Assertion: neural network's output | /r/HasProperty | the same
Facets:
value: on each iteration
type: TEMPORAL
Source sentences:
text: It’s important to note that we shouldn’t initialize all the parameters to zero because doing so will lead the gradients to be equal and on each iteration the output would be the same and the learning algorithm won’t learn anything.
text: Initialize random weight values Execute these steps until the full synchronization is achieved Generate random input vector X Compute the values of the hidden neurons Compute the value of the output neuron Compare the values of both tree parity machines Outputs are the same: go to 2.1 Outputs are different: one of the suitable learning rules is applied to the weights