TensorFlow architecture (eager vs graph execution) - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to enable eager execution in TensorFlow.

TensorFlow

import tensorflow as tf

tf.config.run_functions_eagerly([1])

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

AFalse

BNone

CTrue

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using False disables eager execution.

Passing None or 0 will not enable eager execution.

Setting run_functions_eagerly(True) enables eager execution mode in TensorFlow, which runs operations immediately.

2fill in blank

medium

Complete the code to create a TensorFlow function that runs as a graph.

TensorFlow

import tensorflow as tf

@tf.function
def add(a, b):
    return a [1] b

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

A*

B+

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction or multiplication instead of addition.

Forgetting the decorator to create a graph function.

The @tf.function decorator compiles the function into a graph. The function adds two tensors using the + operator.

3fill in blank

hard

Fix the error in the code to print the result of a TensorFlow tensor in eager mode.

TensorFlow

import tensorflow as tf

a = tf.constant(5)
print([1])

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

Aa.numpy()

Ba.eval()

Ca.value()

Da.data()

Attempts:

3 left

💡 Hint

Common Mistakes

Using eval() which requires a session in graph mode.

Trying to use non-existent methods like value() or data().

In eager mode, use tensor.numpy() to get the value as a NumPy array for printing.

4fill in blank

hard

Fill both blanks to create a TensorFlow graph function that multiplies and then adds two tensors.

TensorFlow

import tensorflow as tf

@tf.function
def compute(x, y):
    mul = x [1] y
    return mul [2] y

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

A*

B+

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the order of operations.

Using subtraction or division instead of addition.

The function multiplies x and y first, then adds y to the result.

5fill in blank

hard

Fill all three blanks to create a TensorFlow function that subtracts, divides, and then multiplies two tensors.

TensorFlow

import tensorflow as tf

@tf.function
def complex_op(a, b):
    sub = a [1] b
    div = sub [2] b
    return div [3] a

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

A-

B/

C*

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of subtraction.

Mixing up division and multiplication operators.

The function subtracts b from a, divides the result by b, then multiplies by a.

Practice

1. What is the main difference between eager execution and graph execution in TensorFlow?

easy

A. Eager execution requires a GPU, graph execution runs only on CPU.

B. Eager execution uses less memory than graph execution in all cases.

C. Graph execution is only for training, eager execution is only for inference.

D. Eager execution runs operations immediately, while graph execution builds a computation plan first.

Solution

1. Step 1: Understand eager execution behavior

   Eager execution runs TensorFlow operations immediately as they are called, making it easy to debug and understand.

2. Step 2: Understand graph execution behavior

   Graph execution builds a computation graph first, then runs it for better performance and optimization.

3. Final Answer:

   Eager execution runs operations immediately, while graph execution builds a computation plan first. -> Option D

4. Quick Check:

   Eager vs Graph = Immediate vs Plan [OK]

Hint: Eager means now, graph means plan first [OK]

Common Mistakes:

• Thinking graph execution runs immediately
• Confusing hardware requirements
• Assuming eager is only for inference

2. Which of the following is the correct way to convert a Python function to a TensorFlow graph function?

easy

A. Use @tf.function decorator above the function definition.

B. Call tf.convert_to_graph(function) before running it.

C. Wrap the function inside tf.Graph() and call it.

D. Set tf.enable_graph_mode(True) before defining the function.

Solution

1. Step 1: Recall TensorFlow's method to switch execution modes

   TensorFlow uses the @tf.function decorator to convert a Python function into a graph function.

2. Step 2: Evaluate other options for correctness

   tf.convert_to_graph and tf.enable_graph_mode do not exist; wrapping in tf.Graph() is not the standard way.

3. Final Answer:

   Use @tf.function decorator above the function definition. -> Option A

4. Quick Check:

   @tf.function converts to graph [OK]

Hint: Remember @tf.function for graph conversion [OK]

Common Mistakes:

• Using non-existent TensorFlow functions
• Trying to enable graph mode globally
• Confusing tf.Graph() usage

3. Consider the following code snippet:

import tensorflow as tf

@tf.function
def add(a, b):
    print('Running add')
    return a + b

result1 = add(1, 2)
result2 = add(3, 4)

What will be printed when this code runs?

medium

A. Running add
Running add

B. Running add

C. No output printed

D. Error due to print inside @tf.function

Solution

1. Step 1: Understand print behavior inside @tf.function

   When a function is decorated with @tf.function, it runs as a graph. Python print runs only once during graph tracing, not on every call.

2. Step 2: Analyze the calls to add()

   The first call triggers tracing and prints 'Running add'. The second call uses the compiled graph and does not print again.

3. Final Answer:

   Running add -> Option B

4. Quick Check:

   Print runs once during tracing [OK]

Hint: Print inside @tf.function runs once [OK]

Common Mistakes:

• Expecting print every call
• Thinking print is disabled
• Assuming error from print usage

4. You wrote this code:

import tensorflow as tf

def multiply(a, b):
    return a * b

@tf.function
def call_multiply(x, y):
    return multiply(x, y)

print(call_multiply(2, 3))

But the output is a Tensor object, not a number. How can you fix it to print the actual number?

medium

A. Wrap multiply inside tf.function as well

B. Remove @tf.function decorator from call_multiply

C. Add .numpy() to the print call: print(call_multiply(2, 3).numpy())

D. Change multiply to use tf.multiply instead of * operator

Solution

1. Step 1: Understand output type of @tf.function

   Functions decorated with @tf.function return TensorFlow tensors, not plain Python numbers.

2. Step 2: Convert tensor to number for printing

   Use the .numpy() method on the tensor to get the actual number value for printing.

3. Final Answer:

   Add .numpy() to the print call: print(call_multiply(2, 3).numpy()) -> Option C

4. Quick Check:

   Tensor to number: use .numpy() [OK]

Hint: Use .numpy() to get number from tensor [OK]

Common Mistakes:

• Expecting tensor to print as number
• Removing @tf.function unnecessarily
• Changing multiply without need

5. You want to speed up a TensorFlow model training loop by switching from eager to graph execution. Which approach correctly applies this change while keeping eager mode for debugging?

hard

A. Decorate the training step function with @tf.function and run training normally.

B. Set tf.config.experimental_run_functions_eagerly(True) before training.

C. Rewrite the entire model using tf.Graph() and tf.Session().

D. Disable eager execution globally using tf.compat.v1.disable_eager_execution().

Solution

1. Step 1: Identify how to switch to graph execution selectively

   Using @tf.function on the training step compiles it to a graph, speeding execution while keeping eager mode elsewhere.

2. Step 2: Evaluate other options for drawbacks

   Setting experimental_run_functions_eagerly(True) forces eager mode (slower). Rewriting with tf.Graph() and tf.Session() is outdated. Disabling eager globally removes debugging ease.

3. Final Answer:

   Decorate the training step function with @tf.function and run training normally. -> Option A

4. Quick Check:

   @tf.function speeds training, keeps eager debugging [OK]

Hint: Use @tf.function on training step for speed [OK]

Common Mistakes:

• Forcing eager mode instead of graph
• Using old TensorFlow 1.x APIs
• Disabling eager globally losing debugging