# Premade Estimators

<br />

|---|---|---|---|
| [![](https://www.tensorflow.org/images/tf_logo_32px.png)View on TensorFlow.org](https://www.tensorflow.org/tutorials/estimator/premade) | [![](https://www.tensorflow.org/images/colab_logo_32px.png)Run in Google Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/estimator/premade.ipynb) | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png)View source on GitHub](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/estimator/premade.ipynb) | [![](https://www.tensorflow.org/images/download_logo_32px.png)Download notebook](https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/estimator/premade.ipynb) |

> > [!WARNING]
> > **Warning:** TensorFlow 2.15 included the final release of the `tf-estimator` package. Estimators will not be available in TensorFlow 2.16 or after. See the [migration guide](https://tensorflow.org/guide/migrate/migrating_estimator) for more information about how to convert off of Estimators.
>
This tutorial shows you
how to solve the Iris classification problem in TensorFlow using Estimators. An Estimator is a legacy TensorFlow high-level representation of a complete model. For more details see
[Estimators](https://www.tensorflow.org/guide/estimator).

> [!NOTE]
> **Note:** In TensorFlow 2.0, the [Keras API](https://www.tensorflow.org/guide/keras) can accomplish these same tasks, and is believed to be an easier API to learn. If you are starting fresh, it is recommended you start with Keras.

## First things first

In order to get started, you will first import TensorFlow and a number of libraries you will need.

    import tensorflow as tf

    import pandas as pd

## The data set

The sample program in this document builds and tests a model that
classifies Iris flowers into three different species based on the size of their
[sepals](https://en.wikipedia.org/wiki/Sepal) and
[petals](https://en.wikipedia.org/wiki/Petal).

You will train a model using the Iris data set. The Iris data set contains four features and one
[label](https://developers.google.com/machine-learning/glossary/#label).
The four features identify the following botanical characteristics of
individual Iris flowers:

- sepal length
- sepal width
- petal length
- petal width

Based on this information, you can define a few helpful constants for parsing the data:

    CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
    SPECIES = ['Setosa', 'Versicolor', 'Virginica']

Next, download and parse the Iris data set using Keras and Pandas. Note that you keep distinct datasets for training and testing.

    train_path = tf.keras.utils.get_file(
        "iris_training.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv")
    test_path = tf.keras.utils.get_file(
        "iris_test.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv")

    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)

You can inspect your data to see that you have four float feature columns and one int32 label.

    train.head()

For each of the datasets, split out the labels, which the model will be trained to predict.

    train_y = train.pop('Species')
    test_y = test.pop('Species')

    # The label column has now been removed from the features.
    train.head()

## Overview of programming with Estimators

Now that you have the data set up, you can define a model using a TensorFlow Estimator. An Estimator is any class derived from `tf.estimator.Estimator`. TensorFlow
provides a collection of
`tf.estimator`
(for example, `LinearRegressor`) to implement common ML algorithms. Beyond
those, you may write your own
[custom Estimators](https://www.tensorflow.org/guide/estimator#custom_estimators).
It is recommended using pre-made Estimators when just getting started.

To write a TensorFlow program based on pre-made Estimators, you must perform the
following tasks:

- Create one or more input functions.
- Define the model's feature columns.
- Instantiate an Estimator, specifying the feature columns and various hyperparameters.
- Call one or more methods on the Estimator object, passing the appropriate input function as the source of the data.

Let's see how those tasks are implemented for Iris classification.

## Create input functions

You must create input functions to supply data for training,
evaluating, and prediction.

An **input function** is a function that returns a [`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) object
which outputs the following two-element tuple:

- [`features`](https://developers.google.com/machine-learning/glossary/#feature) - A Python dictionary in which:
  - Each key is the name of a feature.
  - Each value is an array containing all of that feature's values.
- `label` - An array containing the values of the [label](https://developers.google.com/machine-learning/glossary/#label) for every example.

Just to demonstrate the format of the input function, here's a simple
implementation:

    def input_evaluation_set():
        features = {'SepalLength': np.array([6.4, 5.0]),
                    'SepalWidth':  np.array([2.8, 2.3]),
                    'PetalLength': np.array([5.6, 3.3]),
                    'PetalWidth':  np.array([2.2, 1.0])}
        labels = np.array([2, 1])
        return features, labels

Your input function may generate the `features` dictionary and `label` list any
way you like. However, It is recommended using TensorFlow's [Dataset API](https://www.tensorflow.org/guide/datasets), which can
parse all sorts of data.

The Dataset API can handle a lot of common cases for you. For example,
using the Dataset API, you can easily read in records from a large collection
of files in parallel and join them into a single stream.

To keep things simple in this example you are going to load the data with
[pandas](https://pandas.pydata.org/), and build an input pipeline from this
in-memory data:

    def input_fn(features, labels, training=True, batch_size=256):
        """An input function for training or evaluating"""
        # Convert the inputs to a Dataset.
        dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))

        # Shuffle and repeat if you are in training mode.
        if training:
            dataset = dataset.shuffle(1000).repeat()

        return dataset.batch(batch_size)

## Define the feature columns

A [**feature column**](https://developers.google.com/machine-learning/glossary/#feature_columns)
is an object describing how the model should use raw input data from the
features dictionary. When you build an Estimator model, you pass it a list of
feature columns that describes each of the features you want the model to use.
The [`tf.feature_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column) module provides many options for representing data
to the model.

For Iris, the 4 raw features are numeric values, so you'll build a list of
feature columns to tell the Estimator model to represent each of the four
features as 32-bit floating-point values. Therefore, the code to create the
feature column is:

    # Feature columns describe how to use the input.
    my_feature_columns = []
    for key in train.keys():
        my_feature_columns.append(tf.feature_column.numeric_column(key=key))

Feature columns can be far more sophisticated than those shown here. You can read more about Feature Columns in [this guide](https://www.tensorflow.org/guide/feature_columns).

Now that you have the description of how you want the model to represent the raw
features, you can build the estimator.

## Instantiate an estimator

The Iris problem is a classic classification problem. Fortunately, TensorFlow
provides several pre-made classifier Estimators, including:

- `tf.estimator.DNNClassifier` for deep models that perform multi-class classification.
- `tf.estimator.DNNLinearCombinedClassifier` for wide \& deep models.
- `tf.estimator.LinearClassifier` for classifiers based on linear models.

For the Iris problem, `tf.estimator.DNNClassifier` seems like the best choice.
Here's how you instantiated this Estimator:

    # Build a DNN with 2 hidden layers with 30 and 10 hidden nodes each.
    classifier = tf.estimator.DNNClassifier(
        feature_columns=my_feature_columns,
        # Two hidden layers of 30 and 10 nodes respectively.
        hidden_units=[30, 10],
        # The model must choose between 3 classes.
        n_classes=3)

## Train, Evaluate, and Predict

Now that you have an Estimator object, you can call methods to do the following:

- Train the model.
- Evaluate the trained model.
- Use the trained model to make predictions.

### Train the model

Train the model by calling the Estimator's `train` method as follows:

    # Train the Model.
    classifier.train(
        input_fn=lambda: input_fn(train, train_y, training=True),
        steps=5000)

Note that you wrap up your `input_fn` call in a
[`lambda`](https://docs.python.org/3/tutorial/controlflow.html)
to capture the arguments while providing an input function that takes no
arguments, as expected by the Estimator. The `steps` argument tells the method
to stop training after a number of training steps.

### Evaluate the trained model

Now that the model has been trained, you can get some statistics on its
performance. The following code block evaluates the accuracy of the trained
model on the test data:

    eval_result = classifier.evaluate(
        input_fn=lambda: input_fn(test, test_y, training=False))

    print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))

Unlike the call to the `train` method, you did not pass the `steps`
argument to evaluate. The `input_fn` for eval only yields a single
[epoch](https://developers.google.com/machine-learning/glossary/#epoch) of data.

The `eval_result` dictionary also contains the `average_loss` (mean loss per sample), the `loss` (mean loss per mini-batch) and the value of the estimator's `global_step` (the number of training iterations it underwent).

### Making predictions (inferring) from the trained model

You now have a trained model that produces good evaluation results.
You can now use the trained model to predict the species of an Iris flower
based on some unlabeled measurements. As with training and evaluation, you make
predictions using a single function call:

    # Generate predictions from the model
    expected = ['Setosa', 'Versicolor', 'Virginica']
    predict_x = {
        'SepalLength': [5.1, 5.9, 6.9],
        'SepalWidth': [3.3, 3.0, 3.1],
        'PetalLength': [1.7, 4.2, 5.4],
        'PetalWidth': [0.5, 1.5, 2.1],
    }

    def input_fn(features, batch_size=256):
        """An input function for prediction."""
        # Convert the inputs to a Dataset without labels.
        return tf.data.Dataset.from_tensor_slices(dict(features)).batch(batch_size)

    predictions = classifier.predict(
        input_fn=lambda: input_fn(predict_x))

The `predict` method returns a Python iterable, yielding a dictionary of
prediction results for each example. The following code prints a few
predictions and their probabilities:

    for pred_dict, expec in zip(predictions, expected):
        class_id = pred_dict['class_ids'][0]
        probability = pred_dict['probabilities'][class_id]

        print('Prediction is "{}" ({:.1f}%), expected "{}"'.format(
            SPECIES[class_id], 100 * probability, expec))