[Solved] Identify the error in the following embedding layer usage:embedding = tf.keras.layers.Embedding(input_dim=1000, output_dim=64) input_seq = tf.... | NLP

NLP - Sequence Models for NLP

Identify the error in the following embedding layer usage:

embedding = tf.keras.layers.Embedding(input_dim=1000, output_dim=64)
input_seq = tf.constant([[0, 1, 2], [999, 1000, 500]])
output = embedding(input_seq)

AThe input sequence contains an index equal to input_dim, which is invalid

BThe output_dim is too large for the input_dim

CEmbedding layer requires input_dim and output_dim to be equal

DThe input sequence must be a list, not a tensor

Step-by-Step Solution

Solution:

Step 1: Check input indices validity
Embedding indices must be in [0, input_dim-1]. Here, input_dim=1000, so max index is 999.
Step 2: Identify invalid index
Input sequence contains 1000, which is out of range and causes an error.
Final Answer:
The input sequence contains an index equal to input_dim, which is invalid -> Option A
Quick Check:
Indices must be less than input_dim [OK]

Quick Trick: Indices must be less than input_dim [OK]

Common Mistakes:

MISTAKES

Using index equal to input_dim
Confusing output_dim size limits
Thinking input must be list, not tensor

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More NLP Quizzes

Identify the error in the following embedding layer usage:

Step 1: Check input indices validity

Step 2: Identify invalid index

Final Answer:

Quick Check:

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