Update blog_custom_estimators.py

Added correct link to blogpost
Changed Sentosa to Setosa in a couple of places

samples/outreach/blogs/blog_custom_estimators.py

@@ -14,8 +14,7 @@
 # ==============================================================================
 
 # This is the complete code for the following blogpost:
-# https://developers.googleblog.com/2017/09/introducing-tensorflow-datasets.html
-#   (https://goo.gl/Ujm2Ep)
+# https://developers.googleblog.com/2017/12/creating-custom-estimators-in-tensorflow.html
 
 import tensorflow as tf
 import os
@@ -116,7 +115,7 @@ def my_model_fn(
     h2 = tf.layers.Dense(10, activation=tf.nn.relu)(h1)
 
     # Output 'logits' layer is three number = probability distribution
-    # between Iris Sentosa, Versicolor, and Viginica
+    # between Iris Setosa, Versicolor, and Viginica
     logits = tf.layers.Dense(3)(h2)
 
     # class_ids will be the model prediction for the class (Iris flower type)
@@ -206,14 +205,14 @@ predict_results = classifier.predict(
 tf.logging.info("Prediction on test file")
 for prediction in predict_results:
     # Will print the predicted class, i.e: 0, 1, or 2 if the prediction
-    # is Iris Sentosa, Vericolor, Virginica, respectively.
+    # is Iris Setosa, Vericolor, Virginica, respectively.
     tf.logging.info("...{}".format(prediction["class_ids"]))
 
 # Let create a dataset for prediction
 # We've taken the first 3 examples in FILE_TEST
 prediction_input = [[5.9, 3.0, 4.2, 1.5],  # -> 1, Iris Versicolor
                     [6.9, 3.1, 5.4, 2.1],  # -> 2, Iris Virginica
-                    [5.1, 3.3, 1.7, 0.5]]  # -> 0, Iris Sentosa
+                    [5.1, 3.3, 1.7, 0.5]]  # -> 0, Iris Setosa
 
 def new_input_fn():
     def decode(x):
@@ -234,7 +233,7 @@ tf.logging.info("Predictions on memory")
 for idx, prediction in enumerate(predict_results):
     type = prediction["class_ids"]  # Get the predicted class (index)
     if type == 0:
-        tf.logging.info("...I think: {}, is Iris Sentosa".format(prediction_input[idx]))
+        tf.logging.info("...I think: {}, is Iris Setosa".format(prediction_input[idx]))
     elif type == 1:
         tf.logging.info("...I think: {}, is Iris Versicolor".format(prediction_input[idx]))
     else: