One major addition in WekaDeeplearning4j v1.7.0 is the new Dl4jCNNExplorer and theĪssociated GUI Dl4j Inference Panel. Check out the Usage Instructions alongside This release adds the IsGPUAvailable tool, similar to Keras,TF,etc., which provides a simple way to check whether the OutputLayer: generates classification / regression outputsįurther configurations can be found in the Getting Started and the Examples sections.GlobalPoolingLayer: apply pooling over time for RNNs and pooling for CNNs applied on sequences.LSTM: uses long short term memory approach.BatchNormalization: applies the common batch normalization strategy on the activations of the parent layer.SubsamplingLayer: subsample from groups of units of the parent layer by different strategies (average, maximum, etc.).DenseLayer: all units are connected to all units of its parent layer.ConvolutionLayer: applying convolution, useful for images and text embeddings.The following Neural Network Layers are available to build sophisticated architectures:
FunctionalityĪll functionality of this package is accessible via the Weka GUI, the commandline and programmatically in Java.
#How to cluster colors using weka jar in java code
The source code for this package is available on GitHub. The backend is provided by the Deeplearning4j Java library. It is developed to incorporate the modern techniques of deep learning into Weka. WekaDeeplearning4j is a deep learning package for the Weka workbench.
WekaDeeplearning4j: Deep Learning using Weka