Linear Classifier in Tensorflow

Due to its simplicity and effectiveness, linear classifiers have been a mainstay of machine learning for a long time. A well-liked machine learning framework called TensorFlow provides complete support for these models. This article offers an introduction to TensorFlow's linear classifiers, explaining how they operate and how to use them in your applications.

Understanding Linear Classifiers

Using a line, plane, or hyperplane, a linear classifier divides data into distinct classes. Because the dividing line is linear with respect to the input space, it is called a "linear" boundary. Binary or multi-class linear classifiers are applied to issues where the relationship between the input and output is roughly linear.

TensorFlow: A Brief Overview

The Google Brain Team created the open-source machine learning framework known as TensorFlow. For building machine learning algorithms and models, it offers a complete ecosystem of tools, libraries, and community resources. TensorFlow's main advantage is its capacity for both high-level and low-level computations, which lets users build complex machine learning models with relative ease.

Implementing Linear Classifier with TensorFlow

For the purpose of creating a linear classifier, TensorFlow offers the tf.estimator API, specifically tf.estimator.LinearClassifier. It contains all of the reasoning involved in building, assessing, forecasting, and using the model.

Installing TensorFlow

Make sure TensorFlow is installed first. Use pip to accomplish this:

pip install tensorflow

Example 1: Simple Linear Classifier

Take a look at a straightforward illustration where we categorise the Iris dataset using a linear classifier. The British statistician and biologist Ronald Fisher developed the multivariate Iris dataset. It contains 50 samples of each of the three Iris flower species.

Let's load the Iris dataset first and then import the required libraries −

import tensorflow as tf
from sklearn import datasets

# Load Iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

The linear classifier will then be constructed after we define the feature columns:

# Define feature columns
feature_columns = [tf.feature_column.numeric_column('x', shape=X.shape[1:])]

# Build linear classifier
classifier = tf.estimator.LinearClassifier(feature_columns=feature_columns, n_classes=3)

# Define input function
input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
   x={'x': X},
   y=y,
   num_epochs=None,
   shuffle=True
)

# Train the classifier
classifier.train(input_fn=input_fn, steps=5000)

The feature columns in this code, which describe the kind of data each feature in the dataset is, are first defined. Then, utilising tf.estimator, we construct the linear classifier.LinearClassifier. We feed our data into the classifier using the numpy_input_fn function, and then we train the classifier using the.train() method.

Example 2: Evaluating the Classifier

We can assess the performance of our classifier now that it has been trained. We'll utilise a section of the Iris dataset for this example that we didn't use for training −

# Define the test inputs
test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
   x={'x': X_test},
   y=y_test,
   num_epochs=1,
   shuffle=False
)

# Evaluate accuracy
accuracy_score = classifier.evaluate(input_fn=test_input_fn)['accuracy']

print(f'\nTest Accuracy: {accuracy_score}\n')

In this example, we establish a new input function for our test data and then use the.evaluate() method to assess the precision of our classifier.

Example 3: Making Predictions

On new data, we can make predictions using our trained classifier. Let's illustrate this by predicting the species of a new flower using our classifier:

# New flower data
new_flower = np.array([[5.1, 3.3, 1.7, 0.5]], dtype=float)

# Define the input function for predictions
predict_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
   x={'x': new_flower},
   num_epochs=1,
   shuffle=False
)

# Get the predictions
predictions = list(classifier.predict(input_fn=predict_input_fn))
predicted_class = predictions[0]['class_ids'][0]

print(f'\nPredicted Iris Class: {predicted_class}\n')

In this illustration, we used four metrics to define a new flower. The class of the new flower was then predicted using our trained classifier. The outcome is the Iris species that was expected.

Conclusion

One of the most straightforward yet effective machine learning models is the linear classifier, especially when working with linearly separable data. By providing a simple and adaptable method to create linear classifiers, TensorFlow's tf.estimator API makes it simpler to use these models in your own applications.

In this post, the idea of linear classifiers was presented, and TensorFlow was used to demonstrate how to use them. We talked about how to develop a classifier, assess its efficacy, and generate predictions using fresh data. These examples show the basic procedures for developing and applying a linear classifier.

Keep in mind that the quality of your results will largely depend on the dataset you use and the methods you use to prepare it, such as feature selection and data normalisation. To acquire a meaningful idea of your classifier's performance, always evaluate it using a test set.

TensorFlow is a very potent tool that offers a wide range of capabilities to create sophisticated machine learning models. Just the tip of the iceberg in terms of its support for linear classifiers. You will find a wide range of cutting-edge methods and tactics for creating reliable, effective machine learning models as you do more research.