Table of Contents

## Which activation function is better than ReLU?

The authors of the Swish paper compare Swish to the following other activation functions: Leaky ReLU, where f(x) = x if x ≥ 0, and ax if x < 0, where a = 0.01. This allows for a small amount of information to flow when x < 0, and is considered to be an improvement over ReLU.

**What would happen if we were to initialize the weights to zero?**

Zero initialization: If all the weights are initialized to zeros, the derivatives will remain same for every w in W[l]. As a result, neurons will learn same features in each iterations. This problem is known as network failing to break symmetry. And not only zero, any constant initialization will produce a poor result.

### What is the output range of ReLU activation function?

ReLU activation function formula ReLU function is its derivative both are monotonic. The function returns 0 if it receives any negative input, but for any positive value x, it returns that value back. Thus it gives an output that has a range from 0 to infinity.

**Why is ReLU the best activation function?**

ReLU. The ReLU function is another non-linear activation function that has gained popularity in the deep learning domain. ReLU stands for Rectified Linear Unit. The main advantage of using the ReLU function over other activation functions is that it does not activate all the neurons at the same time.

## Is ReLU better than ReLU?

So, for leaky ReLU, the function f(x) = max(0.001x, x). Now gradient descent of 0.001x will be having a non-zero value and it will continue learning without reaching dead end. Hence, leaky ReLU performs better than ReLU.

**Why is initializing all the weights to zero problematic?**

When there is no change in the Output, there is no gradient and hence no direction. Main problem with initialization of all weights to zero mathematically leads to either the neuron values are zero (for multi layers) or the delta would be zero.

### What is ReLU activation?

ReLU stands for rectified linear unit, and is a type of activation function. Mathematically, it is defined as y = max(0, x). ReLU is the most commonly used activation function in neural networks, especially in CNNs. If you are unsure what activation function to use in your network, ReLU is usually a good first choice.

**Can we train a neural network by initializing weights as 0?**

Initializing all the weights with zeros leads the neurons to learn the same features during training. In fact, any constant initialization scheme will perform very poorly. Thus, both neurons will evolve symmetrically throughout training, effectively preventing different neurons from learning different things.

## What is the range of the ReLU activation function?

The function returns 0 if it receives any negative input, but for any positive value x, it returns that value back. Thus it gives an output that has a range from 0 to infinity. Now let us give some inputs to the ReLU activation function and see how it transforms them and then we will plot them also.

**What is the leaky ReLU activation function?**

Leaky ReLU activation function Leaky ReLU function is an improved version of the ReLU activation function. As for the ReLU activation function, the gradient is 0 for all the values of inputs that are less than zero, which would deactivate the neurons in that region and may cause dying ReLU problem. Leaky ReLU is defined to address this problem.

### What is the difference between ReLU and softmax?

ReLU then sets all negative values in the matrix x to zero and all other values are kept constant. ReLU is computed after the convolution and therefore a nonlinear activation function like tanh or sigmoid. Softmax is a classifier at the end of the neural network.

**Does Relu solve the problem of batch normalization?**

Although ReLU does not have this advantage batch normalization solves this problem. You can also refer here and here for more information. The main reason to use an Activation Function in NN is to introduce Non-Linearity. And ReLU does a great job in introducing the same.