TensorFlow’s highlevel machine learning API (tf.contrib.learn) makes it easy to configure, train, and evaluate a variety of machine learning models. In this tutorial, you’ll use tf.contrib.learn to construct a neural network classifier and train it on the Iris data set to predict flower species based on sepal/petal geometry. You'll write code to perform the following five steps:
 Load CSVs containing Iris training/test data into a TensorFlow
Dataset
 Construct a [neural network classifier] (../../api_docs/python/contrib.learn/estimators#DNNClassifier)
 Fit the model using the training data
 Evaluate the accuracy of the model
 Classify new samples
NOTE: Remember to install TensorFlow on your machine before getting started with this tutorial.
Complete Neural Network Source Code
Here is the full code for the neural network classifier:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TEST = "iris_test.csv"
# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TRAINING,
target_dtype=np.int)
test_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TEST,
target_dtype=np.int)
# Specify that all features have realvalue data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir="/tmp/iris_model")
# Fit model.
classifier.fit(x=training_set.data,
y=training_set.target,
steps=2000)
# Evaluate accuracy.
accuracy_score = classifier.evaluate(x=test_set.data,
y=test_set.target)["accuracy"]
print('Accuracy: {0:f}'.format(accuracy_score))
# Classify two new flower samples.
new_samples = np.array(
[[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
y = classifier.predict(new_samples)
print('Predictions: {}'.format(str(y)))
The following sections walk through the code in detail.
Load the Iris CSV data to TensorFlow
The Iris data set contains 150 rows of data, comprising 50 samples from each of three related Iris species: Iris setosa, Iris virginica, and Iris versicolor.
From left to right, Iris setosa (by Radomil, CC BYSA 3.0), Iris versicolor (by Dlanglois, CC BYSA 3.0), and Iris virginica (by Frank Mayfield, CC BYSA 2.0).
Each row contains the following data for each flower sample: sepal length, sepal width, petal length, petal width, and flower species. Flower species are represented as integers, with 0 denoting Iris setosa, 1 denoting Iris versicolor, and 2 denoting Iris virginica.
Sepal Length  Sepal Width  Petal Length  Petal Width  Species 

5.1  3.5  1.4  0.2  0 
4.9  3.0  1.4  0.2  0 
4.7  3.2  1.3  0.2  0 
…  …  …  …  … 
7.0  3.2  4.7  1.4  1 
6.4  3.2  4.5  1.5  1 
6.9  3.1  4.9  1.5  1 
…  …  …  …  … 
6.5  3.0  5.2  2.0  2 
6.2  3.4  5.4  2.3  2 
5.9  3.0  5.1  1.8  2 
For this tutorial, the Iris data has been randomized and split into two separate CSVs:
 A training set of 120 samples ([iris_training.csv] (http://download.tensorflow.org/data/iris_training.csv))
 A test set of 30 samples ([iris_test.csv] (http://download.tensorflow.org/data/iris_test.csv)).
Place these files in the same directory as your Python code.
To get started, first import TensorFlow and numpy:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
Next, load the training and test sets into Dataset
s using the load_csv()
method in learn.datasets.base
. The load_csv()
method takes two required
arguments:
filename
, which takes the filepath to the CSV filetarget_dtype
, which takes the [numpy
datatype] (http://docs.scipy.org/doc/numpy/user/basics.types.html) of the dataset's target value.
Here, the target (the value you're training the model to predict) is flower
species, which is an integer from 0–2, so the appropriate numpy
datatype
is np.int
:
# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TEST = "iris_test.csv"
# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TRAINING,
target_dtype=np.int)
test_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TEST,
target_dtype=np.int)
Dataset
s in tf.contrib.learn are named tuples
; you
can access feature data and target values via the data
and target
fields.
Here, training_set.data
and training_set.target
contain the feature data and
target values for the training set, respectively, and test_set.data
and
test_set.target
contain feature data and target values for the test set.
Later on, in "Fit the DNNClassifier to the Iris Training Data,"
you'll use training_set.data
and training_set.target
to
train your model, and in "Evaluate Model Accuracy," you'll
use test_set.data
and test_set.target
. But first, you'll construct your
model in the next section.
Construct a Deep Neural Network Classifier
tf.contrib.learn offers a variety of predefined models, called Estimator
s
, which you can use "out of
the box" to run training and evaluation operations on your data. Here, you'll
configure a Deep Neural Network Classifier model to fit the Iris data. Using
tf.contrib.learn, you can instantiate your DNNClassifier
with just a couple lines
of code:
# Specify that all features have realvalue data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir="/tmp/iris_model")
The code above first defines the model's feature columns, which specify the data
type for the features in the data set. All the feature data is continuous, so
tf.contrib.layers.real_valued_column
is the appropriate function to use to
construct the feature columns. There are four features in the data set (sepal
width, sepal height, petal width, and petal height), so dimensions
must be set
accordingly to 4
to hold all the data.
Then, the code creates a DNNClassifier
model using the following arguments:
feature_columns=feature_columns
. The set of feature columns defined above.hidden_units=[10, 20, 10]
. Three [hidden layers] (http://stats.stackexchange.com/questions/181/howtochoosethenumberofhiddenlayersandnodesinafeedforwardneuralnetw), containing 10, 20, and 10 neurons, respectively.n_classes=3
. Three target classes, representing the three Iris species.model_dir=/tmp/iris_model
. The directory in which TensorFlow will save checkpoint data during model training. For more on logging and monitoring with TensorFlow, see [Logging and Monitoring Basics with tf.contrib.learn] (../monitors/index).
Fit the DNNClassifier to the Iris Training Data
Now that you've configured your DNN classifier
model, you can fit it to the
Iris training data using the fit
method. Pass as
arguments your feature data (training_set.data
), target values
(training_set.target
), and the number of steps to train (here, 2000):
# Fit model
classifier.fit(x=training_set.data, y=training_set.target, steps=2000)
The state of the model is preserved in the classifier
, which means you can
train iteratively if you like. For example, the above is equivalent to the
following:
classifier.fit(x=training_set.data, y=training_set.target, steps=1000)
classifier.fit(x=training_set.data, y=training_set.target, steps=1000)
However, if you're looking to track the model while it trains, you'll likely
want to instead use a TensorFlow monitor
to perform logging operations. See the tutorial “Logging and Monitoring
Basics with tf.contrib.learn” for more on this
topic.
Evaluate Model Accuracy
You've fit your DNNClassifier
model on the Iris training data; now, you can
check its accuracy on the Iris test data using the evaluate
method. Like
fit
, evaluate
takes feature data and target values as arguments, and returns
a dict
with the evaluation results. The following code passes the Iris test
data—test_set.data
and test_set.target
—to evaluate
and prints
the accuracy
from the results:
accuracy_score = classifier.evaluate(x=test_set.data, y=test_set.target)["accuracy"]
print('Accuracy: {0:f}'.format(accuracy_score))
Run the full script, and check the accuracy results:
Accuracy: 0.966667
Your accuracy result may vary a bit, but should be higher than 90%. Not bad for a relatively small data set!
Classify New Samples
Use the estimator's predict()
method to classify new samples. For example, say
you have these two new flower samples:
Sepal Length  Sepal Width  Petal Length  Petal Width 

6.4  3.2  4.5  1.5 
5.8  3.1  5.0  1.7 
You can predict their species with the following code:
# Classify two new flower samples.
new_samples = np.array(
[[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
y = classifier.predict(new_samples)
print('Predictions: {}'.format(str(y)))
The predict()
method returns an array of predictions, one for each sample:
Prediction: [1 2]
The model thus predicts that the first sample is Iris versicolor, and the second sample is Iris virginica.
Additional Resources

For further reference materials on tf.contrib.learn, see the official API docs.

To learn more about using tf.contrib.learn to create linear models, see Largescale Linear Models with TensorFlow.

To build your own Estimator using tf.contrib.learn APIs, check out [Building Machine Learning Estimator in TensorFlow] (http://terrytangyuan.github.io/2016/07/08/understandandbuildtensorflowestimator/).

To experiment with neural network modeling and visualization in the browser, check out Deep Playground.

For more advanced tutorials on neural networks, see Convolutional Neural Networks and Recurrent Neural Networks.