<p>使用<a href="https://pytorch.org/docs/master/torch.html#torch.bmm" rel="nofollow noreferrer">^{<cd1>}</a>的示例:</p>
<pre class="lang-python prettyprint-override"><code>import torch
from torch.autograd import Variable
import numpy as np
seq_len = 10
rnn_output = torch.rand((1, 1, 256))
encoder_outputs = torch.rand((seq_len, 1, 256))
# As computed in the tutorial:
attn_score = Variable(torch.zeros(seq_len))
for i in range(seq_len):
attn_score[i] = rnn_output.squeeze().dot(encoder_outputs[i].squeeze())
# note: the code would fail without the "squeeze()". I would assume the tensors in
# the tutorial are actually (,256) and (10, 256)
# Alternative using batched matrix multiplication (bmm) with some data reformatting first:
attn_score_v2 = torch.bmm(rnn_output.expand(seq_len, 1, 256),
encoder_outputs.view(seq_len, 256, 1)).squeeze()
# ... Interestingly though, there are some numerical discrepancies between the 2 methods:
np.testing.assert_array_almost_equal(attn_score.data.numpy(),
attn_score_v2.data.numpy(), decimal=5)
# AssertionError:
# Arrays are not almost equal to 5 decimals
#
# (mismatch 30.0%)
# x: array([60.32436, 69.04288, 72.04784, 70.19503, 71.75543, 67.45459,
# 63.01708, 71.70189, 63.07552, 67.48799], dtype=float32)
# y: array([60.32434, 69.04287, 72.0478 , 70.19504, 71.7554 , 67.4546 ,
# 63.01709, 71.7019 , 63.07553, 67.488 ], dtype=float32)
</code></pre>