我使用预训练的 BERT 权重构建了一个有点复杂的模型。模型结构如下：
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_ids (InputLayer) [(None, 32)] 0
__________________________________________________________________________________________________
attention_mask (InputLayer) [(None, 32)] 0
__________________________________________________________________________________________________
token_type_ids (InputLayer) [(None, 32)] 0
__________________________________________________________________________________________________
tf_bert_model_1 (TFBertModel) ((None, 32, 768), (N 109482240 input_ids[0][0]
attention_mask[0][0]
token_type_ids[0][0]
__________________________________________________________________________________________________
dropout_76 (Dropout) (None, 32, 768) 0 tf_bert_model_1[0][0]
__________________________________________________________________________________________________
lstm_1 (LSTM) (None, 256) 1049600 dropout_76[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 128) 32896 lstm_1[0][0]
__________________________________________________________________________________________________
dropout_77 (Dropout) (None, 128) 0 dense_5[0][0]
__________________________________________________________________________________________________
dense_6 (Dense) (None, 64) 8256 dropout_77[0][0]
__________________________________________________________________________________________________
dense_7 (Dense) (None, 32) 2080 dense_6[0][0]
__________________________________________________________________________________________________
dense_8 (Dense) (None, 16) 528 dense_7[0][0]
__________________________________________________________________________________________________
dense_9 (Dense) (None, 7) 119 dense_8[0][0]
==================================================================================================
Total params: 110,575,719
Trainable params: 110,575,719
Non-trainable params: 0
__________________________________________________________________________________________________
我使用了 RepeatDataset 类型的对象将数据提供给模型。它是使用以下代码创建的：
train_ds = tf.data.Dataset.from_tensor_slices((train_inp,train_mask,train_type_ids,train_out)).map(convert_to_features).shuffle(100).batch(BATCH_SIZE).repeat(5)
type(test_ds)
所以类型是：tensorflow.python.data.ops.dataset_ops.RepeatDataset。现在我想使用 SHAP 添加模型说明。
我已经尝试使用 DeepExplainer 进行实现。它试图获得数据的形状，在我的例子中是 train_ds。但是作为 RepeateDataset 类型的对象，它没有 shape 属性。我怎样才能克服模型？或者还有其他方法可以将 SHAP 与 RepeatDataset 类型的对象一起使用吗？