# Building Input Functions with tf.estimator

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 @{$get_started/estimator$tf.estimator Quickstart tutorial}:

```python
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:

```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 a python array or stored in
[_pandas_](http://pandas.pydata.org/) dataframes or
[numpy](http://www.numpy.org/) arrays, you can use the following methods to
construct `input_fn`:

```python
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),
    ...)
```

```python
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](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>dense_shape</code></dt>
  <dd>The shape of the tensor. Takes a list indicating the number of elements in each dimension. For example, <code>dense_shape=[3,6]</code> specifies a two-dimensional 3x6 tensor, <code>dense_shape=[2,3,4]</code> specifies a three-dimensional 2x3x4 tensor, and <code>dense_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],
                                dense_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 @{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.:

```python
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`:

```python
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:

```python
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`](https://docs.python.org/2/library/functools.html#functools.partial)
function to construct a new function object with all parameter values fixed:

```python
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`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions)
and pass it to the `input_fn` parameter:

```python
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.:

```python
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:

```python
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=pd.DataFrame(...),
      y=pd.Series(...),
      num_epochs=num_epochs,
      shuffle=shuffle)
```

```python
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](#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 to
`INFO` for more detailed log output:

```python
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
([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.feature_column.numeric_column()` function:

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

NOTE: For a more in-depth overview of feature columns, see
@{$linear#feature-columns-and-transformations$this introduction},
and for an example that illustrates how to define `FeatureColumns` for
categorical data, see the @{$wide$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:

```python
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`:

```python
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:

```python
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:

```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=get_input_fn(test_set, num_epochs=1, shuffle=False))
```

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=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:

```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:

*   @{$linear$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.

*   @{$wide$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.

*   @{$wide_and_deep$TensorFlow Wide & Deep Learning Tutorial}: Building on
    the @{$wide$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`.
