fixed wrong variable in gradient and cross-entropy functions

Author: enalisnick

Session 3 - Classification.ipynb

@@ -356,7 +356,7 @@
     "# labels: Numpy array containing the true class labels\n",
     "# f: column vector of predictions (i.e. output of logistic function)\n",
     "def cross_entropy(labels, f):\n",
-    "    return np.sum(-1*np.multiply(class_labels,np.log(f)) - np.multiply((np.ones(N)-class_labels),np.log(np.ones(N)-f)))"
+    "    return np.sum(-1*np.multiply(labels,np.log(f)) - np.multiply((np.ones(N)-labels),np.log(np.ones(N)-f)))"
    ]
   },
   {
@@ -388,7 +388,7 @@
     "# labels: Numpy array containing the true class labels\n",
     "# f: column vector of predictions (i.e. output of logistic function)\n",
     "def compute_Gradient(features, labels, f):\n",
-    "    return np.sum(np.multiply(f-class_labels,features),0)[np.newaxis].T"
+    "    return np.sum(np.multiply(f-labels,features),0)[np.newaxis].T"
    ]
   },
   {
@@ -824,7 +824,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##<span style=\"color:red\">SURVEY (5 MINS):</span> https://www.surveymonkey.com/r/CD56YRB"
+    "##<span style=\"color:red\">SURVEY (5 MINS):</span> "
    ]
   },
   {
@@ -984,7 +984,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.11"
   }
  },
  "nbformat": 4,

Solutions/Session 3 - Solutions.ipynb

@@ -401,7 +401,7 @@
     "# labels: Numpy array containing the true class labels\n",
     "# f: column vector of predictions (i.e. output of logistic function)\n",
     "def cross_entropy(labels, f):\n",
-    "    return np.sum(-1*np.multiply(class_labels,np.log(f)) - np.multiply((np.ones(N)-class_labels),np.log(np.ones(N)-f)))"
+    "    return np.sum(-1*np.multiply(labels,np.log(f)) - np.multiply((np.ones(N)-labels),np.log(np.ones(N)-f)))"
    ]
   },
   {
@@ -433,7 +433,7 @@
     "# labels: Numpy array containing the true class labels\n",
     "# f: column vector of predictions (i.e. output of logistic function)\n",
     "def compute_Gradient(features, labels, f):\n",
-    "    return np.sum(np.multiply(f-class_labels,features),0)[np.newaxis].T"
+    "    return np.sum(np.multiply(f-labels,features),0)[np.newaxis].T"
    ]
   },
   {
@@ -1503,7 +1503,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.11"
   }
  },
  "nbformat": 4,