ANN Visualizer ERROR: Layer not supported for visualizing

I am relatively new to machine learning with Python and Tensorflow/Keras.
I have now got a model running and would like to visualise my network.
For this there is a python library for visualizing Artificial Neural Networks (ANN): ANN Visualizer.

Unfortunately I receive this error message:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
C:\Temp\ipykernel_1787626106579.py in 
     24 history = model.fit(trainx, trainy, batch_size=1, epochs=30, validation_data=(testsetx, testsety))
     25 
---> 26 ann_viz(model)

c:\Users\aalles\Anaconda3\lib\site-packages\ann_visualizer\visualize.py in ann_viz(model, view, filename, title)
    121                 c.node(str(n), label="Image\n"+pxls[1]+" x"+pxls[2]+" pixels\n"+clrmap, fontcolor="white");
    122             else:
--> 123                 raise ValueError("ANN Visualizer: Layer not supported for visualizing");
    124         for i in range(0, hidden_layers_nr):
    125             with g.subgraph(name="cluster_"+str(i+1)) as c:

ValueError: ANN Visualizer: Layer not supported for visualizing

My method of building the model looks like this:

def build_ann(self, nLayers=4, nNeurons=64, LR_init=1e-2, LR_adapt=1e-4, LR_steps=1e5, regularizer=1e-2, dropout=0.3):
   
        model = Sequential()
        for runs in range(nLayers):
            return_seq = True if runs < nLayers-1 else False
            model.add(LSTM(int(Neurons[runs]), return_sequences=return_seq))  # , dropout=0.3
            model.add(Dropout(dropout))
        model.add(Dense(int(Neurons[-1])))
        model.add(Dense(len(self.targets), activation='sigmoid'))
        model.compile(loss='mean_squared_error', optimizer='Adam', metrics=['accuracy'])  
        model.build(input_shape=(1, self.maxlen, len(self.features)))
        model.summary()

        return model

Consequently, the object will be created:

model = ann.build_ann(nLayers=2, nNeurons=2)
history = model.fit(trainx, trainy, batch_size=1, epochs=30, validation_data=(testsetx, testsety))
ann_viz(model)

And the error appears.

Why can't a visualisation be created? In the other examples it was because of the Flatter() layer. I do not have such a layer. Is it the for loop?

Thank You very much!

The reason behind this error could be the update in ann_visualizer API. So you need to use graphviz explicitly to access the ann_visualizer generated file(.gv) for visualizing the model.

from ann_visualizer.visualize import ann_viz
ann_viz(model, filename="iris.gv", title="Iris NN")

import graphviz
graph_file = graphviz.Source.from_file("iris.gv")
graph_file

Please check this replicated gist for your reference.

Note: You can also use tensorflow's plot_model api to visualize the model by using below code:

from tensorflow.keras.utils import plot_model
plot_model(model, to_file="iris_model.png", show_shapes=True)