def forward(self, x, allOuts=False, outputs=[]): 
        # Get parameters in nice form
        W_e, W_x, W_h, W_y = self.param
        
        z1, h, y, p, p_y = {}, {}, {}, {}, {}
        h[-1] = np.zeros(self.n_hidd)
        loss = 0.
        for t in xrange(len(x)):

            z1[t] = W_e[:, x[t]].T

            h[t] = self.apply_activation( W_x.dot(z1[t]) + W_h.dot(h[t-1]),
                                          self.activation_function)
            
            y[t] = W_y.dot(h[t]) 
            
            ymax = max(y[t])
            logsum = ymax + np.log(sum(np.exp(y[t]-ymax)))
            p[t] = np.exp(y[t] - logsum)            
            p_y[t] = p[t] / np.sum(p[t])  ##  
#            # Annother way of computing p_y[t]
#            p_y[t] = self.soft_max(y[t])

            if outputs:
                loss += -np.log(p_y[t][outputs[t]]) # Cross-entropy loss.

        loss = loss/len(x)  # Normalize to get the mean
        
        if allOuts:
            return loss, p_y, p, y, h, z1, x
        else:
            return p_y
Parse the <reported/> element of the form.
dataset/ETHPy150Open LxMLS/lxmls-toolkit/lxmls/deep_learning/rnn.py/NumpyRNN.forward--crossover with--dataset/ETHPy150Open kuri65536/python-for-android/python3-alpha/python-libs/pyxmpp2/ext/dataforms.py/Form.__get_reported