# Building Input Functions with tf.contrib.learn

This tutorial introduces you to creating input functions in tf.contrib.learn.
You'll get an overview of how to construct an `input_fn` to preprocess and feed
data into your models. Then, you'll implement an `input_fn` that feeds training,
evaluation, and prediction data into a neural network regressor for predicting
median house values.

## Custom Input Pipelines with input_fn

When training a neural network using tf.contrib.learn, it's possible to pass
your feature and target data directly into your `fit`, `evaluate`, or `predict`
operations. Here's an example taken from the [tf.contrib.learn quickstart
tutorial](../tflearn/index.md):

```py
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)
...

classifier.fit(x=training_set.data,
               y=training_set.target,
               steps=2000)
```

This approach works well when little to no manipulation of source data is
required. But in cases where more feature engineering is needed,
`tf.contrib.learn` supports using a custom input function (`input_fn`) to
encapsulate the logic for preprocessing and piping data into your models.

### Anatomy of an input_fn

The following code illustrates the basic skeleton for an input function:

```python
def my_input_fn()

    # Preprocess your data here...

    # ...then return 1) a mapping of feature columns to Tensors with
    # the corresponding feature data, and 2) a Tensor containing labels
    return feature_cols, labels
```

The body of the input function contains the specific logic for preprocessing your
input data, such as scrubbing out bad examples or [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling).

Input functions must return the following two values containing the final
feature and label data to be fed into your model (as shown in the above code
skeleton):

<dl>
  <dt><code>feature_cols</code></dt>
  <dd>A dict containing key/value pairs that map feature column
names to <code>Tensor</code>s (or <code>SparseTensor</code>s) containing the corresponding feature
data.</dd>
  <dt><code>labels</code></dt>
  <dd>A <code>Tensor</code> containing your label (target) values: the values your model aims to predict.</dd>
</dl>

### Converting Feature Data to Tensors

If your feature/label data is stored in [_pandas_](http://pandas.pydata.org/)
dataframes or [numpy](http://www.numpy.org/) arrays, you'll need to convert it
to `Tensor`s before returning it from your `input_fn`.

For continuous data, you can create and populate a `Tensor` using `tf.constant`:

```python
feature_column_data = [1, 2.4, 0, 9.9, 3, 120]
feature_tensor = tf.constant(feature_column_data)
```

For [sparse, categorical data](https://en.wikipedia.org/wiki/Sparse_matrix)
(data where the majority of values are 0), you'll instead want to populate a
`SparseTensor`, which is instantiated with three arguments:

<dl>
  <dt><code>shape</code></dt>
  <dd>The shape of the tensor. Takes a list indicating the number of elements in each dimension. For example, <code>shape=[3,6]</code> specifies a two-dimensional 3x6 tensor, <code>shape=[2,3,4]</code> specifies a three-dimensional 2x3x4 tensor, and <code>shape=[9]</code> specifies a one-dimensional tensor with 9 elements.</dd>
  <dt><code>indices</code></dt>
  <dd>The indices of the elements in your tensor that contain nonzero values. Takes a list of terms, where each term is itself a list containing the index of a nonzero element. (Elements are zero-indexed—i.e., [0,0] is the index value for the element in the first column of the first row in a two-dimensional tensor.) For example, <code>indices=[[1,3], [2,4]]</code> specifies that the elements with indexes of [1,3] and [2,4] have nonzero values.</dd>
  <dt><code>values</code></dt>
  <dd>A one-dimensional tensor of values. Term <code>i</code> in <code>values</code> corresponds to term <code>i</code> in <code>indices</code> and specifies its value. For example, given <code>indices=[[1,3], [2,4]]</code>, the parameter <code>values=[18, 3.6]</code> specifies that element [1,3] of the tensor has a value of 18, and element [2,4] of the tensor has a value of 3.6.</dd>
</dl>

The following code defines a two-dimensional `SparseTensor` with 3 rows and 5
columns. The element with index [0,1] has a value of 6, and the element with
index [2,4] has a value of 0.5 (all other values are 0):

```python
sparse_tensor = tf.SparseTensor(indices=[[0,1], [2,4]],
                                values=[6, 0.5],
                                shape=[3, 5])
```

This corresponds to the following dense tensor:

```none
[[0, 6, 0, 0, 0]
 [0, 0, 0, 0, 0]
 [0, 0, 0, 0, 0.5]]
```

For more on `SparseTensor`, see the [TensorFlow API documentation]
(../../api_docs/python/sparse_ops.md#SparseTensor).

### Passing input_fn Data to Your Model

To feed data to your model for training, you simply pass the input function
you've created to your `fit` operation as the value of the `input_fn` parameter,
e.g.:

```python
classifier.fit(input_fn=my_input_fn, steps=2000)
```

Note that the `input_fn` is responsible for supplying both feature and label
data to the model, and replaces both the `x` and `y` parameters in `fit`. If you
supply an `input_fn` value to `fit` that is not `None` in conjunction with
either an `x` or `y` parameter that is not `None`, it will result in a
`ValueError`.

Also note that the `input_fn` parameter must receive a function object (i.e.,
`input_fn=my_input_fn`), not the return value of a function call
(`input_fn=my_input_fn()`). This means that if you try to pass parameters to the input
function in your `fit` call, as in the following code, it will result in a
`TypeError`:

```python
classifier.fit(input_fn=my_input_fn(training_set), steps=2000)
```

However, if you'd like to be able to parameterize your input function, there are
other methods for doing so. You can employ a wrapper function that takes no
arguments as your `input_fn` and use it to invoke your input function
with the desired parameters. For example:

```python
def my_input_function_training_set:
  my_input_function(training_set)

classifier.fit(input_fn=my_input_fn_training_set, steps=2000)
```

Alternatively, you can use Python's [`functools.partial`](https://docs.python.org/2/library/functools.html#functools.partial)
function to construct a new function object with all parameter values fixed:

```python
classifier.fit(input_fn=functools.partial(my_input_function,
                                          data_set=training_set), steps=2000)
```

A third option is to wrap your input_fn invocation in a [`lambda`]
(https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions) and
pass it to the `input_fn` parameter:

```python
classifier.fit(input_fn=lambda: my_input_fn(training_set), steps=2000)
```

One big advantage of architecting your input pipeline as shown above—to accept a
parameter for data set—is that you can pass the same `input_fn` to `evaluate`
and `predict` operations by just changing the data set argument, e.g.:

```python
classifier.evaluate(input_fn=lambda: my_input_fn(test_set), steps=2000)
```

This approach enhances code maintainability: no need to capture `x` and `y`
values in separate variables (e.g., `x_train`, `x_test`, `y_train`, `y_test`)
for each type of operation.

### A Neural Network Model for Boston House Values

In the remainder of this tutorial, you'll write an input function for
preprocessing a subset of Boston housing data pulled from the [UCI Housing Data
Set](https://archive.ics.uci.edu/ml/datasets/Housing) and use it to feed data to
a neural network regressor for predicting median house values.

The [Boston CSV data sets](#setup) you'll use to train your neural network
contain the following [feature data]
(https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.names)
for Boston suburbs:

Feature | Description
------- | ---------------------------------------------------------------
CRIM    | Crime rate per capita
ZN      | Fraction of residential land zoned to permit 25,000+ sq ft lots
INDUS   | Fraction of land that is non-retail business
NOX     | Concentration of nitric oxides in parts per 10 million
RM      | Average Rooms per dwelling
AGE     | Fraction of owner-occupied residences built before 1940
DIS     | Distance to Boston-area employment centers
TAX     | Property tax rate per $10,000
PTRATIO | Student-teacher ratio

And the label your model will predict is MEDV, the median value of
owner-occupied residences in thousands of dollars.

## Setup {#setup}

Download the following data sets: [boston_train.csv]
(http://download.tensorflow.org/data/boston_train.csv), [boston_test.csv]
(http://download.tensorflow.org/data/boston_test.csv), and [boston_predict.csv]
(http://download.tensorflow.org/data/boston_predict.csv).

The following sections provide a step-by-step walkthrough of how to create an
input function, feed these data sets into a neural network regressor, train and
evaluate the model, and make house value predictions. The full, final code is [available
here](https://www.tensorflow.org/code/tensorflow/examples/tutorials/input_fn/boston.py).

### Importing the Housing Data

To start, set up your imports (including `pandas` and `tensorflow`) and [set
logging verbosity](../monitors/index.md#enabling-logging-with-tensorflow) to
`INFO` for more detailed log output:

```python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import tensorflow as tf

tf.logging.set_verbosity(tf.logging.INFO)
```

Define the column names for the data set in `COLUMNS`. To distinguish features
from the label, also define `FEATURES` and `LABEL`. Then read the three CSVs
([train](http://download.tensorflow.org/data/boston_train.csv), [test]
(http://download.tensorflow.org/data/boston_test.csv), and [predict]
(http://download.tensorflow.org/data/boston_predict.csv)) into _pandas_
`DataFrame`s:

```python
COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age",
           "dis", "tax", "ptratio", "medv"]
FEATURES = ["crim", "zn", "indus", "nox", "rm",
            "age", "dis", "tax", "ptratio"]
LABEL = "medv"

training_set = pd.read_csv("boston_train.csv", skipinitialspace=True,
                           skiprows=1, names=COLUMNS)
test_set = pd.read_csv("boston_test.csv", skipinitialspace=True,
                       skiprows=1, names=COLUMNS)
prediction_set = pd.read_csv("boston_predict.csv", skipinitialspace=True,
                             skiprows=1, names=COLUMNS)
```

### Defining FeatureColumns and Creating the Regressor

Next, create a list of `FeatureColumn`s for the input data, which formally
specify the set of features to use for training. Because all features in the
housing data set contain continuous values, you can create their
`FeatureColumn`s using the `tf.contrib.layers.real_valued_column()` function:

```python
feature_cols = [tf.contrib.layers.real_valued_column(k)
                  for k in FEATURES]
```

NOTE: For a more in-depth overview of feature columns, see [this introduction]
(../linear/overview.md#feature-columns-and-transformations), and for an example
that illustrates how to define `FeatureColumns` for categorical data, see the
[Linear Model Tutorial](../wide/index.md).

Now, instantiate a `DNNRegressor` for the neural network regression model.
You'll need to provide two arguments here: `hidden_units`, a hyperparameter
specifying the number of nodes in each hidden layer (here, two hidden layers
with 10 nodes each), and `feature_columns`, containing the list of
`FeatureColumns` you just defined:

```python
regressor = tf.contrib.learn.DNNRegressor(
    feature_columns=feature_cols, hidden_units=[10, 10])
```

### Building the input_fn

To pass input data into the `regressor`, create an input function, which will
accept a _pandas_ `Dataframe` and return feature column and label values as
`Tensor`s:

```python
def input_fn(data_set):
  feature_cols = {k: tf.constant(data_set[k].values
                  for k in FEATURES}
  labels = tf.constant(data_set[LABEL].values)
  return feature_cols, labels
```

Note that the input data is passed into `input_fn` in the `data_set` argument,
which means the function can process any of the `DataFrame`s you've imported:
`training_set`, `test_set`, and `prediction_set`.

### Training the Regressor

To train the neural network regressor, run `fit` with the `training_set` passed
to the `input_fn` as follows:

<!-- TODO(skleinfeld): Decide on the best step value to use here for pedagogical purposes -->

```python
regressor.fit(input_fn=lambda: input_fn(training_set), steps=5000)
```

You should see log output similar to the following, which reports training loss
for every 100 steps:

```none
INFO:tensorflow:Step 1: loss = 483.179
INFO:tensorflow:Step 101: loss = 81.2072
INFO:tensorflow:Step 201: loss = 72.4354
...
INFO:tensorflow:Step 1801: loss = 33.4454
INFO:tensorflow:Step 1901: loss = 32.3397
INFO:tensorflow:Step 2001: loss = 32.0053
INFO:tensorflow:Step 4801: loss = 27.2791
INFO:tensorflow:Step 4901: loss = 27.2251
INFO:tensorflow:Saving checkpoints for 5000 into /tmp/boston_model/model.ckpt.
INFO:tensorflow:Loss for final step: 27.1674.
```

### Evaluating the Model

Next, see how the trained model performs against the test data set. Run
`evaluate`, and this time pass the `test_set` to the `input_fn`:

```python
ev = regressor.evaluate(input_fn=lambda: input_fn(test_set), steps=1)
```

Retrieve the loss from the `ev` results and print it to output:

```python
loss_score = ev["loss"]
print("Loss: {0:f}".format(loss_score))
```

You should see results similar to the following:

```none
INFO:tensorflow:Eval steps [0,1) for training step 5000.
INFO:tensorflow:Saving evaluation summary for 5000 step: loss = 11.9221
Loss: 11.922098
```

### Making Predictions

Finally, you can use the model to predict median house values for the
`prediction_set`, which contains feature data but no labels for six examples:

```python
y = regressor.predict(input_fn=lambda: input_fn(prediction_set))
print ("Predictions: {}".format(str(y)))
```

Your results should contain six house-value predictions in thousands of dollars,
e.g:

```none
Predictions: [ 33.30348587  17.04452896  22.56370163  34.74345398  14.55953979
  19.58005714]
```

## Additional Resources

This tutorial focused on creating an `input_fn` for a neural network regressor.
To learn more about using `input_fn`s for other types of models, check out the
following resources:

*   [Large-scale Linear Models with TensorFlow](../linear/overview.md): This
    introduction to linear models in TensorFlow provides a high-level overview
    of feature columns and techniques for transforming input data.

*   [TensorFlow Linear Model Tutorial](../wide/index.md): This tutorial covers
    creating `FeatureColumn`s and an `input_fn` for a linear classification
    model that predicts income range based on census data.

*   [TensorFlow Wide & Deep Learning Tutorial](../wide/index.md): Building on
    the [Linear Model Tutorial](../wide/index.md), this tutorial covers
    `FeatureColumn` and `input_fn` creation for a "wide and deep" model that
    combines a linear model and a neural network using
    `DNNLinearCombinedClassifier`.
