Deeplay documentation#
Deeplay is a deep learning library in Python that extends PyTorch with additional functionalities focused on modularity and reusability. Deeplay seeks to address the common issue of rigid and non-reusable modules in PyTorch projects by offering a system that allows for easy customization and optimization of neural network components. Specifically, it facilitates the definition, training, and adjustment of neural networks by introducing dynamic modification capabilities for model components after their initial creation.
Contents:
- Documentation
- deeplay.decorators Module
- deeplay.list Module
- deeplay.meta Module
- deeplay.module Module
- deeplay.shapes Module
- deeplay.trainer Module
- deeplay.activelearning.criterion Module
- deeplay.activelearning.data Module
- deeplay.activelearning.strategies.strategy Module
- deeplay.activelearning.strategies.uncertainty Module
- deeplay.activelearning.strategies.uniform Module
- deeplay.activelearning.strategies.adversarial.adversarial Module
- deeplay.applications.application Module
- deeplay.applications.autoencoders.vae Module
- deeplay.applications.autoencoders.wae Module
- deeplay.applications.classification.binary Module
- deeplay.applications.classification.categorical Module
- deeplay.applications.classification.classifier Module
- deeplay.applications.classification.multilabel Module
- deeplay.applications.detection.lodestar.dataset Module
- deeplay.applications.detection.lodestar.lodestar Module
- deeplay.applications.detection.lodestar.transforms Module
- deeplay.applications.regression.regressor Module
- deeplay.blocks.base Module
- deeplay.blocks.block Module
- deeplay.blocks.la Module
- deeplay.blocks.lan Module
- deeplay.blocks.land Module
- deeplay.blocks.lanu Module
- deeplay.blocks.ls Module
- deeplay.blocks.plan Module
- deeplay.blocks.recurrentblock Module
- deeplay.blocks.residual Module
- deeplay.blocks.sequential Module
- deeplay.blocks.conv.conv2d Module
- deeplay.blocks.linear.linear Module
- deeplay.blocks.sequence.sequence1d Module
- deeplay.callbacks.history Module
- deeplay.callbacks.progress Module
- deeplay.components.dict Module
- deeplay.components.mlp Module
- deeplay.components.rnn Module
- deeplay.components.cnn.cnn Module
- deeplay.components.cnn.encdec Module
- deeplay.components.diffusion.attention_unet Module
- deeplay.components.gnn.tpu Module
- deeplay.components.gnn.gcn.gcn Module
- deeplay.components.gnn.gcn.normalization Module
- deeplay.components.gnn.mpn.cla Module
- deeplay.components.gnn.mpn.ct Module
- deeplay.components.gnn.mpn.ldw Module
- deeplay.components.gnn.mpn.mpn Module
- deeplay.components.gnn.mpn.propagation Module
- deeplay.components.gnn.mpn.rmpn Module
- deeplay.components.gnn.mpn.transformation Module
- deeplay.components.gnn.mpn.update Module
- deeplay.components.transformer.enc Module
- deeplay.components.transformer.ldsn Module
- deeplay.components.transformer.pemb Module
- deeplay.components.transformer.satt Module
- deeplay.external.external Module
- deeplay.external.layer Module
- deeplay.external.optimizers.adam Module
- deeplay.external.optimizers.optimizer Module
- deeplay.external.optimizers.rmsprop Module
- deeplay.external.optimizers.sgd Module
- deeplay.initializers.constant Module
- deeplay.initializers.initializer Module
- deeplay.initializers.kaiming Module
- deeplay.initializers.normal Module
- deeplay.models.recurrentmodel Module
- deeplay.models.visiontransformer Module
- deeplay.models.backbones.resnet18 Module
- deeplay.models.discriminators.cyclegan Module
- deeplay.models.discriminators.dcgan Module
- deeplay.models.generators.cyclegan Module
- deeplay.models.generators.dcgan Module
- deeplay.models.gnn.gtoeMAGIK Module
- deeplay.models.gnn.gtoempm Module
- deeplay.models.gnn.gtogmpm Module
- deeplay.models.gnn.gtonmpm Module
- deeplay.models.gnn.mpm Module
- deeplay.models.mlp.sized Module
- deeplay.ops.logs Module
- deeplay.ops.merge Module
- deeplay.ops.shape Module
- deeplay.ops.attention.cross Module
- deeplay.ops.attention.self Module