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Implementing graph neural networks with TensorFlow-Keras
[article]
2021
arXiv
pre-print
Graph neural networks are a versatile machine learning architecture that received a lot of attention recently. In this technical report, we present an implementation of convolution and pooling layers for TensorFlow-Keras models, which allows a seamless and flexible integration into standard Keras layers to set up graph models in a functional way. This implies the usage of mini-batches as the first tensor dimension, which can be realized via the new RaggedTensor class of TensorFlow best suited
arXiv:2103.04318v1
fatcat:qin3mmux2ffn7jctegd2ykxvku