Optimizers (SGD, Adam, RMSprop) in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Stochastic Gradient Descent optimizer with a learning rate of 0.01.

TensorFlow

optimizer = tf.keras.optimizers.[1](learning_rate=0.01)

Drag options to blanks, or click blank then click option'

ASGD

BAdam

CRMSprop

DAdagrad

Attempts:

3 left

💡 Hint

Common Mistakes

Using Adam or RMSprop instead of SGD for this task.

Misspelling the optimizer name.

The SGD optimizer is created using tf.keras.optimizers.SGD. It uses stochastic gradient descent with the specified learning rate.

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medium

Complete the code to create an Adam optimizer with a learning rate of 0.001.

TensorFlow

optimizer = tf.keras.optimizers.[1](learning_rate=0.001)

Drag options to blanks, or click blank then click option'

ASGD

BRMSprop

CAdam

DAdadelta

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing SGD or RMSprop instead of Adam.

Confusing learning rate parameter names.

The Adam optimizer is created using tf.keras.optimizers.Adam. It adapts the learning rate during training and is often used for faster convergence.

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hard

Fix the error in the code to create an RMSprop optimizer with a learning rate of 0.0005.

TensorFlow

optimizer = tf.keras.optimizers.[1](learning_rate=0.0005)

Drag options to blanks, or click blank then click option'

ASGD

BAdam

CAdagrad

DRMSprop

Attempts:

3 left

💡 Hint

Common Mistakes

Using Adam or SGD instead of RMSprop.

Incorrect capitalization of the optimizer name.

The RMSprop optimizer is created using tf.keras.optimizers.RMSprop. It adjusts the learning rate based on a moving average of squared gradients.

4fill in blank

hard

Fill both blanks to create an Adam optimizer with beta_1 set to 0.9 and beta_2 set to 0.999.

TensorFlow

optimizer = tf.keras.optimizers.Adam(beta_1=[1], beta_2=[2])

Drag options to blanks, or click blank then click option'

A0.9

B0.8

C0.999

D0.99

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping beta_1 and beta_2 values.

Using values far from the typical defaults.

Adam optimizer uses beta_1 and beta_2 parameters to control the decay rates of moving averages of gradient and squared gradient. The common default values are 0.9 and 0.999 respectively.

5fill in blank

hard

Fill all three blanks to create an RMSprop optimizer with learning rate 0.001, rho 0.9, and momentum 0.0.

TensorFlow

optimizer = tf.keras.optimizers.RMSprop(learning_rate=[1], rho=[2], momentum=[3])

Drag options to blanks, or click blank then click option'

A0.01

B0.001

C0.9

D0.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0.01 instead of 0.001 for learning rate.

Setting momentum to a non-zero value when not needed.

The RMSprop optimizer parameters include learning_rate, rho (decay rate), and momentum. Typical values are 0.001 for learning rate, 0.9 for rho, and 0.0 for momentum when no momentum is used.