Update binary_logistic_regression_tf_with_hidden_layers.ipynb

Author: fs446

binary_logistic_regression_tf_with_hidden_layers.ipynb

@@ -253,7 +253,7 @@
     "model_log = model.fit(\n",
     "    X_train, Y_train,\n",
     "    batch_size=batch_size,\n",
-    "    shuffle=False,\n",
+    "    shuffle=True,\n",
     "    epochs=num_epochs,\n",
     "    validation_data=(X_test, Y_test),\n",
     "    verbose=verbose)"
@@ -390,6 +390,8 @@
    "outputs": [],
    "source": [
     "if N == 2:  # 2D plot of data and classification (curved) line\n",
+    "    levels = [0.0, 0.05, 0.1, 0.37, 0.5, 0.63, 0.9, 0.95, 1]\n",
+    "\n",
     "    f1, f2 = np.arange(-6, 6, 0.05), np.arange(-6, 6, 0.05)\n",
     "    xv, yv = np.meshgrid(f1, f2)\n",
     "    # create data such that TF can handle it in model.predict():\n",
@@ -398,19 +400,21 @@
     "\n",
     "    ygrid = model.predict(Xgrid, verbose=0)  # probability 0...1\n",
     "\n",
+    "    # hard decision boundary:\n",
     "    # ygrid = predict_class(ygrid)  # binary classes {0,1}\n",
     "\n",
     "    # reshape to plane\n",
     "    ygrid = np.reshape(ygrid, (xv.shape[0], xv.shape[1]))\n",
     "\n",
     "    plt.figure(figsize=(12, 5))\n",
-    "\n",
     "    plt.subplot(1, 2, 1)  # left plot for training data set\n",
     "    plt.plot(X_train[Y_train[:, 0] == 0, 0],\n",
-    "             X_train[Y_train[:, 0] == 0, 1], \"C0o\", ms=1)\n",
+    "             X_train[Y_train[:, 0] == 0, 1],\n",
+    "             \"o\", color='dodgerblue', ms=1)\n",
     "    plt.plot(X_train[Y_train[:, 0] == 1, 0],\n",
-    "             X_train[Y_train[:, 0] == 1, 1], \"C1o\", ms=1)\n",
-    "    plt.contourf(f1, f2, ygrid, cmap=\"RdBu_r\")\n",
+    "             X_train[Y_train[:, 0] == 1, 1],\n",
+    "             \"o\", color='orangered', ms=1)\n",
+    "    plt.contourf(f1, f2, ygrid, cmap=\"RdBu_r\", levels=levels)\n",
     "    plt.colorbar()\n",
     "    plt.axis(\"square\")\n",
     "    plt.xlim(-6, 6)\n",
@@ -421,10 +425,12 @@
     "\n",
     "    plt.subplot(1, 2, 2)  # right plot for test data set\n",
     "    plt.plot(X_test[Y_test[:, 0] == 0, 0],\n",
-    "             X_test[Y_test[:, 0] == 0, 1], \"C0o\", ms=1)\n",
+    "             X_test[Y_test[:, 0] == 0, 1],\n",
+    "             \"o\", color='dodgerblue', ms=1)\n",
     "    plt.plot(X_test[Y_test[:, 0] == 1, 0],\n",
-    "             X_test[Y_test[:, 0] == 1, 1], \"C1o\", ms=1)\n",
-    "    plt.contourf(f1, f2, ygrid, cmap=\"RdBu_r\")\n",
+    "             X_test[Y_test[:, 0] == 1, 1],\n",
+    "             \"o\", color='orangered', ms=1)\n",
+    "    plt.contourf(f1, f2, ygrid, cmap=\"RdBu_r\", levels=levels)\n",
     "    plt.colorbar()\n",
     "    plt.axis(\"square\")\n",
     "    plt.xlim(-6, 6)\n",