Linear Classification Lecture 3 - Artificial...
Transcript of Linear Classification Lecture 3 - Artificial...
Fei-Fei Li & Andrej Karpathy Lecture 2 - 7 Jan 2015Fei-Fei Li & Andrej Karpathy Lecture 2 - 7 Jan 20151
Lecture 3:Linear Classification
Fei-Fei Li & Andrej Karpathy Lecture 2 - 7 Jan 2015Fei-Fei Li & Andrej Karpathy Lecture 2 - 7 Jan 20152
Last time: Image Classification
cat
assume given set of discrete labels{dog, cat, truck, plane, ...}
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k-Nearest Neighbortraining set
test images
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Linear Classification1. define a score function
class scores
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Linear Classification1. define a score function
“weights” “bias vector”
data (image)
class scores“parameters”
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Linear Classification1. define a score function(assume CIFAR-10 example so32 x 32 x 3 images, 10 classes)
weights bias vector
data (image)[3072 x 1]
class scores
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Linear Classification1. define a score function(assume CIFAR-10 example so32 x 32 x 3 images, 10 classes)
weights[10 x 3072]
bias vector[10 x 1]
data (image)[3072 x 1]
class scores[10 x 1]
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Linear Classification
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Interpreting a Linear ClassifierQuestion:what can a linear classifier do?
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Interpreting a Linear Classifier
Example training classifiers on CIFAR-10:
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Interpreting a Linear Classifier
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Bias trick
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So far:We defined a (linear) score function:
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2. Define a loss function (or cost function, or objective)
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2. Define a loss function (or cost function, or objective)
- scores, label loss.
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2. Define a loss function (or cost function, or objective)
- scores, label loss.
Example:
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2. Define a loss function (or cost function, or objective)
- scores, label loss.
Example: Question: if you were to assign a single number to how “unhappy” you are with these scores, what would you do?
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2. Define a loss function (or cost function, or objective)One (of many ways) to do it: Multiclass SVM Loss
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2. Define a loss function (or cost function, or objective)One (of many ways) to do it: Multiclass SVM Loss
(One possible generalization of Binary Support Vector Machine to multiple classes)
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2. Define a loss function (or cost function, or objective)One (of many ways) to do it: Multiclass SVM Loss
loss due to example i sum over all
incorrect labelsdifference between the correct class score and incorrect class score
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loss due to example i sum over all
incorrect labelsdifference between the correct class score and incorrect class score
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Example:
e.g. 10
loss = ?
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Example:
e.g. 10
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There is a bug with the objective…
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L2 Regularization
Regularization strength
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L2 regularization: motivation
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Do we have to cross-validate both and ?
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So far…1. Score function
2. Loss function
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Softmax Classifier score functionis the same
(extension of Logistic Regression to multiple classes)
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Softmax Classifier score functionis the same
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Softmax Classifier score functionis the same
i.e. we’re minimizing the negative log likelihood.
softmax function
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Softmax Classifier score functionis the same
softmax function
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Softmax Classifier score functionis the same
i.e. we’re minimizing the negative log likelihood.
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Softmax vs. SVM
- Interpreting the probabilities from the Softmax
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Softmax vs. SVM
- Interpreting the probabilities from the Softmax
suppose the weights W were only half as large
(we use a higher regularization strength)
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Softmax vs. SVM
- Interpreting the probabilities from the Softmax
suppose the weights W were only half as large:
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Softmax vs. SVM- Interpreting the probabilities from the Softmax
suppose the weights W were only half as large:
What happens in the limit, as the regularization strength goes to infinity?
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Softmax vs. SVM
scores:[10, -2, 3][10, 9, 9][10, -100, -100]
1
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Softmax vs. SVM1
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Interactive Web Demo time....
http://vision.stanford.edu/teaching/cs231n/linear-classify-demo/
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Summary- We introduced a parametric approach to
image classification- We defined a score function (linear map)- We defined a loss function (SVM / Softmax)
One problem remains: How to find W,b ?
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Next class: Optimization,
Backpropagation
Find the W,b that minimizes the loss function.