.. role:: hidden :class: hidden-section torch.nn =================================== These are the basic building block for graphs .. contents:: torch.nn :depth: 2 :local: :backlinks: top .. currentmodule:: torch.nn .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst ~parameter.Parameter Containers ---------------------------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst Module Sequential ModuleList ModuleDict ParameterList ParameterDict .. currentmodule:: torch Convolution Layers ---------------------------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.Conv1d nn.Conv2d nn.Conv3d nn.ConvTranspose1d nn.ConvTranspose2d nn.ConvTranspose3d nn.Unfold nn.Fold Pooling layers ---------------------------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.MaxPool1d nn.MaxPool2d nn.MaxPool3d nn.MaxUnpool1d nn.MaxUnpool2d nn.MaxUnpool3d nn.AvgPool1d nn.AvgPool2d nn.AvgPool3d nn.FractionalMaxPool2d nn.LPPool1d nn.LPPool2d nn.AdaptiveMaxPool1d nn.AdaptiveMaxPool2d nn.AdaptiveMaxPool3d nn.AdaptiveAvgPool1d nn.AdaptiveAvgPool2d nn.AdaptiveAvgPool3d Padding Layers -------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.ReflectionPad1d nn.ReflectionPad2d nn.ReplicationPad1d nn.ReplicationPad2d nn.ReplicationPad3d nn.ZeroPad2d nn.ConstantPad1d nn.ConstantPad2d nn.ConstantPad3d Non-linear Activations (weighted sum, nonlinearity) --------------------------------------------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.ELU nn.Hardshrink nn.Hardsigmoid nn.Hardtanh nn.Hardswish nn.LeakyReLU nn.LogSigmoid nn.MultiheadAttention nn.PReLU nn.ReLU nn.ReLU6 nn.RReLU nn.SELU nn.CELU nn.GELU nn.SiLU nn.Sigmoid nn.Softplus nn.Softshrink nn.Softsign nn.Tanh nn.Tanhshrink nn.Threshold Non-linear Activations (other) ------------------------------ .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.Softmin nn.Softmax nn.Softmax2d nn.LogSoftmax nn.AdaptiveLogSoftmaxWithLoss Normalization Layers ---------------------------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.BatchNorm1d nn.BatchNorm2d nn.BatchNorm3d nn.GroupNorm nn.SyncBatchNorm nn.InstanceNorm1d nn.InstanceNorm2d nn.InstanceNorm3d nn.LayerNorm nn.LocalResponseNorm Recurrent Layers ---------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.RNNBase nn.RNN nn.LSTM nn.GRU nn.RNNCell nn.LSTMCell nn.GRUCell Transformer Layers ---------------------------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.Transformer nn.TransformerEncoder nn.TransformerDecoder nn.TransformerEncoderLayer nn.TransformerDecoderLayer Linear Layers ---------------------------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.Identity nn.Linear nn.Bilinear Dropout Layers -------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.Dropout nn.Dropout2d nn.Dropout3d nn.AlphaDropout Sparse Layers ------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.Embedding nn.EmbeddingBag Distance Functions ------------------ .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.CosineSimilarity nn.PairwiseDistance Loss Functions -------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.L1Loss nn.MSELoss nn.CrossEntropyLoss nn.CTCLoss nn.NLLLoss nn.PoissonNLLLoss nn.KLDivLoss nn.BCELoss nn.BCEWithLogitsLoss nn.MarginRankingLoss nn.HingeEmbeddingLoss nn.MultiLabelMarginLoss nn.SmoothL1Loss nn.SoftMarginLoss nn.MultiLabelSoftMarginLoss nn.CosineEmbeddingLoss nn.MultiMarginLoss nn.TripletMarginLoss Vision Layers ---------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.PixelShuffle nn.Upsample nn.UpsamplingNearest2d nn.UpsamplingBilinear2d DataParallel Layers (multi-GPU, distributed) -------------------------------------------- .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst nn.DataParallel nn.parallel.DistributedDataParallel Utilities --------- From the ``torch.nn.utils`` module .. currentmodule:: torch.nn.utils .. autosummary:: :toctree: generated :nosignatures: clip_grad_norm_ clip_grad_value_ parameters_to_vector vector_to_parameters .. autosummary:: :toctree: generated :nosignatures: :template: classtemplate.rst prune.BasePruningMethod .. autosummary:: :toctree: generated :nosignatures: prune.PruningContainer prune.Identity prune.RandomUnstructured prune.L1Unstructured prune.RandomStructured prune.LnStructured prune.CustomFromMask prune.identity prune.random_unstructured prune.l1_unstructured prune.random_structured prune.ln_structured prune.global_unstructured prune.custom_from_mask prune.remove prune.is_pruned weight_norm remove_weight_norm spectral_norm remove_spectral_norm Utility functions in other modules .. currentmodule:: torch .. autosummary:: :toctree: generated :nosignatures: nn.utils.rnn.PackedSequence nn.utils.rnn.pack_padded_sequence nn.utils.rnn.pad_packed_sequence nn.utils.rnn.pad_sequence nn.utils.rnn.pack_sequence nn.Flatten Quantized Functions -------------------- Quantization refers to techniques for performing computations and storing tensors at lower bitwidths than floating point precision. PyTorch supports both per tensor and per channel asymmetric linear quantization. To learn more how to use quantized functions in PyTorch, please refer to the :ref:`quantization-doc` documentation.