[Solved] What is wrong with this embedding layer usage?embedding = tf.keras.layers.Embedding(input_dim=5000, output_dim=128) input_data = tf.constant([[1.0, 2.0, 3.0]]) output = embedding(input_data) | NLP

NLP - Sequence Models for NLP

What is wrong with this embedding layer usage?

embedding = tf.keras.layers.Embedding(input_dim=5000, output_dim=128)
input_data = tf.constant([[1.0, 2.0, 3.0]])
output = embedding(input_data)

Ainput_dim should be output_dim

BEmbedding dimension is too large

CInput data should be integers, not floats

DEmbedding layer requires 3D input

Step-by-Step Solution

Solution:

Step 1: Check input data type
Embedding layers require integer indices as input, but input_data contains floats.
Step 2: Validate other parameters
Embedding dimension and input_dim are valid. Input shape can be 2D.
Final Answer:
Input data should be integers, not floats -> Option C
Quick Check:
Embedding input must be integer indices [OK]

Quick Trick: Embedding input must be integer indices, not floats [OK]

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What is wrong with this embedding layer usage?