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04. Deploy Model - solution

Deploy a Unet using No-Code nodes1. Reuse the Preprocessing Image component created in 01.Exercise to apply the identical preprocessing to the test data 2. Read in your U-Net from the previous exercise using the Model Reader node3. Use the Conda Environment Propagation node to ensure the correct conda environment (use the Keras environment created in the beginning under Preferences) is selected. 4. Use the Keras Network Executor node to apply the trained model on the test data - connections: - use the preprocessed test data as input - use the model from the Model Reader as model input - connect the conda environment flow variable from the Conda Environment Propagation node to the Flow Variable input port - Node Settings: - input column: preprocessed image column "Img Calc" - add output: select "conv2d_19/Sigmoid:0", Conversion: "To Image", rename column "output" - Executable Selection tab: select conda environment flow variable5. After converting the predicted image to labels, use the Interactive Segmentation View node to evaluate your prediction results trained.modelconvert labelings to byteconvert image to labelmerge predicted labelswith input image PreprocessingImages Model Reader Image Converter Image to Labeling InteractiveSegmentation View List Files/Folders Path to String Image Reader(Table) Joiner Keras NetworkExecutor Conda EnvironmentPropagation Deploy a Unet using No-Code nodes1. Reuse the Preprocessing Image component created in 01.Exercise to apply the identical preprocessing to the test data 2. Read in your U-Net from the previous exercise using the Model Reader node3. Use the Conda Environment Propagation node to ensure the correct conda environment (use the Keras environment created in the beginning under Preferences) is selected. 4. Use the Keras Network Executor node to apply the trained model on the test data - connections: - use the preprocessed test data as input - use the model from the Model Reader as model input - connect the conda environment flow variable from the Conda Environment Propagation node to the Flow Variable input port - Node Settings: - input column: preprocessed image column "Img Calc" - add output: select "conv2d_19/Sigmoid:0", Conversion: "To Image", rename column "output" - Executable Selection tab: select conda environment flow variable5. After converting the predicted image to labels, use the Interactive Segmentation View node to evaluate your prediction results trained.modelconvert labelings to byteconvert image to labelmerge predicted labelswith input image PreprocessingImages Model Reader Image Converter Image to Labeling InteractiveSegmentation View List Files/Folders Path to String Image Reader(Table) Joiner Keras NetworkExecutor Conda EnvironmentPropagation

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: janinamothes
Created at: 2021-08-16
On NodePit since: 2022-09-02
Last update: 2024-11-18
Created with KNIME version: v4.6.1

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