(simple neural network with 2 input and 5 hidden layers)
Take the value from the input, multiply by specific weight and output the result.
Their job is to act together the output from other synapse and apply the activation function
(sigmoid activation function)
And the apply the activation function to every neuron
Idea:
"cripple" neural network by removing hidden unit stochastically
Keep dropout: 0.5
Final result: 0.2
Keep dropout: 0.9
Final result: 0.9
Keep dropout: 1
Final result: 0.8
Learning rate: 0.001
Final result: 0.55
Learning rate: 0.005
Final result: 0.9
Learning rate: 0.05
Final result: 0.2
Optimizer: Gradient Descent
Average: 0.8
Optimizer: Adam
Average: 0.75