Descent(η = 0.1)

Classic gradient descent optimiser with learning rate η. For each parameter p and its gradient δp, this runs p -= η*δp

Parameters

• Learning rate ( η): Amount by which gradients are discounted before updating the weights.

Examples

			
			
			
			opt
			 
			=
			 
			

			Descent
			(
			)
			

			

			
			opt
			 
			=
			 
			

			Descent
			(
			0.3
			)
			

			

			
			ps
			 
			=
			 
			
			

	
			Flux
			.
			

	
			params
			(
			model
			)
			

			

			
			gs
			 
			=
			 
			
			
			gradient
			(
			ps
			)
			 
			do
			
			
    
			
			
			loss
			(
			x
			,
			 
			y
			)
			

			end
			

			

			
			
			

	
			Flux
			.
			

	
			Optimise
			.
			
			update!
			(
			opt
			,
			 
			ps
			,
			 
			gs
			)