= torch.eye(10).reshape((1, 1, 10, 10)) show_heatmaps(attention_weights, xlabel='Keys', ylabel='Queries') 后面的章节内容将经常调用show_heatmaps函数来显示注意力权重。 小结 • 人类的注意力是有限的、有价值和稀缺的资源。 • 受试者使用非自主性和自主性提示有选择性地引导注意力。前者基于突出性，后者则依赖于意识。 Parameter(torch.rand((1,), requires_grad=True)) def forward(self, queries, keys, values): # queries和attention_weights的形状为(查询个数，“键－值”对个数) queries = queries.repeat_interleave(keys.shape[1]).reshape((-1, keys.shape[1])) shape[1])) self.attention_weights = nn.functional.softmax( -((queries - keys) * self.w)**2 / 2, dim=1) # values的形状为(查询个数，“键－值”对个数) return torch.bmm(self.attention_weights.unsqueeze(1), values.unsqueeze(-1))

hms#/media/File:Moving_From_unknown_to_known_feature_spaces_based_on_TS-ELM_with_random_kernels_and_connections.tif https://commons.wikimedia.org/wiki/Category:Machine_learning_algorithms#/media/File: els_and_connections.tif https://commons.wikimedia.org/wiki/Category:Machine_learning_algorithms#/media/File:OPTICS.svg May be re-distributed in accordance with the terms of the CC-SA 4.0 license https://creativecommons

best serve these needs. When applying color in the digital environment; web, app, and social media posts, please reference the digital RGB or hex code equivalent. When printing, please use CMYK best serve these needs. When applying color in the digital environment; web, app, and social media posts, please reference the digital RGB or hex code equivalent. When printing, please use CMYK

interacting 4 Semi-supervised learning: partially supervised learning 5 Active learning: actively making queries Feng Li (SDU) Overview September 6, 2023 22 / 57 Supervised Learning In the ML literature, a supervised

umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf ▪ 2015 https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf AlphaZero http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old

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