Can we save a deep learning model as PMML in KNIME?

[mlauber71]

@somnathdas76 I don’t think that is possible.

When to use PMML model appender and PMML transformation appender?

Hi, Thanks @mlauber71. I had a look at the workflow and I think I know what the main problem is: the Column Filter (PMML) filters the Data Dictionary of an incoming PMML document and outputs that, but PMML does not have any specification for filtering columns. So the new PMML created by the node does not really document the removal of columns, it just deletes every mention of them from the spec of the input data (the DataDictionary). It also keeps the TransformationDictionary as-is, so now the …

The KNIME deep learning integrations do have special formats - that should also work in Python environments.

AttributeError: module ‘keras.optimizers’ has no attribute ‘Adam’

@akhtarameen as it happens I just have collected some example how to set up Keras and Tensor Flow 2 (for classification tasks) with the help of the Conda Environment propagation. If you have a working Miniconda or Anaconda environment configured in KNIME you should be able to just use the propagation and get a working deep learning setup. You would then have to carefully check and plan what you want to d with the setup and what architecture you want to use. [image] [image] The setup in …

Help with Tensorflow

@sfoglia the point is to find the exact combination of python versions and packages that would work with the knime deep learning integrations. And they might be different depending on you use of keras, or Tensorflow 1 or 2. The description could try and make that more obvious and also more clearly provide yaml files about what knime would want to have. https://docs.knime.com/latest/deep_learning_installation_guide/index.html I tried to distill what is there into Conda Environment propagation…