Merge branch 'master' of https://github.com/ageron/handson-ml into upstream

02_end_to_end_machine_learning_project.ipynb

@@ -3898,7 +3898,7 @@
     "forest_reg = RandomForestRegressor(random_state=42)\n",
     "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
     "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
-    "                           scoring='neg_mean_squared_error')\n",
+    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
     "grid_search.fit(housing_prepared, housing_labels)"
    ]
   },

03_classification.ipynb

@@ -364,7 +364,7 @@
    "source": [
     "from sklearn.linear_model import SGDClassifier\n",
     "\n",
-    "sgd_clf = SGDClassifier(random_state=42)\n",
+    "sgd_clf = SGDClassifier(max_iter=5, random_state=42)\n",
     "sgd_clf.fit(X_train, y_train_5)"
    ]
   },
@@ -1292,7 +1292,7 @@
    ],
    "source": [
     "from sklearn.multiclass import OneVsOneClassifier\n",
-    "ovo_clf = OneVsOneClassifier(SGDClassifier(random_state=42))\n",
+    "ovo_clf = OneVsOneClassifier(SGDClassifier(max_iter=5, random_state=42))\n",
     "ovo_clf.fit(X_train, y_train)\n",
     "ovo_clf.predict([some_digit])"
    ]
@@ -1638,7 +1638,7 @@
     }
    ],
    "source": [
-    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n",
+    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3, n_jobs=-1)\n",
     "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")"
    ]
   },
@@ -2133,7 +2133,7 @@
     "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5]}]\n",
     "\n",
     "knn_clf = KNeighborsClassifier()\n",
-    "grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3)\n",
+    "grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3, n_jobs=-1)\n",
     "grid_search.fit(X_train, y_train)"
    ]
   },

extra_capsnets.ipynb

@@ -607,7 +607,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Okay, let's start by creating a trainable variable of shape (1, 1152, 10, 16, 8) that will hold all the transformation matrices. The first dimension of size 1 will make this array easy to tile. We initialize this variable randomly using a normal distribution with a standard deviation to 0.01."
+    "Okay, let's start by creating a trainable variable of shape (1, 1152, 10, 16, 8) that will hold all the transformation matrices. The first dimension of size 1 will make this array easy to tile. We initialize this variable randomly using a normal distribution with a standard deviation to 0.1."
    ]
   },
   {
@@ -616,7 +616,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "init_sigma = 0.01\n",
+    "init_sigma = 0.1\n",
     "\n",
     "W_init = tf.random_normal(\n",
     "    shape=(1, caps1_n_caps, caps2_n_caps, caps2_n_dims, caps1_n_dims),\n",