Why AI image generation creates visual content in Prompt Engineering / GenAI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for AI image generation.

Prompt Engineering / GenAI

import [1]

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Atorch

Bnumpy

Ctensorflow

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing numpy or matplotlib which are not primarily for AI model building.

PyTorch is a popular library used for AI image generation models.

2fill in blank

medium

Complete the code to define a simple neural network layer for image generation.

Prompt Engineering / GenAI

layer = torch.nn.Linear([1], 256)

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A512

B128

C1024

D784

Attempts:

3 left

💡 Hint

Common Mistakes

Using 512 or 1024 which are common hidden layer sizes but not input size here.

784 is the number of pixels in a 28x28 image, common in image generation tasks.

3fill in blank

hard

Fix the error in the code to generate an image tensor with the right shape.

Prompt Engineering / GenAI

image = torch.randn([1], 3, 64, 64)

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A3

B1

C64

D256

Attempts:

3 left

💡 Hint

Common Mistakes

Using 3 which is the number of color channels, not batch size.

The batch size is usually 1 when generating a single image tensor.

4fill in blank

hard

Fill both blanks to create a dictionary that maps image labels to their pixel counts.

Prompt Engineering / GenAI

pixel_counts = {label: [1] for label, pixels in images.items() if len(pixels) [2] 1000}

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Alen(pixels)

B>

C<

Dsum(pixels)

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum instead of len, or using < instead of >.

We use len(pixels) to count pixels and filter those with more than 1000 pixels.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that selects images with width greater than 50.

Prompt Engineering / GenAI

selected_images = {name: data for name, data in dataset.items() if data['width'] [1] 50 and data['height'] [2] 50 and data['channels'] == [3]

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A>

B<

C3

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > or wrong channel number.

We check if width and height are greater than 50 and channels equal 3 for color images.