The probability of the neuron is alive. (Default: 1.0, 100%)
weights for update
Null activation
Null activation
Forward computation
Forward computation
input matrix
output matrix
accumulated delta values
Sugar: Forward computation.
Sugar: Forward computation. Calls apply(x)
input matrix
output matrix
The probability of the neuron is alive.
The probability of the neuron is alive. (Default: 1.0, 100%)
Translate this layer into JSON object (in Play! framework)
Translate this layer into JSON object (in Play! framework)
JSON object describes this layer
Backward computation.
Backward computation.
to be propagated ( dG / dF
is propagated from higher layer )
of this layer (in this case, x = entry of dX / dw
)
of this layer (in this case, y
)
propagated error (in this case, dG/dx
)
Because this layer only mediates two layers, this layer just remove propagated error for unused elements.
Layer that drop-outs its input.
This layer has a function of "pipeline" with drop-out possibility. Because dropping out neurons occurr in the hidden layer, we need some intermediate pipe that handle this feature. This layer only conveys its input to its output synapse if that output is alive.