[Solved] Examine the following code snippet:embedding = tf.keras.layers.Embedding(input_dim=10000, output_dim=64) input_data = tf.... | NLP

NLP - Sequence Models for NLP

Examine the following code snippet:

embedding = tf.keras.layers.Embedding(input_dim=10000, output_dim=64)
input_data = tf.constant([[1, 2, 3], [4, 5, 10001]])
output = embedding(input_data)

What is the issue with this code?

AThe output dimension is too large for the input

BThe embedding layer parameters are reversed

CThe input data should be float type, not integer

DThe input contains an index (10001) that exceeds the vocabulary size (10000)

Step-by-Step Solution

Solution:

Step 1: Check input indices against vocabulary size
Embedding layers accept indices from 0 to input_dim-1. Here, input_dim=10000, so max index is 9999.
Step 2: Identify invalid index
Input contains 10001, which is out of range and will cause an error.
Step 3: Verify other options
Parameters are correctly ordered, input type should be integer, and output dimension size is valid.
Final Answer:
The input contains an index (10001) that exceeds the vocabulary size (10000) -> Option D
Quick Check:
Input indices must be within vocabulary range [OK]

Quick Trick: Input indices must be less than input_dim [OK]

Common Mistakes:

MISTAKES

Using indices outside vocabulary range
Passing float inputs instead of integers
Confusing input_dim with output_dim

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More NLP Quizzes

Examine the following code snippet:

Step 1: Check input indices against vocabulary size

Step 2: Identify invalid index

Step 3: Verify other options

Final Answer:

Quick Check:

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