ntm.py 文件源码

def preprocess_input(self, x):
        print("begin preprocess_input(self, x)")
        if self.consume_less == 'cpu':
            if 0 < self.dropout_W < 1:
                dropout = self.dropout_W
            else:
                dropout = 0
            input_shape = self.input_spec[0].shape
            input_dim = input_shape[2]
            timesteps = input_shape[1]

            # x_i = time_distributed_dense(x, self.W_i, self.b_i, dropout,
            #                              input_dim, self.output_dim, timesteps)
            # x_f = time_distributed_dense(x, self.W_f, self.b_f, dropout,
            #                              input_dim, self.output_dim, timesteps)
            # x_c = time_distributed_dense(x, self.W_c, self.b_c, dropout,
            #                              input_dim, self.output_dim, timesteps)
            # x_o = time_distributed_dense(x, self.W_o, self.b_o, dropout,
            #                              input_dim, self.output_dim, timesteps)
            # add by Robot Steven ****************************************#
            x_i = time_distributed_dense(x, self.W_i, self.b_i, dropout,
                                         input_dim, self.controller_output_dim, timesteps)
            x_f = time_distributed_dense(x, self.W_f, self.b_f, dropout,
                                         input_dim, self.controller_output_dim, timesteps)
            x_c = time_distributed_dense(x, self.W_c, self.b_c, dropout,
                                         input_dim, self.controller_output_dim, timesteps)
            x_o = time_distributed_dense(x, self.W_o, self.b_o, dropout,
                                         input_dim, self.controller_output_dim, timesteps)
            # add by Robot Steven ****************************************#
            print("end preprocess_input(self,x )")
            return K.concatenate([x_i, x_f, x_c, x_o], axis=2)
        else:
            print("end preprocess_input(self,x )\n")
            return x