Exercise:Softmax Regression
From Ufldl
(→Step 2: Implement softmaxCost) |
(→Step 2: Implement softmaxCost) |
||
| Line 18: | Line 18: | ||
| - | ''Implementation Tip'': Computing the ground truth matrix - In your code, you may need to compute the ground truth matrix <tt>M</tt>, such that <tt>M(r, c)</tt> is 1 if <math>y^{(c)} = r</math> and 0 otherwise. This can be done quickly, without a loop, using the MATLAB functions <tt>sparse</tt> and <tt>full</tt>. <tt>sparse(r, c, v)</tt> creates a sparse matrix such that <tt>M(r(i), c(i)) = v(i)</tt> for all i. That is, the vectors <tt>r</tt> and <tt>c</tt> give the position of the elements whose values we wish to set, and <tt>v</tt> the corresponding values of the elements. Running <tt>full</tt> on a sparse matrix gives the full representation of the matrix for use. Note that the code for using <tt>sparse</tt> and <tt>full</tt> to compute the ground truth matrix has already been included in softmaxCost.m. | + | '''Implementation Tip''': Computing the ground truth matrix - In your code, you may need to compute the ground truth matrix <tt>M</tt>, such that <tt>M(r, c)</tt> is 1 if <math>y^{(c)} = r</math> and 0 otherwise. This can be done quickly, without a loop, using the MATLAB functions <tt>sparse</tt> and <tt>full</tt>. <tt>sparse(r, c, v)</tt> creates a sparse matrix such that <tt>M(r(i), c(i)) = v(i)</tt> for all i. That is, the vectors <tt>r</tt> and <tt>c</tt> give the position of the elements whose values we wish to set, and <tt>v</tt> the corresponding values of the elements. Running <tt>full</tt> on a sparse matrix gives the full representation of the matrix for use. Note that the code for using <tt>sparse</tt> and <tt>full</tt> to compute the ground truth matrix has already been included in softmaxCost.m. |
| - | ''Implementation Tip:'' Preventing overflows - in softmax regression, you will have to compute the hypothesis | + | '''Implementation Tip:''' Preventing overflows - in softmax regression, you will have to compute the hypothesis |
<math> | <math> | ||
| Line 82: | Line 82: | ||
<tt>max(M)</tt> yields a row vector with each element giving the maximum value in that column. <tt>bsxfun</tt> (short for binary singleton expansion function) applies minus along each row of <tt>M</tt>, hence subtracting the maximum of each column from every element in the column. | <tt>max(M)</tt> yields a row vector with each element giving the maximum value in that column. <tt>bsxfun</tt> (short for binary singleton expansion function) applies minus along each row of <tt>M</tt>, hence subtracting the maximum of each column from every element in the column. | ||
| - | '''Implementation | + | '''Implementation Tip: ''' Computing the predictions - you may also find <tt>bsxfun</tt> useful in computing your predictions - if you have a matrix <tt>M</tt> containing the <math>e^{\theta_j^T x^{(i)}}</math> terms, such that <tt>M(r, c)</tt> contains the <math>e^{\theta_r^T x^{(c)}}</math> term, you can use the following code to compute the hypothesis (by diving all elements in each column by their column sum): |
% M is the matrix as described in the text | % M is the matrix as described in the text | ||
