Does the tool support other layers, such as dense layer or conv1d layers?

[tinyxuyan]

All the examples in the project use conv2d layers and support 3-d image input. I am wondering whether the tf-explain tool supports other layers, such as dense layer, conv1d layer, and lstm layer. I am trying to explain the model with conv1d layer, but there are some errors when explainers are used. I am not sure whether it is the reason of my codes or the tf-explain tool itself. Some examples of the detailed errors are listed below:

In the model, the input feature is a 1-d vector with length 120 which should be classified into 4 different classes. The detailed network summary is listed below:

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Input_1 (InputLayer)         [(None, 120)]             0         
_________________________________________________________________
Dense_2 (Dense)              (None, 120)               14520     
_________________________________________________________________
Dense_3 (Dense)              (None, 100)               12100     
_________________________________________________________________
Dense_4 (Dense)              (None, 80)                8080      
_________________________________________________________________
Dense_5 (Dense)              (None, 60)                4860      
_________________________________________________________________
Dense_6 (Dense)              (None, 40)                2440      
_________________________________________________________________
Dense_7 (Dense)              (None, 20)                820       
_________________________________________________________________
Dense_8 (Dense)              (None, 10)                210       
_________________________________________________________________
Dense_9 (Dense)              (None, 4)                 44        
=================================================================
Total params: 43,074
Trainable params: 43,074
Non-trainable params: 0
_________________________________________________________________

When I use GradientsInputs API, (test_dataset is a vector with length 120, test_labelsymbol[0] is an integer)

from tf_explain.core.gradients_inputs import GradientsInputs
explainer = GradientsInputs()
data = (test_dataset, None)
grid = explainer.explain(data, model, test_labelsymbol[0])

the output error is:

(neglecting the tedious details)
 ValueError: 'image' must be at least three-dimensional.

When I use GradCAM API,

from tf_explain.core.grad_cam import GradCAM
explainer = GradCAM()
data = (test_dataset, None)
grid = explainer.explain(data, model, test_labelsymbol[0], layer_name="Dense_5")

the output error is:

(neglecting the tedious details)
 ValueError: No such layer: Dense_5

According the model summary, 'Dense_5' layer must exist.

[Yannik1337]

When you instantiate your model, can you try passing (...name="Dense_5") to the appropriate layer? E.g. explicitly naming the layer as you want, and not relying on the automatic naming of TF.