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在Gumbel-Softmax 方法中,我们使用F.gumbel_softmax对smooth_prob进行采样: importtorch.nn.functionalasF sampled_prob = F.gumbel_softmax(smooth_prob, tau=1.0, hard=True)# 近似 one-hot 的采样结果H_gumbel = sampled_prob.mm(res_embeddings) 假设Gumbel-Softmax 采样结果为以下近似 one-hot 向量(每行...
deep-learningvisual-inertial-odometrygumbel-softmaxeccv2022 UpdatedOct 19, 2022 Python Codes for "Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control", ICASSP 2022 adaptive-learninggumbel-softmaxdeep-jscc
本文简要介绍 python 语言中scipy.stats.gumbel_l的用法。 用法: scipy.stats.gumbel_l= <scipy.stats._continuous_distns.gumbel_l_gen object># left-skewed Gumbel 连续随机变量。 作为rv_continuous类的实例,gumbel_l对象从它继承了一组通用方法(完整列表见下文),并用特定于此特定发行版的详细信息来完成它们...
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