build Your CNN Using Tensorflow

1. Technical Skills-2(Tensorflow)
Topic:
CNN WITH TENSOR FLOW
Department of IoT
Exp- 3

2. AIM OF THE SESSION
To familiarize students with the concept of CNN
INSTRUCTIONAL OBJECTIVES
This Session is designed to:
1. CNN application
2. CNN architecture
LEARNING OUTCOMES
At the end of this session, you should be able to:
1. Able to understand CNN learning model

3. The topics covered in the session are
1. What is need for CNN
2. Differences between CNN and ANN
3. CNN architecture
4. Convolution operation
5. Pooling techniques
3
SESSION Introduction

4. 1. WHAT DO YOU SEE, AND HOW?
CAN WE TEACH MACHINES TO SEE?

5. WHAT DO YOU SEE?

6. HOW DO YOU SEE?
How can we help computers see?

7. WHAT COMPUTERS ‘SEE’: IMAGES AS NUMBERS
What you see What you both see What the computer "sees"
Levin
Image
Processing
&
Computer
Vision
Input Image Input Image + values Pixel intensity values
(“pix-el”=picture-element)
An image is just a
matrix of numbers
[0,255]. i.e.,
1080x1080x3 for an
RGB image.
Question: is this
Lincoln? Washington?
Jefferson? Obama?
•How can the computer
answer this question?
Can I just do
classification on the
1,166400-long image
vector directly?
No. Instead: exploit
image spatial
structure. Learn
patches. Build them
up

8. 2. CLASSICAL MACHINE VISION ROOTS
IN STUDY OF HUMAN/ANIMAL BRAINS

9. INSPIRATION: HUMAN/ANIMAL VISUAL CORTEX
• Layers of neurons: pixels, edges, shapes, primitives, scenes
• E.g. Layer 4 responds to bands w/ given slant, contrasting edges

10. PRIMITIVES:NEURONS &ACTION
POTENTIALS
• Chemical accumulation across
dendritic connections Pre-synaptic axon
• post-synaptic dendrite
• neuronal cell body
• Each neuron receives multiple signals from
its many dendrites When threshold crossed,
it fires
• Its axon then sends outgoing signal to
downstream neurons
• Weak stimuli ignored
• Sufficiently strong cross activation
threshold
• Non-linearity within each
neuronal level
• Neurons connected into circuits (neural networks): emergent properties, learning, memory
• Simple primitives arranged in simple, repetitive, and extremely large networks
• 86 billion neurons, each connects to 10k neurons, 1 quadrillion (1012) connections

11. ABSTRACTION LAYERS: EDGES, BARS, DIR., SHAPES,
OBJECTS, SCENES
LGN: Small dots
V1: Orientation, disparity, some
color
V4: Color, basic shapes, 2D/3D, curvature
VTC: Complex features and objects(VTC:
ventral temporal cort
e
• Abstraction layers  visual cortex layers
• Complex concepts from simple parts, hierarchy
• Primitives of visual concepts encoded in
neuronal connection in early cortical layers

12. • Massive recent expanse of human brain has re-used a relatively simple but general
learning architecture
GENERAL“LEARNINGMACHINE”,REUSED WIDELY
• Hearing, taste, smell, sight, touch all re- use similar
learning architecture
Visual Cortex Motor Cortex • Interchangeable circuitry
• Auditory cortex learns to ‘see’
if sent visual signals
• Injury area tasks shift to
uninjured areas
• Not fully-general learning, but well-adapted to our world
• Humans co-opted this circuitry to many new applications
• Modern tasks accessible to any homo sapiens (<70k years)
• ML primitives not too different from animals: more to come?
human
chimp
Hardware
expansion

13. WHAT IS CNN?
Convolutional Neural Networks (CNN) is a feed forward neural
network that is generally used for Image recognition and object
classification.
CNNs are inspired from the visual cortex of the brain and have been
widely applied in image and speech recognition.
Cnn represent a huge breakthrough in image recognition

14. WHY CNN
Until quite recently, computers were not good at tasks
like recognizing puppy in a picture or recognizing
spoken words, which humans excel at.
• The CNN is essential when it comes to image classification
problems since it has such a high degree of accuracy.
• Prior to training an Artificial Neural Network, a 2D image is
converted into a 1-dimensional vector.
• Furthermore, as the image size increases, the number of
training parameters increases exponentially, leading to
storage loss.
• ANNs do not capture the sequence information that
sequence data requires.

15. CNN VS ANN
1.
2 For small images it might work, but for large
images the no of pixels are so many leading to
millions of connections between neurons, leading to
intractable solutions 2. No of features /pixels are reduced thru
convolution and pooling operations
15
Deep learning
• In an ANN, each neuron in the network is
connected to every other neuron in the
adjacent hidden layers.
1. In a CNN, each neuron in the hidden layer is
connected to a small region of the input neurons.

16. CNN ARCHITECTURE
• A CNN typically has three layers: a convolutional layer, a pooling layer, and a fully connected
layer.
Deep learning 16

17. CNN ARCHITECTURE CONT..
17

18. CNN ARCHITECTURE: TYPES OF LAYERS
• Convolutional layer
• a “filter” passes over the image, scanning a
few pixels at a time and creating a feature
map that predicts the class to which each
feature belongs.
• Pooling layer (downsampling)
• reduces the amount of information in each
feature obtained in the convolutional layer
while maintaining the most important
information .
• there are usually several rounds of
convolution and pooling.
2/25/2024 Deep learning 18

19. CNN ARCHITECTURE: TYPES OF LAYERS
• Fully connected input layer (flatten)
• It takes the output of the previous layers,
“flattens” them and turns them into a
single vector that can be an input for the
next stage.
• The first fully connected layer takes the
inputs from the feature analysis and
applies weights to predict the correct
label.
• Fully connected output layer gives the
final probabilities for each label.
2/25/2024 Deep learning 19

20. 4.CONVOLUTIONS AND FILTERS

21. CONVOLUTION OPERATION IS ELEMENT WISE MULTIPLY
AND ADD
Filter / Kernel

22. PRODUCING FEATURE MAPS
Original Sharpen Edge Detect “Strong” Edge
Detect

23. A SIMPLE PATTERN: EDGES
HOW CAN WE DETECT EDGES WITH A KERNEL?
Input
1 -1
Filter
Output

24. SIMPLE KERNELS / FILTERS

25. X OR X?
Image is represented as matrix of pixel values… and computers are literal!
We want to be able to classify an X as an X even if it’s shifted, shrunk, rotated, deformed.

26. CONVOLUTION
PROCESS
• Assume the filter/kernel is weight
matrix wk
. For example, lets
assume a 3 x 3 weighted matrix.
• The kernel is a filter to extract
some particular features from the
original images.
• It could be for extracting curves ,
identifying a specific colour, or
recognizing a particular voice.
Deep learning 26

27. CONVOLUTION
OPERATION
• Kernel/Filter, K =
• 1 0 1
0 1 0
1 0 1
27

28. PROCESS OF CONVOLUTION (CONTD.)
0 1 1
1 0 0
1 0 1
Input layer Filter/Kernel
(Weighted matrix)
515
• For example, when the weighted matrix starts from the top left corner of the input layer, the
output value is calculated as:
(81x0+2x1+209x1)+(24x1+56x0+108X0)+(91x1+0x0+189x1) = 515
81 2 209 44 71 58
24 56 108 98 12 112
91 0 189 65 79 232
12 0 0 5 1 71
2 32 23 58 8 209
49 98 81 112 54 9
EXAMPLE
• As the filter/kernel is slided across the input layer, the convolved layer is obtained by
adding the values obtained by element wise multiplication of the weight matrix.
Output

29. PROCESS OF CONVOLUTION (CONTD.)
0 1 1
1 0 0
1 0 1
Input layer
Filter/Kernel
(Weighted matrix)
Output
(Activation/Feature Map)
515 374
• Dimension of output=n-f+1
• The distance between two consecutive receptive fields is called the stride.
• In this example stride is 1 since the receptive field was moved by 1 pixel at a time.
81 2 209 44 71 58
24 56 108 98 12 112
91 0 189 65 79 232
12 0 0 5 1 71
2 32 23 58 8 209
49 98 81 112 54 9
EXAMPLE
• The filter then moves by 1 pixel to the next receptive field and the process is repeated. The output layer
obtained after the filter slides over the entire image would be a 4X4 matrix.This is called an activation map/
feature map.

30. 30
Calculating output dimension of Convolution
layer

31. ReLU Layer
31
• ReLU stands for Rectified Linear Unit and is a non-linear operation.
• ReLU is an element wise operation (applied per pixel) and replaces all
negative pixel values in the feature map by zero.
Output = Max(zero, Input)

32. Pooling Layer (Sub-sampling or Down-sampling)
• Pooling layer reduce the size of feature maps by using some functions to summarize sub-
regions, such as taking the average or the maximum value

33. FULLY CONNECTED LAYER
After completion of series of convolutional, nonlinear and pooling layers, it is
necessary to attach a fully connected layer.
This layer takes the output information from convolutional networks.
Attaching a fully connected layer to the end of the network results in an N
dimensional vector, where N is the number of classes from which the model
selects the desired class.
33
Deep learning

34. EXAMPLE

35. AN IMAGE CLASSIFICATION CNN

36. CNNS FOR CLASSIFICATION
• Train model with image data.
• Learn weights of filters in convolutional layers. D
tf.keras.activations.
*
tf.keras.layers.MaxPool2
D
Convolution:Apply
filters to generate
feature maps.
Non-linearity: Often
ReLU.
Pooling:
Downsampling
operation on each
feature map.
tf.keras.layers.Conv2

37. CNNS FOR CLASSIFICATION: FEATURE LEARNING
Learn features in input image through convolution
Learn
Introduce non-linearity through activation function (real-world data is non-linear!)
Introduce
Reduce dimensionality and preserve spatial invariance with pooling
Reduce

38. CNNS FOR CLASSIFICATION: CLASS PROBABILITIES
CONV and POOL layers output high-level features of input
Fully connected layer uses these features for classifying input image
Express output as probability of image belonging to a particular class

39. REPRESENTATION LEARNING IN DEEP CNNS
Mid level features
Low level features High level features
Edges, dark spots
Conv Layer 1
Eyes, ears,nose
Conv Layer 2
Facial structure
Conv Layer 3

40. EXAMPLE – SIX CONVOLUTIONAL LAYERS

41. CONVOLUTIONAL LAYERS: LOCAL
CONNECTIVITY
For a neuron in
hidden layer:
• Take inputs from
patch
• Compute
weighted sum
• Apply bias
tf.keras.layers.
Conv2D

42. CONVOLUTIONAL LAYERS: LOCAL
CONNECTIVITY
For a neuron in hidden layer:
• Take inputs from patch
• Compute weighted sum
• Apply bias
4x4 filter:
matrix of
weights wij
for neuron (p,q) in hidden layer
1) applying a window of weights
2) computing linear combinations
3) activating with non-linear function
tf.keras.layers.Conv2D

43. OTHER ARCHITECTURES OFCNN
LeNet-5 (1998) • AlexNet (2012) ZFNet (2013)
GoogLeNet (2014) VGGNet(2014) ResNet (2015)
• Over the years, many variants of this basic architecture have been developed such as:
• These models can be used in applications by developers.

44. THANK YOU
2/25/2024
44
Deep learning