Hub
NodeNode / Source

DL Python Network Creator

This node allows custom creation 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.

Node details

Output ports
  1. Deep Learning Network 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.

Related workflows & nodes

  1. [KNIME Nodes] KN-302 Sales Demand Forecasting Neural Networks
    Neural Network Deep Learning Kaggle
    Provides a Neural Network solution to the Kaggle Store Item Demand Forecasting Challenge. Input contains 5 years of sto…
    scientificstrategy > Public > KNIME Nodes > KN-302 Advanced Demand Forecasting Neural Networks v03
  2. Semantic Segmentation with Deep Learning in KNIME
    deep learning image processing image analysis +8
    This workflow shows how the new KNIME Keras integration can be used to train and deploy a specialized deep neural netwo…
    davek > Public > DL_Workshop_ImgAna_2020 > SemanticSegmentation
  3. Semantic Segmentation with Deep Learning in KNIME
    deep learning image processing image analysis +10
    This workflow shows how the new KNIME Keras integration can be used to train and deploy a specialized deep neural netwo…
    bwilhelm > Public > DL_Workshop > Semantic_Segmentation
  4. Use a TensorFlow Hub model for TensorFlow 2
    tf tfhub TensorFlow Hub +5
    This workflow shows how a model from the TensorFlow Hub can be used in KNIME. MobileNet is used to predict the class of…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 04_TensorFlow2 > 05_Use_a_TFHub_Model
  5. Semantic Segmentation with Deep Learning in KNIME
    deep learning image processing image analysis +10
    This workflow shows how the new KNIME Keras integration can be used to train and deploy a specialized deep neural netwo…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 02_Keras > 06_Semantic_Segmentation
  6. Read Images and Train VGG
    Life Science Image Analysis Deep Learning +1
    This workflow reads image patches downloaded and prepared by the previous workflows in the workflow group. It loads the…
    b_eslami > Public > 31_Histopathology_Blog_Post > _legacy_version > 02_Read_Images_and_Train_VGG
  7. Mixing Deep Learning with XGBoost
    Deep Learning Machine Learning XGBoost +11
    This workflow shows how to train an XGBoost based image classifier that uses a pretrained convolutional neural network …
    christian.dietz > Public > Mixing_DL_with_XGBoost
  8. Map Softmax Output to Class Names Example
    deep learning Keras image classification
    This workflow shows how to map softmax output of a classification network to class names.
    davek > Public > Map Softmax Output to Class Names Example
  9. Read Images and Train VGG
    Life Science Image Analysis Deep Learning +1
    This workflow reads image patches downloaded and prepared by the previous workflows in the workflow group. It loads the…
    knime > Examples > 50_Applications > 31_Histopathology_Blog_Post > _legacy_version > 02_Read_Images_and_Train_VGG
  10. 01_caption_preprocessing
    image processing image analysis image captioning +5
    In this workflow we create image feature vectors for each image in the training data using a pretrained InceptionV3 (ht…
    davek > Public > DL_Workshop_ImgAna_2020 > ImageCaptioning > 02_calculate_InceptionV3_features