In the naive forward pass cell, line 8 is out, _ = sed_conv_im2col_forward(x, w, b, conv_param). But, it should be out, _ = conv_forward_naive(x, w, b, conv_param)

hello,

I just wonder whether the L2 regularization for gradient should also be considered in the third question of NetworkVisualization-TensorFlow. Otherwise, the variable Xi remains unused. The computed gradient should be like dx[0] - 2 * l2_reg * Xi instead of dx[0]

Mahan,

I think I have found an error in the k-fold cross validation snippet of the knn jupyter notebook. In the second segment of this snippet, the line:
train_set = np.concatenate((X_train_folds[:i] + X_train_folds[i+1:]))

I believe that using the "+" operator on these two arrays (X_train_folds[:i] & X_train_folds[i+1:]) will actually add together the array elements instead of concatenating them as you intended. Do you agree with this?

In my own implementation I have the following (the reason for the if-elif-else is that concatenating an empty array gives an error):
if i == 0:
X_train_fold = np.concatenate(X_train_folds[(i + 1):num_folds])
y_train_fold = np.concatenate(y_train_folds[(i + 1):num_folds])
elif i == (num_folds - 1):
X_train_fold = np.concatenate(X_train_folds[0:i])
y_train_fold = np.concatenate(y_train_folds[0:i])
else:
X_train_fold = np.concatenate((np.concatenate(X_train_folds[0:i]), np.concatenate(X_train_folds[(i + 1):num_folds])))
y_train_fold = np.concatenate((np.concatenate(y_train_folds[0:i]), np.concatenate(y_train_folds[(i + 1):num_folds])))
classifier.train(X_train_fold, y_train_fold)

I am open to suggestions on a cleaner way to implement this...

Your feedback is greatly appreciated -- I don't have someone to discuss this type of thing with....

Regards,
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