# cs231n assignment 1

Recently I was following an online course on Convolutional Neural Networks (CNN) provided by Stanford. I find it a very nice hands-on material: slides and notes are easy to understand. Purely reading formulations can be confusing sometimes, but practicing experiments helps better understanding what the formulations and the symbols in them are expressing.

The first assignment is about basic assignments. It also includes some practice on vectorization, which may make Python code faster. I don’t do the nested version in some assignments, and some of code are half-vectorized.

## 1. KNN

KNN is the easiest one; this part is still worth doing, because it helps understand vectorization and cross validation.

### Train

In KNN, the process of training is simply remembering X_train and y_train:

• X_train: Shape as (#features, #train). Each column corresponds to a training sample.
• y_train: Shape as (#train,). Labels.

### Distances

Essence of KNN lies in computing distances. Input is simply X. Output is dists.

• X: Shape as (#features, #test). Each column corresponds to a test sample.
• XT: Shape as (#features, #train). Each column corresponds to a training sample.
• dists: Shape as (#test, #train). dists[i, j] means the distance of $i^{th}$ test sample to $j^{th}$ training sample.

For brevity, denote

• $D$ as #features
• $X^{(i)}$ as $i^{th}$ column of $X$, i.e. $i^{th}$ sample

Since %

, we can vectorize it like this:

def compute_distances_no_loops(self, X):
num_test = X.shape[0]
num_train = self.X_train.shape[0]
dists = np.zeros((num_test, num_train))

# vectorize
ip = X.dot(self.X_train.T) # inner product
XT2 = np.sum(self.X_train ** 2, axis=1)
X2 = np.sum(X ** 2, axis=1)
dists = np.sqrt(-2*ip + XT2 + X2.reshape(-1, 1))

### Predict

from collections import Counter
def predict_labels(self, dists, k=1):
num_test = dists.shape[0]
y_pred = np.zeros(num_test)

for i in xrange(num_test):
# pick nearest neighbors
closest_y = self.y_train[np.argsort(dists[i])[:k]]

# count which class appears most
y_pred[i] = Counter(closest_y).most_common(1)[0][0]

return y_pred

### Cross validation

Cross validation is a process to determine hyper-parameters; in this case, k is a hyper-parameter. We have X_train and X_test; now we subdivide X_train into cv_X_train and cv_X_test, where cv_ means cross validation.

num_folds = 5
k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]

X_train_folds = np.array(np.array_split(X_train, num_folds))
y_train_folds = np.array(np.array_split(y_train, num_folds))

k_to_accuracies = {}
dim = X_train.shape[1]
from cs231n.classifiers import KNearestNeighbor
for k in k_choices:
k_to_accuracies[k] = [0] * num_folds
for test_idx in range(num_folds):
train_ids = [idx for idx in range(num_folds) if idx != test_idx]
cv_X_train = X_train_folds[train_ids].reshape(-1, dim)
cv_X_test = X_train_folds[test_idx]
cv_y_train = y_train_folds[train_ids].reshape(-1)
cv_y_test = y_train_folds[test_idx]

classifier = KNearestNeighbor()
classifier.train(cv_X_train, cv_y_train)
dists = classifier.compute_distances_no_loops(cv_X_test)
cv_y_test_pred = classifier.predict_labels(dists, k)

num_correct = np.sum(cv_y_test_pred == cv_y_test)
num_test = len(cv_y_test)
accuracy = float(num_correct) / num_test
k_to_accuracies[k][test_idx] = accuracy

for k in sorted(k_to_accuracies): # Print out the computed accuracies
for accuracy in k_to_accuracies[k]:
print 'k = %d, accuracy = %f' % (k, accuracy)

## 2. SVM

Here I omit the pre-processing part – subtract the mean image and add an extra dimension ‘1’ to each sample, which has been explained by lecture notes.

Denote

• $x^{i}$ as $i^{th}$ column of $X$, i.e. $i^{th}$ sample
• $w_{j}$ as $j^{th}$ row of $W$
• $N$ as # of training samples
• $C$ as # of classes

Then

• Score function scores or wx: $f(x^{(i)}, W) = W x^{(i)}$

• Loss function: $L_{i} = \sum_{j \ne y_{i}}{\max(0, f(x^{(i)}, W)_{j} - f(x^{(i)}, W) + \Delta)}$

• Gradient of loss ddW: $% 0) \cdot x^{(i)} & \text{, if } j \ne y_{i} \\ -\left( \sum_{j \ne y_{i}}{\bf 1}(w_{j}x^{(i)} - w_{y_{i}}x^{(i)} + \Delta > 0) \right) \cdot x^{(i)} & \text{, otherwise} } %]]>$

• Total loss loss: $L = \frac{1}{N}\sum_{i}{L_{i}} + \frac{1}{2}\lambda\sum_{k,l}{w_{k,l}^{2}}$

• Gradient of total loss dW: $\nabla_{W}L = \frac{1}{N}\sum_{i}{\nabla_{W} L_{i}} + \lambda W$

_variable_s that represent these terms are used in the following snippet.

### Train

Training is to obtain best $W$, minimizing total loss $L$. Here we use gradient descent to find best $W$, and we need to calculate total loss and gradient of it.

#### Loss by nested loop

Outer loop iterates i of $L_{i}$ over $N$, corresponding to a specific sample. Inner loop iterates j of $W_{j}$ over $C$, corresponding to a specific class.

def svm_loss_naive(W, X, y, reg):
dW = np.zeros(W.shape) # initialize the gradient as zero
delta = 1

num_classes = W.shape[0]
num_train = X.shape[1]
loss = 0.0
for i in xrange(num_train):
ddW = np.zeros(W.shape)
ddWyi = np.zeros(W[0].shape)
scores = W.dot(X[:, i])
correct_class_score = scores[y[i]]
for j in xrange(num_classes):
if j == y[i]:
continue
margin = scores[j] - correct_class_score + delta
if margin > 0:
loss += margin
ddW[j] = X[:, i] ## be careful, it's a reference
ddWyi += ddW[j]
ddW[y[i]] = -ddWyi
dW += ddW

# divided by num_train
loss /= num_train
dW /= num_train

loss += 0.5 * reg * np.sum(W * W)
dW += reg * W

return loss, dW

#### Loss by half vectorization

Inner loop can be vectorized. Rather than count margin loop by loop under j, we can count them in a once. It depends on scores and correct_class_score. scores already satisfies, so we’ll try to obtain correct_class_score for each j.

Sorry about naming, but here I name wx as scores, judge as margins. Here’s the snippet:

def svm_loss_vectorized(W, X, y, reg):
dW = np.zeros(W.shape) # initialize the gradient as zero
wx = W.dot(X)
delta = 1

### loss
# wxy chooses scores of right labels
# its shape is (#samples,)
wxy = [ wx[y[i], i] for i in xrange(wx.shape[1]) ]

# judge expression
# remember to exclude on y[i]'s
judge = wx - wxy + delta
# make judge 0 on y[i]
for i in xrange(wx.shape[1]):
judge[y[i], i] = 0

# mass is a matrix holding all useful temp results
# shape of judge is (#class, #train)
mass = np.maximum(0, judge)

loss = np.sum(mass) / X.shape[1]
loss += 0.5 * reg * np.sum(W * W)

Now explain on the dW part. In each iteration under i, dW adds an ddW. Each row of ddW is a weight timing x[:, i]. For most rows who’s not y[i], weight is 1, i.e. ddW[i] = x[:, i]. For the specific row who is y[i], ddW[i]’s weight is the negative sum of all other rows. This logic corresponds to the definition of $\nabla_{w_{j}} L_{i}$:

def svm_loss_vectorized(W, X, y, reg):
# continue on last snippet
# weight to be producted by X
# its shape is (#classes, #samples)
weight = np.array((judge > 0).astype(int))

# weights on y[i] needs special care
weight_yi = -np.sum(weight, axis=0)
for i in xrange(wx.shape[1]):
weight[y[i], i] = weight_yi[i]

# half vectorized
for i in xrange(X.shape[1]):
ddW = X[:, i] * weight[:, i].reshape(-1, 1)
dW += ddW

dW /= X.shape[1]
dW += reg * W

return loss, dW

#### Comparison of the two-loop version and the half vectorized version

Following the ipython notebook and testing on training dataset with shape (3073, 49000) and 10 labels, training of the two-loop version takes around 7s and that of half vectorized version takes around 4s. Actually the complexity of the algorithm doesn’t change, i.e. $O \left( N \cdot D \cdot C \right)$; That speed goes up is because Python is slow in for loops, and maybe numpy has done some optimization in matrix multiplication.

This part is comparatively easy. Having gradient $\nabla_{W}L$, we may just subtract $W$ by $\alpha \nabla_{W}L$ in each iteration, where $\alpha$ is learning rate.

### Predict

The process of prediction is simply choosing the label with highest score. I omit it here.

## 3. Softmax

Keep the symbols defined in SVM, here we have:

• Score function: $f(x^{(i)}, W) = W x^{(i)}$

• Output function: $h(x^{(i)}, W) = \frac{e^{f_i}}{\sum_j e^{f_j}}$

• Loss function: $L_{i} = -f_{y_{i}} + \log \sum_{j}{e^{f_{j}}}$

• Gradient function: %

, where $% $

Or you may refer to this version of gradient function.

• Total loss: $L = \frac{1}{N}\sum_{i}{L_{i}} + \frac{1}{2}\lambda\sum_{k,l}{w_{k,l}^{2}}$

• Gradient of total loss: $\nabla_{W}L = \frac{1}{N}\sum_{i}{\nabla_{W} L_{i}} + \lambda W$

According to the lecture note, here we adjust $f^{(i)}_{j}$ to $f^{(i)}_{j} - \max_{j}{f^{(i)}_{j}}$. Here’s the snippet, very similar to that of SVM:

def softmax_loss_naive(W, X, y, reg):
dW = np.zeros_like(W)

### loss
scores = W.dot(X)
scores_max = np.max(scores, axis=0)
scores -= scores_max
exp_scores = np.exp(scores)
sums = np.sum(exp_scores, axis=0)
log_sums = np.log(sums)
scores_y = np.array([scores[y[i], i] for i in xrange(X.shape[1])])
loss = np.sum(-scores_y + log_sums)

loss /= X.shape[1]
loss += .5 * np.sum(W * W)

### dW
for i in xrange(X.shape[1]):
# dW += 1./sums[i] * log_sums[i] * X[:, i]
dW += 1./sums[i] * exp_scores[:, i].reshape(-1, 1) * X[:, i]
dW[y[i]] -= X[:, i]

dW /= X.shape[1]
dW += reg * W

return loss, dW

Note the sigma term $\sum_{j} \left( e^{f^{(i)}_{j}} \cdot \nabla_{W}f^{(i)}_{j} \right)$. In Python it is interpreted as exp_scores[:, i].reshape(-1, 1) * X[:, i], because for each j, there’s only one none zero row in $\nabla_{W}f^{(i)}_{j}$, whose value is $e^{f^{(i)}}$ dot product $x^{(i)}$. Shape of $e^{f^{(i)}}$ is (C,) and shape of $x^{(i)}$ is (D,). Making use of broadcast, we can reshape $e^{f^{(i)}}$ to (C, 1) and make a (C, D) matrix; that is what we want for ddW.

## Say last

Thank everyone that points out my mistakes. If there’s any more mistake, please don’t hesitate to tell me. The original purpose of this blog is just to note down my immediate thoughts when following cs231n in my spare time. It’s totally written in an amateur’s view; I would be happy if it helps you.

The official version of SVM and Softmax is really nice, and comes in later assignments.

Written on February 1, 2015