Depth is the number of computational layers in a neural network, usually counting hidden layers, output layers, and any embedding layers. Greater depth can allow more hierarchical feature learning, although it also affects training complexity and model behavior.
The sum of the following in a neural network: - the number of hidden layers - the number of output layers, which is typically 1 - the number of any embedding layers For example, a neural network with five hidden layers and one output layer has a depth of 6. Notice that the input layer doesn't influence depth.
The sum of the following in a neural network: 1. the number of hidden layers 2. the number of output layers, which is typically one 3. the number of any embedding layers. For example, a neural network with five hidden layers and one output layer has a depth of 6. Notice that the input layer doesn't influence depth.