Before getting into this post, I want to thank all you guys out there who liked and appreciated my first post! I received a lot of messages and comments appreciating my work. Thank you so much!
Link to the first post —
Can I follow this post without having installed Tensorflow?
Yes! You can easily understand everything about this post and get into the installation to implement things yourself after understanding it because I’ve explained literally every single line and every bit of syntax in the code.
→ You may skip this part of the post and jump to the next question if you already have installed Tensorflow!
Remember to refer to this part to get everything you need to successfully install Tensorflow :-
Basically, we’ll be using Tensorflow and Command Prompt for understanding a neural network and its implementation. Tensorflow installation used to be a hectic job but it’s got a bit easier now. Follow these links -
This video is self sufficient -
GPU version video tutorial -
If you haven’t had much experience with command prompt, here’s a good way to start -
Why am I reading this post?
Some neural network models are not used anymore as researchers have developed better models with these forming the basis, but they’re still essential as the new developments have their roots in these concepts and we can’t understand new concepts without knowing the old ones.
I’ll try to keep the obsolete concepts short yet enough to get them into your heads permanently. Let’s get done with them and move on to the giants of deep learning! So, at the end of this post, you’ll be able to implement a neural network to identify handwritten digits using the MNIST dataset and have a rough time idea about how to build your own neural networks.
Just start, will you?
Alright then! We’ll start off by understanding certain important terms before jumping into the code and visualizing the working of a basic neural network. Here we go!
Remember how we drew columns to differentiate between things in examination papers during school days? Let’s jot down differences between terms most people seem to be utterly confused about. The only twist this time would be to not include unnecessary points just to increase the total number of points for more marks!
Some Important Terms…
Artificial Neural Networks —
- Biologically inspired models to represent how we imagine the human brain functions.
- Form the roots of deep learning.
- The goal is to the most accurate prediction of an unknown function.
- Formed by interconnected neurons.
- These neurons have weights, and bias which are altered during the network training depending upon the cost function.
- Also called neurons and similar to the neurons in the human brain.
- Basic structural units of any neural network.
- A group of nodes together form a layer.
- A neuron receives an input from the previous layer’s neurons, processes it in its own layer and transfers the output to the next layer.
- Any values that are multiplied by the input of previous layer’s node.
- Each connection has a unique weight associated to it.
- Higher the weight, more valuable is the input for an accurate prediction.
- Linear components added to the product of weight and input from the previous layer neurons before passing it through the activation function in its own layer.
- A layer without a bias would mean just the multiplication of an input vector with a matrix of weights ( i.e. using a linear function) and thus an input of all zeros will always be mapped to an output of all zeros.
- They are different from bias nodes that are just a single mode for every layer which always has its value equal to one.
Hidden layers —
- Layers of nodes between the input later and the output layer where the processing of data actually takes place.
- A Deep NN has the number of hidden layers is greater than or equal to 2, else it is a regular one.
- We can keep adding layers until the test error does not improve anymore.
- Typed multidimensional array, basically an array having a bunch of values of any sizes.
- Tons of matrix manipulation libraries are predefined to work on them.
- In Tensorflow, we define a model in abstract terms by making a computation graph.
- The session runs the graph when it’s ready and everything is done on the graph at the backend.
Gradient Descent —
- Gradient is the ratio of the rate of change of parameters to the error produced by it while learning and training itself to make predictions accurately.
- The process of minimizing the error is called gradient descent.
- Descending a gradient has two aspects: choosing the direction to go in (momentum) and choosing the size of the step (learning rate).
Activation function —
- Non linear functions that work on the sum of product of input and weight, and the bias and transfer the result to the next layer neurons.
- Used to make sure that the representation in the input space is mapped to a different space in the output.
- Usually continuous and derivative all over, so that it can capture notable behaviors among the linear inputs passed to it.
- Rectified Linear Unit (ReLU) and Hyperbolic Tangent are some famous activation functions.
- When the output for one iteration is computed, the error is calculated between the predicted value and expected value.
- This error is sent back into the network with the gradient of the cost function to alter the weights accordingly.
- These weights are then updated so that the errors in the following iterations are reduced.
- This updating of weights using the gradient of the cost function is called backpropagation.
- Optimization is the process of finding the values of parameters for which the value of cost function turns out to be minimum and the algorithms that carry out this process are called optimizers.
- Most widely used ones are Adam, Adagrad and Stochastic Gradient Descent Optimizers among several others out there. (We’ll discuss about which one to use when and why in the upcoming posts!)
Softmax Function —
- It is defined as a generalization of the logistic function (S- shaped sigmoid function) that squashes a K-dimensional vector of arbitrary real values to a K-dimensional vector of real values in the range [0, 1] that add up to 1.
Any other terms required to understand the code and basic neural networks are in the Python code’s comments for your convenience.
Okay… so what’s exactly happening over here?
In the following diagram, I’ve tried my best to explain how the neural network works to minimize the cost function and make the prediction as accurate as possible during the forward pass. You’ll have a clearer picture of the working when you reach the next question where we jump into the code.
Umm… but how am I supposed to code this stuff?
Every single bit of code has been explained in detail in the form of comments alongside the lines. Read the code with the comments. Try to copy, paste,execute and experiment with the code to see the output for yourself.
I have made it so easy to understand that you can visualize what’s going on even if you’re not good at coding using Python!
So, what do I get out of all this ?
You should get something like the following screenshot once you save the python file and you open the folder using command prompt where your python code is stored and then type -
Points to Remember -
- This takes time depending on your hardware as well as the process you followed while installing Tensorflow (pip install gives you certain warnings, which are not errors at all). You’ll get your output inspite of those warnings. They’re just to tell you that you could’ve used your hardware features to the fullest to make computations faster -https://stackoverflow.com/questions/43134753/tensorflow-wasnt-compiled-to-use-sse-etc-instructions-but-these-are-availab
- You won’t get the same accuracy values during new executions as random.normal() keeps changing values for every execution. However, same random values can be used by the process called ‘seeding’.
- I have deliberately not included saving our trained model so that it doesn’t get too much to digest for everyone. We shall be discussing about saving our trained model in the upcoming posts, so that we can start training our model from where we left thereby reducing the time to train it right from the scratch. However, you may try googling about ‘pickling’ and Tensorflow’s ‘saver’ methods.
Too much of complicated stuff?
Fret not! The developers and researchers have been very benevolent. When we use ML and Deep Learning libraries to solve real life problems, most of these scary looking variables and formulas will be taken care of for us. So cool, right?
But that certainly doesn’t mean at all that you forget all this basic stuff. It might sound lunatic but it’ll be great if after you’ve studied the code, you try to type this post’s code on your own without seeing the code and experiment with it by changing certain parts atleast once. You might wonder… How on Earth am I supposed to remember so many complicated things!
Investing just a little time in this exercise will help you more than you can imagine when we get into RNNs, CNNs, Reinforcement Learning and many other architectures that I don’t want to name and scare you even more! At that time, coding and understanding those architectures would be a cakewalk for all of you if you do this.
How can I make use of neural networks to design my own model ?
According to Hugo Larochelle’s very recently released amazing slides (link at the end of the post, don’t forget to check it out!), there are two ways for creating new architectures —
- Imagine how the human brain would have handled the same problem.
- Making a neural network out of an existing algorithm.
This might seem very easy to say but really difficult to actually do it but as you proceed with the posts one by one, eventually you’ll be able to make your very own neural network.
Wasn’t too difficult, right?
You just need the urge to learn. Seriously, that’s all you need. The entire community is here to help. It’s so damn simple that if you get stuck at some point, just blindly copy and paste the error in the Google search box and hit go! There, you have your answer! There’s a solution to almost every query on Stack Overflow to help you out. All you need to do is ask!
An interesting quote by Sebastian Thrun, the Founder and President at Udacity, that pretty much sums up what’s round the corner for all of us -
“I’m really looking forward to a time when generations after us look back and say how ridiculous it was that humans were driving cars.”
Just before I end this post, I would like to mention over here that I have decided to make everything all by myself - all the graphs, flowcharts, and anything else included in my posts that could make understanding a particular concept easier for all my readers, just like I’ve been doing till now.
It would’ve been so convenient to just pick up pictures by googling whatever I wish to add in my posts but then I couldn’t call my posts completely mine, right?
Making these diagrams does take time but then, somewhere down the line I want to feel proud of the content I provided to all my readers and don’t want a single person to feel that their time was wasted reading even a single post on my blog.
On that note, I wind up this post. We’ll be diving deep into a comparative and comprehensive study of Convolutional and Recurrent Neural Networks in the upcoming posts. Until then, stay tuned guys! :)
I’m leaving two links that I find quite good, for those readers who want a glossary kind of a site with them -
Check out Hugo Larochelle’s latest awesome slides about ML and Deep Learning -