CNN architecture for image classification in TensorFlow - 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.

TensorFlow

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

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

A32

B128

C16

D64

Attempts:

3 left

💡 Hint

Common Mistakes

Using too few filters like 16 may limit feature extraction.

Using too many filters like 128 can increase computation unnecessarily.

The first convolutional layer typically 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.

TensorFlow

model.add(tf.keras.layers.MaxPooling2D(pool_size=[1]))

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

A(3, 3)

B(1, 1)

C(2, 2)

D(4, 4)

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using (3, 3) or larger may reduce size too much.

MaxPooling2D with pool size (2, 2) reduces the spatial dimensions by half, helping to reduce computation and control overfitting.

3fill in blank

hard

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

TensorFlow

model.add(tf.keras.layers.[1]())

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

ADense

BDropout

CConv2D

DFlatten

Attempts:

3 left

💡 Hint

Common Mistakes

Using Dense instead of Flatten causes shape errors.

Using Conv2D or Dropout here is incorrect.

Flatten layer converts the 2D feature maps into a 1D vector so it can be fed into dense layers.

4fill in blank

hard

Fill both blanks to add a dense layer with 128 units and ReLU activation.

TensorFlow

model.add(tf.keras.layers.Dense([1], activation=[2]))

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

A128

B'sigmoid'

C'relu'

D64

Attempts:

3 left

💡 Hint

Common Mistakes

Using sigmoid activation in hidden layers can slow learning.

Choosing too few units like 64 may limit model capacity.

A dense layer with 128 units and ReLU activation helps the model learn complex patterns.

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'

A'sgd'

B'sparse_categorical_crossentropy'

C'accuracy'

D'adam'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sgd' optimizer may require tuning learning rate.

Using 'categorical_crossentropy' instead of sparse version causes errors if labels are integers.

Adam optimizer is popular for training CNNs. Sparse categorical crossentropy is used for multi-class classification with integer labels. Accuracy measures how often predictions are correct.

Practice

1. What is the main role of the Conv2D layer in a CNN for image classification?

easy

A. To reduce the size of the image by pooling

B. To decide the final class of the image

C. To flatten the image into a 1D array

D. To detect patterns like edges and shapes in small parts of the image

Solution

1. Step 1: Understand the function of Conv2D

   The Conv2D layer scans small parts of the image to find patterns such as edges and textures.

2. Step 2: Compare with other layers

   Pooling layers reduce size, flatten layers reshape data, and dense layers classify. Conv2D focuses on pattern detection.

3. Final Answer:

   To detect patterns like edges and shapes in small parts of the image -> Option D

4. Quick Check:

   Conv2D = pattern detection [OK]

Hint: Conv2D finds patterns; pooling shrinks images [OK]

Common Mistakes:

• Confusing Conv2D with pooling layers
• Thinking Conv2D flattens the image
• Believing Conv2D decides the final class

2. Which of the following is the correct way to add a MaxPooling2D layer in TensorFlow Keras?

easy

A. tf.keras.layers.MaxPooling2D(pool_size=(2, 2))

B. tf.keras.layers.MaxPooling(pool=(2, 2))

C. tf.keras.layers.MaxPool2D(size=(2, 2))

D. tf.keras.layers.Pooling2D(pool_size=2)

Solution

1. Step 1: Recall correct TensorFlow layer syntax

   The correct class name is MaxPooling2D and it uses the argument pool_size to set the window size.

2. Step 2: Check each option

   tf.keras.layers.MaxPooling2D(pool_size=(2, 2)) uses correct class and argument. Others have wrong class names or argument names.

3. Final Answer:

   tf.keras.layers.MaxPooling2D(pool_size=(2, 2)) -> Option A

4. Quick Check:

   MaxPooling2D with pool_size=(2,2) is correct [OK]

Hint: MaxPooling2D uses pool_size, not pool or size [OK]

Common Mistakes:

• Using wrong layer class names
• Using incorrect argument names like pool or size
• Omitting parentheses or tuple for pool_size

3. What will be the output shape after applying this CNN block on an input image of shape (64, 64, 3)?

model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(64,64,3)),
  tf.keras.layers.MaxPooling2D(pool_size=(2,2))
])
output_shape = model.output_shape

medium

A. (None, 32, 32, 32)

B. (None, 62, 62, 32)

C. (None, 31, 31, 32)

D. (None, 64, 64, 32)

Solution

1. Step 1: Calculate Conv2D output shape

   Conv2D with kernel size 3x3 and default padding='valid' reduces each spatial dimension by 2: 64 - 3 + 1 = 62, so output shape is (62, 62, 32).

2. Step 2: Calculate MaxPooling2D output shape

   MaxPooling2D with pool size 2x2 halves each spatial dimension: 62 / 2 = 31, so output shape is (31, 31, 32).

3. Final Answer:

   (None, 31, 31, 32) -> Option C

4. Quick Check:

   Conv2D valid padding reduces size; pooling halves it [OK]

Hint: Conv2D valid padding shrinks; pooling halves size [OK]

Common Mistakes:

• Assuming padding='same' by default
• Forgetting spatial dimension reduction by Conv2D
• Misapplying pooling size effect

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

model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28,28,1)),
  tf.keras.layers.MaxPooling2D(pool_size=2),
  tf.keras.layers.Dense(10, activation='softmax')
])

medium

A. Missing Flatten layer before Dense

B. Wrong input_shape format

C. MaxPooling2D pool_size should be a tuple

D. Dense layer should have no activation

Solution

1. Step 1: Check layer order and compatibility

   Conv2D and MaxPooling2D output 3D tensors, but Dense expects 1D input.

2. Step 2: Identify missing layer

   A Flatten layer is needed to convert 3D output to 1D before Dense.

3. Final Answer:

   Missing Flatten layer before Dense -> Option A

4. Quick Check:

   Dense needs 1D input; add Flatten [OK]

Hint: Add Flatten before Dense after Conv/Pooling [OK]

Common Mistakes:

• Skipping Flatten layer before Dense
• Assuming Dense accepts 3D input
• Confusing pool_size tuple requirement

5. You want to build a CNN to classify 100x100 RGB images into 5 classes. Which architecture below is best suited for this task?

hard

A. Dense(128, relu) -> Dense(64, relu) -> Dense(5, softmax)

B. Conv2D(16, (3,3)) -> MaxPooling2D(2,2) -> Conv2D(32, (3,3)) -> MaxPooling2D(2,2) -> Flatten -> Dense(5, softmax)

C. Conv2D(64, (5,5), padding='same') -> Flatten -> Dense(5, softmax)

D. Conv2D(32, (3,3)) -> Conv2D(32, (3,3)) -> Dense(5, softmax)

Solution

1. Step 1: Analyze each architecture's suitability

   Conv2D(16, (3,3)) -> MaxPooling2D(2,2) -> Conv2D(32, (3,3)) -> MaxPooling2D(2,2) -> Flatten -> Dense(5, softmax) uses multiple Conv2D and MaxPooling layers to extract features and reduce size, then flattens before classification. This is a standard CNN design.

2. Step 2: Identify issues in other options

   Dense(128, relu) -> Dense(64, relu) -> Dense(5, softmax) uses only Dense layers, ignoring spatial info. Conv2D(64, (5,5), padding='same') -> Flatten -> Dense(5, softmax) lacks pooling, risking large feature maps. Conv2D(32, (3,3)) -> Conv2D(32, (3,3)) -> Dense(5, softmax) misses Flatten and pooling, causing shape errors.

3. Final Answer:

   Conv2D(16, (3,3)) -> MaxPooling2D(2,2) -> Conv2D(32, (3,3)) -> MaxPooling2D(2,2) -> Flatten -> Dense(5, softmax) -> Option B

4. Quick Check:

   Multiple Conv+Pooling layers + Flatten + Dense = best CNN [OK]

Hint: Use Conv + Pooling layers before Flatten and Dense [OK]

Common Mistakes:

• Using only Dense layers for images
• Skipping pooling layers
• Missing Flatten before Dense