Update to Custom Estimators docs to use input_fn for both .fit() and

.evaluate(). Follow-up to PR #9617

PiperOrigin-RevId: 155792276

1 files changed, 25 insertions, 7 deletions

diff --git a/tensorflow/docs_src/extend/estimators.md b/tensorflow/docs_src/extend/estimators.md
index c5444c59ca..f972ee5f50 100644
--- a/tensorflow/docs_src/extend/estimators.md
+++ b/tensorflow/docs_src/extend/estimators.md
@@ -37,14 +37,17 @@ measurements. You'll learn how to do the following:
 ## Prerequisites
 
 This tutorial assumes you already know tf.contrib.learn API basics, such as
-feature columns and `fit()` operations. If you've never used tf.contrib.learn
-before, or need a refresher, you should first review the following tutorials:
+feature columns, input functions, and `fit()`/`evaluate()`/`predict()`
+operations. If you've never used tf.contrib.learn before, or need a refresher,
+you should first review the following tutorials:
 
 *   @{$tflearn$tf.contrib.learn Quickstart}: Quick introduction to
     training a neural network using tf.contrib.learn.
 *   @{$wide$TensorFlow Linear Model Tutorial}: Introduction to
     feature columns, and an overview on building a linear classifier in
     tf.contrib.learn.
+*   @{$input_fn$Building Input Functions with tf.contrib.learn}: Overview of how
+    to construct an input_fn to preprocess and feed data into your models.
 
 ## An Abalone Age Predictor {#abalone-predictor}
 
@@ -239,7 +242,7 @@ nn = tf.contrib.learn.Estimator(
 *   `params`: An optional dict of hyperparameters (e.g., learning rate, dropout)
     that will be passed into the `model_fn`.
 
-NOTE: Just like `tf.contrib.learn`'s predefined regressors and classifiers, the
+Note: Just like `tf.contrib.learn`'s predefined regressors and classifiers, the
 `Estimator` initializer also accepts the general configuration arguments
 `model_dir` and `config`.
 
@@ -252,7 +255,7 @@ code (highlighted in bold below), right after the logging configuration:
 <strong># Learning rate for the model
 LEARNING_RATE = 0.001</strong></code></pre>
 
-NOTE: Here, `LEARNING_RATE` is set to `0.001`, but you can tune this value as
+Note: Here, `LEARNING_RATE` is set to `0.001`, but you can tune this value as
 needed to achieve the best results during model training.
 
 Then, add the following code to `main()`, which creates the dict `model_params`
@@ -576,7 +579,7 @@ required arguments:
         algorithm
         (@{tf.train.RMSPropOptimizer})
 
-NOTE: The `optimize_loss` function supports additional optional arguments to
+Note: The `optimize_loss` function supports additional optional arguments to
 further configure the optimizer, such as for implementing decay. See the
 @{tf.contrib.layers.optimize_loss$API docs} for more info.
 
@@ -654,15 +657,30 @@ Add the following code to the end of `main()` to fit the neural network to the
 training data and evaluate accuracy:
 
 ```python
+def get_train_inputs():
+  x = tf.constant(training_set.data)
+  y = tf.constant(training_set.target)
+  return x, y
+
 # Fit
-nn.fit(x=training_set.data, y=training_set.target, steps=5000)
+nn.fit(input_fn=get_train_inputs, steps=5000)
+
+def get_test_inputs():
+  x = tf.constant(test_set.data)
+  y = tf.constant(test_set.target)
+  return x, y
 
 # Score accuracy
-ev = nn.evaluate(x=test_set.data, y=test_set.target, steps=1)
+ev = nn.evaluate(input_fn=get_test_inputs, steps=1)
 print("Loss: %s" % ev["loss"])
 print("Root Mean Squared Error: %s" % ev["rmse"])
 ```
 
+Note: The above code uses input functions to feed feature (`x`) and label (`y`)
+`Tensor`s into the model for both training (`get_train_inputs()`) and evaluation
+(`get_test_inputs()`). To learn more about input functions, see the tutorial
+@{$input_fn$Building Input Functions with tf.contrib.learn}.
+
 Then run the code. You should see output like the following:
 
 ```none