Assertion: neural network's neuron | /r/HasAspect | same weights
Facets:
value: exactly
type: DEGREE
value: w =  - b
type: OTHER
Source sentences:
text: Let’s use the network pictured above and assume all neurons have the same weights w = [ 0 , 1 ] w =  
text: Each neuron has the same weights and bias: - w =  - b = 0 ''' def __init__ ( self ) : weights = np . array ( [ 0 , 1 ] ) bias = 0 self .
text: It is advisable to initialize the weights with small random values to break the symmetry between different neurons, if two neurons have exactly the same weights they will always have the same gradient; that supposes that both have the same values in the subsequent iterations, so they will not be able to learn different characteristics.
text: Let’s use the network pictured above and assume all neurons have the same weights w=, the same bias b=0, and the same sigmoid activation function.