NodeDL Python Network Editor

Manipulator

This node allows custom editing of a (Python compatible) deep learning network in a local Python installation via a user-defined script. The path to the Python executable has to be configured in Preferences → KNIME → Python. It also allows to import Jupyter notebooks as Python modules via the knime_jupyter module that is part of the Python workspace.

Input Ports

  1. Port Type: DLPythonNetworkPortObject
    The deep learning network. Referred to as input_network in the Python script. Can be any deep learning network which can be used in Python.

Output Ports

  1. Port Type: DLPythonNetworkPortObject
    The output deep learning network. This is the content of the variable output_network which has to be defined in the script and has to be a deep learning network known to KNIME. The network can then be used in common "DL Python" nodes or in nodes which are specific to the network type.

  1. Generate Mountain Namesn (Training)

    deep learning keras text generation RNN LSTM text analysis sequence analysis neural network text processing
    The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive mountain names. The brown nodes…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 02_Keras > 10_Generate_Product_Names_With_LSTM > 01_Training

  2. Semantic Segmentation with Deep Learning in KNIME

    deep learning image processing image analysis computer vision unet neural network segmentation semantic segmentation encoder decoder pixel classification
    This workflow shows how the new KNIME Keras integration can be used to train and deploy a specialized deep neural networ…
    shima0011 > 04_Analytics > 14_Deep_Learning > 02_Keras > 06_Semantic_Segmentation

  3. Edit MNIST SavedModel

    deep learning TensorFlow image classification
    This workflow shows how to edit a TensorFlow model using the TensorFlow Python API by adding an additional output to a m…
    shima0011 > 04_Analytics > 14_Deep_Learning > 03_TensorFlow > 04_Edit_MNIST_SavedModel

  4. Neural Machine Translation from English to German: Training Workflow

    deep learning keras sequence analysis text analysis text processing machine translation neural machine translation neural language translation lstm encoder decoder neural network sequence to sequence NLP Natural Language Processing
    Uses a character level encoder-decoder network of LSTMs. The encoder network reads the input sentence character by chara…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 02_Keras > 12_Machine_Translation > 01_Training_a_Neural_Machine_Translation_Model

  5. Neural Machine Translation from English to German: Training Workflow

    deep learning keras sequence analysis text analysis text processing machine translation neural machine translation neural language translation lstm encoder decoder neural network sequence to sequence
    Uses a character level encoder-decoder network of LSTMs. The encoder network reads the input sentence character by chara…
    shima0011 > 04_Analytics > 14_Deep_Learning > 02_Keras > 12_Machine_Translation > 01_Training_a_Neural_Machine_Translation_Model

  6. Fine-tune VGG16

    deep learning Keras image classification
    In this workflow we are fine-tuning a VGG1G network, similar to "Fine-tune VGG16 (Python)". However, we won't make use o…
    shima0011 > 04_Analytics > 14_Deep_Learning > 02_Keras > 04_Cats_and_Dogs > 04_Fine-tune_VGG16

  7. Generate Mountain Names (Training)

    deep learning keras text generation RNN LSTM text analysis sequence analysis neural network text processing
    The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive mountain names. The brown nodes…
    kathrin > Forum_Question_How_to_Generate_Product_Names_with_my_own_list > 01_Training 2

  8. Semantic Segmentation with Deep Learning in KNIME

    deep learning image processing image analysis computer vision unet neural network segmentation semantic segmentation encoder decoder pixel classification NLP Natural Language Processing Python
    This workflow shows how the new KNIME Keras integration can be used to train and deploy a specialized deep neural networ…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 02_Keras > 06_Semantic_Segmentation

  9. Edit MNIST SavedModel

    deep learning TensorFlow image classification
    This workflow shows how to edit a TensorFlow model using the TensorFlow Python API by adding an additional output to a m…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 03_TensorFlow > 04_Edit_MNIST_SavedModel

  10. Fine-tune VGG16 (Python)

    deep learning Keras image classification
    Instead of creating our own network architecture as in the previous workflow "Train simple CNN", in this workflow we use…
    shima0011 > 04_Analytics > 14_Deep_Learning > 02_Keras > 04_Cats_and_Dogs > 03_Fine-tune_VGG16_Python