Embedding dimensionality considerations in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an embedding layer with the correct input dimension.

Prompt Engineering / GenAI

embedding_layer = Embedding(input_dim=[1], output_dim=50)

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

A1000

B50

C10

D500

Attempts:

3 left

💡 Hint

Common Mistakes

Using output_dim instead of input_dim for vocabulary size.

Confusing embedding size with vocabulary size.

The input_dim should be the size of the vocabulary, which is 1000 in this example.

2fill in blank

medium

Complete the code to set the embedding output dimension to 128.

Prompt Engineering / GenAI

embedding_layer = Embedding(input_dim=1000, output_dim=[1])

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

A256

B64

C128

D32

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing input_dim with output_dim.

Choosing too small or too large embedding size without reason.

The output_dim is the size of each embedding vector, set here to 128.

3fill in blank

hard

Fix the error in the code to correctly initialize an embedding layer with vocabulary size 5000 and embedding size 100.

Prompt Engineering / GenAI

embedding_layer = Embedding([1], output_dim=100)

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

Ainput_shape=5000

Boutput_dim=5000

Cinput_length=5000

Dinput_dim=5000

Attempts:

3 left

💡 Hint

Common Mistakes

Using output_dim instead of input_dim for vocabulary size.

Using input_length or input_shape incorrectly.

The correct parameter for vocabulary size is input_dim=5000.

4fill in blank

hard

Fill both blanks to create an embedding layer with vocabulary size 2000 and embedding size 64.

Prompt Engineering / GenAI

embedding_layer = Embedding([1], output_dim=[2])

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

Ainput_dim=2000

Binput_dim=64

C64

D2000

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping input_dim and output_dim values.

Using numeric values without parameter names for input_dim.

input_dim=2000 sets vocabulary size; output_dim=64 sets embedding size.

5fill in blank

hard

Fill all three blanks to create an embedding layer with vocabulary size 3000, embedding size 128, and input length 50.

Prompt Engineering / GenAI

embedding_layer = Embedding([1], [2], input_length=[3])

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

Ainput_dim=3000

Boutput_dim=128

C50

Dinput_length=50

Attempts:

3 left

💡 Hint

Common Mistakes

Using input_length without parameter name.

Confusing output_dim with input_dim.

Putting input_length in wrong position.

input_dim=3000 sets vocabulary size, output_dim=128 sets embedding size, input_length=50 sets input sequence length.