Assertion: neural network's weight | /r/HasProperty | the same
Facets:
value: into a single recurrent layer
type: OTHER
value: for the time steps
type: OTHER
value: at each time step
type: TEMPORAL
Source sentences:
text: We can now combines these layers together, that the weights and bias of all the hidden layers is the same.
text: The weights as we can see are the same at each time step.
text: The cross entropy error is first computed using the current output and the actual output Remember that the network is unrolled for all the time steps For the unrolled network, the gradient is calculated for each time step with respect to the weight parameter Now that the weight is the same for all the time steps the gradients can be combined together for all time steps
text: Hence these three layers can be joined together such that the weights and bias of all the hidden layers is the same, into a single recurrent layer.