If you want a chase to get more help, tell what you expected and what you got.
Fred Le 8 nov. 2016 05:53, "Mohammad Najafi" <[email protected]> a écrit : > Hi, > > I want the softmax of the RNN output, to be computed within the step > function, like below: > > > def step(x_t, h_tm1): > h_t = self.activation(T.dot(x_t, self.W_in) + T.dot(h_tm1, self.W) + > self.bh) > y_t = T.dot(h_t, self.W_out) + self.by > y_t = T.nnet.softmax(y_t) > return h_t, y_t > > > However I do not get expected results and I am not sure where the problem > is. > Can anybody give me a hint why this method does not work? > > This is the scan function: > > > [self.h, self.y_pred], _ = theano.scan(step, > sequences=self.input, > outputs_info=[T.alloc(self.h0, self.input.shape[1], > n_hidden), None]) > > > > and I directly pass self.y_pred to p_y_given_x: > > > self.p_y_given_x = self.y_pred > > > > Thanks in advance. > > -- > > --- > You received this message because you are subscribed to the Google Groups > "theano-users" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > For more options, visit https://groups.google.com/d/optout. > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
