Association·arcadia
Network activations and implementation with PyTorch
Network layers use leaky ReLU activation with batch normalization; output layers for phenotypes have linear activation; implemented in PyTorch v2.2.2.
Confidence
100%
active
Evidence Quote
“All internal layers used leaky ReLU activation and batch normalization; output layer used linear activation. Networks instantiated using PyTorch v2.2.2.”
Relationship
Leaky ReLU activation and batch normalization utilizes PyTorch v2.2.2 framework
Evidence
“Reference introducing the attention mechanism in deep learning”
“Study analyzing rectified activation functions leading to improved image classification performance.”
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification doi:10.48550/ARXIV.1502.01852 ↗
“Introduction of batch normalization technique to accelerate deep neural network training.”
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift doi:10.48550/ARXIV.1502.03167 ↗
“Description of PyTorch deep learning framework enabling imperative style programming and high performance.”
PyTorch: An Imperative Style, High-Performance Deep Learning Library doi:10.48550/ARXIV.1912.01703 ↗
“Reference for Adam optimization algorithm for stochastic gradient descent”
Adam: A Method for Stochastic Optimization doi:10.48550/ARXIV.1412.6980 ↗
“Reference for Captum model interpretability library for PyTorch”
Kokhlikyan N et al.. Captum: A unified and generic model interpretability library for PyTorch doi:10.48550/ARXIV.2009.07896 ↗
“Supporting references for denoising autoencoder designs and implementation with PyTorch.”
G–P Atlas architecture and training description