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diff --git a/tensorflow/examples/skflow/mnist_rnn.py b/tensorflow/examples/skflow/mnist_rnn.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.
+
+"""
+This example builds rnn network for mnist data.
+Borrowed structure from here: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/recurrent_network.py
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from sklearn import metrics, preprocessing
+
+import tensorflow as tf
+from tensorflow.contrib import learn
+
+# Parameters
+learning_rate = 0.1
+training_steps = 3000
+batch_size = 128
+
+# Network Parameters
+n_input = 28  # MNIST data input (img shape: 28*28)
+n_steps = 28  # timesteps
+n_hidden = 128  # hidden layer num of features
+n_classes = 10  # MNIST total classes (0-9 digits)
+
+### Download and load MNIST data.
+mnist = learn.datasets.load_dataset('mnist')
+
+X_train = mnist.train.images
+y_train = mnist.train.labels
+X_test = mnist.test.images
+y_test = mnist.test.labels
+
+# It's useful to scale to ensure Stochastic Gradient Descent will do the right thing
+scaler = preprocessing.StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.fit_transform(X_test)
+
+
+def rnn_model(X, y):
+  X = tf.reshape(X, [-1, n_steps, n_input])  # (batch_size, n_steps, n_input)
+  # # permute n_steps and batch_size
+  X = tf.transpose(X, [1, 0, 2])
+  # # Reshape to prepare input to hidden activation
+  X = tf.reshape(X, [-1, n_input])  # (n_steps*batch_size, n_input)
+  # # Split data because rnn cell needs a list of inputs for the RNN inner loop
+  X = tf.split(0, n_steps, X)  # n_steps * (batch_size, n_input)
+
+  # Define a GRU cell with tensorflow
+  lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden)
+  # Get lstm cell output
+  _, encoding = tf.nn.rnn(lstm_cell, X, dtype=tf.float32)
+
+  return learn.models.logistic_regression(encoding, y)
+
+
+classifier = learn.TensorFlowEstimator(model_fn=rnn_model, n_classes=n_classes,
+                                       batch_size=batch_size,
+                                       steps=training_steps,
+                                       learning_rate=learning_rate)
+
+classifier.fit(X_train, y_train, logdir="/tmp/mnist_rnn")
+score = metrics.accuracy_score(y_test, classifier.predict(X_test))
+print('Accuracy: {0:f}'.format(score))