<p>The number is <strong>4672</strong> from Google.</p>

<blockquote>
  <p><a href="https://arxiv.org/pdf/1712.01815.pdf" rel="noreferrer">https://arxiv.org/pdf/1712.01815.pdf</a></p>
</blockquote>

<pre><code>A move in chess may be described in two parts: selecting the piece to move, and then
selecting among the legal moves for that piece. We represent the policy π(a|s) by a 8 × 8 × 73
stack of planes encoding a probability distribution over 4,672 possible moves. Each of the 8×8
positions identifies the square from which to “pick up” a piece.
</code></pre>

<p>4672 might sound a big number but it's nothing to what Google (and many other competitors) has been doing to deep learning for image analysis. </p>

CC BY-SA 4.0
2023-07-21T15:35:52.390Z
The paper explains the 4672 number: 8 * 8 * (56 + 8 + 9). Can someone help me understand why there are 9 planes for promotion, instead of 12? I understand there are 3 pawn moves: straight, diagonal left, diagonal right. But pawn can be promoted to 4 kinds: knight, rook, bishop, and queen. So that would have been 12. Why queen was not considered in the paper?
bizi
CC BY-SA 3.0
2018-04-16T06:24:07.380Z
2018-04-16T06:24:07.380Z
SmallChess
<p>In a neural network for chess (or checkers), the output is a piece or square on the board and an end position.</p>

<p>How would one encode this?</p>

<p>As far as I can see choosing a starting square is 8x8=64 outputs and an ending square is 8x8=64 outputs. So the total number of possible moves is 64x64 4096 outputs. Giving a probability for every possible move.</p>

<p>Is this correct? This seems like an awful lot of outputs!</p>

<neural-networks><chess>
How do you encode a chess move in a neural network?
zooby
# How do you encode a chess move in a neural network?

(Asked by: username_0 on 2018-04-15)

In a neural network for chess (or checkers), the output is a piece or square on the board and an end position.

How would one encode this?

As far as I can see choosing a starting square is 8x8=64 outputs and an ending square is 8x8=64 outputs. So the total number of possible moves is 64x64 4096 outputs. Giving a probability for every possible move.

Is this correct? This seems like an awful lot of outputs!

**Tags:** neural-networks, chess


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## 1 answers

### Answer by username_1 on 2018-04-16. Score: 5 | Accepted answer

The number is 4672 from Google.

https://arxiv.org/pdf/1712.01815.pdf

A move in chess may be described in two parts: selecting the piece to move, and then
selecting among the legal moves for that piece. We represent the policy π(a|s) by a 8 × 8 × 73
stack of planes encoding a probability distribution over 4,672 possible moves. Each of the 8×8
positions identifies the square from which to “pick up” a piece.

4672 might sound a big number but it's nothing to what Google (and many other competitors) has been doing to deep learning for image analysis.

> **Comments:**
> **username_2**: The paper explains the 4672 number: 8 * 8 * (56 + 8 + 9). Can someone help me understand why there are 9 planes for promotion, instead of 12? I understand there are 3 pawn moves: straight, diagonal left, diagonal right. But pawn can be promoted to 4 kinds: knight, rook, bishop, and queen. So that would have been 12. Why queen was not considered in the paper?  

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