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:
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.
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:
When I use GradientsInputs API, (test_dataset is a vector with length 120, test_labelsymbol[0] is an integer)
the output error is:
When I use GradCAM API,
the output error is:
According the model summary, 'Dense_5' layer must exist.