Bridging TensorFlow and Scikit-Learn through Keras Wrappers

Greetings and welcome! In today's lesson, we explore a crucial and yet often overlooked topic: Bridging TensorFlow and Scikit-Learn through Keras Wrappers. Despite TensorFlow's powerful capabilities for machine learning, it doesn't offer the extensive utilities for model selection and evaluation that Scikit-learn provides. Tools such as cross-validation, grid search, and various metrics are invaluable. But how can we use them with TensorFlow models? The answer lies in Wrappers, specifically the KerasClassifier Wrapper, which will be our primary focus today.

KerasClassifier is a wrapper designed to bridge the functionality of TensorFlow's Keras deep learning module with Scikit-learn's powerful tools for model selection and evaluation. Keras, an integral part of TensorFlow, is an open-source software library that provides a Python interface for artificial neural networks. For newer versions of TensorFlow, these wrappers are included in a separate library called scikeras, which specifically facilitates the integration between TensorFlow and Scikit-learn. To understand it better, let's dive into the implementation:

The first step is to define a build function thats return a model to the wrapper, this model is suited for the Iris dataset we have been using throughout this course:

import tensorflow as tf

def build_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Input(shape=(4,)),
        tf.keras.layers.Dense(16, activation='relu'),
        tf.keras.layers.Dense(3, activation='softmax')
    ])
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

Next, we import the module and create an instance of KerasClassifier. It requires a function that constructs and compiles a model, which will be the value of the model parameter. Additional arguments can be set that are passed to fit when called like epochs and verbose.

from scikeras.wrappers import KerasClassifier

model = KerasClassifier(model=build_model, epochs=30, verbose=0)

In this instance, we use the function build_model we defined earlier. epochs is provided a value of 30, representing the number of iterations over the dataset to train the model, and verbose is set to 0, meaning no output is shown during training.