Using IntegratedGradients to explain LSTM model

[qishubo]

Hi
The TIME_STEPS of my LSTM model is 10.The code for the explanation section is：
input.requires_grad_()
ig = IntegratedGradients(model)
attr, delta = ig.attribute(input,target=0, return_convergence_delta=True)
attr = attr.detach().numpy()
def visualize_importances(feature_names, importances, title="Average Feature Importances", plot=True, axis_title="Features"):
print(title)
for i in range(len(feature_names)):
print(feature_names[i], ": ", '%.3f'%(importances[i]))
x_pos = (np.arange(len(feature_names)))
if plot:
plt.figure(figsize=(12,6))
plt.bar(x_pos, importances, align='center')
plt.xticks(x_pos, feature_names, wrap=True)
plt.xlabel(axis_title)
plt.title(title)
visualize_importances(feature_names, np.mean(attr, axis=0))

For one problem, print(np.mean(attr, axis=0).shape) gets (10, 7). Where 10 is TIME_STEPS and 7 is the number of features. print(np.mean(attr, axis=0))
[[-6.14843489e-03 1.23128297e-02 0.00000000e+00 0.00000000e+00 -2.44566928e-03 9.71663677e-03 0.00000000e+00]
[1.01105891e-02 -1.33561019e-02 0.00000000e+00 0.00000000e+00 -1.08681414e-02 -8.36898667e-03 0.00000000e+00]
[-3.01603199e-03 2.83478058e-05 0.00000000e+00 0.00000000e+00 5.63220110e-03 3.27828258e-03 0.00000000e+00]
[6.62596261e-04 -4.36685487e-03 0.00000000e+00 0.00000000e+00 -5.13588440e-04 2.89923891e-06 0.00000000e+00]
[-1.46707092e-03 1.61141641e-03 0.00000000e+00 0.00000000e+00 -4.48725778e-03 4.45105709e-03 0.00000000e+00]
[-4.85951945e-04 2.61684348e-03 0.00000000e+00 0.00000000e+00 -8.63782548e-03 6.60564365e-03 0.00000000e+00]
[1.44520150e-03 -4.12249724e-03 0.00000000e+00 0.00000000e+00 -3.75068304e-03 6.29765388e-03 0.00000000e+00]
[2.51529449e-03-1.04613991e-02 0.00000000e+00 0.00000000e+00 -5.62792612e-03 8.99994289e-03 0.00000000e+00]
[-2.50925849e-02 3.11531167e-02 0.00000000e+00 0.00000000e+00 1.79945677e-02 5.28183730e-02 0.00000000e+00]
[5.15326855e-02 -5.28545470e-02 0.00000000e+00 0.00000000e+00 -7.92302065e-02 -1.16893978e-02 0.00000000e+00]]
The correct result is that each feature corresponds to one importance value, and now there are 10, so how do we do that