[Theano]如何基于共享变量评估梯度

因此，您需要更改self.step的签名以采用与共享变量相对应的三个以上参数，然后将参数non_sequences=[W_x, W_h, W_y]添加到theano.scan.此外，我怀疑您可能在倒数第二行中打了错字-应该是error = ((self.y - t) ** 2).sum()吗?I'm currently facing this issue:I can't manage to evaluate my gradient symbolic variables in a Recurrent Neural Network coded with Theano.Here's the code : W_x = theano.shared(init_W_x, name='W_x') W_h = theano.shared(init_W_h, name='W_h') W_y = theano.shared(init_W_y, name='W_y') [self.y, self.h], _ = theano.scan(self.step, sequences=self.x, outputs_info=[None, self.h0]) error = ((self.y - self.t) ** 2).sum() gW_x, gW_y, gW_h = T.grad(self.error, [W_x, W_h, W_y]) [...] def step(self, x_t, h_tm1): h_t = T.nnet.sigmoid(T.dot(self.W_x, x_t) + T.dot(h_tm1, self.W_h)) y_t = T.dot(self.W_y, h_t) return y_t, h_tI kept just the things I thought were appropriate.I would like to be able to compute for instance 'gW_x' but when I try to embbed it as a theano function it doesn't work because it's dependencies (W_x, W_h, W_y) are shared variables.Thank you very much 解决方案 I believe that in this instance, you need to pass the shared variables to the function self.step in the non_sequences argument of theano.scan.Therefore you need to change the signature of self.step to take three more arguments, corresponding to the shared variables, and then add the argument non_sequences=[W_x, W_h, W_y] to theano.scan.Also, I suspect you may have made a typo in the penultimate line - should it be error = ((self.y - t) ** 2).sum()? 这篇关于[Theano]如何基于共享变量评估梯度的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！