CNN architecture review in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a convolutional layer with 32 filters and a 3x3 kernel.

Computer Vision

model.add(Conv2D([1], kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))

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

A32

B64

C16

D128

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing too few or too many filters like 16 or 128 which may underfit or overfit early.

Confusing kernel size with number of filters.

The first convolutional layer commonly uses 32 filters to extract features from the input images.

2fill in blank

medium

Complete the code to add a max pooling layer with pool size 2x2.

Computer Vision

model.add(MaxPooling2D(pool_size=[1]))

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

A(3, 3)

B(4, 4)

C(2, 2)

D(1, 1)

Attempts:

3 left

💡 Hint

Common Mistakes

Using (1, 1) which does not reduce size.

Using too large pool sizes like (4, 4) which may lose too much information.

Max pooling with a 2x2 window reduces the spatial size by half, helping reduce computation.

3fill in blank

hard

Fix the error in the code to flatten the output before the dense layer.

Computer Vision

model.add([1]())

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

AFlatten

BDense

CConv2D

DMaxPooling2D

Attempts:

3 left

💡 Hint

Common Mistakes

Using Dense directly without flattening causes shape errors.

Using Conv2D or MaxPooling2D here is incorrect.

Flatten layer converts 2D feature maps into 1D vectors for dense layers.

4fill in blank

hard

Fill both blanks to add a dropout layer with rate 0.5 and a dense output layer with 10 units.

Computer Vision

model.add(Dropout([1]))
model.add(Dense([2], activation='softmax'))

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

A0.5

B10

C5

D0.25

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropout rates too low or too high.

Setting output units incorrectly for classification.

Dropout with 0.5 rate helps prevent overfitting. The output layer has 10 units for 10 classes.

5fill in blank

hard

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

Computer Vision

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

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

Aadam

Bcategorical_crossentropy

Caccuracy

Dsgd

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong loss function like 'mse' for classification.

Choosing incorrect optimizer or metric.

Adam optimizer is popular for CNNs. Categorical crossentropy is used for multi-class classification. Accuracy measures performance.

Practice

1. What is the main purpose of a Convolutional Neural Network (CNN) in computer vision?

easy

A. To perform text translation

B. To sort numbers in a list

C. To generate random images

D. To detect patterns and features in images

Solution

1. Step 1: Understand CNN function

   CNNs scan images to find important patterns like edges and shapes.

2. Step 2: Match purpose to options

   Only To detect patterns and features in images describes detecting patterns in images, which is CNN's main job.

3. Final Answer:

   To detect patterns and features in images -> Option D

4. Quick Check:

   CNN purpose = detect image patterns [OK]

Hint: CNNs find image features, not unrelated tasks like sorting [OK]

Common Mistakes:

• Confusing CNNs with general neural networks
• Thinking CNNs generate images
• Mixing CNNs with text processing models

2. Which of the following is the correct way to add a 2D convolutional layer in Keras?

easy

A. Dense(units=32, activation='relu')

B. Conv1D(filters=32, kernel_size=3, activation='relu')

C. Conv2D(filters=32, kernel_size=(3,3), activation='relu')

D. MaxPooling2D(pool_size=(2,2))

Solution

1. Step 1: Identify Conv2D syntax

   Conv2D requires filters, kernel_size as a tuple, and activation function.

2. Step 2: Compare options

   Conv2D(filters=32, kernel_size=(3,3), activation='relu') matches Conv2D syntax correctly; others are different layers or wrong dimensions.

3. Final Answer:

   Conv2D(filters=32, kernel_size=(3,3), activation='relu') -> Option C

4. Quick Check:

   Conv2D syntax = Conv2D(filters=32, kernel_size=(3,3), activation='relu') [OK]

Hint: Conv2D uses 2D kernel size tuple, not single int [OK]

Common Mistakes:

• Using Conv1D instead of Conv2D for images
• Confusing Dense layer with Conv2D
• Wrong kernel_size format

3. Given this Keras CNN snippet, what is the output shape after the Conv2D layer?

model = Sequential()
model.add(Conv2D(16, (3,3), input_shape=(28,28,1)))

medium

A. (26, 26, 16)

B. (28, 28, 16)

C. (30, 30, 16)

D. (28, 28, 1)

Solution

1. Step 1: Calculate output size after Conv2D

   With default 'valid' padding and kernel size 3, output dims = input - kernel + 1 = 28 - 3 + 1 = 26.

2. Step 2: Determine output channels

   Filters=16 means output depth is 16 channels.

3. Final Answer:

   (26, 26, 16) -> Option A

4. Quick Check:

   Output shape = (26,26,16) [OK]

Hint: Output size = input - kernel + 1 with 'valid' padding [OK]

Common Mistakes:

• Assuming output size equals input size without padding
• Confusing number of filters with spatial dimensions
• Forgetting default padding is 'valid'

4. Identify the error in this CNN model code snippet:

model = Sequential()
model.add(Conv2D(32, (3,3), activation='relu', input_shape=(28,28)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))

medium

A. Dense layer should come before Flatten

B. input_shape missing channel dimension

C. Activation function 'relu' is invalid

D. Conv2D filters must be 64 or more

Solution

1. Step 1: Check input_shape format

   Conv2D expects input_shape with 3 dimensions: height, width, channels. Here channels are missing.

2. Step 2: Validate other parts

   Activation 'relu' is valid, Flatten before Dense is correct, filters can be any positive integer.

3. Final Answer:

   input_shape missing channel dimension -> Option B

4. Quick Check:

   Input shape must include channels [OK]

Hint: Conv2D input_shape needs (height, width, channels) [OK]

Common Mistakes:

• Ignoring channel dimension in input_shape
• Misordering Flatten and Dense layers
• Thinking filters must be >=64

5. You want to build a CNN for classifying 64x64 RGB images into 5 classes. Which architecture choice is best?

hard

A. Conv2D(32, (3,3)) + MaxPooling2D + Conv2D(64, (3,3)) + Flatten + Dense(5, softmax)

B. Dense(128) + Dense(64) + Dense(5, softmax)

C. Conv1D(32, 3) + Flatten + Dense(5, softmax)

D. Flatten + Dense(5, softmax)

Solution

1. Step 1: Identify suitable layers for image data

   Conv2D layers extract spatial features from 2D images; MaxPooling reduces size; Flatten prepares for Dense.

2. Step 2: Evaluate options

   Conv2D(32, (3,3)) + MaxPooling2D + Conv2D(64, (3,3)) + Flatten + Dense(5, softmax) uses Conv2D and pooling correctly for images. The Dense-only option lacks feature extraction, Conv1D is unsuitable for 2D images, and Flatten + Dense skips convolutions.

3. Final Answer:

   Conv2D(32, (3,3)) + MaxPooling2D + Conv2D(64, (3,3)) + Flatten + Dense(5, softmax) -> Option A

4. Quick Check:

   Use Conv2D + pooling for images [OK]

Hint: Use Conv2D layers for images, not Dense-only or Conv1D [OK]

Common Mistakes:

• Using Dense layers only for image input
• Applying Conv1D to 2D images
• Skipping pooling layers for downsampling