Batch indexing for rnn

username: Sascha
cooked: <p>So the output of my network looks like this:</p>
<pre><code>output = tensor([[[ 0.0868, -0.2623],
     [ 0.0716, -0.2668],
     [ 0.0584, -0.2549],
     [ 0.0482, -0.2386],
     [ 0.0410, -0.2234],
     [ 0.0362, -0.2111],
     [ 0.0333, -0.2018],
     [ 0.0318, -0.1951],
     [ 0.0311, -0.1904],
     [ 0.0310, -0.1873],
     [ 0.0312, -0.1851],
     [ 0.0315, -0.1837],
     [ 0.0318, -0.1828],
     [ 0.0322, -0.1822],
     [ 0.0324, -0.1819],
     [ 0.0327, -0.1817],
     [ 0.0328, -0.1815],
     [ 0.0330, -0.1815],
     [ 0.0331, -0.1814],
     [ 0.0332, -0.1814],
     [ 0.0333, -0.1814],
     [ 0.0333, -0.1814],
     [ 0.0334, -0.1814],
     [ 0.0334, -0.1814],
     [ 0.0334, -0.1814]],

    [[ 0.0868, -0.2623],
     [ 0.0716, -0.2668],
     [ 0.0584, -0.2549],
     [ 0.0482, -0.2386],
     [ 0.0410, -0.2234],
     [ 0.0362, -0.2111],
     [ 0.0333, -0.2018],
     [ 0.0318, -0.1951],
     [ 0.0311, -0.1904],
     [ 0.0310, -0.1873],
     [ 0.0312, -0.1851],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164]],

    [[ 0.0868, -0.2623],
     [ 0.0716, -0.2668],
     [ 0.0584, -0.2549],
     [ 0.0482, -0.2386],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164]],

    [[ 0.0868, -0.2623],
     [ 0.0716, -0.2668],
     [ 0.0584, -0.2549],
     [ 0.0482, -0.2386],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164]],

    [[ 0.0868, -0.2623],
     [ 0.0716, -0.2668],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164]],

    [[ 0.0868, -0.2623],
     [ 0.0716, -0.2668],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164]],

    [[ 0.0868, -0.2623],
     [ 0.0716, -0.2668],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164]],

    [[ 0.0868, -0.2623],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164],
     [ 0.1003, -0.2164]]])
</code></pre>
<p>Which is a shape of <code>[8, 24, 2]</code><br>
Now 8 is my batch size. And i would like to get a data point from every batch, at the following locations:</p>
<pre><code>index = tensor([24, 10,  3,  3,  1,  1,  1,  0])
</code></pre>
<p>So the 24th value from the first batch, the 10th value from the second batch, and so on.</p>
<p>Now i have problems figuring out the syntax.<br>
I’ve tried</p>
<pre><code>torch.gather(output, 0, index)
</code></pre>
<p>But it keeps telling me, that my dimensions don’t match.<br>
And trying</p>
<pre><code>output[ : ,index]
</code></pre>
<p>Just gets me the values at all the indexes for each batch.<br>
What would be the correct syntax here, to get these values?</p>

username: googlebot
cooked: <p>output[ torch.arange(batch_size) ,index] should work, as well as output[index_2d] ([[0,24],[1,10],…])</p>