Skip to content
#

softmax-function

Star

Here are 5 public repositories matching this topic...

Language: All
Filter by language
All 5 Jupyter Notebook 4 Java 1

mahnoorsheikh16 / Node-Classification-with-Graph-Neural-Networks

Star 1

Evaluation of multiple graph neural network models—GCN, GAT, GraphSAGE, MPNN and DGI—for node classification on graph-structured data. Preprocessing includes feature normalization and adjacency-matrix regularization, and an ensemble of model predictions boosts performance. The best ensemble achieves 83.47% test accuracy.

deep-neural-networks deep-learning ensemble-learning regularization loss-functions relu-layer node-classification graphsage graph-neural-networks pytorch-implementation deep-infomax deep-graph-infomax gcn-architecture feature-normalization mpnn-model pytorch-tensors graph-attention-network softmax-function dense-array
  • Updated May 12, 2025
  • Jupyter Notebook

p-dirac / javafx-convolution-network

Star 0

JavaFx Application for Convolutional Network to perfom Image Classification using Softmax Output Layer, Back Propagation, Gradient Descent, Partial Derivatives, Matrix Flattening, Matrix Unfolding, Concurrent Task, Performance Histogram, Confusion Matrix

javafx convolutional-layers image-classification convolution performance-visualization convolutional-networks javafx-application gradient-descent convolutional-neural-network backpropagation maxpooling unfolding matrix-library concurrent-tasks activation-functions partial-derivatives softmax-function matrix-flattening matrix-unfolding
  • Updated Oct 26, 2024
  • Java

Improve this page

Add a description, image, and links to the softmax-function topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the softmax-function topic, visit your repo's landing page and select "manage topics."

Learn more