Sparse output from sparse encoder

Let's create an example of a sparse output from a hypothetical sparse encoder. This encoder takes an input vector and outputs an encoded representation where most of the values are zero (i.e., sparse).

Example: Sparse Encoder Output

Input:

A dense input vector:

[0.9, 0.1, 0.8, 0.6, 0.2, 0.3]

Output:

A sparse encoded vector (after applying sparsity constraints, e.g., thresholding small values):

[0.9, 0.0, 0.8, 0.6, 0.0, 0.0]

Code Example (Python)

Here's a Python code snippet that demonstrates how sparsity might be applied to an encoder's output:

import numpy as np

# Example input: Dense vector
input_vector = np.array([0.9, 0.1, 0.8, 0.6, 0.2, 0.3])

# Sparsity threshold: Values below this are set to zero
threshold = 0.5

# Applying sparsity
sparse_output = np.where(input_vector > threshold, input_vector, 0.0)

print("Dense Input: ", input_vector)
print("Sparse Output: ", sparse_output)

Output:

Dense Input:  [0.9 0.1 0.8 0.6 0.2 0.3]
Sparse Output:  [0.9 0.  0.8 0.6 0.  0. ]

Explanation

  • The thresholding operation introduces sparsity by zeroing out small values.
  • The sparse output retains only the significant features (above the threshold).

Real-World Example in NLP (Sparse Attention)

In transformers like Longformer or Reformer, sparse outputs are achieved through sparse attention mechanisms. For example, if processing a sentence:

Input:
["The", "cat", "sat", "on", "the", "mat"]

Attention Output (Sparse):
A sparse attention matrix focusing only on relevant words:

[
 [1.0, 0.0, 0.0, 0.0, 0.5, 0.0],
 [0.0, 1.0, 0.8, 0.0, 0.0, 0.0],
 [0.0, 0.8, 1.0, 0.0, 0.0, 0.0],
 [0.0, 0.0, 0.0, 1.0, 0.6, 0.0],
 [0.5, 0.0, 0.0, 0.6, 1.0, 0.0],
 [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]
]

Here, most connections have zero weight, reducing computation while maintaining key relationships.

Let me know if you'd like further details or code implementations!

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