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Convolution Layer

KNIME Deeplearning4J Integration version 4.2.0.v202007031306 by KNIME AG, Zurich, Switzerland

This node adds a Convolutional layer to the Deep Learning Model supplied by the input port. The layer performs convolution of the inputs (usually images) using a user defined number of convolution kernels.

Options

Number of Kernels
The number of convolution kernels to use. Hence, the number of feature maps produced by the layer.
Learning Rate
The learning rate that should be used for this layer.
Kernel Size
The size if the convolution kernel in each dimension. The values are given in pixels and are separated by a comma.
Stride
The stride for the convolution kernel in each dimension. Hence, the step size the kernel will be shifted over the input. The values are given in pixels and are separated by a comma.
Activation Function
The type of activation function that should be used for this layer.
Drop Out Rate
Drop Out probability for neurons.

Input Ports

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The model which will be extended by this layer.

Output Ports

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The model from the input port additionally containing this layer.

Best Friends (Incoming)

Best Friends (Outgoing)

Workflows

Installation

To use this node in KNIME, install KNIME Deeplearning4J Integration (64bit only) from the following update site:

KNIME 4.2

A zipped version of the software site can be downloaded here. Read our FAQs to get instructions about how to install nodes from a zipped update site.

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