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Deprecated**KNIME Deep Learning - Keras Integration** version **4.3.0.v202012011122** by **KNIME AG, Zurich, Switzerland**

This layer works similarly to a convolution layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input. Corresponds to the Keras Locally Connected 2D Layer.

- Name prefix
- The name prefix of the layer. The prefix is complemented by an index suffix to obtain a unique layer name. If this option is unchecked, the name prefix is derived from the layer type.
- Filters
- The dimensionality of the output space (i.e. the number of output filters in the convolution).
- Kernel size
- A tuple of 2 integers, specifying the height and width of the 2D convolution window.
- Strides
- A tuple of 2 integers, specifying the strides of the convolution along the height and width. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
- Padding
- Locally Connected Layers only support 'valid' padding.
- Activation function
- The activation function to use.
- Use bias?
- If checked, a bias vector will be used.
- Kernel initializer
- Initializer for the kernel weights matrix.
- Bias initializer
- Initializer for the bias vector.
- Kernel regularizer
- Regularizer function applied to the kernel weights matrix.
- Bias regularizer
- Regularizer function applied to the bias vector.
- Activation regularizer
- Regularizer function applied to the output of the layer (its "activation").
- Kernel constraint
- Constraint function applied to the kernel matrix.
- Bias constraint
- Constraint function applied to the bias vector.

- Keras Network Learner (100 %)

To use this node in KNIME, install KNIME Deep Learning - Keras Integration from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

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