How to Use TensorFlow Feature Columns with Keras Models

TensorFlow and Keras are two powerful libraries that allow developers to build, train, and deploy machine learning models with ease. A key component in preprocessing and defining input data for a model in Keras is the Feature Columns API in TensorFlow. This API offers an elegant way to handle a wide variety of data preparation tasks, converting raw data into a format that a machine learning model can consume. This article will guide you through the essentials of using TensorFlow Feature Columns with Keras models to transform raw data and improve model performance.

Understanding Feature Columns

Feature columns are an abstraction layer that provides a bridge between raw data and Keras models. They help you transform data and specify how it should be represented in your models.

Types of Feature Columns

Feature columns provide tremendous flexibility. Consider the various types of feature columns you can employ:

• Numeric Columns: Used for floating-point features.
• Categorical Columns with Vocabulary Lists: Converts strings to one-hot or multi-hot vectors.
• Categorical Columns with Identifiers: Used for integer indices instead of strings.
• Bucketized Columns: Splits numeric data into discrete ranges.
• Crossed Columns: Combines several categorical data features to create a categorical feature.

Setting Up the Environment

To get started with TensorFlow feature columns, it's necessary to ensure TensorFlow is installed. You can check the installation with this command:

!pip install tensorflow

Now, let’s dive into a basic example of incorporating TensorFlow feature columns in a Keras model.

Example: Using Feature Columns in a Keras Model

Let's assume you're working on a dataset with both numerical and categorical features that need to be fed into a neural network. We will begin by creating some foundational feature columns using TensorFlow.

Step 1: Define Feature Columns

First, define the feature columns, including both numeric and categorical features. For example:

import tensorflow as tf

# Numeric column
age = tf.feature_column.numeric_column("age")

# Categorical column with vocabulary
occupation = tf.feature_column.categorical_column_with_vocabulary_list(
    "occupation", ["doctor", "engineer", "teacher"])

# Embedding column to convert categorical data into dense vectors
occupation_embedded = tf.feature_column.embedding_column(occupation, dimension=8)

Step 2: Create a Feature Layer

Next, wrap the feature columns into a dense feature layer that is compatible with Keras models:

feature_layer = tf.keras.layers.DenseFeatures([age, occupation_embedded])

Step 3: Build the Keras Model

Integrate the feature layer into a Keras sequential model:

model = tf.keras.Sequential([
    feature_layer,
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

Training the Model

To train the model, prepare the data. For instance:

train_data = {
    "age": [25, 32, 47, 51],
    "occupation": ["engineer", "teacher", "doctor", "engineer"]
}
labels = [0, 1, 0, 1]

Use the dataset to fit the model:

model.fit(x=dict(train_data), y=labels, epochs=10)

Conclusion

Incorporating TensorFlow Feature Columns into Keras models provides a structured way to handle and preprocess raw data effectively. By using feature columns, developers can leverage advanced feature engineering and turn their datasets into powerful inputs suitable for deep learning models. Exploring more of TensorFlow's feature column capabilities allows further refinement and optimization of your machine learning pipelines.