Which of the following is the correct way to create an embedding layer in TensorFlow Keras for 1000 words with 50 dimensions?

easy📝 Syntax Q12 of 15

NLP - Sequence Models for NLP

A<code>Embedding(input_dim=1000, output_dim=50)</code>

B<code>Embedding(output_dim=1000, input_dim=50)</code>

C<code>Embedding(input_dim=50, output_dim=1000)</code>

D<code>Embedding(1000, 100)</code>

Step-by-Step Solution

Solution:

Step 1: Recall embedding layer parameters
The first parameter input_dim is vocabulary size (1000), second output_dim is embedding size (50).
Step 2: Match parameters to options
Only Embedding(input_dim=1000, output_dim=50) has the correct parameters: input_dim as vocabulary size (1000) and output_dim as embedding dimension (50). The others either swap these values or use incorrect dimensions.
Final Answer:
Embedding(input_dim=1000, output_dim=50) -> Option A
Quick Check:
input_dim = vocab size, output_dim = vector size [OK]

Quick Trick: input_dim = vocab size, output_dim = vector size [OK]

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