Activation Functions In Neural Networks Explained | Deep Learning Tutorial

Summary

This video from the 'Deep Learning Explained' series by Assembly AI explores the importance and types of activation functions in neural networks. It explains how activation functions introduce non-linearity, enabling networks to handle complex problems. The video covers various functions like step, sigmoid, hyperbolic tangent, ReLU, leaky ReLU, and softmax, discussing their applications and advantages. It also demonstrates how to implement these functions in TensorFlow and PyTorch, simplifying the process for viewers.

Takeaways

• 🧠 Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns.
• 🔄 Without activation functions, neural networks would only perform linear transformations, limiting their ability to model complex data.
• 📶 The step function is a basic activation function that outputs 1 if the input is above a threshold, otherwise 0, but is too simplistic for practical use.
• 📉 The sigmoid function outputs a probability between 0 and 1, useful for binary classification, but has limitations in deeper networks.
• 🔽 The hyperbolic tangent function, or tanh, outputs values between -1 and 1, making it a common choice for hidden layers.
• 🚀 The ReLU (Rectified Linear Unit) function is widely used in hidden layers due to its simplicity and effectiveness in avoiding the vanishing gradient problem.
• 💧 The leaky ReLU is a variation of ReLU that allows a small, non-zero output for negative inputs, helping to mitigate the dying ReLU problem.
• 🔮 The softmax function is used in the output layer of multi-class classification problems to output probabilities for each class.
• 🛠️ Deep learning frameworks like TensorFlow and PyTorch provide easy-to-use implementations of activation functions, either as layers or functions.
• 🔗 Assembly AI offers a state-of-the-art speech-to-text API, and the video provides a link to obtain a free API token for use.

Q & A

What is the primary purpose of activation functions in neural networks?

The primary purpose of activation functions in neural networks is to introduce non-linearity, which allows the network to learn complex patterns and make decisions on whether a neuron should be activated or not.

Why are activation functions necessary for neural networks?

Without activation functions, a neural network would only perform linear transformations, which would limit it to solving linearly separable problems. Activation functions enable the network to model non-linear relationships, which are crucial for complex tasks.

What is the step function in the context of activation functions?

The step function is a simple activation function that outputs 1 if the input is greater than a threshold and 0 otherwise, demonstrating the concept of whether a neuron should be activated based on the input.

What does the sigmoid function do and where is it commonly used?

The sigmoid function outputs a probability between 0 and 1 based on the input value. It is commonly used in hidden layers and particularly in the last layer for binary classification problems.

How does the hyperbolic tangent function differ from the sigmoid function?

The hyperbolic tangent function is similar to the sigmoid but outputs values between -1 and 1, making it a scaled and shifted version of the sigmoid function, and it is commonly used in hidden layers.

What is the ReLU (Rectified Linear Unit) function and why is it popular?

The ReLU function outputs the input value if it is positive and 0 if it is negative. It is popular because it can improve the learning speed and performance of neural networks, and it is often the default choice for hidden layers.

What is the dying ReLU problem and how can it be addressed?

The dying ReLU problem refers to a situation where a neuron only outputs 0 for any input after many training iterations, halting further weight updates. This can be addressed by using the leaky ReLU, which allows a small, non-zero output for negative inputs to prevent the neuron from becoming completely inactive.

What is the softmax function and where is it typically used?

The softmax function is used to squash the input values to probabilities between 0 and 1, with the highest input value corresponding to the highest probability. It is typically used in the last layer of a neural network for multi-class classification problems.

How can activation functions be implemented in TensorFlow and PyTorch?

In TensorFlow, activation functions can be specified as an argument in the layer definition or used as layers from `tensorflow.keras.layers`. In PyTorch, they can be used as layers from `torch.nn` or as functions from `torch.nn.functional` in the forward pass of a neural network.

What is the role of Assembly AI in the context of this video?

Assembly AI is a company that creates state-of-the-art speech-to-text APIs. The video is part of the 'Deep Learning Explained' series by Assembly AI, and they offer a free API token for users to try their services.

Outlines

00:00

🧠 Introduction to Activation Functions

This paragraph introduces the concept of activation functions in neural networks. It explains that activation functions are non-linear transformations that determine whether a neuron should be activated. The importance of these functions is highlighted by discussing the limitations of linear transformations in modeling complex problems. The paragraph also mentions that without activation functions, a neural network would essentially be a stacked linear regression model, incapable of learning complex patterns. The video promises to cover various types of activation functions and their practical applications in coding with deep learning frameworks like PyTorch and TensorFlow.

05:00

📊 Exploring Different Activation Functions

This paragraph delves into the specifics of different activation functions, including the step function, sigmoid, hyperbolic tangent, ReLU (Rectified Linear Unit), leaky ReLU, and softmax. It describes the step function's binary output based on a threshold, the sigmoid function's output range between 0 and 1, suitable for binary classification, and the hyperbolic tangent's output range between -1 and 1, often used in hidden layers. The paragraph emphasizes ReLU as a popular choice for hidden layers due to its simplicity and effectiveness in preventing the vanishing gradient problem. Leaky ReLU is introduced as a variant to address the 'dying ReLU' issue, where negative inputs are scaled by a small factor to allow for some gradient flow. Lastly, the softmax function is explained as a means to convert input values into probabilities, typically used in the output layer for multi-class classification problems. The paragraph concludes with a brief mention of how these functions can be implemented in TensorFlow and PyTorch.

Keywords

💡Actuation Functions

Actuation functions, also known as activation functions, are mathematical equations that determine whether a neuron in a neural network should be activated or not. They introduce non-linearity into the network, allowing it to learn complex patterns. In the video, the importance of actuation functions is emphasized as they enable neural networks to model non-linear relationships, which is crucial for solving complex problems. Examples from the script include the step function, sigmoid, hyperbolic tangent, ReLU (Rectified Linear Unit), leaky ReLU, and softmax.

💡Neural Networks

Neural networks are a series of algorithms modeled loosely after the human brain. They are designed to recognize patterns. The video explains that neural networks consist of an input layer, one or more hidden layers, and an output layer, all composed of neurons. Each neuron applies a linear transformation to the input data. The video uses neural networks as the primary context for discussing the role and application of actuation functions.

💡Non-linear Transformation

A non-linear transformation is a function that does not preserve the property of straight lines within the data. In the context of the video, non-linear transformations are applied through actuation functions to enable the neural network to model complex, non-linear relationships. Without these transformations, the network would only be capable of linear regression, severely limiting its ability to learn from data.

💡Hidden Layers

Hidden layers are the layers of neurons in a neural network that lie between the input and output layers. They are called 'hidden' because their inputs and outputs are not directly observable. The video discusses how hidden layers, along with actuation functions, contribute to the network's ability to learn from data, with each neuron in these layers applying a linear transformation followed by an actuation function.

💡Deep Learning Frameworks

Deep learning frameworks are software libraries that are used to design, build, and deploy neural networks. The video mentions PyTorch and TensorFlow, two popular frameworks that facilitate the application of actuation functions in neural networks. These frameworks provide built-in functions and layers for various actuation functions, making it easier for developers to experiment with different types of functions.

💡Step Function

The step function, also known as the Heaviside step function, is a simple actuation function that outputs 1 if the input is greater than a certain threshold and 0 otherwise. It is used to illustrate the basic concept of actuation functions in the video, where it decides whether a neuron should be activated based on the input value. Despite its simplicity, the step function is not commonly used in practice due to its discontinuity.

💡Sigmoid Function

The sigmoid function is an S-shaped function that maps input values to a range between 0 and 1. It is used in the video to demonstrate how actuation functions can output probabilities, which is particularly useful for binary classification problems. The sigmoid function is noted for its smooth curve and its ability to represent the likelihood of a neuron being activated.

💡Hyperbolic Tangent

The hyperbolic tangent, or tanh function, is another actuation function that outputs values between -1 and 1. It is a scaled and shifted version of the sigmoid function. The video mentions that tanh is commonly used in hidden layers of neural networks because it centers the output around zero, which can lead to faster convergence during training.

💡ReLU (Rectified Linear Unit)

ReLU is a widely used actuation function that outputs the input directly if it is positive, and 0 if it is negative. The video explains that ReLU is a good default choice for hidden layers due to its simplicity and effectiveness in speeding up learning. It also touches upon the 'dying ReLU' problem, where neurons can become inactive, and suggests leaky ReLU as a solution.

💡Leaky ReLU

Leaky ReLU is a variation of the ReLU function that allows a small, non-zero gradient when the unit is not active. This helps to prevent the 'dying ReLU' problem where neurons can become inactive and stop learning. The video suggests using leaky ReLU in situations where standard ReLU neurons are not updating their weights, by applying a small scaling factor to negative inputs.

💡Softmax Function

The softmax function is used in the video to explain how actuation functions can be used in the output layer of a neural network for multi-class classification problems. It transforms input values into probabilities, ensuring that the output values sum up to 1. This allows the network to predict the probability of each class, with the class having the highest probability being selected as the prediction.

Highlights

Activation functions apply a non-linear transformation and decide whether a neuron should be activated.

Without activation functions, a neural network would only perform linear transformations, limiting its ability to learn complex patterns.

The step function is a basic activation function that outputs 1 if the input is above a threshold, otherwise 0.

The sigmoid function outputs a probability between 0 and 1, useful for binary classification problems.

The hyperbolic tangent function is a scaled and shifted version of the sigmoid, outputting values between -1 and 1.

The ReLU (Rectified Linear Unit) function is a popular choice for hidden layers, outputting the input if positive, otherwise 0.

Leaky ReLU is a variation of ReLU that allows a small, non-zero output for negative inputs to prevent 'dying ReLU' problems.

The softmax function is used in the output layer of multi-class classification problems to output probabilities.

Deep learning frameworks like TensorFlow and PyTorch make it easy to apply activation functions.

In TensorFlow, activation functions can be specified as layers or used directly in the forward pass.

In PyTorch, activation functions are available as layers in torch.nn and can be used in the forward pass or as functional calls.

Choosing the right activation function is crucial for the performance of a neural network.

For hidden layers, ReLU is often the default choice due to its effectiveness in learning.

The sigmoid function is historically significant but has limitations, such as the vanishing gradient problem.

Leaky ReLU can be a better choice than standard ReLU in certain scenarios to prevent neurons from becoming inactive.

Softmax is essential for interpreting the output of a neural network as probabilities in classification tasks.

Practical demonstration of using activation functions in code with TensorFlow and PyTorch is provided.

The video is part of the 'Deep Learning Explained' series by Assembly AI, focusing on making complex topics accessible.

Transcripts

in this video we are going to learn

about actuation functions we go over the

definition of actuation functions why

they are used then we have a look at

different kinds of actuation functions

and at the end i also show you how to

use them in your code and don't worry

because deep learning frameworks like

pytorch and tensorflow make it extremely

easy to apply them this video is part of

the deep learning explained series by

assembly ai which is a company that

creates a state-of-the-art

speech-to-text api and if you want to

use assembly ai for free then grab your

api token using the link in the

description below and now let's get

started so what are activation functions

and why do we need them actuation

functions apply a non-linear

transformation and decide whether a

neuron should be activated or not now

let's take a step back and see what this

means in a previous video we learned how

neural networks work in a neural network

we have the input layer where we accept

an input and an output layer that gives

the actual prediction or the outcome of

the network and in between we have the

hidden layers all of these layers

consist of neurons and at each neuron we

apply a linear transformation it

multiplies the input with some weights

and maybe adds a bias now this is fine

as long as we have a simple problem like

this where we can model the predictions

with a linear function but let's say we

have a more complex problem one thing we

can do is of course add more layers to

our network but here's a big problem

without activation functions we only get

linear transformations after each other

so our whole network is basically just a

stacked linear regression model that is

not able to learn complex patterns and

this is exactly why actuation functions

come into play so after each layer we

want to apply an activation function

this applies a non-linear transformation

and helps our network to solve complex

tasks now let's have a look at different

kinds of actuation functions there are

many different actuation functions you

can choose so we take a look at the most

popular ones we'll have a look at the

step function sigmoid hyperbolic tangent

value leaky value and the softmax the

step function will just output 1 if our

input is greater than a threshold and 0

otherwise this perfectly demonstrates

the underlying concept that the

activation function decides if a neuron

will be activated or not if the input is

greater than the threshold the neuron is

actuated and otherwise not while this

transformation should be easy to

understand the step function is actually

a little bit too simple and not used in

practice a very popular choice in

practice is the sigmoid function the

formula is 1 over 1 plus e to the minus

x this outputs a probability between 0

and 1. if the input is a very negative

number then sigmoid outputs a number

close to 0 and for a very positive

number sigmoid transforms it to a number

close to 1 and for numbers close to 0 we

have this rising curve between 0 and 1.

this again means that the more positive

the input number is the more our neuron

will be activated the sigmoid function

is sometimes used in hidden layers but

most of the time it is used in the last

layer for binary classification problems

until now we have only seen activation

functions that output numbers between 0

and 1 but this is not a requirement for

actuation functions so in the next

examples you will see transformations

that can output numbers also in a

different range the hyperbolic tangent

is a common choice for hidden layers it

is basically a scaled and shifted

sigmoid function that outputs a number

between -1 and plus 1. value is probably

the most popular choice in hidden layers

the formula is rather simple it just

takes the maximum of 0 and the input x

so if the input is negative it outputs 0

and if the input is positive it simply

returns this output without modification

it does not look that fancy but it can

actually improve the learning of our

neural network a lot so the rule of

thumb is that if you are not sure which

actuation function you should use in

your hidden layers then just use value

there is only one problem that sometimes

happens during training this is the

so-called dying value problem after many

training iterations our neuron can reach

a dead state where it only outputs 0 for

any given input which means there will

be no more updates for your weights so

to avoid this problem you can use a

slightly adapted function which is the

leaky value the leaky value is the same

as the regular value for positive

numbers here it just returns the input

but for negative numbers it does not

simply return 0 but it applies a small

scaling factor a times x a is usually

very small for example

0.001 so the output is close to zero but

it avoids that the neuron will be

completely dead so this is also a very

good choice for hidden layers so

whenever you notice that your weights

won't update during training then try

using leaky value instead of the normal

value and the last function i want to

show you is the softmax function the

softmax squashes the input numbers to

output numbers between 0 and 1 so that

you will get a probability value at the

end so the higher the raw input number

the higher will be the probability value

this is usually used in the last layer

in multi-class classification problems

after applying the softmax in the end

you then decide for the class with the

highest probability now that we've seen

different actuation functions in theory

let's have a look at how we can use them

in tensorflow and pytorch it is quite

easy with both frameworks in tensorflow

i recommend using the keras api with

this we have two options for each layer

we can specify the optional argument

actuation and then just use the name of

the actuation function or we just leave

this actuation argument away and create

the layer ourself all the functions i

just showed you are available as a layer

in

tensorflow.keras.layers in pytorch we

also find all actuation functions as a

layer under torch.nn in our init

function of the neural network we can

create instances of the actuation

function layers and then in the forward

pass we call these layers or as a second

option we can use the functions directly

in the forward pass by using the

functions defined in torch.nn.functional

and that's basically all we have to do

to use actuation functions in our code

alright so i hope you now have a clear

understanding of what actuation

functions are and how you can use them

and if you have any questions let me

know in the comments and also if you

enjoyed this video then please hit the

like button and consider subscribing to

the channel for more content like this

and before you leave don't forget to

grab your free api token using the link

in the description below and then i hope

to see you in the next video bye