DeepExplainer with Shap ValueError: 层 sequential_1被调用时输入的不是符号张量。

Question

我试着使用Keras和Shap库来获取一个经典神经网络的特征导入，但我有以下错误。ValueError.Layer sequential_1 was called with an input that isn't symbolic tensor: Layer sequential_1 was called with an input that isn't a symbolic tensor. 我在论坛上看了一下，但答案只针对卷积网络。请看下面我的代码。

import pandas as pd
import pickle 
import numpy as np

from sklearn.utils import shuffle

    # Train

dataset_train_shuffle = shuffle(list_dataset_train[0], random_state = 24) 
dataset_train_shuffle = dataset_train_shuffle.reset_index(drop=True)

X_train = dataset_train_shuffle.iloc[:,1:8]
label_train = dataset_train_shuffle.iloc[:,[-1]]

    # Validation

X_validation = list_dataset_validation[0]
X_validation = X_validation.iloc[:,1:8]

label_validation = list_dataset_validation[0]
label_validation = label_validation.iloc[:,[-1]]

    # Test

X_test = list_dataset_test[0]
X_test = X_test.iloc[:,1:8]

label_test = list_dataset_test[0]
label_test = label_test.iloc[:,[-1]]

我的Xs是数据框，形状如下。

      BookEquityToMarketEquity    Market  ...  EPSGrowth1yrFwd  LowVolatility
0                    -0.725018 -0.531440  ...         0.551760      -1.111092
1                     0.622943 -0.372537  ...        -0.036427      -0.391065
2                    -1.123209  2.099897  ...         1.885993      -1.762509
3                    -3.047993  2.582608  ...         2.272227      -2.906862
4                     0.461661  0.562763  ...        -0.524000      -0.155260
                       ...       ...  ...              ...            ...
3007                 -1.466322 -2.234277  ...        -0.493226       1.712511
3008                  0.061376  0.294030  ...         0.411817      -0.057478
3009                  0.807521  0.357246  ...        -0.169811      -0.713736
3010                 -0.396623  0.320133  ...        -0.096492      -0.287331
3011                 -1.308371  1.074483  ...         1.447048      -1.062359

我的标签是数据框，形状如下：

      NYSE:AEE
0            0
1            0
2            0
3            0
4            1
       ...
3007         0
3008         0
3009         0
3010         0
3011         1

我的模型如下。

from keras.models import Sequential
from keras.layers.core import Dense, Dropout
from keras import optimizers
import tensorflow as tf

model = Sequential()
model.add(Dense(32,input_dim=len(X_train.columns), activation = 'relu',))
model.add(Dropout(0.25))

model.add(Dense(16, activation = 'relu'))
model.add(Dropout(0.25))

model.add(Dense(8, activation ='relu')) 
model.add(Dropout(0.25))

model.add(Dense(1,activation ='sigmoid'))

model.compile(loss = 'binary_crossentropy',
              optimizer = 'adam',
              metrics = [tf.keras.metrics.AUC()],
              )

model.fit(X_train,
          label_train,
          validation_data = (X_validation, label_validation),
          epochs = 100, 
          batch_size = 50,
          verbose = 1,
          )

当我试图获取特征导入时，我在DeepExplainer上遇到了一个问题。

background = X_train[:1000]
explainer = shap.DeepExplainer(model, background)
shap_values = explainer.shap_values(X_test)

shap.force_plot(explainer.expected_value, shap_values[0,:], X_train.iloc[0,:])

ValueError: Layer sequential_1 was called with an input that isn't a symbolic tensor. Received type: <class 'pandas.core.frame.DataFrame'>. Full input: [     BookEquityToMarketEquity    Market  ...  EPSGrowth1yrFwd  LowVolatility
0                   -0.725018 -0.531440  ...         0.551760      -1.111092
1                    0.622943 -0.372537  ...        -0.036427      -0.391065
2                   -1.123209  2.099897  ...         1.885993      -1.762509
3                   -3.047993  2.582608  ...         2.272227      -2.906862
4                    0.461661  0.562763  ...        -0.524000      -0.155260
..                        ...       ...  ...              ...            ...
995                 -1.552939 -0.102533  ...         0.852491      -0.383818
996                  1.311711  1.659371  ...         1.028700      -0.967370
997                  1.013556 -1.029374  ...        -1.386222       0.319806
998                  0.374137 -1.736694  ...        -0.433354      -0.220381
999                  0.353116 -0.631120  ...        -0.227051       0.475108

[1000 rows x 7 columns]]. All inputs to the layer should be tensors.

有谁有办法吗？先谢谢你的帮助。

Answer 1

我也有同样的错误。我发现使用 tensorflow.Keras 而不是 Keras 来解决这个问题。也可以看看这个 联系所以，你需要做的是更改

from keras.models import Sequential
from keras.layers.core import Dense, Dropout
from keras import optimizers

到模块中，从 tensorflow.keras

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers.core import Dense, Dropout
from tensorflow.keras import optimizers