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

07_ensemble_learning_and_random_forests.ipynb

@@ -71,7 +71,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Voting classifiers"
+    "This notebook assumes you have installed Scikit-Learn ≥0.20."
    ]
   },
   {
@@ -79,6 +79,23 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Voting classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "heads_proba = 0.51\n",
     "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
@@ -87,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,27 +137,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Warning**: In Scikit-Learn 0.20, some hyperparameters (`solver`, `n_estimators`, `gamma`, etc.) start issuing warnings about the fact that their default value will change in Scikit-Learn 0.22. To avoid these warnings and ensure that this notebooks keeps producing the same outputs as in the book, I set the hyperparameters to their old default value.  In your own code, you can simply rely on the latest default values instead."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.ensemble import VotingClassifier\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
-    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
-    "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
-    "\n",
-    "voting_clf = VotingClassifier(\n",
-    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
-    "    voting='hard')"
+    "**Note**: to be future-proof, we set `solver=\"lbfgs\"`, `n_estimators=100`, and `gamma=\"scale\"` since these will be the default values in upcoming Scikit-Learn versions."
    ]
   },
   {
@@ -149,7 +146,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "voting_clf.fit(X_train, y_train)"
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.ensemble import VotingClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n",
+    "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "svm_clf = SVC(gamma=\"scale\", random_state=42)\n",
+    "\n",
+    "voting_clf = VotingClassifier(\n",
+    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
+    "    voting='hard')"
    ]
   },
   {
@@ -157,6 +165,15 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "voting_clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "\n",
@@ -166,15 +183,22 @@
     "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Soft voting:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
-    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
-    "svm_clf = SVC(gamma=\"auto\", probability=True, random_state=42)\n",
+    "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n",
+    "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "svm_clf = SVC(gamma=\"scale\", probability=True, random_state=42)\n",
     "\n",
     "voting_clf = VotingClassifier(\n",
     "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
@@ -184,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,14 +238,14 @@
     "\n",
     "bag_clf = BaggingClassifier(\n",
     "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
-    "    max_samples=100, bootstrap=True, n_jobs=-1, random_state=42)\n",
+    "    max_samples=100, bootstrap=True, random_state=42)\n",
     "bag_clf.fit(X_train, y_train)\n",
     "y_pred = bag_clf.predict(X_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,18 +317,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
     "bag_clf = BaggingClassifier(\n",
     "    DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
-    "    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)"
+    "    n_estimators=500, max_samples=1.0, bootstrap=True, random_state=42)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,39 +338,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.ensemble import RandomForestClassifier\n",
     "\n",
-    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
+    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, random_state=42)\n",
     "rnd_clf.fit(X_train, y_train)\n",
     "\n",
     "y_pred_rf = rnd_clf.predict(X_test)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # almost identical predictions"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.datasets import load_iris\n",
-    "iris = load_iris()\n",
-    "rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)\n",
-    "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n",
-    "for name, score in zip(iris[\"feature_names\"], rnd_clf.feature_importances_):\n",
-    "    print(name, score)"
+    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # almost identical predictions"
    ]
   },
   {
@@ -355,7 +365,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "rnd_clf.feature_importances_"
+    "from sklearn.datasets import load_iris\n",
+    "iris = load_iris()\n",
+    "rnd_clf = RandomForestClassifier(n_estimators=500, random_state=42)\n",
+    "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n",
+    "for name, score in zip(iris[\"feature_names\"], rnd_clf.feature_importances_):\n",
+    "    print(name, score)"
    ]
   },
   {
@@ -363,6 +378,15 @@
    "execution_count": 21,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "rnd_clf.feature_importances_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(6, 4))\n",
     "\n",
@@ -384,20 +408,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "bag_clf = BaggingClassifier(\n",
     "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
-    "    bootstrap=True, n_jobs=-1, oob_score=True, random_state=40)\n",
+    "    bootstrap=True, oob_score=True, random_state=40)\n",
     "bag_clf.fit(X_train, y_train)\n",
     "bag_clf.oob_score_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -406,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,29 +446,16 @@
     "## Feature importance"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    from sklearn.datasets import fetch_openml\n",
-    "    mnist = fetch_openml('mnist_784', version=1)\n",
-    "    mnist.target = mnist.target.astype(np.int64)\n",
-    "except ImportError:\n",
-    "    from sklearn.datasets import fetch_mldata\n",
-    "    mnist = fetch_mldata('MNIST original')"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
-    "rnd_clf.fit(mnist[\"data\"], mnist[\"target\"])"
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "mnist = fetch_openml('mnist_784', version=1)\n",
+    "mnist.target = mnist.target.astype(np.uint8)"
    ]
   },
   {
@@ -452,6 +463,16 @@
    "execution_count": 27,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "rnd_clf.fit(mnist[\"data\"], mnist[\"target\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def plot_digit(data):\n",
     "    image = data.reshape(28, 28)\n",
@@ -462,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -498,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -507,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -518,7 +539,7 @@
     "    sample_weights = np.ones(m)\n",
     "    plt.subplot(subplot)\n",
     "    for i in range(5):\n",
-    "        svm_clf = SVC(kernel=\"rbf\", C=0.05, gamma=\"auto\", random_state=42)\n",
+    "        svm_clf = SVC(kernel=\"rbf\", C=0.05, gamma=\"scale\", random_state=42)\n",
     "        svm_clf.fit(X_train, y_train, sample_weight=sample_weights)\n",
     "        y_pred = svm_clf.predict(X_train)\n",
     "        sample_weights[y_pred != y_train] *= (1 + learning_rate)\n",
@@ -537,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -553,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -576,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -587,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -598,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -607,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -616,7 +637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -625,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -674,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -686,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -696,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -723,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -746,7 +767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -755,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -781,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -805,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -814,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -830,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -843,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -851,22 +872,8 @@
     "    xgb_reg = xgboost.XGBRegressor(random_state=42)\n",
     "    xgb_reg.fit(X_train, y_train)\n",
     "    y_pred = xgb_reg.predict(X_val)\n",
-    "    val_error = mean_squared_error(y_val, y_pred)\n",
-    "    print(\"Validation MSE:\", val_error)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if xgboost is not None:  # not shown in the book\n",
-    "    xgb_reg.fit(X_train, y_train,\n",
-    "                eval_set=[(X_val, y_val)], early_stopping_rounds=2)\n",
-    "    y_pred = xgb_reg.predict(X_val)\n",
-    "    val_error = mean_squared_error(y_val, y_pred)\n",
-    "    print(\"Validation MSE:\", val_error)"
+    "    val_error = mean_squared_error(y_val, y_pred) # Not shown\n",
+    "    print(\"Validation MSE:\", val_error)           # Not shown"
    ]
   },
   {
@@ -875,7 +882,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None"
+    "if xgboost is not None:  # not shown in the book\n",
+    "    xgb_reg.fit(X_train, y_train,\n",
+    "                eval_set=[(X_val, y_val)], early_stopping_rounds=2)\n",
+    "    y_pred = xgb_reg.predict(X_val)\n",
+    "    val_error = mean_squared_error(y_val, y_pred)  # Not shown\n",
+    "    print(\"Validation MSE:\", val_error)            # Not shown"
    ]
   },
   {
@@ -883,6 +895,15 @@
    "execution_count": 54,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%timeit GradientBoostingRegressor().fit(X_train, y_train)"
    ]
@@ -933,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -942,7 +963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -961,7 +982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -972,19 +993,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
-    "random_forest_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
-    "extra_trees_clf = ExtraTreesClassifier(n_estimators=10, random_state=42)\n",
+    "random_forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "extra_trees_clf = ExtraTreesClassifier(n_estimators=100, random_state=42)\n",
     "svm_clf = LinearSVC(random_state=42)\n",
     "mlp_clf = MLPClassifier(random_state=42)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -996,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1019,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1028,7 +1049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1042,7 +1063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1051,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1060,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1069,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1085,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1101,7 +1122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1117,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1133,7 +1154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1149,7 +1170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1165,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1174,7 +1195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1197,7 +1218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1206,7 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1236,7 +1257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1248,7 +1269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1257,7 +1278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1267,7 +1288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1290,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1302,7 +1323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1311,7 +1332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1320,7 +1341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1337,7 +1358,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 - tf2",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },