When developing machine learning models, feature engineering is an essential component of enhancing model performance. TensorFlow provides an effective way to handle and preprocess different types of features through its feature columns API. Feature columns act as intermediaries between raw input data and the Estimator API, making them pivotal in constructing deep models efficiently.
Understanding Feature Columns
Feature columns serve three main purposes: they can transform raw data into formats understandable by a model, define feature cross-products, and enhance low-dimensional representation through embeddings.
Categorical Columns
TensorFlow supports two types of categorical columns:
- categorical_column_with_vocabulary_list - Maps strings to continuous integers.
- categorical_column_with_hash_bucket - Offers a compact representation of larger categorical values.
import tensorflow as tf
# Define a categorical column
gender_column = tf.feature_column.categorical_column_with_vocabulary_list(
'gender', ['male', 'female'])
hashed_feature = tf.feature_column.categorical_column_with_hash_bucket(
'category_name', hash_bucket_size=50)
Numerical Columns
Numerical columns are straightforward and represent raw numeric data as it is.
age_column = tf.feature_column.numeric_column('age')
Bucketized Columns
Often, it's beneficial to convert continuous numerical information into categorical form using buckets.
# Bucketizing column into age groups
age_buckets = tf.feature_column.bucketized_column(
age_column, boundaries=[18, 25, 30, 50, 65])
Combining Features
We can improve the representation by combining multiple feature columns. This is useful when you believe that individual features interact with each other.
Crossed Features
Crossed columns improve your model's capacity to learn associations between categorical variables.
# Feature cross for gender and age
crossed_feature = tf.feature_column.crossed_column(
['age_bucket', 'gender'], hash_bucket_size=100)
Embedding Columns
To manage high-dimensional categorical columns efficiently, embedding them into low-dimensional spaces is helpful.
# Embedding column for the hased_feature
embedded_feature = tf.feature_column.embedding_column(hashed_feature, dimension=8)
Integrating Feature Columns into a Model
After defining the required feature columns, you can integrate them into TensorFlow's model functions:
feature_layer = tf.keras.layers.DenseFeatures([age_column, age_buckets, gender_column, crossed_feature, embedded_feature])
# Sample input data
inputs = {
'age': tf.constant([[23], [45], [28]]),
'gender': tf.constant([['male'], ['female'], ['female']]),
'category_name': tf.constant([['smartphone'], ['tablet'], ['smartphone']])
}
output = feature_layer(inputs)
By using this flexible feature engineering approach, you can design resilient TensorFlow models that can work with intricate datasets and lay a solid foundation for model learning. Remember, the effectiveness will largely depend on how you select and combine the features, so experiment and iterate based on your model's performance.
