Assertion: neural network's input | /r/HasProperty | positive
Facets:
value: for other
type: TRANSITIVE-OBJECT
value: regardless of the input
type: OTHER
value: always
type: DEGREE
Source sentences:
text: When inputs of both the nodes are either positive or negative, then a strong positive weight exists between the nodes.
text: When inputs of both the nodes are either positive or negative, then a strong positive weight exists between the nodes.
text: If the input of a node is positive and negative for other, a strong negative weight exists between the nodes.
text: The last step which is the rightmost neuron is just a function called activation function which outputs 1 if the input to it is positive and 0 if the input it negative.
text: If the input z is reasonably large and positive, “e”
text: If the input of a node is positive and negative for other, a strong negative weight exists between the nodes.
text: Key among the limitations of ReLU is the case where large weight updates can mean that the summed input to the activation function is always negative, regardless of the input to the network.