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Convolutional Neural networks
this slide deck:
-
Machine Learning basic concepts
- interpretability
- parameters vs hyperparameters
- supervised/unsupervised
- CART
- Clustering methods
- Neural Networks
Neural Networks
- the brain connection
- perceptron
- activation functions
- shallow nets
- deep nets architecture
- back-propagation
- convolutional NN
- preprocessing and whitening (minibatch)
mean square error
mean absolute error
L_1 = \Sigma \left| y_{true} - y_{predicted}\right|
L_2= \Sigma \left( y_{true} - y_{predicted}\right)^2
recap
How do we fit a model to data?
minimize loss function
optimization schemes
Gradient descent
.
.
.
x_1
x_2
x_N
+b
\vec{y} = \vec{x}W + b
Any linear model
w_2
w_1
w_N
y
.
.
.
x_1
x_2
x_N
+b
f
\vec{y} = f(\vec{x}W + b)
perceptron or
shallow NN
w_2
w_1
w_N
input layer
hidden layer
output layer
.
.
.
x_1
x_2
x_N
+b
\vec{y} = \vec{x}W + b
Any linear model
w_2
w_1
w_N
y
y : prediction
ytrue : target
Error: e.g.
intercept
slope
L2
L_2~=~(y - y_\mathrm{true})^2
x
features
y = \sum_i w_i x_i + b
Find the best parameters by finding the minimum of the L2 hyperplane
.
.
.
x_1
x_2
x_N
+b
\vec{y} = \vec{x}W + b
Any linear model
w_2
w_1
w_N
y
y : prediction
ytrue : target
Error: e.g.
intercept
slope
L2
L_2~=~(y - y_\mathrm{true})^2
Find the best parameters by finding the minimum of the L2 hyperplane
x
y = ax + b
y = ax + b
.
.
.
x_1
x_2
x_N
+b
\vec{y} = \vec{x}W + b
Any linear model
w_2
w_1
w_N
y
y : prediction
ytrue : target
Error: e.g.
intercept
slope
L2
L_2~=~(y - y_\mathrm{true})^2
Find the best parameters by finding the minimum of the L2 hyperplane
at every step look around and choose the best direction
global minimum
x
initial
guess
.
\vec{y} = a\vec{x}+b
intercept
slope
L2
global minimum
x
How do I know in which direction to go?
find the direction where L2 decreases by taking the gradient of the loss function
w respect to the parameters (a,b)
global minimum
L2
\vec{L_2} = \sum_i{(y_i - (a{x_i} + b))^2}
initial
guess
.
Find the best parameters by finding the minimum of the L2 hyperplane
at every step look around and choose the best direction
Gradient Descent
Used to optimize parameters of a function fit to data
- Start at a random location in parameters space
- Calculate partial derivative w respect to each parameter
- Update parameters according to the (partial) derivative
m=m_0,b=b_0
f(m,b)
f' = (\frac{\partial f}{\partial m}, ~ \frac{\partial f}{\partial b} )
m_\mathrm{new} = m_\mathrm{old} - \frac{\partial f}{\partial m}\cdot\frac{\alpha}{N}\\
b_\mathrm{new} = b_\mathrm{old} - \frac{\partial f}{\partial b}\cdot \frac{\alpha}{N}
0. Choose a learning rate hyperparameter α
{
(m,b)
\vec{x}=x_1...x_N
Gradient Descent
Used to optimize parameters of a function fit to data
- Start at a random location in parameters space
- Calculate partial derivative w respect to each parameter
- Update parameters according to the (partial) derivative
m_\mathrm{new} = m_\mathrm{old} - \frac{\partial f}{\partial m}\cdot\frac{\alpha}{N}\\
b_\mathrm{new} = b_\mathrm{old} - \frac{\partial f}{\partial b}\cdot \frac{\alpha}{N}
0. Choose a learning rate hyperparameter α
{
\vec{x}=x_1...x_N
parameter
function f
\frac{\partial{f}}{\partial{w}} > 0
\frac{\partial{f}}{\partial{w}} < 0
\vec{y} = a\vec{x}+b
Find the best parameters by finding the minimum of the L2 hyperplane
at every step look around and choose the best direction
How do I know in which direction to go?
find the direction where L2 decreases by taking the gradient of the loss function
w respect to the parameters (a,b)
global minimum
L2
\vec{L_2} = \frac{1}{N}\sum_i{(y_i - (a{x_i} + b))^2}
Gradient Descent
Used to optimize parameters of a function fit to data
\vec{x} = x_1...x_N\\
f(\vec{x},m,b) = m\vec{x} + b\\
L_2 = \frac{1}{N}\sum(y -( m\vec{x} + b))^2
def gradDesc(m, b, x, y, alpha, ax=None):
N = len(x)
#partial derivative: -2x(y - (mx + b)), -2(y - (mx + b))
f_m = (-2 * x * (y - (m * x + b))).sum()
f_b = (-2 * (y - (m * x + b))).sum()
# We subtract because the derivatives point in direction of steepest ascent
m -= f_m / float(N) * alpha
b -= f_b / float(N) * alpha
return m, b
#initial setup
m_new, b_new = 11, 11
#hyperparmeters
epsilon = 1 #convergence threshold
alpha = 0.0005 #learning rate
print (loss(m, b, x, y))
while loss(m_new, b_new, x, y) > epsilon:
#time.sleep(1)
m_old, b_old = m_new, b_new
print (loss(m_new, b_new, x, y))
m_new, b_new = gradDesc(m_old, b_old, x, y, alpha, ax=ax)neural networks
recap
Perceptrons are linear classifiers: makes its predictions based on a linear predictor function
combining a set of weights (=parameters) with the feature vector.
y ~= ~\sum_i w_ix_i ~+~ b
y ~= ~wx ~+~ b
y ~= ~f(\sum_i w_ix_i ~+~ b)
.
.
.
x_1
x_2
x_N
+b
f
output
f
activation function
weights
w_i
bias
b
w_2
w_1
w_N
recap
perceptrons
multilayer perceptron
x_2
x_3
output
Fully connected: all nodes go to all nodes of the next layer.
input layer
hidden layer
output layer
1970: multilayer perceptron architecture
x_1
recap
multilayer perceptron
x_2
x_3
output
Fully connected: all nodes go to all nodes of the next layer.
layer of perceptrons
w_{11}x_1 + w_{12}x_2 + w_{13}x_3 + b1
w_{21}x_1 + w_{22}x_2 + w_{23}x_3 + b1
w_{31}x_1 + w_{32}x_2 + w_{33}x_3 + b1
w_{41}x_1 + w_{42}x_2 + w_{43}x_3 + b1
x_1
activation functions
0
Back
Propagation
Training ANN
back-propagation
how does linear descent look when you have a whole network structure with hundreds of weights and biases to optimize??
x_{j}~=~\sum_i y_{i}w_{ji} ~~~~~~ y_j~=\frac{1}{1+e^{-x_j}}
.
.
.
x_1
x_N
f
+b
f
w_2
output
Seminal paper
Y. LeCun 1998
\vec{y} = f_N(....(f_1(\vec{x}{ W_i + b_1}...W_N + b_N)))
W4
y = \frac{1}{ 1+e^{-\frac{w7}{1+e^{-w_1x_1-w_4x_2 - b_1}} - \frac{w8}{1+e^{-x_1w_2-w_5x_2 - b_2}}- \frac{w9}{1+e^{-x_1w_3-x_2w_6 - b_3}}-b_4}}
\vec{y} = f_N(....(f_1(\vec{x}{ W_i + b_1}...W_N + b_N)))
Training models with this many parameters requires a lot of care:
- defining the metric
- choose optimization schemes
- training/validation/testing sets
Small changes in the parameters leads to small changes in the output for the right activation functions.
Training a DNN
feed data forward through network and calculate cost metric
for each layer, calculate effect of small changes on next layer
C=\frac{1}{2}|y−a^L|^2~=~\frac{1}{2}\sum_j(y_j−a^L_j)^2
define a cost function, e.g.
\vec{y} = f_N(....(f_1(\vec{x}{ W_i + b_1}...W_N + b_N)))
back-propagation
how does linear descent look when you have a whole network structure with hundreds of weights and biases to optimize??
think of applying just gradient to a function of a function of a function... use:
1) partial derivatives, 2) chain rule
C=\frac{1}{2}|y−a^L|^2~=~\frac{1}{2}\sum_j(y_j−a^L_j)^2
define a cost function, e.g.
Training a DNN
ISSUES TO WATCH FOR
Training a DNN
Exploding Gradient
the gradients of the network's loss with respect to the parameters (weights) become excessively large.
The "explosion" of the gradient can lead to numerical instability and the inability of the network to converge.
Erratic learning, with the loss becoming NaN (not a number) or Inf (infinity)
see link
Vanishing Gradient
when the gradients are very small, they can diminish as they are propagated back through the network, leading to minimal or no updates to the weights in the initial layers.
Activation functions like the sigmoid or hyperbolic tangent (tanh) have gradients that are in the range of 0 to 0.25 for sigmoid and -1 to 1 for tanh: the gradients of the loss function with respect to the parameters can become very small
Slow and stalled learning
see link
CNN
1
Convolutional Neural Nets
@akumadog
Brain Programming and the Random Search in Object Categorization
The visual cortex learns hierarchically: first detects simple features, then more complex features and ensembles of features
CNN
CNN
1a
Convolution
Convolution
convolution is a mathematical operator on two functions
f and g
that produces a third function
f x g
expressing how the shape of one is modified by the other.
o
Convolution Theorem
f * g= \mathcal{F}^{-1}\big\{\mathcal{F}\{f\}\cdot\mathcal{F}\{g\}\big\}
\mathcal{F}
fourier transform
{\displaystyle {\begin{aligned}F(\nu )&=\int _{\mathbb {R} ^{n}}f(x)e^{-2\pi ix\cdot \nu }\,dx,\\
G(\nu )&=\int _{\mathbb {R} ^{n}}g(x)e^{-2\pi ix\cdot \nu }\,dx,\end{aligned}}}
two images.
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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feature maps
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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convolution
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model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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(-1*1) + (-1*-1) + (-1*-1) + \\
(-1*-1)+(1*1)+(-1*-1)\\
(-1*-1)+(-1*-1)+(1*1)\\
= 7
| 7 | ||
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=
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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1
1
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(-1*1) + (-1*-1) + (-1*-1) + \\
(-1*1)+(-1*1)+(-1*1)\\
(-1*-1)+(-1*1)+(-1*1)\\
= -3
| 7 | -3 | |
|---|---|---|
=
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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| 7 | -3 | 3 |
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=
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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| ? | ||
=
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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| 7 | -1 | 3 |
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| ? | ? | |
=
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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| ? | ? | |
=
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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| 7 | -1 | 3 |
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| ? | ? | |
=
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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| 7 | -1 | 3 |
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=
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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=
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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=
input layer
feature map
convolution layer
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
1
1
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| 7 | -3 | 3 |
| -3 | 5 | -3 |
| 3 | -1 | 7 |
=
input layer
feature map
convolution layer
the feature map is "richer": we went from binary to R
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
1
1
1
1
1
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| -1 | 1 | -1 |
| -1 | -1 | 1 |
| 7 | -3 | 3 |
| -3 | 5 | -3 |
| 3 | -1 | 7 |
=
input layer
feature map
convolution layer
the feature map is "richer": we went from binary to R
and it is reminiscent of the original layer
7
5
7
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
Convolve with different feature: each neuron is 1 feature
CNN
1b
ReLu
| 7 | -3 | 3 |
| -3 | 5 | -3 |
| 3 | -1 | 7 |
7
5
7
ReLu: normalization that replaces negative values with 0's
| 7 | 0 | 3 |
| 0 | 5 | 0 |
| 3 | 0 | 7 |
7
5
7
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
1c
Max-Pool
CNN
MaxPooling: reduce image size, generalizes result
| 7 | 0 | 3 |
| 0 | 5 | 0 |
| 0 | 0 | 7 |
7
5
7
MaxPooling: reduce image size, generalizes result
| 7 | 0 | 3 |
| 0 | 5 | 0 |
| 3 | 0 | 7 |
7
5
7
2x2 Max Poll
| 7 | 5 |
MaxPooling: reduce image size, generalizes result
| 7 | 0 | 3 |
| 0 | 5 | 0 |
| 3 | 0 | 7 |
7
5
7
2x2 Max Poll
| 7 | 5 |
| 5 |
MaxPooling: reduce image size, generalizes result
| 7 | 0 | 3 |
| 0 | 5 | 0 |
| 3 | 0 | 7 |
7
5
7
2x2 Max Poll
| 7 | 5 |
| 5 | 7 |
MaxPooling: reduce image size & generalizes result
By reducing the size and picking the maximum of a sub-region we make the network less sensitive to specific details
model.add(MaxPooling2D(pool_size=(2, 2)))CNN
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
model = Sequencial()
model.add(Conv2D(32, kernel_size=(10, 10),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))final layer:
the final layer is fully connected MPL
x
O
last hidden layer
output layer
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
model = Sequencial()
model.add(Conv2D(32, kernel_size=(10, 10),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(2, activation='softmax'))
Stack multiple convolution layers
overfitting
3
Minibatch
&
Dropout
Overfitting
What are the symptoms
How can we fix it?
model performance (accuracy)
model performance (accuracy)
tree depth
tree depth
ANN training epochs
- If one updates model parameters after processing the whole training data (i.e., epoch), it would take too long to get a model update in training, and the entire training data probably won’t fit in the memory.
- If one updates model parameters after processing every instance (i.e., stochastic gradient descent), model updates would be too noisy, and the process is not computationally efficient.
- Therefore, minibatch gradient descent is introduced as a trade-off between fast model updates (memory efficiency) and accurate model updates (computational efficiency).
Split your training set into many smaller subsets and train on each small set separately
overfitting
overfitting
Dropout
Artificially remove some neurons for different minibatches to avoid overfitting
output
from keras.models import Sequential
from keras.layers import Dropout
model.add(Dropout(0.5))Class Imbalance
what is the simplest classifier you can build for this dataset ?
what is the accuracy?
x
y
Class Imbalance
If your dataset is imbalanced (more of one class than the other)
your model will learn that it is better to guess the most common class
this will contaminate the prediction
what is the simplest classifier you can build for this dataset ?
what is the accuracy?
x
y
Class Imbalance
If your dataset is imbalanced (more of one class than the other)
key concepts
Architecture components: neurons, activation function
- basically each neuron is a multivariate regression with an activation function that turns the output into a probability
- changing the weights and biases in the linear regression gives different results
Single layer NN: perceptrons
- perceptrons were developed in the 50s but a long time passed since then till people figured out how to build complex layered architectures and especially how to train them
Deep NN:
- DNN are multi-layer architectures of neurons. They can be fully connected (each neuron goes to each neuron of the next layer) or not (a neuron goes only to some neurons in the next layer)
- DNN have a lot of parameters (thousands!) which makes the interpretability and feature extraction of NN difficult.
key concepts
Convolutional NN
- convolutional NN are DNN with three types of layers:
- convolutional layers: run filters through an image to detect features like edges or colors
- maxpool layers: decrease the size of the previous layer outputs and removes some details
- ReLU : rectified linear units: normalizes the output of conv layers so that it is all positive (sets negatives to 0)
- CNNs are great for the stud of structure in large datasets (images are large datasets)
Training an NN:
- most ML methods are trained by gradient descent: change weights and biases based on the derivative of the loss (or cost) function
- DLL are difficult to train cause of the layer structure
- backpropagation propagates changes to the weights to the entire NN
- Minibatch: split the training set into many (100s!) subset and use these to train the NN
- Dropout: set some neurons to zero to avoid overfitting
Galaxyzoo challenge on Kaggle
data science for (physical) scientists 13
By federica bianco
data science for (physical) scientists 13
convolutional neural networks
