test_concat_model.py 文件源码

def test_convert_conv1d_model_compute_scores(self): 
        if (self.run_graph_tests==False):
            return
        deeplift_model = kc.convert_graph_model(
                            model=self.keras_model,
                            nonlinear_mxts_mode=NonlinearMxtsMode.Rescale)
        deeplift_contribs_func = deeplift_model.\
                                     get_target_contribs_func(
                              find_scores_layer_name=["inp1", "inp2"],
                              pre_activation_target_layer_name="output_preact")

        grads_inp1, grads_inp2 = self.grad_func(self.inp1, self.inp2)
        np.testing.assert_almost_equal(
            np.array(deeplift_contribs_func(task_idx=0,
                                      input_data_list={
                                       'inp1': self.inp1,
                                       'inp2': self.inp2},
                                      input_references_list={
                                       'inp1': np.zeros_like(self.inp1),
                                       'inp2': np.zeros_like(self.inp2)},
                                      batch_size=10,
                                      progress_update=None)),
            #when biases are 0 and ref is 0, deeplift is the same as grad*inp 
            np.array([grads_inp1*self.inp1,
                      grads_inp2*self.inp2]), decimal=6)