since ReLU doesn’t have a derivative. No, ReLU has derivative. I assumed you are using ReLU function f(x)=max(0,x). It means if x<=0 then f(x)=0, else f(x)=x. In the first case, when x<0 so the derivative of f(x) with respect to x gives result f'(x)=0. In the second case, it’s clear to compute f'(x)=1.
To understand how backpropagation is even possible with functions like ReLU you need to understand what is the most important property of derivative that makes backpropagation algorithm works so well. This property is that : f(x) ~ f(x0) + f'(x0)(x – x0) If you treat x0 as actual value of your parameter at the moment … Read more
if x <= 0, output is 0. if x > 0, output is 1 The ReLU function is defined as: For x > 0 the output is x, i.e. f(x) = max(0,x) So for the derivative f ‘(x) it’s actually: if x < 0, output is 0. if x > 0, output is 1. The … Read more
The “forward pass” refers to calculation process, values of the output layers from the inputs data. It’s traversing through all neurons from first to last layer. A loss function is calculated from the output values. And then “backward pass” refers to process of counting changes in weights (de facto learning), using gradient descent algorithm (or … Read more
A Feed-Forward Neural Network is a type of Neural Network architecture where the connections are “fed forward”, i.e. do not form cycles (like in recurrent nets). The term “Feed forward” is also used when you input something at the input layer and it travels from input to hidden and from hidden to output layer. The … Read more
You seem to be a bit confused (I remember I was too) so I am going to simplify things for you. 😉 Sample Neural Network Scenario Whenever you are given a task such as devising a neural network you are often also given a sample dataset to use for training purposes. Let us assume you … Read more
The tutorial you posted here is actually doing it wrong. I double checked it against Bishop’s two standard books and two of my working implementations. I will point out below where exactly. An important thing to keep in mind is that you are always searching for derivatives of the error function with respect to a … Read more
Backpropagation is an efficient method of computing gradients in directed graphs of computations, such as neural networks. This is not a learning method, but rather a nice computational trick which is often used in learning methods. This is actually a simple implementation of chain rule of derivatives, which simply gives you the ability to compute … Read more
Cross-entropy is prefered for classification, while mean squared error is one of the best choices for regression. This comes directly from the statement of the problems itself – in classification you work with very particular set of possible output values thus MSE is badly defined (as it does not have this kind of knowledge thus … Read more
From model documentation: loss: String (name of objective function) or objective function. See losses. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all … Read more