Multi-input and multi-output models in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define two input layers for a multi-input model.

TensorFlow

input1 = tf.keras.Input(shape=[1])
input2 = tf.keras.Input(shape=(10,))

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

ANone

B32

C[32]

D(32,)

Attempts:

3 left

💡 Hint

Common Mistakes

Using an integer instead of a tuple for input shape.

Using a list instead of a tuple for input shape.

The input shape must be a tuple representing the shape of the input data excluding the batch size. Here, (32,) means the input has 32 features.

2fill in blank

medium

Complete the code to concatenate two input tensors in the model.

TensorFlow

concat = tf.keras.layers.Concatenate()([input1, [1]])

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

Ainput2

Binput3

Cinputs

Dinput_2

Attempts:

3 left

💡 Hint

Common Mistakes

Using an undefined variable name.

Using a string instead of a tensor variable.

To concatenate two inputs, you must pass both input tensors. Here, input2 is the second input tensor to concatenate with input1.

3fill in blank

hard

Fix the error in the model output definition by selecting the correct output layer.

TensorFlow

output1 = tf.keras.layers.Dense(1, activation='sigmoid')([1])
output2 = tf.keras.layers.Dense(10, activation='softmax')(dense2)

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

Ainput2

Binput1

Cconcat

Ddense1

Attempts:

3 left

💡 Hint

Common Mistakes

Passing input layers directly to output layers.

Passing unrelated layer outputs.

The output layer should receive the concatenated tensor 'concat' as input, not the raw inputs or unrelated layers.

4fill in blank

hard

Fill both blanks to compile the multi-output model with appropriate loss functions and metrics.

TensorFlow

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

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

A{'output1': 'binary_crossentropy', 'output2': 'categorical_crossentropy'}

B['mse', 'mae']

C{'output1': ['accuracy'], 'output2': ['accuracy']}

D['accuracy', 'accuracy']

Attempts:

3 left

💡 Hint

Common Mistakes

Using lists instead of dictionaries for losses and metrics.

Not matching output names in loss and metrics.

For multi-output models, loss must be a dictionary mapping output names to loss functions. Metrics can also be a dictionary mapping output names to metric lists.

5fill in blank

hard

Fill all three blanks to create a multi-input, multi-output model with correct inputs, outputs, and model definition.

TensorFlow

input_a = tf.keras.Input(shape=[1])
input_b = tf.keras.Input(shape=[2])
model = tf.keras.Model(inputs=[input_a, input_b], outputs=[3])

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

A(64,)

B(10,)

C[output1, output2]

D(32,)

Attempts:

3 left

💡 Hint

Common Mistakes

Using integers instead of tuples for input shapes.

Passing outputs as a tuple instead of a list.

The inputs must have correct shapes as tuples. The outputs must be a list of output layers for multi-output models.

Practice

1. What is the main purpose of a multi-input model in TensorFlow?

easy

A. To accept more than one data source at the same time

B. To predict multiple outputs from a single input

C. To train faster using GPU acceleration

D. To reduce the number of layers in the model

Solution

1. Step 1: Understand multi-input models

   Multi-input models are designed to take multiple data sources as inputs simultaneously.

2. Step 2: Differentiate from multi-output models

   Multi-output models predict multiple outputs but usually from a single input source.

3. Final Answer:

   To accept more than one data source at the same time -> Option A

4. Quick Check:

   Multi-input = multiple data sources [OK]

Hint: Multi-input means many inputs, not many outputs [OK]

Common Mistakes:

• Confusing multi-input with multi-output
• Thinking multi-input reduces layers
• Assuming multi-input speeds training automatically

2. Which of the following is the correct way to define two inputs in a TensorFlow Keras model?

easy

A. inputs = tf.keras.Input(shape=(10, 5))

B. inputs = tf.keras.Input(shape=(10,)), tf.keras.Input(shape=(5,))

C. inputs = [tf.keras.Input(shape=(10,)), tf.keras.Input(shape=(5,))]

D. inputs = tf.keras.Input(shape=(10,)); inputs = tf.keras.Input(shape=(5,))

Solution

1. Step 1: Recall how to define multiple inputs

   Multiple inputs should be stored as a list of Input layers in Keras.

2. Step 2: Check each option

   inputs = [tf.keras.Input(shape=(10,)), tf.keras.Input(shape=(5,))] correctly creates a list of two Input layers. inputs = tf.keras.Input(shape=(10,)), tf.keras.Input(shape=(5,)) creates a tuple but does not assign it properly. inputs = tf.keras.Input(shape=(10, 5)) defines a single input with combined shape. inputs = tf.keras.Input(shape=(10,)); inputs = tf.keras.Input(shape=(5,)) overwrites the first input with the second.

3. Final Answer:

   inputs = [tf.keras.Input(shape=(10,)), tf.keras.Input(shape=(5,))] -> Option C

4. Quick Check:

   Multiple inputs = list of Input layers [OK]

Hint: Use a list to hold multiple Input layers [OK]

Common Mistakes:

• Using a tuple instead of a list for inputs
• Overwriting inputs instead of storing both
• Combining shapes into one input incorrectly

3. What will be the output shape of the following multi-output model?

input1 = tf.keras.Input(shape=(8,))
input2 = tf.keras.Input(shape=(4,))
x1 = tf.keras.layers.Dense(5)(input1)
x2 = tf.keras.layers.Dense(3)(input2)
output1 = tf.keras.layers.Dense(2)(x1)
output2 = tf.keras.layers.Dense(1)(x2)
model = tf.keras.Model(inputs=[input1, input2], outputs=[output1, output2])
print([o.shape for o in model.outputs])

medium

A. [TensorShape([None, 2]), TensorShape([None, 1])]

B. [TensorShape([8, 2]), TensorShape([4, 1])]

C. [TensorShape([None, 5]), TensorShape([None, 3])]

D. [TensorShape([None, 8]), TensorShape([None, 4])]

Solution

1. Step 1: Trace output layers shapes

   output1 is Dense(2) applied to x1, so shape is (None, 2). output2 is Dense(1) applied to x2, so shape is (None, 1).

2. Step 2: Understand batch dimension

   TensorFlow uses None for batch size, so output shapes include None as first dimension.

3. Final Answer:

   [TensorShape([None, 2]), TensorShape([None, 1])] -> Option A

4. Quick Check:

   Output shapes match Dense layer units [OK]

Hint: Output shape = batch size null + Dense units [OK]

Common Mistakes:

• Confusing input shape with output shape
• Ignoring batch dimension null
• Mixing intermediate layer shapes with output shapes

4. Identify the error in this multi-output model definition:

input = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(8)(input)
output1 = tf.keras.layers.Dense(4)(x)
output2 = tf.keras.layers.Dense(3)(x)
model = tf.keras.Model(inputs=input, outputs=[output1, output2])

medium

A. inputs should be a list when there are multiple outputs

B. outputs should be a single tensor, not a list

C. inputs must be a list even if only one input exists

D. No error, the model is defined correctly

Solution

1. Step 1: Check inputs parameter

   inputs can be a single Input layer if there is only one input source.

2. Step 2: Check outputs parameter

   outputs can be a list of tensors to define multiple outputs.

3. Final Answer:

   No error, the model is defined correctly -> Option D

4. Quick Check:

   Single input + multiple outputs = valid model [OK]

Hint: Single input can be passed directly, outputs can be list [OK]

Common Mistakes:

• Thinking inputs must always be a list
• Believing outputs cannot be a list
• Assuming multiple outputs require multiple inputs

5. You want to build a model that takes two inputs: an image (shape 64x64x3) and a vector of 10 features. It should output two predictions: a 5-class classification and a single continuous value. Which is the correct way to define the model inputs and outputs?

hard

A. inputs = [tf.keras.Input(shape=(64,64,3)), tf.keras.Input(shape=(10,))]; outputs = tf.keras.layers.Dense(6)(inputs)

B. inputs = [tf.keras.Input(shape=(64,64,3)), tf.keras.Input(shape=(10,))]; outputs = [tf.keras.layers.Dense(5, activation='softmax')(x), tf.keras.layers.Dense(1)(y)]

C. inputs = tf.keras.Input(shape=(64,64,3)); outputs = [tf.keras.layers.Dense(5)(inputs), tf.keras.layers.Dense(1)(inputs)]

D. inputs = tf.keras.Input(shape=(74,)); outputs = [tf.keras.layers.Dense(5)(inputs), tf.keras.layers.Dense(1)(inputs)]

Solution

1. Step 1: Define inputs separately for image and vector

   Two inputs require two Input layers with correct shapes: (64,64,3) for image and (10,) for vector.

2. Step 2: Define outputs separately for classification and regression

   Outputs are two layers: one Dense with 5 units and softmax for classification, one Dense with 1 unit for continuous value.

3. Final Answer:

   inputs = [tf.keras.Input(shape=(64,64,3)), tf.keras.Input(shape=(10,))]; outputs = [tf.keras.layers.Dense(5, activation='softmax')(x), tf.keras.layers.Dense(1)(y)] -> Option B

4. Quick Check:

   Separate inputs and outputs for multi-input/output model [OK]

Hint: Match each input and output with separate Input and Dense layers [OK]

Common Mistakes:

• Combining inputs into one vector incorrectly
• Using single input for different data types
• Outputting combined units instead of separate outputs