Backpropagation algorithm in Neural Network

Backpropagation algorithm in Neural Network

• 1.
  i1=0.05, i2=0.10 w1=0.15, w2=0.20 w3=0.25, w4=0.30 b1=0.3 w5=0.4. w6=0.45 w7=0.5, w8=0.55 b2=0.6
• 2.
  For war d Pass Squashit using logistic function to get output
• 3.
  For war dPass
• 4.
  Cal cul ating Tot al Er r or
• 5.
  The Backwar dsPass Update the weights using gradient descent. So that they cause the actual output to be closer the target output. Thereby minimizing the error for each output neuron and the network as a whole.
• 6.
  Consider w5. total partial derivative of E wrtw5. Out put Layer :
• 10.
  Update the weight. Todecrease the error, we then subtract this value from the current weight. = learning rate (eta)
• 11.
  We perform theactual updates in the neural network after we have the new weights leading into the hidden layer neurons (ie, we use the original weights, not the updated weights, when we continue the backpropagation algorithm below).
• 12.
  Hidden Layer :
• 16.
  Finally, we’ve updatedall of our weights! When we fed forward the 0.05 and 0.1 inputs originally, the error on the network was 0.298371109. After this first round of backpropagation, the total error is now down to 0.291027924.