Conv2D layers in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Conv2D layer with 32 filters.

TensorFlow

layer = tf.keras.layers.Conv2D([1], kernel_size=(3,3), activation='relu')

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

A16

B64

C32

D128

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a very large number of filters can slow training.

Using zero or negative numbers causes errors.

The number of filters in a Conv2D layer defines how many feature detectors it has. Here, 32 filters is the correct choice.

2fill in blank

medium

Complete the code to add padding to the Conv2D layer to keep the output size same as input.

TensorFlow

layer = tf.keras.layers.Conv2D(32, kernel_size=(3,3), padding=[1], activation='relu')

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

A'valid'

B'same'

C'full'

D'none'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'valid' padding reduces output size.

Using invalid strings causes errors.

Padding='same' adds zeros around the input so the output has the same width and height as the input.

3fill in blank

hard

Fix the error in the Conv2D layer that causes a shape mismatch.

TensorFlow

layer = tf.keras.layers.Conv2D(64, kernel_size=[1], activation='relu')

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

A(3, 3)

B3

C(5, 5)

D[3, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single integer instead of a tuple.

Using square brackets instead of parentheses.

The kernel_size must be a tuple of two integers, like (3, 3). Using a single integer or list causes errors.

4fill in blank

hard

Fill both blanks to create a Conv2D layer with 16 filters and stride of 2.

TensorFlow

layer = tf.keras.layers.Conv2D([1], kernel_size=(3,3), strides=[2], activation='relu')

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

A16

B(1, 1)

C(2, 2)

D32

Attempts:

3 left

💡 Hint

Common Mistakes

Using stride (1, 1) does not reduce output size.

Choosing too many filters can slow training.

16 filters and strides of (2, 2) reduce the output size by skipping pixels.

5fill in blank

hard

Fill all three blanks to create a Conv2D layer with 64 filters, kernel size (5,5), and 'valid' padding.

TensorFlow

layer = tf.keras.layers.Conv2D([1], kernel_size=[2], padding=[3], activation='relu')

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

A32

B(5, 5)

C'valid'

D64

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'same' padding when 'valid' is required.

Passing kernel size as a single integer.

64 filters, kernel size (5,5), and 'valid' padding means no padding, so output shrinks.

Practice

1. What does a Conv2D layer primarily do in a neural network?

easy

A. It scans images with small windows to find features.

B. It reduces the size of the input image by half.

C. It converts images into text descriptions.

D. It sorts images based on color.

Solution

1. Step 1: Understand the role of Conv2D

   A Conv2D layer uses small filters to scan over images and detect patterns like edges or textures.

2. Step 2: Compare options with Conv2D function

   Only It scans images with small windows to find features. correctly describes this scanning and feature detection process.

3. Final Answer:

   It scans images with small windows to find features. -> Option A

4. Quick Check:

   Conv2D = feature scanning [OK]

Hint: Conv2D finds image features using small filters [OK]

Common Mistakes:

• Thinking Conv2D reduces image size directly
• Confusing Conv2D with image captioning
• Assuming Conv2D sorts images

2. Which of the following is the correct way to create a Conv2D layer with 32 filters and a 3x3 kernel in TensorFlow?

easy

A. tf.keras.layers.Conv2D(32, kernel=3)

B. tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3))

C. tf.keras.layers.Conv2D(filters=3, kernel_size=32)

D. tf.keras.layers.Conv2D(32, size=(3,3))

Solution

1. Step 1: Recall Conv2D syntax

   The correct syntax uses filters for number of filters and kernel_size as a tuple for filter size.

2. Step 2: Check each option

   tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3)) matches the correct syntax. Options B, C, and D use wrong parameter names or values.

3. Final Answer:

   tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3)) -> Option B

4. Quick Check:

   Correct parameters = tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3)) [OK]

Hint: Use filters= and kernel_size=(h,w) in Conv2D [OK]

Common Mistakes:

• Using wrong parameter names like kernel or size
• Swapping filters and kernel_size values
• Passing kernel_size as a single integer without tuple

3. What will be the output shape of this Conv2D layer in TensorFlow?

layer = tf.keras.layers.Conv2D(filters=16, kernel_size=(3,3), padding='valid')
input_shape = (None, 28, 28, 1)
output_shape = layer.compute_output_shape(input_shape)

medium

A. (None, 30, 30, 16)

B. (None, 28, 28, 16)

C. (None, 26, 26, 16)

D. (None, 25, 25, 16)

Solution

1. Step 1: Understand padding='valid'

   Valid padding means no padding, so output size shrinks by kernel_size - 1 in height and width.

2. Step 2: Calculate output dimensions

   Input is 28x28, kernel is 3x3, so output height and width = 28 - 3 + 1 = 26. Filters = 16.

3. Final Answer:

   (None, 26, 26, 16) -> Option C

4. Quick Check:

   Output size = input - kernel + 1 [OK]

Hint: Valid padding shrinks output by kernel_size - 1 [OK]

Common Mistakes:

• Assuming output size stays same with valid padding
• Confusing padding='same' with 'valid'
• Forgetting to apply kernel size reduction

4. Identify the error in this Conv2D layer code:

model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(64, kernel_size=3, input_shape=(28,28)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10, activation='softmax')
])

medium

A. Missing channel dimension in input_shape

B. kernel_size should be a tuple, not an integer

C. Dense layer should come before Flatten

D. Activation function 'softmax' is invalid here

Solution

1. Step 1: Check input_shape format

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

2. Step 2: Validate other parts

   kernel_size=3 is valid (interpreted as (3,3)), Dense after Flatten is correct, softmax is valid for classification.

3. Final Answer:

   Missing channel dimension in input_shape -> Option A

4. Quick Check:

   Input shape must include channels [OK]

Hint: Input shape for Conv2D needs channels (e.g., (28,28,1)) [OK]

Common Mistakes:

• Ignoring channel dimension in input_shape
• Thinking kernel_size must be tuple always
• Misordering Flatten and Dense layers

5. You want to build a Conv2D model that keeps the input image size unchanged after convolution. Which setting should you use for the padding parameter?

hard

A. 'valid' to avoid padding

B. 'none' to disable convolution

C. 'full' to increase output size

D. 'same' to keep output size equal to input size

Solution

1. Step 1: Understand padding options

   'valid' means no padding, output shrinks; 'same' adds padding to keep output size same as input.

2. Step 2: Choose padding to keep size

   To keep input and output size equal, use padding='same'. Options 'full' and 'none' are invalid in TensorFlow Conv2D.

3. Final Answer:

   'same' to keep output size equal to input size -> Option D

4. Quick Check:

   Padding='same' keeps size [OK]

Hint: Use padding='same' to keep image size after Conv2D [OK]

Common Mistakes:

• Choosing 'valid' and expecting same size
• Using unsupported padding values like 'full' or 'none'
• Confusing padding effect on output size