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The console output from a multi-layer neural network.
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Stage 1) Random starting synaptic weights: | |
Layer 1 (4 neurons, each with 3 inputs): | |
[[-0.16595599 0.44064899 -0.99977125 -0.39533485] | |
[-0.70648822 -0.81532281 -0.62747958 -0.30887855] | |
[-0.20646505 0.07763347 -0.16161097 0.370439 ]] | |
Layer 2 (1 neuron, with 4 inputs): | |
[[-0.5910955 ] | |
[ 0.75623487] | |
[-0.94522481] | |
[ 0.34093502]] | |
Stage 2) New synaptic weights after training: | |
Layer 1 (4 neurons, each with 3 inputs): | |
[[ 0.3122465 4.57704063 -6.15329916 -8.75834924] | |
[ 0.19676933 -8.74975548 -6.1638187 4.40720501] | |
[-0.03327074 -0.58272995 0.08319184 -0.39787635]] | |
Layer 2 (1 neuron, with 4 inputs): | |
[[ -8.18850925] | |
[ 10.13210706] | |
[-21.33532796] | |
[ 9.90935111]] | |
Stage 3) Considering a new situation [1, 1, 0] -> ?: | |
[ 0.0078876] |
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