nn_layers.py 文件源码

def output_func(self, input):
        # P(Y|X) = softmax(W.X + b)
        features = input[0]
        session_info = input[1]
        exam = 1 / (1 + T.exp(-T.dot(features, self.W[0]) - self.b[0]))
        rel = 1 / (1 + T.exp(-T.dot(features, self.W[1]) - self.b[1]))
        p_1 = exam * rel
        #p_1 = 1 / (1 + T.exp(-T.dot(features, self.W) - self.b))
        self.y_pred = p_1 > 0.5
        self.p_y_given_x = T.horizontal_stack(1 - p_1, p_1)
        #self.p_y_given_x = T.nnet.softmax(self._dot(input, self.W) + self.b)
        #self.y_pred = T.argmax(self.p_y_given_x, axis=1)

        #comput add loss
        #q_info = session_info[:,0]
        #u_info = session_info[:,1:]
        r_info = session_info[:,1 + self.dim:]
        self.rel_model_loss = T.pow(rel - r_info, 2)

        #prev_rel = 1 / (1 + T.exp(-T.dot(features, self.R_W) - self.R_b))
        #self.rel_const_loss = T.pow(rel - prev_rel, 2)

        return self.y_pred