Merge pull request tensorflow#4379 from stantonxu/patch-1

MarkDaoust · web-flow · commit d57fdb382549 · 2018-05-26T05:07:59.000-07:00
Update eager.ipynb fixing typo
diff --git a/samples/core/get_started/eager.ipynb b/samples/core/get_started/eager.ipynb
@@ -428,7 +428,7 @@
         "  </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 are 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: `0.03` 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*."
       ]
     },
     {

Original file line number	Diff line number	Diff line change
`@@ -428,7 +428,7 @@`
`428`	`428`	`" </td></tr>\n",`
`429`	`429`	`"</table>\n",`
`430`	`430`	`"\n",`
`431`		- "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 are 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."
	`431`	+ "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."
`432`	`432`	`]`
`433`	`433`	`},`
`434`	`434`	`{`