Code not executed

[abdulrehman192]

Describe the bug When I reload or press update button its given an error code not executed.

Network ID https://viscom.net2vis.uni-ulm.de/OG1Br2BAkYSwwrV6CADl4X5EfErFjUzvuUwXWDdLbdsIXNhb9L

Expected behavior architecture should be drawn

Screenshots If applicable, add screenshots to help explain your problem.

Desktop (please complete the following information):

• OS: Windows
• Browser chrome
• Version [e.g. 22]

Additional context also if I upload a new model it gives an error

[Sparkier]

It seems like the link you send does not have valid data anymore. Can you either try to paste your model code again or share it with me so I can debug?

[abdulrehman192]

I run the default code which is pre loaded. And get the following error in attached screenshot.

On Wed, Nov 29, 2023, 7:39 PM Alex Bäuerle @.***> wrote:

It seems like the link you send does not have valid data anymore. Can you either try to paste your model code again or share it with me so I can debug?

— Reply to this email directly, view it on GitHub https://github.com/viscom-ulm/Net2Vis/issues/50#issuecomment-1832021510, or unsubscribe https://github.com/notifications/unsubscribe-auth/AS2CTJW4XZ7MWWYM34ZMG5TYG5CINAVCNFSM6AAAAAA77IKUL6VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQMZSGAZDCNJRGA . You are receiving this because you authored the thread.Message ID: @.***>

[Sparkier]

For me, the default code works, see for example here.

When I open your link, however, this is the code I see:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import SimpleRNN, Dense

def get_model(input_shape, rnn_units, num_classes):
    model = Sequential()
    
    # Simple RNN layer
    model.add(SimpleRNN(units=rnn_units, input_shape=input_shape))
    
    # Dense layer for classification
    model.add(Dense(num_classes, activation='softmax'))
    
    return model

# Example usage:
input_shape = (10, 15)  # Define the input shape based on your data
rnn_units = 32  # Number of units in the SimpleRNN layer
num_classes = 10  # Number of output classes

# Create the model
model = get_model(input_shape, rnn_units, num_classes)

This won't work because we call the get_model() function to render the model which you changed to include some parameters. If you want this code to work you'd have to change it like this.