1 / 7

Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure

Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure. Prof. Carolina Ruiz Department of Computer Science Worcester Polytechnic Institute. A 2-layer feedforward ANN. Input hidden layer output layer. -1. -1. -1. -1. -1. -1.

petersenr
Download Presentation

Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure Prof. Carolina Ruiz Department of Computer Science Worcester Polytechnic Institute

  2. A 2-layer feedforward ANN Input hidden layer output layer -1 -1 -1

  3. -1 -1 -1 Error Backpropagation 1. Initialize the weights to small random values 0.5 0.1 A C -0.2 -0.1 E 0.05 0.3 D B 0.2 0.5

  4. -1 -1 -1 Error Backpropagation 2. For each of the examples: 2.1. Present example to input layer 2.2. Propagate the example forward 0.5 0 0.1 0.377 A C -0.2 -0.1 E 0.5094 0.05 0.3 D 0.377 B 0.2 0 0.5

  5. -1 -1 -1 Error Backpropagation 2. For each of the examples: 2.3. Compute node errors for output layer 2.4. Compute node errors for hidden layer 0.025 0.5 0 0.1 0.377 A C -0.2 -0.5094 -0.1 E 0.5094 0.05 0.3 D 0.377 B 0.2 0 -0.0382 0.5

  6. -1 -1 -1 Error Backpropagation 2. For each of the examples: 2.5. Compute and record weight change for each connection 0.025 0.5 0 0.1 0.377 A C -0.2 -0.5094 -0.1 E 0.5094 0.05 0.3 D 0.377 B 0.2 0 -0.0382 0.5

  7. -1 -1 -1 Error Backpropagation 3. After processing all examples update weight 4. Repeat process until obtaining “good” weights 0.025 0.5 0 0.1 0.377 A C -0.2 -0.5094 -0.1 E 0.5094 0.05 0.3 D 0.377 B 0.2 0 -0.0382 0.5

More Related