Resolve review comments - part 1

Author: MarkDaoust

samples/core/tutorials/estimators/wide.ipynb

@@ -356,14 +356,12 @@
       "source": [
         "ds = easy_input_function(train_df, label_key='income_bracket', num_epochs=5, shuffle=True, batch_size=10)\n",
         "\n",
-        "for feature_batch, label_batch in ds:\n",
-        "    break\n",
-        "    \n",
-        "print('Some feature keys:', list(feature_batch.keys())[:5])\n",
-        "print()\n",
-        "print('A batch of Ages  :', feature_batch['age'])\n",
-        "print()\n",
-        "print('A batch of Labels:', label_batch )"
+        "for feature_batch, label_batch in ds.take(1):\n",
+        "  print('Some feature keys:', list(feature_batch.keys())[:5])\n",
+        "  print()\n",
+        "  print('A batch of Ages  :', feature_batch['age'])\n",
+        "  print()\n",
+        "  print('A batch of Labels:', label_batch )"
       ],
       "execution_count": 0,
       "outputs": []
@@ -426,14 +424,12 @@
       },
       "cell_type": "code",
       "source": [
-        "for feature_batch, label_batch in ds:\n",
-        "    break\n",
-        "    \n",
-        "print('Feature keys:', list(feature_batch.keys())[:5])\n",
-        "print()\n",
-        "print('Age batch   :', feature_batch['age'])\n",
-        "print()\n",
-        "print('Label batch :', label_batch )"
+        "for feature_batch, label_batch in ds.take(1):\n",
+        "  print('Feature keys:', list(feature_batch.keys())[:5])\n",
+        "  print()\n",
+        "  print('Age batch   :', feature_batch['age'])\n",
+        "  print()\n",
+        "  print('Label batch :', label_batch )"
       ],
       "execution_count": 0,
       "outputs": []
@@ -546,7 +542,7 @@
       },
       "cell_type": "code",
       "source": [
-        "classifier = tf.estimator.LinearClassifier(feature_columns=[age], n_classes=2)\n",
+        "classifier = tf.estimator.LinearClassifier(feature_columns=[age])\n",
         "classifier.train(train_inpf)\n",
         "result = classifier.evaluate(test_inpf)\n",
         "\n",
@@ -627,7 +623,7 @@
       },
       "cell_type": "code",
       "source": [
-        "classifier = tf.estimator.LinearClassifier(feature_columns=my_numeric_columns, n_classes=2)\n",
+        "classifier = tf.estimator.LinearClassifier(feature_columns=my_numeric_columns)\n",
         "classifier.train(train_inpf)\n",
         "\n",
         "result = classifier.evaluate(test_inpf)\n",
@@ -876,7 +872,7 @@
       },
       "cell_type": "code",
       "source": [
-        "classifier = tf.estimator.LinearClassifier(feature_columns=my_numeric_columns+my_categorical_columns, n_classes=2)\n",
+        "classifier = tf.estimator.LinearClassifier(feature_columns=my_numeric_columns+my_categorical_columns)\n",
         "classifier.train(train_inpf)\n",
         "result = classifier.evaluate(test_inpf)\n",
         "\n",