TensorFlow - Gradient Descent Optimization
TensorFlow - Gradient Descent Optimization - Gradient descent optimization is considered to be an important concept in data science
Gradient descent optimization is considered to be an important concept in data science.
Consider the steps shown below to understand the implementation of gradient descent optimization −
Include necessary modules and declaration of x and y variables through which we are going to define the gradient descent optimization.
import tensorflow as tf x = tf.Variable(2, name = 'x', dtype = tf.float32) log_x = tf.log(x) log_x_squared = tf.square(log_x) optimizer = tf.train.GradientDescentOptimizer(0.5) train = optimizer.minimize(log_x_squared)
Initialize the necessary variables and call the optimizers for defining and calling it with respective functions.
init = tf.initialize_all_variables() def optimize(): with tf.Session() as session: session.run(init) print("starting at", "x:", session.run(x), "log(x)^2:", session.run(log_x_squared)) for step in range(10): session.run(train) print("step", step, "x:", session.run(x), "log(x)^2:", session.run(log_x_squared)) optimize()
The above line of code generates an output as shown in the screenshot below −
We can see that the necessary epochs and iterations are calculated as shown in the output.