## 선형 분류 (Linear Classification)

지난 섹션에서는 특정 카테고리에서 하나의 라벨을 이미지에 붙이는 문제인 이미지 분류에 대해 소개하였다. 또한, 학습 데이터셋에 있는 (라벨링된) 이미지들 중 가까이 있는 것들의 라벨을 활용하는 k-Nearest Neighbor (kNN) 분류기에 대해 설명하였다. 앞서 살펴보았듯이, kNN은 몇 가지 단점이 있다:

• 이 분류기는 모든 학습 데이터를 기억 해야 하고, 나중에 테스트 데이터와 비교하기 위해 저장해 두어야 한다. 이것은 메모리 공간 관점에서 매우 비효율적이고, 일반적인 데이터셋들은 용량이 기가바이트 단위를 쉽게 넘기는 것이 많기 때문에 문제가 된다.
• 테스트 이미지를 분류할 때 모든 학습 이미지와 다 비교를 해야 하기 때문에 매우 계산량/시간이 많이 소요된다.

Overview. 이번 노트에서는 이미지 분류를 위한 보다 강력한 방법들을 발전시켜나갈 것이고, 이는 나중에 뉴럴 네트워크와 컨볼루션 신경망으로 확장될 것이다. 이 방법들은 두 가지 중요한 요소가 있다: 데이터를 클래스 스코어로 매핑시키는 스코어 함수, 그리고 예측한 스코어와 실제(ground truth) 라벨과의 차이를 정량화해주는 손실 함수 가 그 두 가지이다. 우리는 이를 최적화 문제로 바꾸어서 스코어 함수의 파라미터들에 대한 손실 함수를 최소화할 것이다.

### 이미지에서 라벨 스코어로의 파라미터화된 매핑(mapping)

먼저, 이미지의 픽셀 값들을 각 클래스에 대한 신뢰도 점수 (confidence score)로 매핑시켜주는 스코어 함수를 정의한다. 여기서는 구체적인 예시를 통해 각 과정을 살펴볼 것이다. 이전 노트에서처럼, 학습 데이터셋 이미지들인 $x_i \in R^D$가 있고, 각각이 해당 라벨 $y_i$를 갖고 있다고 하자. 여기서 $i = 1 \dots N$, 그리고 $y_i \in \{ 1 \dots K \}$이다. 즉, 학습할 데이터 N 개가 있고 (각각은 D 차원의 벡터이다.), 총 K 개의 서로 다른 카테고리(클래스)가 있다. 예를 들어, CIFAR-10 에서는 N = 50,000 개의 학습 데이터 이미지들이 있고, 각각은 D = 32 x 32 x 3 = 3072 픽셀로 이루어져 있으며, (dog, cat, car, 등등) 10개의 서로 다른 클래스가 있으므로 K = 10 이다. 이제 이미지의 픽셀값들을 클래스 스코어로 매핑해 주는 스코어 함수 $f: R^D \mapsto R^K$ 을 아래에 정의할 것이다.

선형 분류기 (Linear Classifier). 이 파트에서는 가장 단순한 함수라고 할 수 있는 선형 매핑 함수로 시작할 것이다.

위 식에서, 우리는 각 이미지 $x_i$의 모든 픽셀들이 [D x 1] 모양을 갖는 하나의 열 벡터로 평평하게 했다고 가정하였다. [K x D] 차원의 행렬 W 와 [K x 1] 차원의 벡터 b 는 이 함수의 파라미터 이다. CIFAR-10 에서 $x_i$ 는 i번째 이미지의 모든 픽셀을 [3072 x 1] 크기로 평평하게 모양을 바꾼 열 벡터가 될 것이고, W 는 [10 x 3072], b 는 [10 x 1] 여서 3072 개의 숫자가 함수의 입력(이미지 픽셀 값들)으로 들어와 10개의 숫자가 출력(클래스 스코어)으로 나오게 된다. W 안의 파라미터들은 보통 weight 라고 불리고, bbias 벡터 라 불리는데, 그 이유는 b가 실제 입력 데이터인 $x_i$와의 아무런 상호 작용이 없이 출력 스코어 값에는 영향을 주기 때문이다. 그러나 보통 일반적으로 사람마다 weight파라미터(parameter) 두 개의 용어를 혼용해서 사용하는 경우가 많다.

여기서 몇 가지 짚고 넘어갈 점이 있다.

• 먼저, 한 번의 행렬곱 $W x_i$ 만으로 10 개의 로 다른 분류기(각 클래스마다 하나씩)를 병렬로 계산하는 효과를 나타내고 있다는 점을 살펴보자. 이 때 W 행렬의 각 열이 각각 하나의 분류기가 된다.
• 또한, 여기서 입력 데이터 $(x_i, y_i)$는 주어진 값이고 고정되어 있지만, 파라미터들인 W, b 의 세팅은 우리가 조절할 수 있다는 점을 생각하자. 우리의 최종 목표는 전체 학습 데이터에 대해서 우리가 계산할 스코어 값들이 실제 (ground truth) 라벨과 가장 잘 일치하도록 이 파라미터 값들을 정하는 것이다. 이후(아래)에 자세한 방법에 대해 다룰 것이지만, 직관적으로 간략하게 말하자면 올바르게 잘 맞춘 클래스가 틀린 클래스들보다 더 높은 스코어를 갖도록 조절할 것이다.
• 이러한 방식의 장점은, 학습 데이터가 파라미터들인 W, b 를 학습하는데 사용되지만 학습이 끝난 이후에는 학습된 파라미터들만 남기고, 학습에 사용된 데이터셋은 더 이상 필요가 없다는 (따라서 메모리에서 지워버려도 된다는) 점이다. 그 이유는, 새로운 테스트 이미지가 입력으로 들어올 때 위의 함수에 의해 스코어를 계산하고, 계산된 스코어를 통해 바로 분류되기 때문이다.
• 마지막으로, 테스트 이미지를 분류할 때 행렬곱 한 번과 덧셈 한 번을 하는 계산만 필요하다는 점을 주목하자. 이것은 테스트 이미지를 모든 학습 이미지와 비교하는 것에 비하면 매우 빠르다.

스포일러: 컨볼루션 신경망(Convolutional Neural Networks)은 정확히 위의 방식처럼 이미지 픽셀 값을 스코어 값으로 매핑시켜 주지만, 매핑시켜주는 함수 ( f ) 가 훨씬 더 복잡해지고 더 많은 수의 파라미터를 갖고 있을 것이다.

### 선형 분류기 분석하기

선형 분류기는 클래스 스코어를 이미지의 모든 픽셀 값들의 가중치 합으로 스코어를 계산하고, 이 때 각 픽셀의 3 개의 색 채널을 모두 고려하는 것에 주목하자. 이 때 각 가중치(파라미터, weights)에 어떤 값을 주느냐에 따라 스코어 함수는 이미지의 특정 위치에서 특정 색깔을 선호하거나 선호하지 않거나 (가중치 값의 부호에 따라) 할 수 있다. 예를 들어, “ship” 클래스는 이미지의 가장자리 부분에 파란색이 많은 경우에 (강, 바다 등의 물에 해당하는 색) 스코어 값이 더 높아질 것이라고 추측해 볼 수 있을 것이다. 즉, “ship” 분류기는 파란색 채널의 파라미터(weights)들이 양의 값을 갖고 (파란색이 존재하는 것이 ship의 스코어를 증가시키도록), 빨강/초록색 채널에는 음의 값을 갖는 파라미터들이 많을 것이라고 (빨간색/초록색의 존재는 ship의 스코어를 감소시키도록) 예상할 수 있다. 이미지에서 클래스 스코어로의 매핑 예시. 시각화를 위해서, 이미지가 픽셀 4 개 만으로 이루어져 있고 (색 채널도 고려하지 않고, 단일 채널이라고 생각하자), 3 개의 클래스가 있다고 하자 (빨강 (cat), 초록 (dog), 파랑 (ship) 클래스). (주: 여기에서의 색깔은 3 개의 클래스를 나타내기 위함이고, RGB 채널과는 전혀 상관이 없다.) 이제 이미지 픽셀들을 펼쳐서 열 벡터로 만들고 각 클래스에 대해 행렬곱을 수행하면 스코어 값을 얻을 수 있다. 여기서 정해준 파라미터 W 값들은 매우 안 좋은 예시인 것을 확인하자: 현재의 파라미터로는 고양이(cat) 이미지를 매우 낮은 cat 스코어를 갖도록 한다. 이 경우, 현재의 파라미터 값은 우리가 dog 이미지를 보고있다고 생각하고 있다.

이미지와 고차원 공간 상의 점에 대한 비유. 이미지들을 고차원 열 벡터로 펼쳤기 때문에, 우리는 각 이미지를 이 고차원 공간 상의 하나의 점으로 생각할 수 있다 (e.g. CIFAR-10 데이터셋의 각 이미지는 32x32x3 개의 픽셀로 이루어진 3072-차원 공간 상의 한 점이 된다). 마찬가지로 생각하면, 전체 데이터셋은 라벨링된 고차원 공간 상의 점들의 집합이 될 것이다.

위에서 각 클래스에 대한 스코어를 이미지의 모든 픽셀에 대한 가중치 합으로 정의했기 때문에, 각 클래스 스코어는 이 공간 상에서의 선형 함수값이 된다. 3072-차원 공간은 시각화할 수 없지만, 2차원으로 축소시켰다고 상상해보면 우리의 분류기가 어떤 행동을 하는지를 시각화하려고 시도해볼 수 있을 것이다: 이미지 공간의 시각화. 각 이미지는 하나의 점에 해당되고, 3 개의 분류기가 표시되어 있다. 자동차(car) 분류기(빨간색)를 예로 들어보면, 빨간색 선이 이 공간 상에서 car 클래스에 대해 스코어 값이 0이 되는 모든 점을 나타낸 것이다. 빨간색 화살표는 스코어가 증가하는 방향을 나타낸 것으로, 빨간색 선의 오른쪽에 있는 점들은 양의 (그리고 선형적으로 증가하는) 스코어 값을 가질 것이고, 왼쪽의 점들은 음의 (그리고 선형적으로 감소하는) 스코어 값을 가질 것이다.

위에서 살펴보았듯이, $W$의 각 행은 각각의 클래스를 구별하는 분류기이다. 각 행에 있는 숫자들을 기하학적으로 해석해보자면, 우리가 $W$의 하나의 행을 바꾸면 픽셀 공간에서 해당하는 선이 다른 방향으로 회전할 것이다. 반면에, bias인 $b$는 분류기가 그 선들을 평행이동 할 수 있도록 해준다. 특히, bias가 없다면 $x_i = 0$가 입력으로 들어왔을 때 파라미터 값들에 상관없이 항상 스코어가 0이 될 것이고, 모든 (분류) 선들이 원점을 지나야만 할 것이다.

템플릿 매칭으로서의 선형 분류기 해석. 파라미터 $W$에 대해 다른 방식으로 해석해보면, $W$의 각 행은 각 클래스별 템플릿 (또는 프로토타입)에 해당된다. 이미지의 각 클래스 스코어는 각 템플릿들을 이미지와 내적(inner product, 또는 dot product)을 통해 하나하나 비교함으로써 계산되고, 이 스코어를 기준으로 가장 잘 “맞는” 것이 무엇인지 정한다. 즉, 선형 분류기가 결국 템플릿 매칭을 하고 있고, 각 템플릿이 학습을 통해 배워진다고 할 수 있다. 또다른 방식으로 생각해보면, 우리는 Nearest Neighbor와 비슷한 것을 하고 있는데, 수 천 장의 학습 이미지를 갖고 있지 않고 각 클래스마다 한 장의 이미지만 사용한다고 볼 수 있다. (다만, 그 이미지를 학습하고, 학습 데이터셋에 실제로 존재하는 이미지일 필요는 없다.) 이 때, 거리 함수로는 L1이나 L2 거리를 사용하지 않고 서로 내적한 것(의 반대 부호인 값)을 사용한다. 약간의 선행학습: CIFAR-10 데이터셋에 학습된 파라미터들의 시각화 예시. 예를 들어 ship 템플릿을 보면, 예상할 수 있듯이 많은 수의 파란색 픽셀들로 이루어져 있다는 점에 주목하자. 이 템플릿은 배(ship)가 바다 위에 떠있는 이미지와 내적을 통해 비교되었을 때, 높은 스코어 값을 가질 것이다.

추가적으로, horse 템플릿은 머리가 두 개인 말(horse)이 있는 것처럼 보이는데, 이것은 데이터셋 안에 왼쪽을 보고 있는 말과 오른쪽을 보고 있는 말이 섞여있기 때문이다. 선형 분류기는 말에 대한 이 두 가지 모드를 하나의 템플릿으로 합친 것을 확인할 수 있다. 이와 비슷한 현상으로, car 분류기는 모든 방향 및 색깔의 자동차 모양들을 하나의 템플릿으로 합쳐 놓았다. 특히, 이 템플릿이 결과적으로 붉은 색을 띄는 것으로 보아 CIFAR-10 데이터셋에는 다른 색깔에 비해 빨간색 자동차가 더 많다는 점을 알 수 있다. 선형 분류기는 여러 가지 색깔의 자동차를 제대로 분류하기에는 너무 모델이 단순하지만, 나중에 배울 뉴럴 네트워크는 이를 해결할 수 있다. 약간만 미리 살펴보자면, 뉴럴 네트워크는 히든 레이어의 각 뉴런들이 특정 자동차 타입 (e.g. 왼쪽을 바라보고 있는 초록색 자동차, 정면을 보고 있는 파란색 차, 등등)을 검출하도록 할 수 있고, 다음 레이어의 뉴런들이 이 정보들을 종합하여 각각의 자동차 타입 검출기의 점수의 가중치 합을 통해 보다 정확한 (자동차에 대한) 스코어를 계산할 수 있다.

Bias 트릭. 다음 내용으로 넘어가기 전에, 두 파라미터 $W, b$를 하나로 표현하는 간단한 트릭을 소개한다. 앞에서 스코어 함수는 아래와 같이 정의되었다.

앞으로 내용을 전개해 나갈 때 두 가지 파라미터를 (bias $b$와 weight $W$) 매번 동시에 고려해야 한다면 표현이 번거로워진다. 흔히 사용하는 트릭은 이 두 파라미터들을 하나의 행렬로 합치고, $x_i$를 항상 $1$의 값을 갖는 한 차원 - 디폴트 bias 차원 - 을 늘리는 방식이다. 이 한 차원 추가하는 것으로, 새 스코어 함수는 행렬곱 한 번으로 계산이 가능해진다:

With our CIFAR-10 example, $x_i$ is now [3073 x 1] instead of [3072 x 1] - (with the extra dimension holding the constant 1), and $W$ is now [10 x 3073] instead of [10 x 3072]. The extra column that $W$ now corresponds to the bias $b$. An illustration might help clarify: Illustration of the bias trick. Doing a matrix multiplication and then adding a bias vector (left) is equivalent to adding a bias dimension with a constant of 1 to all input vectors and extending the weight matrix by 1 column - a bias column (right). Thus, if we preprocess our data by appending ones to all vectors we only have to learn a single matrix of weights instead of two matrices that hold the weights and the biases.

Image data preprocessing. As a quick note, in the examples above we used the raw pixel values (which range from [0…255]). In Machine Learning, it is a very common practice to always perform normalization of your input features (in the case of images, every pixel is thought of as a feature). In particular, it is important to center your data by subtracting the mean from every feature. In the case of images, this corresponds to computing a mean image across the training images and subtracting it from every image to get images where the pixels range from approximately [-127 … 127]. Further common preprocessing is to scale each input feature so that its values range from [-1, 1]. Of these, zero mean centering is arguably more important but we will have to wait for its justification until we understand the dynamics of gradient descent.

### 손실함수(Loss function)

In the previous section we defined a function from the pixel values to class scores, which was parameterized by a set of weights $W$. Moreover, we saw that we don’t have control over the data $(x_i,y_i)$ (it is fixed and given), but we do have control over these weights and we want to set them so that the predicted class scores are consistent with the ground truth labels in the training data.

For example, going back to the example image of a cat and its scores for the classes “cat”, “dog” and “ship”, we saw that the particular set of weights in that example was not very good at all: We fed in the pixels that depict a cat but the cat score came out very low (-96.8) compared to the other classes (dog score 437.9 and ship score 61.95). We are going to measure our unhappiness with outcomes such as this one with a loss function (or sometimes also referred to as the cost function or the objective). Intuitively, the loss will be high if we’re doing a poor job of classifying the training data, and it will be low if we’re doing well.

#### Multiclass Support Vector Machine 손실함수

There are several ways to define the details of the loss function. As a first example we will first develop a commonly used loss called the Multiclass Support Vector Machine (SVM) loss. The SVM loss is set up so that the SVM “wants” the correct class for each image to a have a score higher than the incorrect classes by some fixed margin $\Delta$. Notice that it’s sometimes helpful to anthropomorphise the loss functions as we did above: The SVM “wants” a certain outcome in the sense that the outcome would yield a lower loss (which is good).

Let’s now get more precise. Recall that for the i-th example we are given the pixels of image $x_i$ and the label $y_i$ that specifies the index of the correct class. The score function takes the pixels and computes the vector $f(x_i, W)$ of class scores, which we will abbreviate to $s$ (short for scores). For example, the score for the j-th class is the j-th element: $s_j = f(x_i, W)_j$. The Multiclass SVM loss for the i-th example is then formalized as follows:

Example. Lets unpack this with an example to see how it works. Suppose that we have three classes that receive the scores $s = [13, -7, 11]$, and that the first class is the true class (i.e. $y_i = 0$). Also assume that $\Delta$ (a hyperparameter we will go into more detail about soon) is 10. The expression above sums over all incorrect classes ($j \neq y_i$), so we get two terms:

You can see that the first term gives zero since [-7 - 13 + 10] gives a negative number, which is then thresholded to zero with the $max(0,-)$ function. We get zero loss for this pair because the correct class score (13) was greater than the incorrect class score (-7) by at least the margin 10. In fact the difference was 20, which is much greater than 10 but the SVM only cares that the difference is at least 10; Any additional difference above the margin is clamped at zero with the max operation. The second term computes [11 - 13 + 10] which gives 8. That is, even though the correct class had a higher score than the incorrect class (13 > 11), it was not greater by the desired margin of 10. The difference was only 2, which is why the loss comes out to 8 (i.e. how much higher the difference would have to be to meet the margin). In summary, the SVM loss function wants the score of the correct class $y_i$ to be larger than the incorrect class scores by at least by $\Delta$ (delta). If this is not the case, we will accumulate loss.

Note that in this particular module we are working with linear score functions ( $f(x_i; W) = W x_i$ ), so we can also rewrite the loss function in this equivalent form:

where $w_j$ is the j-th row of $W$ reshaped as a column. However, this will not necessarily be the case once we start to consider more complex forms of the score function $f$.

A last piece of terminology we’ll mention before we finish with this section is that the threshold at zero $max(0,-)$ function is often called the hinge loss. You’ll sometimes hear about people instead using the squared hinge loss SVM (or L2-SVM), which uses the form $max(0,-)^2$ that penalizes violated margins more strongly (quadratically instead of linearly). The unsquared version is more standard, but in some datasets the squared hinge loss can work better. This can be determined during cross-validation.

The loss function quantifies our unhappiness with predictions on the training set The Multiclass Support Vector Machine "wants" the score of the correct class to be higher than all other scores by at least a margin of delta. If any class has a score inside the red region (or higher), then there will be accumulated loss. Otherwise the loss will be zero. Our objective will be to find the weights that will simultaneously satisfy this constraint for all examples in the training data and give a total loss that is as low as possible.

Regularization. There is one bug with the loss function we presented above. Suppose that we have a dataset and a set of parameters W that correctly classify every example (i.e. all scores are so that all the margins are met, and $L_i = 0$ for all i). The issue is that this set of W is not necessarily unique: there might be many similar W that correctly classify the examples. One easy way to see this is that if some parameters W correctly classify all examples (so loss is zero for each example), then any multiple of these parameters $\lambda W$ where $\lambda > 1$ will also give zero loss because this transformation uniformly stretches all score magnitudes and hence also their absolute differences. For example, if the difference in scores between a correct class and a nearest incorrect class was 15, then multiplying all elements of W by 2 would make the new difference 30.

In other words, we wish to encode some preference for a certain set of weights W over others to remove this ambiguity. We can do so by extending the loss function with a regularization penalty $R(W)$. The most common regularization penalty is the L2 norm that discourages large weights through an elementwise quadratic penalty over all parameters:

In the expression above, we are summing up all the squared elements of $W$. Notice that the regularization function is not a function of the data, it is only based on the weights. Including the regularization penalty completes the full Multiclass Support Vector Machine loss, which is made up of two components: the data loss (which is the average loss $L_i$ over all examples) and the regularization loss. That is, the full Multiclass SVM loss becomes:

Or expanding this out in its full form:

Where $N$ is the number of training examples. As you can see, we append the regularization penalty to the loss objective, weighted by a hyperparameter $\lambda$. There is no simple way of setting this hyperparameter and it is usually determined by cross-validation.

In addition to the motivation we provided above there are many desirable properties to include the regularization penalty, many of which we will come back to in later sections. For example, it turns out that including the L2 penalty leads to the appealing max margin property in SVMs (See CS229 lecture notes for full details if you are interested).

The most appealing property is that penalizing large weights tends to improve generalization, because it means that no input dimension can have a very large influence on the scores all by itself. For example, suppose that we have some input vector $x = [1,1,1,1]$ and two weight vectors $w_1 = [1,0,0,0]$, $w_2 = [0.25,0.25,0.25,0.25]$. Then $w_1^Tx = w_2^Tx = 1$ so both weight vectors lead to the same dot product, but the L2 penalty of $w_1$ is 1.0 while the L2 penalty of $w_2$ is only 0.25. Therefore, according to the L2 penalty the weight vector $w_2$ would be preferred since it achieves a lower regularization loss. Intuitively, this is because the weights in $w_2$ are smaller and more diffuse. Since the L2 penalty prefers smaller and more diffuse weight vectors, the final classifier is encouraged to take into account all input dimensions to small amounts rather than a few input dimensions and very strongly. As we will see later in the class, this effect can improve the generalization performance of the classifiers on test images and lead to less overfitting.

Note that biases do not have the same effect since, unlike the weights, they do not control the strength of influence of an input dimension. Therefore, it is common to only regularize the weights $W$ but not the biases $b$. However, in practice this often turns out to have a negligible effect. Lastly, note that due to the regularization penalty we can never achieve loss of exactly 0.0 on all examples, because this would only be possible in the pathological setting of $W = 0$.

Code. Here is the loss function (without regularization) implemented in Python, in both unvectorized and half-vectorized form:

def L_i(x, y, W):
"""
unvectorized version. Compute the multiclass svm loss for a single example (x,y)
- x is a column vector representing an image (e.g. 3073 x 1 in CIFAR-10)
with an appended bias dimension in the 3073-rd position (i.e. bias trick)
- y is an integer giving index of correct class (e.g. between 0 and 9 in CIFAR-10)
- W is the weight matrix (e.g. 10 x 3073 in CIFAR-10)
"""
delta = 1.0 # see notes about delta later in this section
scores = W.dot(x) # scores becomes of size 10 x 1, the scores for each class
correct_class_score = scores[y]
D = W.shape # number of classes, e.g. 10
loss_i = 0.0
for j in xrange(D): # iterate over all wrong classes
if j == y:
# skip for the true class to only loop over incorrect classes
continue
# accumulate loss for the i-th example
loss_i += max(0, scores[j] - correct_class_score + delta)
return loss_i

def L_i_vectorized(x, y, W):
"""
A faster half-vectorized implementation. half-vectorized
refers to the fact that for a single example the implementation contains
no for loops, but there is still one loop over the examples (outside this function)
"""
delta = 1.0
scores = W.dot(x)
# compute the margins for all classes in one vector operation
margins = np.maximum(0, scores - scores[y] + delta)
# on y-th position scores[y] - scores[y] canceled and gave delta. We want
# to ignore the y-th position and only consider margin on max wrong class
margins[y] = 0
loss_i = np.sum(margins)
return loss_i

def L(X, y, W):
"""
fully-vectorized implementation :
- X holds all the training examples as columns (e.g. 3073 x 50,000 in CIFAR-10)
- y is array of integers specifying correct class (e.g. 50,000-D array)
- W are weights (e.g. 10 x 3073)
"""
# evaluate loss over all examples in X without using any for loops
# left as exercise to reader in the assignment


The takeaway from this section is that the SVM loss takes one particular approach to measuring how consistent the predictions on training data are with the ground truth labels. Additionally, making good predictions on the training set is equivalent to minimizing the loss.

All we have to do now is to come up with a way to find the weights that minimize the loss.

### Practical Considerations

Setting Delta. Note that we brushed over the hyperparameter $\Delta$ and its setting. What value should it be set to, and do we have to cross-validate it? It turns out that this hyperparameter can safely be set to $\Delta = 1.0$ in all cases. The hyperparameters $\Delta$ and $\lambda$ seem like two different hyperparameters, but in fact they both control the same tradeoff: The tradeoff between the data loss and the regularization loss in the objective. The key to understanding this is that the magnitude of the weights $W$ has direct effect on the scores (and hence also their differences): As we shrink all values inside $W$ the score differences will become lower, and as we scale up the weights the score differences will all become higher. Therefore, the exact value of the margin between the scores (e.g. $\Delta = 1$, or $\Delta = 100$) is in some sense meaningless because the weights can shrink or stretch the differences arbitrarily. Hence, the only real tradeoff is how large we allow the weights to grow (through the regularization strength $\lambda$).

Relation to Binary Support Vector Machine. You may be coming to this class with previous experience with Binary Support Vector Machines, where the loss for the i-th example can be written as:

where $C$ is a hyperparameter, and $y_i \in \{ -1,1 \}$. You can convince yourself that the formulation we presented in this section contains the binary SVM as a special case when there are only two classes. That is, if we only had two classes then the loss reduces to the binary SVM shown above. Also, $C$ in this formulation and $\lambda$ in our formulation control the same tradeoff and are related through reciprocal relation $C \propto \frac{1}{\lambda}$.

Aside: Optimization in primal. If you’re coming to this class with previous knowledge of SVMs, you may have also heard of kernels, duals, the SMO algorithm, etc. In this class (as is the case with Neural Networks in general) we will always work with the optimization objectives in their unconstrained primal form. Many of these objectives are technically not differentiable (e.g. the max(x,y) function isn’t because it has a kink when x=y), but in practice this is not a problem and it is common to use a subgradient.

Aside: Other Multiclass SVM formulations. It is worth noting that the Multiclass SVM presented in this section is one of few ways of formulating the SVM over multiple classes. Another commonly used form is the One-Vs-All (OVA) SVM which trains an independent binary SVM for each class vs. all other classes. Related, but less common to see in practice is also the All-vs-All (AVA) strategy. Our formulation follows the Weston and Watkins 1999 (pdf) version, which is a more powerful version than OVA (in the sense that you can construct multiclass datasets where this version can achieve zero data loss, but OVA cannot. See details in the paper if interested). The last formulation you may see is a Structured SVM, which maximizes the margin between the score of the correct class and the score of the highest-scoring incorrect runner-up class. Understanding the differences between these formulations is outside of the scope of the class. The version presented in these notes is a safe bet to use in practice, but the arguably simplest OVA strategy is likely to work just as well (as also argued by Rikin et al. 2004 in In Defense of One-Vs-All Classification (pdf)).

### Softmax 분류기

It turns out that the SVM is one of two commonly seen classifiers. The other popular choice is the Softmax classifier, which has a different loss function. If you’ve heard of the binary Logistic Regression classifier before, the Softmax classifier is its generalization to multiple classes. Unlike the SVM which treats the outputs $f(x_i,W)$ as (uncalibrated and possibly difficult to interpret) scores for each class, the Softmax classifier gives a slightly more intuitive output (normalized class probabilities) and also has a probabilistic interpretation that we will describe shortly. In the Softmax classifier, the function mapping $f(x_i; W) = W x_i$ stays unchanged, but we now interpret these scores as the unnormalized log probabilities for each class and replace the hinge loss with a cross-entropy loss that has the form:

where we are using the notation $f_j$ to mean the j-th element of the vector of class scores $f$. As before, the full loss for the dataset is the mean of $L_i$ over all training examples together with a regularization term $R(W)$. The function $f_j(z) = \frac{e^{z_j}}{\sum_k e^{z_k}}$ is called the softmax function: It takes a vector of arbitrary real-valued scores (in $z$) and squashes it to a vector of values between zero and one that sum to one. The full cross-entropy loss that involves the softmax function might look scary if you’re seeing it for the first time but it is relatively easy to motivate.

Information theory view. The cross-entropy between a “true” distribution $p$ and an estimated distribution $q$ is defined as:

The Softmax classifier is hence minimizing the cross-entropy between the estimated class probabilities ( $q = e^{f_{y_i}} / \sum_j e^{f_j}$ as seen above) and the “true” distribution, which in this interpretation is the distribution where all probability mass is on the correct class (i.e. $p = [0, \ldots 1, \ldots, 0]$ contains a single 1 at the $y_i$ -th position.). Moreover, since the cross-entropy can be written in terms of entropy and the Kullback-Leibler divergence as $H(p,q) = H(p) + D_{KL}(p||q)$, and the entropy of the delta function $p$ is zero, this is also equivalent to minimizing the KL divergence between the two distributions (a measure of distance). In other words, the cross-entropy objective wants the predicted distribution to have all of its mass on the correct answer.

Probabilistic interpretation. Looking at the expression, we see that

can be interpreted as the (normalized) probability assigned to the correct label $y_i$ given the image $x_i$ and parameterized by $W$. To see this, remember that the Softmax classifier interprets the scores inside the output vector $f$ as the unnormalized log probabilities. Exponentiating these quantities therefore gives the (unnormalized) probabilities, and the division performs the normalization so that the probabilities sum to one. In the probabilistic interpretation, we are therefore minimizing the negative log likelihood of the correct class, which can be interpreted as performing Maximum Likelihood Estimation (MLE). A nice feature of this view is that we can now also interpret the regularization term $R(W)$ in the full loss function as coming from a Gaussian prior over the weight matrix $W$, where instead of MLE we are performing the Maximum a posteriori (MAP) estimation. We mention these interpretations to help your intuitions, but the full details of this derivation are beyond the scope of this class.

Practical issues: Numeric stability. When you’re writing code for computing the Softmax function in practice, the intermediate terms $e^{f_{y_i}}$ and $\sum_j e^{f_j}$ may be very large due to the exponentials. Dividing large numbers can be numerically unstable, so it is important to use a normalization trick. Notice that if we multiply the top and bottom of the fraction by a constant $C$ and push it into the sum, we get the following (mathematically equivalent) expression:

We are free to choose the value of $C$. This will not change any of the results, but we can use this value to improve the numerical stability of the computation. A common choice for $C$ is to set $\log C = -\max_j f_j$. This simply states that we should shift the values inside the vector $f$ so that the highest value is zero. In code:

f = np.array([123, 456, 789]) # example with 3 classes and each having large scores
p = np.exp(f) / np.sum(np.exp(f)) # Bad: Numeric problem, potential blowup

# instead: first shift the values of f so that the highest number is 0:
f -= np.max(f) # f becomes [-666, -333, 0]
p = np.exp(f) / np.sum(np.exp(f)) # safe to do, gives the correct answer



Possibly confusing naming conventions. To be precise, the SVM classifier uses the hinge loss, or also sometimes called the max-margin loss. The Softmax classifier uses the cross-entropy loss. The Softmax classifier gets its name from the softmax function, which is used to squash the raw class scores into normalized positive values that sum to one, so that the cross-entropy loss can be applied. In particular, note that technically it doesn’t make sense to talk about the “softmax loss”, since softmax is just the squashing function, but it is a relatively commonly used shorthand.

### SVM vs. Softmax

A picture might help clarify the distinction between the Softmax and SVM classifiers: Example of the difference between the SVM and Softmax classifiers for one datapoint. In both cases we compute the same score vector f (e.g. by matrix multiplication in this section). The difference is in the interpretation of the scores in f: The SVM interprets these as class scores and its loss function encourages the correct class (class 2, in blue) to have a score higher by a margin than the other class scores. The Softmax classifier instead interprets the scores as (unnormalized) log probabilities for each class and then encourages the (normalized) log probability of the correct class to be high (equivalently the negative of it to be low). The final loss for this example is 1.58 for the SVM and 1.04 for the Softmax classifier, but note that these numbers are not comparable; They are only meaningful in relation to loss computed within the same classifier and with the same data.

Softmax classifier provides “probabilities” for each class. Unlike the SVM which computes uncalibrated and not easy to interpret scores for all classes, the Softmax classifier allows us to compute “probabilities” for all labels. For example, given an image the SVM classifier might give you scores [12.5, 0.6, -23.0] for the classes “cat”, “dog” and “ship”. The softmax classifier can instead compute the probabilities of the three labels as [0.9, 0.09, 0.01], which allows you to interpret its confidence in each class. The reason we put the word “probabilities” in quotes, however, is that how peaky or diffuse these probabilities are depends directly on the regularization strength $\lambda$ - which you are in charge of as input to the system. For example, suppose that the unnormalized log-probabilities for some three classes come out to be [1, -2, 0]. The softmax function would then compute:

Where the steps taken are to exponentiate and normalize to sum to one. Now, if the regularization strength $\lambda$ was higher, the weights $W$ would be penalized more and this would lead to smaller weights. For example, suppose that the weights became one half smaller ([0.5, -1, 0]). The softmax would now compute:

where the probabilites are now more diffuse. Moreover, in the limit where the weights go towards tiny numbers due to very strong regularization strength $\lambda$, the output probabilities would be near uniform. Hence, the probabilities computed by the Softmax classifier are better thought of as confidences where, similar to the SVM, the ordering of the scores is interpretable, but the absolute numbers (or their differences) technically are not.

In practice, SVM and Softmax are usually comparable. The performance difference between the SVM and Softmax are usually very small, and different people will have different opinions on which classifier works better. Compared to the Softmax classifier, the SVM is a more local objective, which could be thought of either as a bug or a feature. Consider an example that achieves the scores [10, -2, 3] and where the first class is correct. An SVM (e.g. with desired margin of $\Delta = 1$) will see that the correct class already has a score higher than the margin compared to the other classes and it will compute loss of zero. The SVM does not care about the details of the individual scores: if they were instead [10, -100, -100] or [10, 9, 9] the SVM would be indifferent since the margin of 1 is satisfied and hence the loss is zero. However, these scenarios are not equivalent to a Softmax classifier, which would accumulate a much higher loss for the scores [10, 9, 9] than for [10, -100, -100]. In other words, the Softmax classifier is never fully happy with the scores it produces: the correct class could always have a higher probability and the incorrect classes always a lower probability and the loss would always get better. However, the SVM is happy once the margins are satisfied and it does not micromanage the exact scores beyond this constraint. This can intuitively be thought of as a feature: For example, a car classifier which is likely spending most of its “effort” on the difficult problem of separating cars from trucks should not be influenced by the frog examples, which it already assigns very low scores to, and which likely cluster around a completely different side of the data cloud.

### 선형 분류 웹 데모 We have written an interactive web demo to help your intuitions with linear classifiers. The demo visualizes the loss functions discussed in this section using a toy 3-way classification on 2D data. The demo also jumps ahead a bit and performs the optimization, which we will discuss in full detail in the next section.

### 요약

In summary,

• We defined a score function from image pixels to class scores (in this section, a linear function that depends on weights W and biases b).
• Unlike kNN classifier, the advantage of this parametric approach is that once we learn the parameters we can discard the training data. Additionally, the prediction for a new test image is fast since it requires a single matrix multiplication with W, not an exhaustive comparison to every single training example.
• We introduced the bias trick, which allows us to fold the bias vector into the weight matrix for convenience of only having to keep track of one parameter matrix.
• We defined a loss function (we introduced two commonly used losses for linear classifiers: the SVM and the Softmax) that measures how compatible a given set of parameters is with respect to the ground truth labels in the training dataset. We also saw that the loss function was defined in such way that making good predictions on the training data is equivalent to having a small loss.

We now saw one way to take a dataset of images and map each one to class scores based on a set of parameters, and we saw two examples of loss functions that we can use to measure the quality of the predictions. But how do we efficiently determine the parameters that give the best (lowest) loss? This process is optimization, and it is the topic of the next section.

### 추가 읽기 자료

These readings are optional and contain pointers of interest.