# 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](https://en.wikipedia.org/wiki/Artificial_neural_network) classifier
and train it on [Fisher’s Iris data
set](https://en.wikipedia.org/wiki/Iris_flower_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](
../../api_docs/python/contrib.learn.html#DNNClassifier)
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](../../get_started/os_setup.html#download-and-setup) before getting
started with this tutorial.

Here is the full code for our neural network:

```python
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 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](https://en.wikipedia.org/wiki/Iris_flower_data_set) contains
150 rows of data, comprising 50 samples from each of three related Iris species:
*Iris setosa*, *Iris virginica*, and *Iris versicolor*.

![Petal geometry compared for three iris species: Iris setosa, Iris virginica,
and Iris versicolor](../../images/iris_three_species.jpg) **From left to right,
[*Iris setosa*](https://commons.wikimedia.org/w/index.php?curid=170298) (by
[Radomil](https://commons.wikimedia.org/wiki/User:Radomil), CC BY-SA 3.0),
[*Iris versicolor*](https://commons.wikimedia.org/w/index.php?curid=248095) (by
[Dlanglois](https://commons.wikimedia.org/wiki/User:Dlanglois), CC BY-SA 3.0),
and [*Iris virginica*](https://www.flickr.com/photos/33397993@N05/3352169862)
(by [Frank Mayfield](https://www.flickr.com/photos/33397993@N05), CC BY-SA
2.0).**

Each row contains the following data for each flower sample: [sepal]
(https://en.wikipedia.org/wiki/Sepal) length, sepal width, [petal]
(https://en.wikipedia.org/wiki/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
&hellip;     | &hellip;    | &hellip;     | &hellip;    | &hellip;
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
&hellip;     | &hellip;    | &hellip;     | &hellip;    | &hellip;
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)). and 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:

```python
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()`]
(https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/datasets/base.py)  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](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&ndash;2, so the appropriate `numpy`
datatype is `np.int`:

```python
# 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]
(https://docs.python.org/2/library/collections.html#collections.namedtuple),
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,"]
(#fit-dnnclassifier) you'll use `training_set.data` and `training_set.target`
to train your model, and in ["Evaluate Model Accuracy,"](#evaluate-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]
(../../api_docs/python/contrib.learn.html#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`]
(../../api_docs/python/contrib.learn.html#DNNClassifier) with just a couple lines of
code:

```python
# 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](http://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw),
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 {#fit-dnnclassifier}

Now that you've configured your DNN `classifier` model, you can fit it to the
Iris training data using the [`fit`]
(../../api_docs/python/contrib.learn.html#BaseEstimator.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):

```python
# 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:

```python
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`]
(https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/monitors.py)
to perform logging operations. See the tutorial [&ldquo;Logging and Monitoring
Basics with tf.contrib.learn&rdquo;](../monitors/index.md) for more on this
topic.

## Evaluate Model Accuracy {#evaluate-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`
](../../api_docs/python/contrib.learn.html#BaseEstimator.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&mdash;`test_set.data` and `test_set.target`&mdash;to `evaluate`
and prints the `accuracy` from the results:

```python
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:

```python
# 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:

```python
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](../../api_docs/python/contrib.learn.md).

* To learn more about using tf.contrib.learn to create linear models, see 
[Large-scale Linear Models with TensorFlow](../linear/).

* 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/understand-and-build-tensorflow-estimator/).

* To experiment with neural network modeling and visualization in the browser,
check out [Deep Playground](http://playground.tensorflow.org/).

* For more advanced tutorials on neural networks, see [Convolutional Neural
Networks](../deep_cnn/) and [Recurrent Neural Networks](../recurrent/).
