Icon

02. Define U-Net - solution

Create a Unet Architecture using No-Code nodesCreate a conda environment for Keras under Preferences -> KNIME -> Python Deep Learning. Name it "UNet-Keras-CellSegmentation".1. Use the Keras Input Layer node to create the input layer of shape (224,224,1)2. Create 5 Encoding Layers using the U-Net 2D Encoding Layer component with the following settings - Filter = 16, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.1, MaxPooling Pool Size = 2,2 - Filter = 32, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.1, MaxPooling Pool Size = 2,2 - Filter = 64, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.2, MaxPooling Pool Size = 2,2 - Filter = 128, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.2, MaxPooling Pool Size = 2,2 - Filter = 256, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.3, MaxPooling Pool Size = 2,23. Create 4 Decoding Layers using the U-Net 2D DEconding Layer component (the settings will be taken automatically from the Encoding Layers)4. Create on final convolutional layer using the Keras Convolution 2D Layer node with Padding = "same" and Activation function = "Sigmoid"5. Use the Keras Network Writer node to write your network to a file named u-net.h5 in the data folder (Hint: use workflow relative path: knime://knime.workflow/../../data/u-net.h5) 16113264128256128643216u-net.h5 U-Net 2D -Encoding Layer Keras Input Layer Keras Convolution2D Layer U-Net 2D -Encoding Layer U-Net 2D -Encoding Layer U-Net 2D -Encoding Layer U-Net 2D -Encoding Layer U-Net 2D -Decoding Layer U-Net 2D -Decoding Layer U-Net 2D -Decoding Layer U-Net 2D -Decoding Layer Keras NetworkWriter Conda EnvironmentPropagation Create a Unet Architecture using No-Code nodes Create a conda environment for Keras under Preferences -> KNIME -> Python Deep Learning. Name it "UNet-Keras-CellSegmentation".1. Use the Keras Input Layer node to create the input layer of shape (224,224,1)2. Create 5 Encoding Layers using the U-Net 2D Encoding Layer component with the following settings - Filter = 16, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.1, MaxPooling Pool Size = 2,2 - Filter = 32, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.1, MaxPooling Pool Size = 2,2 - Filter = 64, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.2, MaxPooling Pool Size = 2,2 - Filter = 128, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.2, MaxPooling Pool Size = 2,2 - Filter = 256, Activation function = "ELU”, Kernel Size = 3,3, Dropout Rate = 0.3, MaxPooling Pool Size = 2,23. Create 4 Decoding Layers using the U-Net 2D DEconding Layer component (the settings will be taken automatically from the Encoding Layers)4. Create on final convolutional layer using the Keras Convolution 2D Layer node with Padding = "same" and Activation function = "Sigmoid"5. Use the Keras Network Writer node to write your network to a file named u-net.h5 in the data folder (Hint: use workflow relative path: knime://knime.workflow/../../data/u-net.h5) 16113264128256128643216u-net.h5 U-Net 2D -Encoding Layer Keras Input Layer Keras Convolution2D Layer U-Net 2D -Encoding Layer U-Net 2D -Encoding Layer U-Net 2D -Encoding Layer U-Net 2D -Encoding Layer U-Net 2D -Decoding Layer U-Net 2D -Decoding Layer U-Net 2D -Decoding Layer U-Net 2D -Decoding Layer Keras NetworkWriter Conda EnvironmentPropagation

Nodes

Extensions

Links

Download

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

Download Workflow
Created by: janinamothes
Created at: 2021-08-16
On NodePit since: 2022-09-02
Last update: 2024-04-26
Created with KNIME version: v4.6.1

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!