Question

1. Multi-layer BP neural networks have no proof of converging to an optimal solution. Is this true? If it is, then why do we bother to use them?

1. What is the fundamental equation that guides changes to a weight wij in a BP network. Describe its components.

Answer 1

1). ANSWER :

GIVENTHAT :

Backpropagation, short for “backward propagation of errors”, is a mechanism used to update the weights using gradient descent. It calculates the gradient of the error function with respect to the neural network’s weights. The calculation proceeds backwards through the network.

Gradient descent is an iterative optimization algorithm for finding the minimum of a function; in our case we want to minimize th error function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient of the function at the current point.

For example, to update w6, we take the current w6 and subtract the partial derivative of error function with respect to w6. Optionally, we multiply the derivative of the error function by a selected number to make sure that the new updated weight is minimizing the error function; this number is called learning rate.

The derivation of the error function is evaluated by applying the chain rule as following

So to update w6 we can apply the following formula

Similarly, we can derive the update formula for w5 and any other weights existing between the output and the hidden layer.

However, when moving backward to update w1, w2, w3 and w4 existing between input and hidden layer, the partial derivative for the error function with respect to w1, for example, will be as following.

We can find the update formula for the remaining weights w2, w3 and w4 in the same way.

In summary, the update formulas for all weights will be as following:

We can rewrite the update formulas in matrices as following

Backward Pass

Using derived formulas we can find the new weights.

Learning rate: is a hyperparameter which means that we need to manually guess its value.

Now, using the new weights we will repeat the forward passed

We can notice that the prediction 0.26 is a little bit closer to actual output than the previously predicted one 0.191. We can repeat the same process of backward and forward pass until error is close or equal to zero.

Backpropagation Visualization

You can see visualization of the forward pass and backpropagation here.

You can build your neural network using netflow.js