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CNN PPT

The document discusses Convolutional Neural Networks (CNNs), explaining how they utilize convolutional layers with filters to detect patterns in images while reducing the number of parameters through shared weights and pooling layers. It highlights the importance of max pooling for subsampling and retaining essential features, leading to more efficient networks. The document also touches on the structure of CNNs and their implementation in frameworks like Keras.

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44 views37 pages

CNN PPT

The document discusses Convolutional Neural Networks (CNNs), explaining how they utilize convolutional layers with filters to detect patterns in images while reducing the number of parameters through shared weights and pooling layers. It highlights the importance of max pooling for subsampling and retaining essential features, leading to more efficient networks. The document also touches on the structure of CNNs and their implementation in frameworks like Keras.

Uploaded by

qwertyluzzluli
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Convolutional Neural Network

Dr. Gaurav Trivedi


Indian Institute of Technology, Guwahati
Consider learning an image:
• Some patterns are much smaller than the whole image

Can represent a small region with fewer parameters

“beak” detector
Same pattern appears in different places:
They can be compressed!
What about training a lot of such “small” detectors
and each detector must “move around”.

“upper-left
beak” detector

They can be compressed


to the same parameters.

“middle beak”
detector
A convolutional layer
A CNN is a neural network with some convolutional layers
(and some other layers). A convolutional layer has a
number of filters that does convolutional operation.

Beak detector

A filter
Convolution These are the network
parameters to be learned.

1 -1 -1
1 0 0 0 0 1 -1 1 -1 Filter 1
0 1 0 0 1 0 -1 -1 1
0 0 1 1 0 0
1 0 0 0 1 0 -1 1 -1
0 1 0 0 1 0 -1 1 -1 Filter 2
0 0 1 0 1 0 -1 1 -1

…
…
6 x 6 image
Each filter detects a
small pattern (3 x 3).
1 -1 -1
-1 1 -1
Convolution
Filter 1
-1 -1 1
stride=1

1 0 0 0 0 1 Dot
product
0 1 0 0 1 0 3 -1
0 0 1 1 0 0
1 0 0 0 1 0
0 1 0 0 1 0
0 0 1 0 1 0

6 x 6 image
1 -1 -1
-1 1 -1
Convolution
Filter 1
-1 -1 1
If stride=2

1 0 0 0 0 1
0 1 0 0 1 0 3 -3
0 0 1 1 0 0
1 0 0 0 1 0
0 1 0 0 1 0
0 0 1 0 1 0

6 x 6 image
1 -1 -1
-1 1 -1
Convolution
Filter 1
-1 -1 1
stride=1

1 0 0 0 0 1
0 1 0 0 1 0 3 -1 -3 -1
0 0 1 1 0 0
1 0 0 0 1 0 -3 1 0 -3
0 1 0 0 1 0
0 0 1 0 1 0 -3 -3 0 1

6 x 6 image 3 -2 -2 -1
-1 1 -1
-1 1 -1 Filter 2
Convolution -1 1 -1
stride=1
Repeat this for each filter
1 0 0 0 0 1
0 1 0 0 1 0 3 -1 -3 -1
-1 -1 -1 -1
0 0 1 1 0 0
1 0 0 0 1 0 -3 1 0 -3
-1 -1 -2 1
0 1 0 0 1 0 Feature
0 0 1 0 1 0 -3 -3 Map
0 1
-1 -1 -2 1
6 x 6 image 3 -2 -2 -1
-1 0 -4 3
Two 4 x 4 images
Forming 2 x 4 x 4 matrix
Color image: RGB 3 channels
Filter 2
11 -1-1 -1-1 -1-1 11 -1-1
1 -1 -1 -1 1 -1
-1 1 -1 -1-1 11 -1-1
-1-1 11 -1-1 Filter 1 -1 1 -1
-1-1 -1-1 11 -1-1-1 111 -1-1-1
-1 -1 1
Color image
1 0 0 0 0 1
1 0 0 0 0 1
0 11 00 00 01 00 1
0 1 0 0 1 0
0 00 11 01 00 10 0
0 0 1 1 0 0
1 00 00 10 11 00 0
1 0 0 0 1 0
0 11 00 00 01 10 0
0 1 0 0 1 0
0 00 11 00 01 10 0
0 0 1 0 1 0
0 0 1 0 1 0
Convolution v.s. Fully Connected

1 0 0 0 0 1 1 -1 -1 -1 1 -1
0 1 0 0 1 0 -1 1 -1 -1 1 -1
0 0 1 1 0 0 -1 -1 1 -1 1 -1
1 0 0 0 1 0
0 1 0 0 1 0
0 0 1 0 1 0
convolution
image

x1
1 0 0 0 0 1
0 1 0 0 1 0 x2
Fully- 0 0 1 1 0 0
1 0 0 0 1 0
connected
…
…

…
…
0 1 0 0 1 0
0 0 1 0 1 0
x36
1 -1 -1 Filter 1 1 1
-1 1 -1 2 0
-1 -1 1 3 0
4: 0 3

…
1 0 0 0 0 1
0 1 0 0 1 0 0
0 0 1 1 0 0 8 1
1 0 0 0 1 0 9 0
0 1 0 0 1 0 10: 0

…
0 0 1 0 1 0
13 0
6 x 6 image
14 0
fewer parameters! 15 1 Only connect
to 9 inputs, not
16 1 fully connected
…
1 -1 -1 1: 1
-1 1 -1 Filter 1 2: 0
-1 -1 1 3: 0
4: 0 3

…
1 0 0 0 0 1
0 1 0 0 1 0 7: 0
0 0 1 1 0 0 8: 1
1 0 0 0 1 0 9: 0 -1
0 1 0 0 1 0 10: 0

…
0 0 1 0 1 0
1 0
6 x 6 image
3: 0
14:
Fewer parameters 15: 1
Even fewer parameters 16: 1
Shared weights
…
The whole CNN
cat dog ……
Convolution

Max Pooling
Can
Fully Connected repeat
Feedforward network
Convolution many
times

Max Pooling

Flattened
Max Pooling
1 -1 -1 -1 1 -1
-1 1 -1 Filter 1 -1 1 -1 Filter 2
-1 -1 1 -1 1 -1

3 -1 -3 -1 -1 -1 -1 -1

-3 1 0 -3 -1 -1 -2 1

-3 -3 0 1 -1 -1 -2 1

3 -2 -2 -1 -1 0 -4 3
Why Pooling
• Subsampling pixels will not change the
object
bird
bird

Subsampling

We can subsample the pixels to make image smaller


fewer parameters to characterize the image
A CNN compresses a fully
connected network in two ways:
• Reducing number of connections

• Shared weights on the edges

• Max pooling further reduces the complexity


Max Pooling
New image
1 0 0 0 0 1 but smaller
0 1 0 0 1 0 Conv
3 0
0 0 1 1 0 0 -1 1
1 0 0 0 1 0
0 1 0 0 1 0 Max 3 1
0 3
0 0 1 0 1 0 Pooling
2 x 2 image
6 x 6 image
Each filter
is a channel
The whole CNN
3 0
-1 1 Convolution

3 1
0 3
Max Pooling
Can
A new image
repeat
Convolution many
Smaller than the original
times
image
The number of channels Max Pooling

is the number of filters


The whole CNN
cat dog ……
Convolution

Max Pooling

Fully Connected A new image


Feedforward network
Convolution

Max Pooling

A new image
Flattened
Flattening 3

1
3 0
-1 1 3

3 1 -1
0 3 Flattened

1 Fully Connected
Feedforward network
0

3
Only modified the network structure and
CNN in Keras input format (vector -> 3-D tensor)

input

Convolution
1 -1 -1
-1 1 -1
-1 1 -1
-1 1 -1 … There are
-1 -1 1 25 3x3
-1 1 -1 … Max Pooling
filters.
Input_shape = ( 28 , 28 , 1)

28 x 28 pixels 1: black/white, 3: RGB Convolution

3 -1 3 Max Pooling

-3 1
Only modified the network structure and
CNN in Keras input format (vector -> 3-D array)

Input
1 x 28 x 28

Convolution
How many parameters for
each filter? 9 25 x 26 x 26

Max Pooling
25 x 13 x 13

Convolution
How many parameters 225=
for each filter? 50 x 11 x 11
25x9
Max Pooling
50 x 5 x 5
Only modified the network structure and
CNN in Keras input format (vector -> 3-D array)

Input
1 x 28 x 28

Output Convolution

25 x 26 x 26
Fully connected Max Pooling
feedforward network
25 x 13 x 13

Convolution
50 x 11 x 11

Max Pooling
1250 50 x 5 x 5
Flattened
Pooling Layers
Reduce the spatial dimensions (height
and width) of the feature maps while
retaining the most important
information.

Translation invariance, meaning the CNN


becomes less sensitive to small changes in the
input image.

Make the network more efficient by reducing


the number of parameters and computations.
Types of Pooling Layers
Max Pooling:

Takes the maximum value from each region

-1 -1.33 1.3

2.1 -1.11 -3.2

1 0.55 1.56
Types of Pooling Layers
Max Pooling:

Takes the maximum value from each region

-1 -1.33 1.3

2.1 -1.11 -3.2

1 0.55 1.56
Types of Pooling Layers
Max Pooling:

Takes the maximum value from each region

-1 -1.33 1.3

2.1 -1.11 -3.2

1 0.55 1.56

Significance:

Retains the most important features (strongest activations)

Helps detect important patterns like edges and textures


Types of Pooling Layers
Average Pooling:

Computes the average of all values in the region

-1 -1.33 1.3

2.1 -1.11 -3.2

1 0.55 1.56
Types of Pooling Layers
Average Pooling:

Computes the average of all values in the region

-1 -1.33 1.3

2.1 -1.11 -3.2 = -0.014


1 0.55 1.56

Significance:

Provides a smoother representation of the feature map

Used when preserving overall intensity is important


CNN Architectures

Lenet-5 1989- Earliest One

2012-
AlexNet
Revolutionized DNN

VGGNet 2014- (Powerful)

Resnet 2015

EfficientNet 2019
CNN Architectures
Lenet-5
CNN Architectures
Lenet-5
CNN Architectures
AlexNet
CNN Architectures
AlexNet
Challenges on Hardware
Implementation
Convolutional Operator

Max Pooling

Activation Function

Data Flow Management

Memory Requirement

FPGA Constraints
THANK YOU

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