[Solved] Given the code below, what is the shape of the output tensor after the embedding layer?import tensorflow as tf embedding = tf.keras.layers.Embedding(input_dim=5000, output_dim=16) input_seq = tf.... | NLP

NLP - Sequence Models for NLP

Given the code below, what is the shape of the output tensor after the embedding layer?

import tensorflow as tf
embedding = tf.keras.layers.Embedding(input_dim=5000, output_dim=16)
input_seq = tf.constant([[1, 2, 3], [4, 5, 6]])
output = embedding(input_seq)
print(output.shape)

A(3, 16)

B(3, 2, 16)

C(2, 16)

D(2, 3, 16)

Step-by-Step Solution

Solution:

Step 1: Understand input shape
Input is a 2D tensor with shape (2, 3) representing 2 sequences each of length 3.
Step 2: Embedding output shape
Embedding converts each integer to a 16-dimensional vector, so output shape is (2, 3, 16).
Final Answer:
(2, 3, 16) -> Option D
Quick Check:
Output shape = (batch_size, sequence_length, embedding_dim) [OK]

Quick Trick: Output shape adds embedding dim to input shape [OK]

Common Mistakes:

MISTAKES

Mixing batch and sequence dimensions
Forgetting embedding dimension in output
Assuming output shape matches input shape exactly

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Given the code below, what is the shape of the output tensor after the embedding layer?

Step 1: Understand input shape

Step 2: Embedding output shape

Final Answer:

Quick Check:

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