This tutorial introduces you to creating input functions in tf.estimator.
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

The `input_fn`

is used to pass feature and target data to the `train`

,
`evaluate`

, and `predict`

methods of the `Estimator`

.
The user can do feature engineering or pre-processing inside the `input_fn`

.
Here's an example taken from the tf.estimator Quickstart tutorial:

```
import numpy as np
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float32)
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.array(training_set.data)},
y=np.array(training_set.target),
num_epochs=None,
shuffle=True)
classifier.train(input_fn=train_input_fn, steps=2000)
```

### Anatomy of an input_fn

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

```
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.

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):

`feature_cols`

- A dict containing key/value pairs that map feature column
names to
`Tensor`

s (or`SparseTensor`

s) containing the corresponding feature data. `labels`

- A
`Tensor`

containing your label (target) values: the values your model aims to predict.

### Converting Feature Data to Tensors

If your feature/label data is a python array or stored in
*pandas* dataframes or
numpy arrays, you can use the following methods to
construct `input_fn`

:

```
import numpy as np
# numpy input_fn.
my_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.array(x_data)},
y=np.array(y_data),
...)
```

```
import pandas as pd
# pandas input_fn.
my_input_fn = tf.estimator.inputs.pandas_input_fn(
x=pd.DataFrame({"x": x_data}),
y=pd.Series(y_data),
...)
```

For sparse, categorical data
(data where the majority of values are 0), you'll instead want to populate a
`SparseTensor`

, which is instantiated with three arguments:

`dense_shape`

- The shape of the tensor. Takes a list indicating the number of elements in each dimension. For example,
`dense_shape=[3,6]`

specifies a two-dimensional 3x6 tensor,`dense_shape=[2,3,4]`

specifies a three-dimensional 2x3x4 tensor, and`dense_shape=[9]`

specifies a one-dimensional tensor with 9 elements. `indices`

- 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,
`indices=[[1,3], [2,4]]`

specifies that the elements with indexes of [1,3] and [2,4] have nonzero values. `values`

- A one-dimensional tensor of values. Term
`i`

in`values`

corresponds to term`i`

in`indices`

and specifies its value. For example, given`indices=[[1,3], [2,4]]`

, the parameter`values=[18, 3.6]`

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.

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):

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

This corresponds to the following dense tensor:

```
[[0, 6, 0, 0, 0]
[0, 0, 0, 0, 0]
[0, 0, 0, 0, 0.5]]
```

For more on `SparseTensor`

, see `tf.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 `train`

operation as the value of the `input_fn`

parameter, e.g.:

```
classifier.train(input_fn=my_input_fn, steps=2000)
```

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_fn`

in your `train`

call, as in the following code, it will result in a
`TypeError`

:

```
classifier.train(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:

```
def my_input_fn(data_set):
...
def my_input_fn_training_set():
return my_input_fn(training_set)
classifier.train(input_fn=my_input_fn_training_set, steps=2000)
```

Alternatively, you can use Python's `functools.partial`

function to construct a new function object with all parameter values fixed:

```
classifier.train(
input_fn=functools.partial(my_input_fn, data_set=training_set),
steps=2000)
```

A third option is to wrap your `input_fn`

invocation in a
`lambda`

and pass it to the `input_fn`

parameter:

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

One big advantage of designing 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.:

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

This approach enhances code maintainability: no need to define multiple
`input_fn`

(e.g. `input_fn_train`

, `input_fn_test`

, `input_fn_predict`

) for each
type of operation.

Finally, you can use the methods in `tf.estimator.inputs`

to create `input_fn`

from numpy or pandas data sets. The additional benefit is that you can use
more arguments, such as `num_epochs`

and `shuffle`

to control how the `input_fn`

iterates over the data:

```
import pandas as pd
def get_input_fn_from_pandas(data_set, num_epochs=None, shuffle=True):
return tf.estimator.inputs.pandas_input_fn(
x=pdDataFrame(...),
y=pd.Series(...),
num_epochs=num_epochs,
shuffle=shuffle)
```

```
import numpy as np
def get_input_fn_from_numpy(data_set, num_epochs=None, shuffle=True):
return tf.estimator.inputs.numpy_input_fn(
x={...},
y=np.array(...),
num_epochs=num_epochs,
shuffle=shuffle)
```

### 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 and use it to feed data to a neural network regressor for predicting median house values.

The Boston CSV data sets you'll use to train your neural network contain the following feature data 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

Download the following data sets: boston_train.csv, boston_test.csv, and 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.

### Importing the Housing Data

To start, set up your imports (including `pandas`

and `tensorflow`

) and set logging verbosity to
`INFO`

for more detailed log output:

```
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
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
(`tf.train`

,
`tf.test`

, and
predict) into *pandas*
`DataFrame`

s:

```
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:

```
feature_cols = [tf.feature_column.numeric_column(k) for k in FEATURES]
```

NOTE: For a more in-depth overview of feature columns, see
this introduction,
and for an example that illustrates how to define `FeatureColumns`

for
categorical data, see the Linear Model Tutorial.

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:

```
regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
hidden_units=[10, 10],
model_dir="/tmp/boston_model")
```

### Building the input_fn

To pass input data into the `regressor`

, write a factory method that accepts a
*pandas* `Dataframe`

and returns an `input_fn`

:

```
def get_input_fn(data_set, num_epochs=None, shuffle=True):
return tf.estimator.inputs.pandas_input_fn(
x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),
y = pd.Series(data_set[LABEL].values),
num_epochs=num_epochs,
shuffle=shuffle)
```

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`

.

Two additional arguments are provided:
* `num_epochs`

: controls the number of
epochs to iterate over data. For training, set this to `None`

, so the
`input_fn`

keeps returning data until the required number of train steps is
reached. For evaluate and predict, set this to 1, so the `input_fn`

will
iterate over the data once and then raise `OutOfRangeError`

. That error will
signal the `Estimator`

to stop evaluate or predict.
* `shuffle`

: Whether to shuffle the data. For evaluate and predict, set this to
`False`

, so the `input_fn`

iterates over the data sequentially. For train,
set this to `True`

.

### Training the Regressor

To train the neural network regressor, run `train`

with the `training_set`

passed to the `input_fn`

as follows:

```
regressor.train(input_fn=get_input_fn(training_set), steps=5000)
```

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

```
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`

:

```
ev = regressor.evaluate(
input_fn=get_input_fn(test_set, num_epochs=1, shuffle=False))
```

Retrieve the loss from the `ev`

results and print it to output:

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

You should see results similar to the following:

```
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:

```
y = regressor.predict(
input_fn=get_input_fn(prediction_set, num_epochs=1, shuffle=False))
# .predict() returns an iterator of dicts; convert to a list and print
# predictions
predictions = list(p["predictions"] for p in itertools.islice(y, 6))
print("Predictions: {}".format(str(predictions)))
```

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

```
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: 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: 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: Building on the Linear Model Tutorial, 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`

.