35: Logistic Regression Tutorial with TensorFlow Core API

In this tutorial, we will be looking at how to implement logistic regression using TensorFlow’s Core API. Logistic regression is a popular machine learning algorithm used for binary classification tasks.

What is Logistic Regression?

Logistic regression is a statistical model that predicts the probability of a binary outcome based on one or more input variables. It is commonly used in machine learning for binary classification tasks, where we want to predict whether an input belongs to one of two classes.

Implementing Logistic Regression with TensorFlow Core API

First, we need to import the necessary libraries:


import tensorflow as tf
import numpy as np


Next, we will define our model using TensorFlow’s Core API:


X = tf.placeholder(tf.float32, shape=(None, num_features))
y = tf.placeholder(tf.float32, shape=(None, 1))

W = tf.Variable(tf.random_normal([num_features, 1]))
b = tf.Variable(tf.zeros([1]))

logits = tf.add(tf.matmul(X, W), b)
y_pred = tf.sigmoid(logits)


Once we have defined our model, we can train it using gradient descent:


loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)

with tf.Session() as sess:
sess.run(tf.global_variables_initializer())

for i in range(num_epochs):
_, loss_val = sess.run([optimizer, loss], feed_dict={X: X_train, y: y_train})
print("Epoch: {}, Loss: {:.4f}".format(i, loss_val))


Conclusion

Logistic regression is a powerful and versatile algorithm that can be used for a variety of binary classification tasks. By using TensorFlow’s Core API, we can easily implement and train logistic regression models for our machine learning projects.