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diff --git a/tensorflow/examples/skflow/dnn_autoencoder_iris.py b/tensorflow/examples/skflow/dnn_autoencoder_iris.py
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+#  Copyright 2015-present The Scikit Flow 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.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import random
+
+import tensorflow as tf
+from tensorflow.contrib import learn
+from tensorflow.contrib.learn import datasets
+
+# Load Iris Data
+iris = datasets.load_iris()
+
+# Initialize a deep neural network autoencoder
+# You can also add noise and add dropout if needed
+# Details see TensorFlowDNNAutoencoder documentation.
+autoencoder = learn.TensorFlowDNNAutoencoder(hidden_units=[10, 20])
+
+# Fit with Iris data
+transformed = autoencoder.fit_transform(iris.data)
+
+print(transformed)