backprop algo.txt

Step 1: START

Step 2: Initialise weights and biases

Step 3: DO

Step 4:		FOR EACH training input image:
	
Step 5: 		error = output - target

Step 6: 		FOR EACH layer:

Step 7:				Compute deltaW

Step 8:				Update weights and biases

Step 9:	UNTIL threshold value of loss is reached

Step 10: END





   forEach training example named ex
        prediction = neural-net-output(network, ex)  // forward pass
        actual = teacher-output(ex)
        compute error (prediction - actual) at the output units
        compute {\displaystyle \Delta w_{h}} \Delta w_h for all weights from hidden layer to output layer  // backward pass
        compute {\displaystyle \Delta w_{i}} \Delta w_i for all weights from input layer to hidden layer   // backward pass continued
        update network weights // input layer not modified by error estimate
  until all examples classified correctly or another stopping criterion satisfied
  return the network