Neural network architecture in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple neural network layer using Keras.

ML Python

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

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

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

ASequential

B'relu'

C10

D32

Attempts:

3 left

💡 Hint

Common Mistakes

Using the activation function name instead of the number of neurons.

Using the input shape as the number of neurons.

The first Dense layer needs the number of neurons as the first argument, here 32 neurons.

2fill in blank

medium

Complete the code to compile the model with the correct loss function for classification.

ML Python

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

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

Acategorical_crossentropy

Bmean_absolute_error

Cmean_squared_error

Dhinge

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean squared error loss for classification.

Using hinge loss which is for SVMs.

For multi-class classification, 'categorical_crossentropy' is the correct loss function.

3fill in blank

hard

Fix the error in the code to add a dropout layer with 0.5 dropout rate.

ML Python

from tensorflow.keras.layers import Dropout

model.add(Dropout([1]))

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

A50

B0.5

C5

D1.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using 50 instead of 0.5 for dropout rate.

Using values greater than 1.

Dropout rate should be a float between 0 and 1, so 0.5 means 50% dropout.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps layer names to their output shapes for layers with output shape length greater than 2.

ML Python

layer_shapes = {layer.name: layer.output_shape for layer in model.layers if len(layer.output_shape) [1] [2]

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

A>

B2

C<

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' in the condition.

Using 1 instead of 2 as the threshold.

We want layers where the output shape length is greater than 2, so use '>' and 2.

5fill in blank

hard

Fill all three blanks to create a dictionary that maps layer names in uppercase to their output shapes, but only for layers with output shape length greater than 1.

ML Python

layer_info = {layer.name[1]: layer.output_shape for layer in model.layers if len(layer.output_shape) [2] [3]

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

A.upper()

B>

C1

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Not using .upper() to convert names.

Using '<' instead of '>' in the condition.

Using 2 instead of 1 as the threshold.

We convert layer names to uppercase with .upper(), and filter layers with output shape length greater than 1.

Practice

1. What does the architecture of a neural network mainly describe?

easy

A. The type of keyboard used to code the network

B. How layers and neurons are connected to learn from data

C. The speed of the computer running the network

D. The color of the network's interface

Solution

1. Step 1: Understand the role of architecture in neural networks

   The architecture defines the structure, including layers and neuron connections, which determines how the network learns.

2. Step 2: Eliminate unrelated options

   Options about color, speed, or keyboard do not relate to network design or learning.

3. Final Answer:

   How layers and neurons are connected to learn from data -> Option B

4. Quick Check:

   Neural network architecture = connections and layers [OK]

Hint: Think about what controls learning structure [OK]

Common Mistakes:

• Confusing hardware with architecture
• Thinking architecture is about UI design

2. Which of the following is the correct way to start a simple Sequential model in Python using Keras?

easy

A. model = Sequential()

B. model = sequence()

C. model = Sequential[]

D. model = Sequential{}

Solution

1. Step 1: Recall the correct syntax for creating a Sequential model

   In Keras, the Sequential model is created by calling Sequential() with parentheses.

2. Step 2: Identify incorrect syntax

   Using lowercase, square brackets, or curly braces is invalid for this class instantiation.

3. Final Answer:

   model = Sequential() -> Option A

4. Quick Check:

   Use parentheses to create Sequential model [OK]

Hint: Remember classes need parentheses to create instances [OK]

Common Mistakes:

• Using square or curly brackets
• Using lowercase 'sequence' instead of 'Sequential'

3. Given this code snippet, what will be the output shape of the model's last layer?

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(10, input_shape=(5,), activation='relu'))
model.add(Dense(3, activation='softmax'))
print(model.output_shape)

medium

A. (None, 3)

B. (5, 10)

C. (None, 10)

D. (3, 5)

Solution

1. Step 1: Understand the model layers and their output shapes

   The first Dense layer outputs shape (None, 10) because input is (5,) and units=10. The second Dense layer outputs (None, 3) because it has 3 units.

2. Step 2: Identify the final output shape

   The model's output shape is the output of the last layer, which is (None, 3). None means batch size is flexible.

3. Final Answer:

   (None, 3) -> Option A

4. Quick Check:

   Last Dense units = output shape [OK]

Hint: Output shape matches last layer units [OK]

Common Mistakes:

• Confusing input shape with output shape
• Ignoring batch size dimension

4. Identify the error in this neural network code snippet:

model = Sequential()
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='categorical_crossentropy')

medium

A. Activation function 'relu' is invalid

B. Missing input_shape in first Dense layer

C. Loss function 'categorical_crossentropy' is incorrect for single output

D. Optimizer 'adam' is not supported

Solution

1. Step 1: Check the output layer and loss function compatibility

   The output layer has 1 unit, likely for regression or binary classification. 'categorical_crossentropy' expects multiple classes, so it's incorrect here.

2. Step 2: Verify other parts

   Input shape can be set later or inferred, 'relu' is valid, and 'adam' optimizer is supported.

3. Final Answer:

   Loss function 'categorical_crossentropy' is incorrect for single output -> Option C

4. Quick Check:

   Loss must match output type [OK]

Hint: Match loss to output layer type [OK]

Common Mistakes:

• Assuming input_shape is always mandatory
• Confusing loss functions for output types

5. You want to build a neural network for a multi-class classification problem with 4 classes. Which architecture is best suited?

hard

A. Input layer -> Dense(1, softmax) -> Dense(4, relu)

B. Input layer -> Dense(4, relu) -> Dense(1, sigmoid)

C. Input layer -> Dense(10, sigmoid) -> Dense(4, relu)

D. Input layer -> Dense(16, relu) -> Dense(4, softmax)

Solution

1. Step 1: Understand output layer requirements for multi-class classification

   For 4 classes, output layer should have 4 units with softmax activation to output class probabilities.

2. Step 2: Check each option's output layer and activations

   Input layer -> Dense(16, relu) -> Dense(4, softmax) ends with Dense(4, softmax), which is correct. Others have wrong units or activations for output.

3. Final Answer:

   Input layer -> Dense(16, relu) -> Dense(4, softmax) -> Option D

4. Quick Check:

   Output units = number of classes, softmax for multi-class [OK]

Hint: Output units = classes, use softmax for multi-class [OK]

Common Mistakes:

• Using sigmoid for multi-class output
• Wrong number of output units
• Misplacing activation functions