IconKeras Dropout Layer0 ×

KNIME Deep Learning - Keras Integration version 3.6.0.v201807091039 by KNIME AG, Zurich, Switzerland

Applies dropout to the layer input. Dropout consists in randomly setting a fraction rate of input units to 0 at each update during training time, which helps prevent overfitting. Corresponds to the Keras Dropout 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.
Drop rate
Fraction of the input units to drop.
Noise shape
The shape of the binary dropout mask that will be multiplied with the input. The noise shape has to include the batch dimension which means in case of 2D images with shape [height, width, channels], the noise shape must must have rank 4 i.e. [batch, height, width, channels]. In order to reuse the dropout mask along specific dimensions set those to '1' in the noise shape other dimensions can be set to '?'. Spatial dropout where whole feature maps are dropped can be achieved by setting noise shape to '?, 1, 1, ?'.
Random seed
Random seed to use for the dropping.

Input Ports

The Keras deep learning network to which to add a Dropout layer.

Output Ports

The Keras deep learning network with an added Dropout layer.

Update Site

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

Wait a sec! You want to explore and install nodes even faster? We highly recommend our NodePit for KNIME extension for your KNIME Analytics Platform.