REACTIVE MACHINES (Scenarii reactive)

LIMITED MEMORY (+ aware of changes in an environment)

THEORY OF MIND (+ aware of the people in it)

SELF AWARE (+ capable of self councious choices)

IN

?

IN

?

OUT

IN

?

OUT

PERCEPTRON

?

Or NOT

?

0 -> 10

Or NOT

?

0 -> 1

0 -> 10

Or NOT

?

0 -> 1

0 -> 10

Or NOT

0 -> 1

?

0 -> 1

0 -> 10

Or NOT

0 -> 1

0 -> 1

?

0 -> 1

0 -> 10

Or NOT

0 -> 1

0 -> 1

0 -> 1

?

0 -> 1

activation

0 -> 10

Or NOT

0 -> 1

activation

0 -> 1

0 -> 1

?

0 -> 1

• BinaryStep

• Gaussian

• HyperbolicTangent

• Parametric Rectified Linear Unit

• Sigmoid (default)

• Thresholded Rectified Linear Unit

0 -> 10

Or NOT

activation

0 -> 1

activation

0 -> 1

0 -> 1

?

0 -> 1

0 -> 10

Or NOT

• BinaryStep

• Gaussian

• HyperbolicTangent

• Parametric Rectified Linear Unit

• Sigmoid (default)

• Thresholded Rectified Linear Unit

activation

0 -> 1

activation

0 -> 1

0 -> 1

?

0 -> 1

0 -> 10

Or NOT

• BinaryStep

• Gaussian

• HyperbolicTangent

• Parametric Rectified Linear Unit

• Sigmoid (default)

• Thresholded Rectified Linear Unit

activation

0 -> 1

activation

0 -> 1

0 -> 1

?

0.2

8

Or NOT

0.3

Sigmoid

0.4

0.8

Sigmoid

H

Or NOT

0.3

0.4

0.8

0.2

8

Sigmoid

Sigmoid

H = sigmoid( (8x0.2) + 0.4 )

H

Or NOT

0.3

0.4

0.8

0.2

8

Sigmoid

Sigmoid

H = sigmoid( (8x0.2) + 0.4 )

H = 0.88079707797788

H

Or NOT

0.3

0.4

0.8

H = sigmoid( (8x0.2) + 0.4 )

H = 0.88079707797788

0.2

8

Sigmoid

Sigmoid

O = sigmoid( (Hx0.3) + 0.8 )

O = 0.74349981350761

H

0.3

0.4

0.8

H = sigmoid( (8x0.2) + 0.4 )

H = 0.88079707797788

0.2

8

Sigmoid

Sigmoid

O = sigmoid( (Hx0.3) + 0.8 )

O = 0.74349981350761

H

0.3

0.4

0.8

H = sigmoid( (8x0.2) + 0.4 )

H = 0.68997448112761

0.2

2

Sigmoid

Sigmoid

O = sigmoid( (Hx0.3) + 0.8 )

O = 0.73243113381927

H

0.3

0.4

0.8

0.2

2

Sigmoid

Sigmoid

TRAINING

H

0.3

0.4

0.8

0.2

2

Sigmoid

Sigmoid

TRAINING

BACK PROPAGATION

H

0.3

0.4

0.8

0.2

2

Sigmoid

Sigmoid

BACK PROPAGATION

H

0.3

0.4

0.8

0.2

2

Sigmoid

Sigmoid

BACK PROPAGATION

H

0.3

0.4

0.8

0.2

Sigmoid

Sigmoid

BACK PROPAGATION

2

H

0.3

0.4

0.8

0.2

Sigmoid

Sigmoid

BACK PROPAGATION

2

ERROR = EXPECTATION - OUTPUT

H

0.3

0.4

0.8

0.2

Sigmoid

Sigmoid

BACK PROPAGATION

2

LINEAR GRADIENT DESCENT

ERROR = EXPECTATION - OUTPUT

H

0.3

0.4

0.8

0.2

Sigmoid

Sigmoid

BACK PROPAGATION

2

LINEAR GRADIENT DESCENT

ERROR = EXPECTATION - OUTPUT

H

0.3

0.4

0.8

0.2

Sigmoid

Sigmoid

BACK PROPAGATION

2

LINEAR GRADIENT DESCENT

ERROR = EXPECTATION - OUTPUT

BACK PROPAGATION

BACK PROPAGATION

BACK PROPAGATION

BACK PROPAGATION

BACK PROPAGATION

BACK PROPAGATION

BACK PROPAGATION

BACK PROPAGATION

derivative

learning
rate

H

0.3

0.4

0.8

0.2

DSigmoid

DSigmoid

BACK PROPAGATION

2

LINEAR GRADIENT DESCENT

ERROR = EXPECTATION - OUTPUT

3blue1Brown

H

0.3

0.4

0.8

0.2

DSigmoid

DSigmoid

BACK PROPAGATION

2

GRADIENT = Derivative of Sigmoid (OUTPUT)

LINEAR GRADIENT DESCENT

ERROR = EXPECTATION - OUTPUT

H

0.3

0.4

0.8

0.2

DSigmoid

DSigmoid

BACK PROPAGATION

2

multiply by the ERROR

ERROR = EXPECTATION - OUTPUT

LINEAR GRADIENT DESCENT

GRADIENT = Derivative of Sigmoid (OUTPUT)

ERROR = EXPECTATION - OUTPUT

H

0.3

0.4

0.8

0.2

DSigmoid

DSigmoid

BACK PROPAGATION

2

LINEAR GRADIENT DESCENT

multiply by the ERROR
                multiply by the LEARNING RATE

ERROR = EXPECTATION - OUTPUT

GRADIENT = Derivative of Sigmoid (OUTPUT)

ERROR = EXPECTATION - OUTPUT

H

0.3

0.4

0.8

0.2

DSigmoid

DSigmoid

BACK PROPAGATION

2

GRADIENT = DSigmoid (OUTPUT) * ERROR * LR

ERROR = EXPECTATION - OUTPUT

LINEAR GRADIENT DESCENT

H

0.3

0.4

0.8

0.2

DSigmoid

DSigmoid

BACK PROPAGATION

2

GRADIENT = DSigmoid (OUTPUT) * ERROR * LR

ERROR = EXPECTATION - OUTPUT

LINEAR GRADIENT DESCENT

ΔWeights = GRADIENT * H

H

0.3

0.4

0.8

0.2

DSigmoid

DSigmoid

BACK PROPAGATION

2

GRADIENT = DSigmoid (OUTPUT) * ERROR * LR

ERROR = EXPECTATION - OUTPUT

LINEAR GRADIENT DESCENT

ΔWeights = GRADIENT * H

Weights = Weights + ΔWeights

H

0.3

0.4

0.8

0.2

DSigmoid

DSigmoid

BACK PROPAGATION

2

GRADIENT = DSigmoid (OUTPUT) * ERROR * LR

ERROR = EXPECTATION - OUTPUT

LINEAR GRADIENT DESCENT

ΔWeights = GRADIENT * H

Weights = Weights + ΔWeights

Bias = Bias + GRADIENT

H

0.4

0.8

0.2

DSigmoid

BACK PROPAGATION

2

Sigmoid

0.3

DSigmoid

Sigmoid

H

-26.6143

-3.75104

4.80418

DSigmoid

BACK PROPAGATION

2

Sigmoid

7.62213

DSigmoid

Sigmoid

H

-26.6143

-3.75104

4.80418

DSigmoid

BACK PROPAGATION

2

Sigmoid

7.62213

DSigmoid

Sigmoid

0.02295

H

-26.6143

-3.75104

4.80418

DSigmoid

BACK PROPAGATION

2

Sigmoid

7.62213

DSigmoid

Sigmoid

0.02295

H

-26.6143

-3.75104

4.80418

DSigmoid

BACK PROPAGATION

8

Sigmoid

7.62213

DSigmoid

Sigmoid

0.97988

CONGRATULATIONS !

CONGRATULATIONS !
Let's play together :)

https://github.com/GregoireHebert/sflive-nn

Hungry

EAT

MULTI LAYER

PERCEPTRON

Hungry

EAT

Hungry

EAT

Sleepy

Excited

RUN

SLEEP

Hungry

EAT

Sleepy

Excited

RUN

SLEEP

Hungry

EAT

Sleepy

Excited

RUN

SLEEP

?

NEAT

NEAT

Hungry

EAT

Sleepy

Excited

RUN

SLEEP

?

Neuro Evolution of Augmented Topologie

NEAT

Hungry

EAT

Sleepy

Excited

RUN

SLEEP

?

Neuro Evolution of Augmented Topologie

https://github.com/GregoireHebert/tamagotchi

MERCI !