Functional API basics in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an input layer with shape (28, 28).

TensorFlow

inputs = tf.keras.Input(shape=[1])

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

A(28, 28)

B28

C[28, 28]

D(784,)

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list instead of a tuple for shape.

Specifying the total number of pixels instead of the shape tuple.

The Input layer requires the shape as a tuple. (28, 28) correctly specifies the input shape for images of 28 by 28 pixels.

2fill in blank

medium

Complete the code to add a Dense layer with 64 units and ReLU activation.

TensorFlow

x = tf.keras.layers.Dense([1], activation='relu')(inputs)

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

A128

B64

C32

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing the wrong number of units.

Confusing activation function with units.

The Dense layer should have 64 units as specified, with ReLU activation.

3fill in blank

hard

Fix the error in the code to create the model using the Functional API.

TensorFlow

model = tf.keras.Model(inputs=[1], outputs=x)

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

Aoutputs

Bx

Cmodel

Dinputs

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the output tensor as inputs.

Passing the model variable itself.

The inputs argument must be the input tensor, which is 'inputs' here, not 'x' or other variables.

4fill in blank

hard

Fill both blanks to flatten the input and add a Dense layer with 10 units.

TensorFlow

x = tf.keras.layers.[1]()(inputs)
x = tf.keras.layers.Dense([2])(x)

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

AFlatten

B10

CDense

D64

Attempts:

3 left

💡 Hint

Common Mistakes

Using Dense instead of Flatten for the first layer.

Choosing wrong number of units for the Dense layer.

First, the Flatten layer converts the input to 1D. Then, a Dense layer with 10 units is added for output classes.

5fill in blank

hard

Fill all three blanks to compile the model with Adam optimizer, sparse categorical crossentropy loss, and accuracy metric.

TensorFlow

model.compile(optimizer='[1]', loss='[2]', metrics=['[3]'])

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

Aadam

Bsparse_categorical_crossentropy

Caccuracy

Dsgd

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong optimizer like 'sgd' when 'adam' is expected.

Using categorical_crossentropy instead of sparse_categorical_crossentropy.

Forgetting to include accuracy metric.

Adam optimizer is popular for training. Sparse categorical crossentropy is used for integer labels in classification. Accuracy is a common metric to track.

Practice

1. What is the main advantage of using TensorFlow's Functional API over the Sequential API?

easy

A. It allows building models with multiple inputs and outputs.

B. It automatically tunes hyperparameters.

C. It requires less code to build simple models.

D. It only supports linear stacks of layers.

Solution

1. Step 1: Understand Functional API capabilities

   The Functional API allows explicit connections between layers, supporting complex architectures.

2. Step 2: Compare with Sequential API

   Sequential API only supports simple linear stacks, while Functional API supports multiple inputs and outputs.

3. Final Answer:

   It allows building models with multiple inputs and outputs. -> Option A

4. Quick Check:

   Functional API = multiple inputs/outputs [OK]

Hint: Functional API supports complex models with multiple inputs/outputs [OK]

Common Mistakes:

• Thinking Functional API is simpler for linear models
• Confusing hyperparameter tuning with model building
• Assuming Sequential API supports multiple inputs

2. Which of the following is the correct way to start defining a model using the Functional API?

easy

A. inputs = tf.keras.Input(shape=(32,))

B. inputs = tf.keras.layers.Dense(32)

C. model = tf.keras.Model()

D. model = tf.keras.Sequential()

Solution

1. Step 1: Identify how to define input in Functional API

   Functional API starts with tf.keras.Input() to define the input shape.

2. Step 2: Check other options

   Sequential() is for Sequential API, Model() requires inputs and outputs, Dense is a layer, not input.

3. Final Answer:

   inputs = tf.keras.Input(shape=(32,)) -> Option A

4. Quick Check:

   Start Functional API with Input() [OK]

Hint: Use Input() to start Functional API models [OK]

Common Mistakes:

• Using Sequential() instead of Input() to start
• Trying to create Model() without inputs and outputs
• Confusing layers with input definitions

3. What will be the output shape of the model defined below?

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(5)(inputs)
outputs = tf.keras.layers.Dense(2)(x)
model = tf.keras.Model(inputs, outputs)
print(model.output_shape)

medium

A. (None, 10)

B. (10, 2)

C. (None, 5)

D. (None, 2)

Solution

1. Step 1: Trace the model layers

   Input shape is (10,), first Dense layer outputs (5,), second Dense outputs (2,).

2. Step 2: Understand output shape format

   Output shape includes batch size None, so final output shape is (None, 2).

3. Final Answer:

   (None, 2) -> Option D

4. Quick Check:

   Output shape = (None, 2) [OK]

Hint: Output shape matches last layer units with batch None [OK]

Common Mistakes:

• Confusing input shape with output shape
• Ignoring batch dimension None
• Mixing layer output sizes

4. Identify the error in this Functional API model code:

inputs = tf.keras.Input(shape=(8,))
x = tf.keras.layers.Dense(4)(inputs)
outputs = tf.keras.layers.Dense(1)(inputs)
model = tf.keras.Model(inputs=inputs, outputs=outputs)

medium

A. Input shape must be (4,), not (8,).

B. Output layer should connect to x, not inputs.

C. Model() requires no arguments.

D. Dense layers cannot be used in Functional API.

Solution

1. Step 1: Check layer connections

   The output layer is connected directly to inputs, skipping the intermediate Dense layer x.

2. Step 2: Correct the output connection

   Output should connect to x to use the transformed data, not inputs.

3. Final Answer:

   Output layer should connect to x, not inputs. -> Option B

4. Quick Check:

   Output must connect to last layer, not input [OK]

Hint: Connect outputs to last layer, not inputs [OK]

Common Mistakes:

• Connecting output directly to inputs
• Changing input shape unnecessarily
• Misunderstanding Model() arguments

5. You want to build a model with two inputs: one for images (shape 64x64x3) and one for metadata (shape 10). Which Functional API code snippet correctly defines the inputs?

hard

A. img_input = tf.keras.layers.Input(shape=(64,64,3))
meta_input = tf.keras.layers.Input(shape=(10,))

B. inputs = tf.keras.Input(shape=(64,64,3,10))

C. img_input = tf.keras.Input(shape=(64,64,3))
meta_input = tf.keras.Input(shape=(10,))

D. inputs = tf.keras.Input(shape=(64,64,3))
inputs = tf.keras.Input(shape=(10,))

Solution

1. Step 1: Define separate inputs for each data type

   Functional API allows multiple inputs by defining each with tf.keras.Input and correct shapes.

2. Step 2: Check each option for correctness

   img_input = tf.keras.Input(shape=(64,64,3)) meta_input = tf.keras.Input(shape=(10,)) correctly defines two inputs separately; B merges shapes incorrectly; A uses wrong module; D overwrites inputs variable.

3. Final Answer:

   img_input = tf.keras.Input(shape=(64,64,3)) meta_input = tf.keras.Input(shape=(10,)) -> Option C

4. Quick Check:

   Multiple inputs need separate Input() calls [OK]

Hint: Use separate Input() for each input tensor [OK]

Common Mistakes:

• Combining input shapes incorrectly
• Using layers.Input instead of keras.Input
• Overwriting input variables