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What is a Bidirectional Layer?

Visualize how a Bidirectional layer processes a sequence in both forward and backward directions to capture full context before merging the results.

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Architecture Flow

$X_t$ (Input Sequence)
$H^f_t$ (Forward Layer)
$H^b_t$ (Backward Layer)
$Y_t$ (Concatenated Output)
Total Output Size
Y = [H_f, H_b]
Output size is exactly 16 ($2 \times$ Hidden)

Bidirectional RNNs: Looking into the Future

Standard Recurrent layers process text from left to right. But what if understanding a word requires knowing the words that come after it? Enter the Bidirectional layer.

The Context Problem

Consider the sentence: "He went to the bank to fish."

When a standard left-to-right RNN reaches the word "bank", its hidden state only contains the history of "He went to the". It has no idea if this is a financial bank or a river bank. It must guess.

A Bidirectional layer solves this by deploying two independent networks. One reads forward, and the other reads backward (starting from "fish"). When both reach "bank", their states are merged, granting perfect contextual awareness of both the past and the future!

Step-by-Step Architecture

When you run the simulation, you will see the architecture process the sequence "The bank of the river":

  1. Dual Inputs: The exact same input sequence ($X_1 \dots X_5$) is fed into two completely separate hidden layers simultaneously.
  2. The Forward Pass ($H^f$): The green layer acts like a standard RNN, reading the sequence from left to right ($t=1$ to $t=5$).
  3. The Backward Pass ($H^b$): The purple layer is entirely independent. It reads the exact same sequence, but starts at the end ($t=5$) and processes backwards to the beginning ($t=1$).
  4. Concatenation ($Y_t$): At each time step $t$, the hidden state from the forward layer ($H^f_t$) and the backward layer ($H^b_t$) are glued (concatenated) together. If your hidden size is 8, your final output vector for each word will have a size of 16.

The Parameter Cost (2x)

It's important to remember that a Bidirectional layer is literally just two standard layers wrapped in a trenchcoat. The forward layer has its own weights ($W^f$), and the backward layer has its own completely separate weights ($W^b$). Because of this, a Bidirectional layer has exactly twice as many parameters as a standard layer of the same size. You can verify this by clicking the Parameters button.

Cheat sheet

What is a Bidirectional Layer?

Visualize how a Bidirectional layer processes a sequence in both forward and backward directions to capture full context before merging the results.

NLP · vizlearn.in/natural_language_processing/what_is_bi_directional_layer.html

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