In order to make the use of tensorflow simpler to experiment machine learning, google offered a library that stays on top of tensorflow. Skflow make life easier.
Import library
import tensorflow.contrib.learn as skflow
from sklearn import datasets, metrics
from sklearn import cross_validation
Load dataset
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
# Feature columns is required for new versions
feature_columns = skflow.infer_real_valued_columns_from_input(x_train)