From the course: Deep Learning: Getting Started
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The output layer
From the course: Deep Learning: Getting Started
The output layer
The output layer is the final layer in the neural network where desired predictions are obtained. There is one output layer in a neural network that produces the desired final prediction. It has its own set of weights and biases that are applied before the final output is arrived. The activation function for the output layer may be different than that of the hidden layers based on the problem. For example, softmax activation is used to derive the final classes in a classification problem. The output is a vector of values that may require further post-processing to convert them to business-related values. For example, in a classification problem, the output is a set of probabilities that then need to be mapped to the corresponding business classes. How do we determine the number of nodes in the output layer? It depends on the problem we are trying to solve and the type of output we expect. The output layer should match the format of the prediction. In a binary classification problem…
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