bruceoutdoors/cs231n

my assignment solutions for CS231n Convolutional Neural Networks for Visual Recognition

cs231n's Issues

Assignment 1 TwoLayerNet.loss: reg_loss compute error

CS231n/assignment1/cs231n/classifiers/neural_net.py

Line 104 in c92ca90

reg_loss = 0.5 * reg * np.sum(W1 * W1) + 0.5 * reg * np.sum(W2 * W2)

Here should be

    reg_loss = reg * np.sum(W1 * W1) + reg * np.sum(W2 * W2)

to gain the correct loss.

Error in svm_loss_naive method in linear_svm.py

the dW should be multiplied by 2regW instead of just reg*W due to the presence of square in R(W)

Assigment 2 - Train Model

Hi, can you please explain me a bit how did you come to the best train model in Assignment 2 - Fully Connected Nets - Train a good model!?

How did you end with this?

weight_scale = 5e-2
learning_rate = 1e-3 
model = FullyConnectedNet([100, 75, 50, 25],
              weight_scale=weight_scale, dtype=np.float64)

Thanks !

Andres

Assignment1, facing import error for knn

Assignment1- Svm_loss_vetorized

Line 133: Linear_svm.py
X_mask[np.arange(num_train), y] = -incorrect_counts
should be:
X_mask[np.arange(num_train), y] -= incorrect_counts

Using SGD with Momentum for Two Layer Neural Net in Assignment 1

In the Notebook file for Two layer neural net implementation in assignment one, it is mentioned:

To train our network we will use SGD with momentum.

So, SGD with momentum must be implemented in the task.

multiple definitions of "loss"

CS231n/assignment1/cs231n/classifiers/softmax.py

About the best k

Hi, there is a problem that is a little wired.

When test the best_k, or in cross_validation, the result shows that the best k is near k == 10, because of the highest peak. However, when I did the following step, in which I change the k's value and get the result greater than 0.28. When k == 10, the result is not very good, but when k == 5, the result is highest .

So, My question is:

1. What's your best k of cross validation?
2. If your best k also is 10, is the best accuracy when k == 10 in the last test step?

Thanks!

ps: The plot diagram has been uploaded in my github, you could see the details: https://github.com/fortyMiles/cs231n/blob/master/assignment1/knn.ipynb

Incorrect Cross-validation for knn.

In knn.ipynb file, you have:

y_cross_validation_pred = classifier_k.predict_labels(X_train_folds[n], k)

This is incorrect because predict_labels takes in a distance matrix but you pass in a raw test matrix. So, you need to have an additional step as:

dists = classifier.compute_distances_no_loops(X_train_folds[n])
y_cross_validation_pred = classifier_k.predict_labels(dists, k)

Or you can use predict function in k_nearest_neighbor which technically does the same thing:

y_cross_validation_pred = classifier_k.predict(dists, k)