Deletes iris_val_based_early_stopping example, which uses deprecated ValidationMonitor.

PiperOrigin-RevId: 160154863

4 files changed, 0 insertions, 96 deletions

diff --git a/tensorflow/examples/learn/BUILD b/tensorflow/examples/learn/BUILD
index 1606e1a947..91fc105f27 100644
--- a/tensorflow/examples/learn/BUILD
+++ b/tensorflow/examples/learn/BUILD
@@ -70,16 +70,6 @@ py_binary(
 )
 
 py_binary(
-    name = "iris_val_based_early_stopping",
-    srcs = ["iris_val_based_early_stopping.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/learn",
-    ],
-)
-
-py_binary(
     name = "iris_with_pipeline",
     srcs = ["iris_with_pipeline.py"],
     srcs_version = "PY2AND3",
@@ -201,7 +191,6 @@ sh_test(
         ":iris_custom_decay_dnn",
         ":iris_custom_model",
         ":iris_run_config",
-        ":iris_val_based_early_stopping",
         ":iris_with_pipeline",
         ":random_forest_mnist",
         ":resnet",
diff --git a/tensorflow/examples/learn/README.md b/tensorflow/examples/learn/README.md
index 3166f37e78..6671d68831 100644
--- a/tensorflow/examples/learn/README.md
+++ b/tensorflow/examples/learn/README.md
@@ -19,7 +19,6 @@ processing (`sudo pip install pandas`).
 
 ## Techniques
 
-* [Improving Performance Using Early Stopping with Iris Data]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_val_based_early_stopping.py)
 * [Using skflow with Pipeline]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_with_pipeline.py)
 * [Deep Neural Network with Customized Decay Function]( https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_decay_dnn.py)
 
diff --git a/tensorflow/examples/learn/examples_test.sh b/tensorflow/examples/learn/examples_test.sh
index 8942720271..77b245ab15 100755
--- a/tensorflow/examples/learn/examples_test.sh
+++ b/tensorflow/examples/learn/examples_test.sh
@@ -49,7 +49,6 @@ test iris
 test iris_custom_decay_dnn
 test iris_custom_model
 test iris_run_config
-test iris_val_based_early_stopping
 test iris_with_pipeline
 test random_forest_mnist
 test resnet
diff --git a/tensorflow/examples/learn/iris_val_based_early_stopping.py b/tensorflow/examples/learn/iris_val_based_early_stopping.py
deleted file mode 100644
index 991d1831d7..0000000000
--- a/tensorflow/examples/learn/iris_val_based_early_stopping.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-"""Example of DNNClassifier for Iris plant dataset, with early stopping."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import shutil
-
-from sklearn import datasets
-from sklearn import metrics
-from sklearn.cross_validation import train_test_split
-import tensorflow as tf
-
-learn = tf.contrib.learn
-
-
-def clean_folder(folder):
-  """Cleans the given folder if it exists."""
-  try:
-    shutil.rmtree(folder)
-  except OSError:
-    pass
-
-
-def main(unused_argv):
-  iris = datasets.load_iris()
-  x_train, x_test, y_train, y_test = train_test_split(
-      iris.data, iris.target, test_size=0.2, random_state=42)
-
-  x_train, x_val, y_train, y_val = train_test_split(
-      x_train, y_train, test_size=0.2, random_state=42)
-  val_monitor = learn.monitors.ValidationMonitor(
-      x_val, y_val, early_stopping_rounds=200)
-
-  model_dir = '/tmp/iris_model'
-  clean_folder(model_dir)
-
-  # classifier with early stopping on training data
-  classifier1 = learn.DNNClassifier(
-      feature_columns=learn.infer_real_valued_columns_from_input(x_train),
-      hidden_units=[10, 20, 10],
-      n_classes=3,
-      model_dir=model_dir)
-  classifier1.fit(x=x_train, y=y_train, steps=2000)
-  predictions1 = list(classifier1.predict(x_test, as_iterable=True))
-  score1 = metrics.accuracy_score(y_test, predictions1)
-
-  model_dir = '/tmp/iris_model_val'
-  clean_folder(model_dir)
-
-  # classifier with early stopping on validation data, save frequently for
-  # monitor to pick up new checkpoints.
-  classifier2 = learn.DNNClassifier(
-      feature_columns=learn.infer_real_valued_columns_from_input(x_train),
-      hidden_units=[10, 20, 10],
-      n_classes=3,
-      model_dir=model_dir,
-      config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1))
-  classifier2.fit(x=x_train, y=y_train, steps=2000, monitors=[val_monitor])
-  predictions2 = list(classifier2.predict(x_test, as_iterable=True))
-  score2 = metrics.accuracy_score(y_test, predictions2)
-
-  # In many applications, the score is improved by using early stopping
-  print('score1: ', score1)
-  print('score2: ', score2)
-  print('score2 > score1: ', score2 > score1)
-
-
-if __name__ == '__main__':
-  tf.app.run()