I have six image layers with 32-bit float. I created a CNN model using the following parameters:

  • Sample patch size: 32
  • Number of image layers: 6
  • Model classes: Class-1, Class2
  • Number of hidden layers: 3
  • Use batch normalization: Yes
  • Hidden layer 1: Kernel size 7, Number of feature maps 20, Max pooling No
  • Hidden layer 2: Kernel size 3, Number of feature maps 12, Max pooling No
  • Hidden layer31: Kernel size 3, Number of feature maps 12, Max pooling No

Model training was successful. When I executed the CNN model, though, I've got an error message "Invalid argument: input depth must be evenly divisible by filter depth: 5 vs 6 [[{{node BiasAdd_8}}]]"

This very same parameters worked alright when I used a 4-band, 8-bit unsigned integer image. Has anyone experienced with this error message and came up with a fix?


  • Gi-Choul Ahn

    Oops. It turned out that my model was built using six image layers, but I used just five input layers when I executed the model by mistake. It works great now!

  • Trimble Support

    Dear Gi-Choul Ahn,

    Thank you for the update!


Recent Discussions

Need more help?

Enter Knowledge Base Contact Support