Merge pull request #4765 from erssebaggala/erssebaggala-patch-1

Correct prediction probabilities in custom training walkthrough

Author: MarkDaoust

samples/core/tutorials/eager/custom_training_walkthrough.ipynb

@@ -573,7 +573,7 @@
         "  </td></tr>\n",
         "</table>\n",
         "\n",
-        "When the model from Figure 2 is trained and fed an unlabeled example, it yields three predictions: the likelihood that this flower is the given Iris species. This prediction is called *[inference](https://developers.google.com/machine-learning/crash-course/glossary#inference)*. For this example, the sum of the output predictions is 1.0. In Figure 2, this prediction breaks down as: `0.03` for *Iris setosa*, `0.95` for *Iris versicolor*, and `0.02` for *Iris virginica*. This means that the model predicts—with 95% probability—that an unlabeled example flower is an *Iris versicolor*."
+        "When the model from Figure 2 is trained and fed an unlabeled example, it yields three predictions: the likelihood that this flower is the given Iris species. This prediction is called *[inference](https://developers.google.com/machine-learning/crash-course/glossary#inference)*. For this example, the sum of the output predictions is 1.0. In Figure 2, this prediction breaks down as: `0.02` for *Iris setosa*, `0.95` for *Iris versicolor*, and `0.03` for *Iris virginica*. This means that the model predicts—with 95% probability—that an unlabeled example flower is an *Iris versicolor*."
       ]
     },
     {
@@ -1225,4 +1225,4 @@
       "outputs": []
     }
   ]
-}
+}