Assertion: neural network's layer | /r/HasProperty | one
Facets:
value: in any neural network
type: OTHER
Source sentences:
text: Rather than training the whole network through a random initialization of weights, we can use the weights of the pre-trained model (and freeze them) and focus on the more important layers (ones that are higher up) for training.
text: Input layer - where information comes in; Hidden layer - the one where all the action is; Output layer - the results of the operation;
text: If the hidden layer is more than one then that network is called a deep neural network.
text: If the hidden layer is more than two in any neural network than it is known as a deep neural network.