Dense (fully connected) layers in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a dense layer with 10 units.

TensorFlow

layer = tf.keras.layers.Dense([1])

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

A0

BNone

C10

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 or None will cause errors or no neurons to be created.

Choosing a smaller number like 5 does not meet the requirement.

The number 10 specifies the number of neurons in the dense layer.

2fill in blank

medium

Complete the code to add a ReLU activation to the dense layer.

TensorFlow

layer = tf.keras.layers.Dense(10, activation=[1])

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

A'relu'

B'sigmoid'

C'softmax'

D'tanh'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sigmoid' or 'softmax' activates differently and are usually for output layers.

Forgetting quotes around the activation name causes syntax errors.

The 'relu' activation function is commonly used for hidden layers to add non-linearity.

3fill in blank

hard

Fix the error in the code by completing the missing argument for input shape.

TensorFlow

model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=[1])
])

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

A(10,)

B{10}

C[10]

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using an integer alone causes errors because shape must be a tuple.

Using list or set brackets is invalid for input_shape.

The input_shape argument expects a tuple describing the shape of each input sample.

4fill in blank

hard

Fill both blanks to create a model with two dense layers: first with 64 units and ReLU activation, second with 10 units and softmax activation.

TensorFlow

model = tf.keras.Sequential([
    tf.keras.layers.Dense([1], activation='relu'),
    tf.keras.layers.Dense([2], activation='softmax')
])

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

A64

B10

C32

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the number of units between layers.

Using incorrect numbers like 32 or 5 which do not match the task.

The first dense layer has 64 units with ReLU activation, and the second has 10 units with softmax for classification.

5fill in blank

hard

Fill all three blanks to create a dense layer with 128 units, 'tanh' activation, and use it as the first layer with input shape (20,).

TensorFlow

model = tf.keras.Sequential([
    tf.keras.layers.Dense([1], activation=[2], input_shape=[3])
])

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

A128

B'tanh'

C(20,)

D'relu'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong activation like 'relu' instead of 'tanh'.

Not using a tuple for input_shape.

Using wrong number of units.

The dense layer has 128 units, uses 'tanh' activation, and expects input samples shaped (20,).

Practice

1. What does a Dense (fully connected) layer do in a neural network?

easy

A. Does not connect any neurons, only passes data through

B. Connects every input neuron to every output neuron with weights

C. Connects neurons randomly without weights

D. Only connects input neurons to output neurons with zero weights

Solution

1. Step 1: Understand the role of Dense layers

   A Dense layer connects each input neuron to every output neuron using weights and biases to learn patterns.

2. Step 2: Compare options with Dense layer behavior

   Only Connects every input neuron to every output neuron with weights correctly describes this full connection with weights; others are incorrect or incomplete.

3. Final Answer:

   Connects every input neuron to every output neuron with weights -> Option B

4. Quick Check:

   Dense layer = full weighted connections [OK]

Hint: Dense means all inputs connect to all outputs [OK]

Common Mistakes:

• Thinking Dense layers connect neurons randomly
• Believing Dense layers have zero weights
• Assuming Dense layers do not connect neurons

2. Which of the following is the correct way to add a Dense layer with 10 neurons and ReLU activation in TensorFlow?

easy

A. tf.keras.layers.Dense(10, activation='relu')

B. tf.keras.DenseLayer(10, activation='relu')

C. tf.layers.Dense(activation='relu', units=10)

D. tf.keras.layers.Dense(activation='relu', neurons=10)

Solution

1. Step 1: Recall TensorFlow Dense layer syntax

   The correct syntax is tf.keras.layers.Dense(units, activation='function').

2. Step 2: Match options to correct syntax

   tf.keras.layers.Dense(10, activation='relu') matches this exactly. Others have wrong class names or parameter names.

3. Final Answer:

   tf.keras.layers.Dense(10, activation='relu') -> Option A

4. Quick Check:

   Correct Dense syntax = tf.keras.layers.Dense(10, activation='relu') [OK]

Hint: Use tf.keras.layers.Dense(units, activation) [OK]

Common Mistakes:

• Using wrong class name like DenseLayer
• Swapping parameter names (neurons vs units)
• Placing activation before units

3. What will be the output shape of this model?

model = tf.keras.Sequential([
  tf.keras.layers.Dense(5, input_shape=(3,)),
  tf.keras.layers.Dense(2)
])
output = model(tf.constant([[1.0, 2.0, 3.0]]))
print(output.shape)

medium

A. (3, 2)

B. (1, 5)

C. (1, 2)

D. (3, 5)

Solution

1. Step 1: Analyze model layers and input shape

   Input shape is (3,), first Dense outputs 5 units, second Dense outputs 2 units.

2. Step 2: Determine output shape after second Dense

   Batch size is 1 (one input), final output shape is (1, 2).

3. Final Answer:

   (1, 2) -> Option C

4. Quick Check:

   Output shape = (batch_size, last layer units) = (1, 2) [OK]

Hint: Output shape = (batch, last Dense units) [OK]

Common Mistakes:

• Confusing input shape with output shape
• Mixing up units of first and second Dense layers
• Ignoring batch dimension

4. Identify the error in this code snippet:

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(10, input_shape=(4,)))
model.add(tf.keras.layers.Dense(5, activation='relu'))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='adam', loss='mse')
model.fit(x_train, y_train, epochs=5)

medium

A. Loss function 'mse' is invalid

B. Input shape should be specified in the first layer only

C. Missing activation in the first Dense layer

D. No error, code is correct

Solution

1. Step 1: Check Dense layer usage and input shape

   Input shape is correctly specified in the first Dense layer only.

2. Step 2: Verify loss function and activation usage

   Loss 'mse' is valid for regression; activation in second layer is fine; first layer activation is optional.

3. Final Answer:

   No error, code is correct -> Option D

4. Quick Check:

   Code syntax and usage are correct [OK]

Hint: Input shape only in first layer; 'mse' is valid loss [OK]

Common Mistakes:

• Thinking activation is mandatory in every Dense layer
• Specifying input_shape in multiple layers
• Believing 'mse' is invalid loss

5. You want to build a model to classify images into 3 categories. Which Dense layer setup is best for the output layer?

hard

A. Dense(3, activation='softmax')

B. Dense(1, activation='sigmoid')

C. Dense(3, activation='relu')

D. Dense(3)

Solution

1. Step 1: Understand classification output needs

   For 3 categories, output layer should have 3 units, one per class.

2. Step 2: Choose activation for multi-class classification

   Softmax activation outputs probabilities summing to 1, ideal for multi-class.

3. Step 3: Evaluate options

   Dense(3, activation='softmax') uses 3 units with softmax, perfect for 3-class classification; others are unsuitable.

4. Final Answer:

   Dense(3, activation='softmax') -> Option A

5. Quick Check:

   Multi-class output = units=classes + softmax [OK]

Hint: Use softmax with units = number of classes [OK]

Common Mistakes:

• Using sigmoid for multi-class output
• Omitting activation in output layer
• Using relu activation for output