Update binary_logistic_regression_torch_with_hidden_layers.ipynb

bugfix

Author: fs446

binary_logistic_regression_torch_with_hidden_layers.ipynb

@@ -575,8 +575,8 @@
    "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 10))\n",
-    "N = 500\n",
-    "predict_threshold = np.linspace(0, 1, N, endpoint=False)\n",
+    "Nt = 500\n",
+    "predict_threshold = np.linspace(0, 1, Nt, endpoint=False)\n",
     "TPR = np.zeros_like(predict_threshold)\n",
     "FPR = np.zeros_like(predict_threshold)\n",
     "TNR = np.zeros_like(predict_threshold)\n",
@@ -593,7 +593,7 @@
     "    TPR[idx] = TP / (FN+TP)  # recall, sensitivity, test power\n",
     "    FNR[idx] = FN / (FN+TP)  # type II error\n",
     "    TNR[idx] = TN / (TN+FP)  # specificity, selectivity\n",
-    "    if idx == N//2:  # indicate 0.5 probability decision point\n",
+    "    if idx == Nt//2:  # indicate 0.5 probability decision point\n",
     "        plt.subplot(2, 2, 1)\n",
     "        plt.text(FPR[idx], TPR[idx], '. %0.2f' % val)\n",
     "        plt.subplot(2, 2, 2)\n",