如何在 Lime 中使用 1D-CNN 模型？

Question

我有一个数字健康记录数据集。我在分类步骤中使用了 1D CNN keras 模型。

我在 Python 中给出一个可重现的例子：

import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Conv1D, Activation, Flatten, Dense
import numpy as np

a = np.array([[0,1,2,9,3], [0,5,1,33,6], [1, 12,1,8,9]])
train = np.reshape(a[:,1:],(a[:,1:].shape[0], a[:,1:].shape[1],1))
y_train = keras.utils.to_categorical(a[:,:1])

model = Sequential()
model.add(Conv1D(filters=2, kernel_size=2, strides=1, activation='relu', padding="same", input_shape=(train.shape[1], 1), kernel_initializer='he_normal'))

model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))

model.compile(loss=keras.losses.binary_crossentropy,
                 optimizer=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),
                 metrics=['accuracy'])

model.fit(train, y_train, epochs=3, verbose=1)

当我将石灰应用于我的 1D CNN 模型时出现此错误

IndexError: boolean index did not match indexed array along dimension 1; dimension is 4 but corresponding boolean dimension is 1

import lime
import lime.lime_tabular

explainer = lime.lime_tabular.LimeTabularExplainer(train)

有解决办法吗？

Answer 1

您应该尝试 lime_tabular.RecurrentTabularExplainer 而不是 LimeTabularExplainer。它是 keras 风格的递归神经网络的解释器。查看 LIME 文档中的示例以获得更好的理解。祝你好运:)