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 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 Tensors (or SparseTensors) 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 DataFrames:

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 FeatureColumns 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 FeatureColumns 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 DataFrames 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_fns 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 FeatureColumns 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.