Classifying the MNIST dataset with Convolutional Neural Networks

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From the course by IBM

Applied AI with DeepLearning

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IBM

Applied AI with DeepLearning

99 оценки

Course 3 of 4 in the Specialization Advanced Data Science with IBM

>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<< This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras one real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs. IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018 Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide. Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

From the lesson

DeepLearning Applications

Classifying the MNIST dataset with Convolutional Neural Networks5:55

Image classification with Imagenet and Resnet503:24

Meet the Instructors

Romeo Kienzler
Chief Data Scientist, Course Lead
IBM Watson IoT
Niketan Pansare
Senior Software Engineer
IBM Research
Tom Hanlon
Training Director
Skymind
Max Pumperla
Deep Learning Engineer
Ilja Rasin
Data Scientist
IBM Watson Health

In this example, we will improve our hand written digit recognizer which will

implement it in week two.

If we use the [INAUDIBLE] for this.

Remember, in week two, if you used soft [INAUDIBLE] flow and

got an accuracy of around 92%.

So let's see if we can do better.

We will start with a couple of imports.

Again, most of the frameworks

are providing the most interesting data sets as imports.

So in this case, we will used the end of this data set provided by We will

use the all ready known sequential model type and we import through layer types.

Then you all ready know where it is.

So, drop out which is a functionality to prevent over fitting is usually only

a parameter to a layer but it carries, it is moderate as a separate layer.

Since deep learning is in anyway nothing else than taking functions.

This perfectly make sense.

So that during every iteration,

a number of random neurons gets freezed and deviates an updated.

So this prevents over-fitting, and that model generalizes better.

Is a layer which reduces the dimensionality

of a which we will see later.

Then you put a tool in important layers for convolution in your networks.

Is the two dimensional convolutions layer which perfectly fits image data.

And next, pulling 2D as the name suggests is a pulling

layer using the max pulling function again in 2D.

The we can specify later.

Allows us to access properties of the underlying backend with

a special function in this case, we could access those functions using capital k.

We defined some constants, you should all ready know what those mean,

then we need to find the shape of the images which are 28 by 28 pixels.

Note that in this example we are not using a vector of 784 pixels,

but the 3D tensor of 28 by 28 by 1.

The third dimension usually is reserved for the color venue, and

has a shape of three.

Let's load the data.

This is a very convenient method,

because it will try to [INAUDIBLE] off four non paid arrays.

The FE input vector to neural network with the size of 784, and

the effort target vector in the dimension one which contains only the class labels.

From zero to nine, and it all through the times to split between training and

test data.

Now it's time to access the properties of the tensor flow back end by using

the key variable.

Since I auto supported we have to define the way,

how the color information is encoded in the image.

Some case of the it would expect a 28 by 28 by 1.

But here we needed 1 by 28 by 28.

If only one here, because its a mono-form image and not the image.

You apply some data pre-processing by casting the data and normalizing it.

Analyzing the straight forward here, but just to write by 55 and

we obtain a very range between zero and one.

It was on in order to verify if the shape is correct, and

also to count the number of training and text examples.

So since the label is recorded as a singular mention

with variables between zero and nine, we transform this to one hot and

call it vector, using the caret two category invention.

We started a sequential model and added a 2D convolutional layer.

This layer has 32 neurons, and

uses a three by three matrix to scan our body image.

We're using activation function, and we define the input shape as 28 by 28 by 1,

because you're using the This is followed by narrow 2D convolution layer.

This time, we are specifying 64 neurons.

Followed by this, we are adding a 2D max pooling layer,

which is counting the image by a 2 by 2 matrix.

Now we drop our 25% of the neurons in each iteration.

Now we've down the 28 by 28 by 1 into a vector of 784 elements.

And then, we add a fully connected layer with 128 neurons.

Again, we drop out, this time 50% of the neurons in each iteration.

Finally, we end up with a fully connected layer, with ten output nuerons.

Note that we change the activation function from value to soft next,

which is an ideal fit for our ten element one hot encoded vector.

As cost function, we defined categorical across entropy

which is a very good fit for my declassification task.

It's great to send out data, we use other data which turns out

to converge best given the dataset, and that you're in a topology.

Then we start training with the usual parameters.

After training, we checked the accuracy using the test data set.

Finally, you print loss in the accuracy on a test data set, and we are done.

Let's run this.

After training the 60,000 examples,

we noticed that we are doing quite well on the training data.

And if you look at the test data, you see that we are doing an amazing job.

We went up from 92% accuracy with softmax equation,

to 99% accuracy using this very simple convolutional neural network.

In the next video, you will see how more complex convolutional neural network

can give us amazing recites, and image classification.

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