to change the shape of the neural network. Let’s try changing the number of middle hidden layer nodes. We’ve had them set to for far too long!
Before we jump in and run experiments with different numbers of hidden nodes, let’s think about what might happen. The hidden layer is the layer where the learning happens. Remember, the input nodes simply bring in the input signals, and the output nodes simply push out the network’s answer. It’s the hidden layer (or layers), that has to turn the input into the answer. It’s where the learning happens. Actually, it’s the link weights before and after the hidden nodes ...