9c158b87f5fb2dca1ed95884e667ab2fc218e1b7,dragonn/models.py,SequenceDNN,deeplift,#SequenceDNN#,175

Before Change

        
        Returns (num_task, num_samples, 1, num_bases, sequence_length) deeplift score array.
        
        if sys.version_info[0] != 2:
            raise RuntimeError("DeepLIFT requires Python2!")
        assert len(np.shape(X)) == 4 and np.shape(X)[1] == 1
        from deeplift.conversion import keras_conversion as kc
        from deeplift.blobs import NonlinearMxtsMode

        // normalize sequence convolution weights
        kc.mean_normalise_first_conv_layer_weights(self.model, True,None)
        // run deeplift
        deeplift_model = kc.convert_sequential_model(
           self.model, nonlinear_mxts_mode=NonlinearMxtsMode.DeepLIFT)
        target_contribs_func = deeplift_model.get_target_contribs_func(
            find_scores_layer_idx=0)
        input_reference_shape = tuple([1] + list(X.shape[1:]))
        return np.asarray([
            target_contribs_func(task_idx=i, input_data_list=[X],
                                 batch_size=batch_size, progress_update=None,
                                 input_references_list=[np.zeros(input_reference_shape)])
            for i in range(self.num_tasks)])

    def in_silico_mutagenesis(self, X):
        

After Change

        score_func = deeplift_model.get_target_contribs_func(
            find_scores_layer_idx=0)
        // use a 40% GC reference
        input_references = [np.array([0.3, 0.2, 0.2, 0.3])[None, None, :, None]]
        // get deeplift scores
        deeplift_scores = np.zeros((self.num_tasks,) + X.shape)
        for i in range(self.num_tasks):
            deeplift_scores[i] = score_func(
                task_idx=i,
                input_data_list=[X],
                batch_size=batch_size,
                progress_update=None,
                input_references_list=input_references)
        return deeplift_scores

    def in_silico_mutagenesis(self, X):
        

Instances