# tf.contrib.learn Quickstart

TensorFlow’s high-level machine learning API (tf.contrib.learn) makes it easy to configure, train, and evaluate a variety of machine learning models. In this quickstart tutorial, you’ll use tf.contrib.learn to construct a neural network classifier and train it on Fisher’s Iris data set to predict flower species based on sepal/petal geometry. You’ll perform the following five steps:

1. Load CSVs containing Iris training/test data into a TensorFlow Dataset
2. Construct a neural network classifier
3. Fit the model using the training data
4. Evaluate the accuracy of the model
5. Classify new samples

## Get Started

Remember to install TensorFlow on your machine before getting started with this tutorial.

Here is the full code for our neural network:

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"

target_dtype=np.int)
target_dtype=np.int)

# Specify that all features have real-value 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 BY-SA 3.0), Iris versicolor (by Dlanglois, CC BY-SA 3.0), and Iris virginica (by Frank Mayfield, CC BY-SA 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 ). and a test set of 30 samples (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 Datasets using the load_csv() method in learn.datasets.base. The load_csv() method has two required arguments:

• filename, which takes the filepath to the CSV file, and
• target_dtype, which takes the numpy datatype 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"

target_dtype=np.int)
target_dtype=np.int)


Datasets in tf.contrib.learn are named tuples , and 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 Estimators , 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 real-value 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 with the feature_columns defined above; three hidden layers, containing 10, 20, and 10 neurons, respectively (hidden_units=[10, 20, 10]); and three target classes (n_classes=3). Model data will be stored in /tmp/iris_model.

## 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. You should get:

Accuracy: 0.966667


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.