How to use various optimizers using tensorflow

This recipe helps you use various optimizers using tensorflow

Recipe Objective

How to use various optimizers using tensorflow?

The optimizers are nothing but the extended class, to train the specific model it includes the added information. For the given parameters the optimizers class in being initialized but one important thing to remember is that there is no tensor is needed. For training a specific model the speed and performance is improved by optimizers. The classification of optimizers is as follows:

--Adagrad
--Adam
--Adamax
--Adadelta
--RMSProp
--Nesterov momentum
--Momentum
--Stochastic Gradient descent with gradient clipping
--Stochastic Gradient descent --SMORMS3

We are going to use Gradient decent and Adam for our practical example.

Step 1 - Import library

import tensorflow as tf import numpy as np

Step 2 - Perform Gradient decent

var_1 = tf.Variable(2, name = 'var_1', dtype = tf.float32) var_1_log = tf.compat.v1.log(var_1) var_1_log_squared = tf.square(var_1_log) gradient_opt = tf.compat.v1.train.GradientDescentOptimizer(0.5) train_data = gradient_opt.minimize(var_1_log_squared)

Step 3 - Initialize variables and print results

initialize = tf.compat.v1.initialize_all_variables() def funct_optimize(): with tf.compat.v1.Session() as sess: sess.run(initialize) print("starting at", "var_1:", sess.run(var_1), "log(var_1)^2:", sess.run(var_1_log_squared)) for stp in range(10): sess.run(train_data) print("step", stp, "var_1:", sess.run(var_1), "log(x)^2:", sess.run(var_1_log_squared))

starting at var_1: 2.0 log(var_1)^2: 0.480453
step 0 var_1: 1.6534264 log(x)^2: 0.25285786
step 1 var_1: 1.3493005 log(x)^2: 0.08975197
step 2 var_1: 1.1272696 log(x)^2: 0.014351669
step 3 var_1: 1.0209966 log(x)^2: 0.0004317744
step 4 var_1: 1.0006447 log(x)^2: 4.1534943e-07
step 5 var_1: 1.0000006 log(x)^2: 3.5527118e-13
step 6 var_1: 1.0 log(x)^2: 0.0
step 7 var_1: 1.0 log(x)^2: 0.0
step 8 var_1: 1.0 log(x)^2: 0.0
step 9 var_1: 1.0 log(x)^2: 0.0

Step 4 - Adam optimizer

adam_opt = tf.keras.optimizers.Adam( learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, name="Adam" ) opt = tf.keras.optimizers.Adam(learning_rate=0.2) var_2 = tf.Variable(30.0) loss_fun = lambda: (var_2 ** 3)/3.0 count_step = np.array([opt.minimize(loss_fun, [var_2])]) np.array([var_2])

array([<tf.Variable 'Variable_8:0' shape=() dtype=float32>], dtype=object)

Here in the above Adam optimizer we have used various parameters which are:

--learning rate: It is a Tensor or floating point value or a schedule that is a "tf.keras.optimizers.schedules.LearningRateSchedule" or a callable which takes no arguments and will return the actual for use.
--beta_1 - It is a constant float tensor or float value or a callable which takes no arguments and will return the actual for use. In this the exponential moment for the first moment estimates which is default to 0.9.
--beta_2 - It is a constant float tensor or float value or a callable which takes no arguments and will return the actual for use. In this the exponential moment for the second moment estimates which is default to 0.999.
--epsilon - This is a small constant for the numerical stability. Its default to 1e-7.
--amsgrad - Boolean. Whether to apply the AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond". Defaults to False.
--name - This is for the operations created when applying the gradients by default it is to "Adam".

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