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An Introduction to Convolutional Neural Networks Alessandro Giusti Dalle Molle Institute for Artificial Intelligence Lugano, Switzerland

An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

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Page 1: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

An Introduction toConvolutional Neural Networks

Alessandro GiustiDalle Molle Institute for Artificial Intelligence

Lugano, Switzerland

Page 2: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

Sources & Resources

- Andrej Karpathy, CS231nhttp://cs231n.github.io/convolutional-networks/

- Ian Goodfellow et al., Deep Learninghttp://www.deeplearningbook.org/

- F. Chollet, Deep Learning with Pythonhttps://www.manning.com/books/deep-learning-with-python

- Jonathan Hui, CNN Tutorialhttps://jhui.github.io/2017/03/16/CNN-Convolutional-neural-network/

- Tim Demetters, Understanding Convolutionshttp://timdettmers.com/2015/03/26/convolution-deep-learning/

Some images in this presentation are extracted from the sources listed above

http://cs231n.github.io/convolutional-networks/
http://www.deeplearningbook.org/
https://www.manning.com/books/deep-learning-with-python
https://jhui.github.io/2017/03/16/CNN-Convolutional-neural-network/
http://timdettmers.com/2015/03/26/convolution-deep-learning/
Page 3: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

What is a convolution?

Page 4: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

What is a convolution?

Page 5: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

How does a convolution look like?

1 3x3 kernel1 input map 1 output map

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What about multiple maps?

1 input map 1 output map1 3x3 kernel

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1 input map 2 output maps2 3x3 kernels

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3 input maps 2 output maps3x2 3x3 kernels

Page 9: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

3 input maps 2 output maps3x2 3x3 kernels

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3 input maps 2 output maps3x2 3x3 kernels

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3 input maps 2 output maps3x2 3x3 kernels

Quiz: how many parameters does this layer have?

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3 input maps 2 output maps3x2 3x3 kernels

= 54 ...

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3 input maps 2 output maps3x2 3x3 kernels

= 54 ... + 2 biases

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3 input maps 2 output maps3x2 3x3 kernels

= 54 ... + 2 biases= 56 trainable parameters (weights)

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Details: padding

How many 3x3 patchesare fully contained in a5x5 map?

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Details: padding

9: the output map is 3x3

Page 17: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

Details: padding

• This is known as “valid” padding mode (default)

• An alternative pads the input map with zeros to yield a same-sized map

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Details: striding

• Stride 1x1 is most frequently used: shift 1 pixel at a time patches are heavily overlapping

• Stride 2x2 skips one patch horizontally and vertically

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Why convolutional layers?

• Sparse connectivity

• Parameter sharing

• Translation invariance

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Sparse connectivity

Fully connected

3x1 convolutional

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Sparse connectivity

Fully connected

3x1 convolutional

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Receptive fields

Fully connected

3x1 convolutional

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Receptive fields

3x1 convolutional

3x1 convolutional

Deeper neurons depend on wider patches of the input

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Parameter sharing

Fully connected

3x1 convolutional

5x5 = 25 weights(+ 5 bias)

3 weights!(+ 1 bias)

Quiz: how many parameters does this layer have?

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Translational invariance

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Max pooling layers

Quiz: how many parameters does this layer have?

... on many maps?

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Max pooling downsamples activation maps

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ExerciseInput:

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

map

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

38x38

38x38

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Receptive fieldsInput

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

map

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

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Receptive fieldsInput

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

map

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

?

?

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

?

How large is the receptive field of the black neuron?

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Receptive fieldsInput

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

Co

nv

3x3

map

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

?

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

Co

nv

3x3

MP

2x2

22x22

How large is the receptive field of the black neuron?

13x13

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Why convnets work

Convnets learn a hierarchy of translation-invariant spatial pattern detectors

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What are layers looking for?Data from a convnet trained on

ImageNet

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Shallow layers respond to fine, low-level patterns

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Intermediate layers ...

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Deep layers respond to complex, high-level patterns

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Detail: backprop with max pooling

The gradient is only routed through the input pixel that contributes to the output value; e.g.:

Gradient of • with respect to • = 0

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A typical architectureAs we move to deeper layers:• spatial resolution is reduced• the number of maps increasesWe search for higher-level patterns, and don’t care too much about their exact location.There are more high-level patterns than low-level details!

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A typical architecture

Extract high-level features from pixel data Classify

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We will manipulate 4D tensors

Images are represented in 4D tensors:Tensorflow convention:(samples, height, width, channels)

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The software stack

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What is Keras?

• A model-level library, providing high-level building blocks for developing deep-learning models.

• Doesn’t handle low-level operations such as tensor manipulation and differentiation.

• Relies on backends (such as Tensorflow)

• Allows full access to the backend

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Why Keras?

Pros:

• Higher level fewer lines of code

• Modular backend not tied to tensorflow

• Way to go if you focus on applications

Cons:

• Not as flexible

• Need more flexibility? Access the backend directly!

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More about ConvNets

Alessandro GiustiDalle Molle Institute for Artificial Intelligence

Lugano, Switzerland

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1. UNDERFITTING & OVERFITTING ON OUR ROCK PAPER SCISSORS NET

Rock Paper Scissors

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1, 2, 4, 8

1

24

8 1

2

4

8

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8, 16

1632

1632

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16

32

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32,64

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64

128

12864

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The ML Pipeline (Chollet)

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2. VISUALIZATION TECHNIQUESRock Paper Scissors

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Visualizing the weights of the net

...

We want to see this

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Visualizing the weights of the net

11x11x3 filters (visualized in RGB) in the first convolutional layers

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Visualizing the activations of intermediate layers

...

We want to see this

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Visualizing the activations of intermediate layers

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Visualizing the input that maximally activates some neurons

...

We want to compute (and see) the input that maximally activates this guy

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Step 1

...

Compute the gradient of this with respect to the input

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Step 2

...

Nudge the input accordingly: our guy will increase its activation

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Goto step 1

...

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Shallow layers respond to fine, low-level patterns

Page 70: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

Intermediate layers ...

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Deep layers respond to complex, high-level patterns

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USING PRETRAINED WEIGHTSStand on the shoulder of giants

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Using pretrained weights

Step 1 Step 2 Step 3

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Option 1

Co

nv

MP

Co

nv

MP

Co

nv

MPInput

Flat

ten

Den

se

Den

se

Ou

tpu

ts

Page 75: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

Option 1

Co

nv

MP

Co

nv

MP

Co

nv

MPInput

Flat

ten

Den

se

Den

se

Ou

tpu

ts

Page 76: An Introduction to Convolutional Neural Networks...Details: padding •This is known as “valid” padding mode (default) •An alternative pads the input map with zeros to yield

Option 1

Co

nv

MP

Co

nv

MP

Co

nv

MPInput

Flat

ten

Save these featuresfor the whole training and testing datasets.

Then, train a new classifier that uses these features as input

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Option 2

Co

nv

MP

Co

nv

MP

Co

nv

MPInput

Flat

ten

Den

se

Den

se

Ou

tpu

ts

Freeze Train only

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Option 3

Co

nv

MP

Co

nv

MP

Co

nv

MPInput

Flat

ten

Den

se

Den

se

Ou

tpu

ts

Freeze Train onlyFinetune

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A MILE-HIGH OVERVIEW OF FULLY CONVOLUTIONAL NETWORKS FOR SEGMENTATION

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Overall idea

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“Convolutionalization” of a dense layer

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SOME POSSIBLE PROJECTS

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Deep Learning on vibration data for detecting fence violations

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Deep Learning on wearable sensor data for robot control

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Learning to predict errors in weather forecasts


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