Update all notebooks assuming we are all in the future now: sklearn 0.20+, python 3.5+, TF 2.0 preview

10_neural_nets_with_keras.ipynb

@@ -92,7 +92,7 @@
     "X = iris.data[:, (2, 3)]  # petal length, petal width\n",
     "y = (iris.target == 0).astype(np.int)\n",
     "\n",
-    "per_clf = Perceptron(max_iter=100, tol=-np.infty, random_state=42)\n",
+    "per_clf = Perceptron(max_iter=1000, tol=1e-3, random_state=42)\n",
     "per_clf.fit(X, y)\n",
     "\n",
     "y_pred = per_clf.predict([[2, 0.5]])"
@@ -474,7 +474,7 @@
     "        plt.axis('off')\n",
     "        plt.title(class_names[y_train[index]], fontsize=12)\n",
     "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
-    "save_fig('fashion_mnist', tight_layout=False)\n",
+    "save_fig('fashion_mnist_diagram', tight_layout=False)\n",
     "plt.show()"
    ]
   },
@@ -668,7 +668,7 @@
    "source": [
     "model.compile(loss=keras.losses.sparse_categorical_crossentropy,\n",
     "              optimizer=keras.optimizers.SGD(),\n",
-    "              metrics=[keras.metrics.Accuracy()])"
+    "              metrics=[keras.metrics.sparse_categorical_accuracy])"
    ]
   },
   {
@@ -719,7 +719,7 @@
     "pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
     "plt.grid(True)\n",
     "plt.gca().set_ylim(0, 1)\n",
-    "save_fig(\"keras_learning_curve_graph\")\n",
+    "save_fig(\"keras_learning_curves_graph\")\n",
     "plt.show()"
    ]
   },
@@ -1656,7 +1656,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 - tf2",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },