torch.nn

These are the basic building blocks for graphs:

Parameter	A kind of Tensor that is to be considered a module parameter.
UninitializedParameter	A parameter that is not initialized.
UninitializedBuffer	A buffer that is not initialized.

Containers

Module	Base class for all neural network modules.
Sequential	A sequential container.
ModuleList	Holds submodules in a list.
ModuleDict	Holds submodules in a dictionary.
ParameterList	Holds parameters in a list.
ParameterDict	Holds parameters in a dictionary.

Global Hooks For Module

register_module_forward_pre_hook	Registers a forward pre-hook common to all modules.
register_module_forward_hook	Registers a global forward hook for all the modules
register_module_backward_hook	Registers a backward hook common to all the modules.
register_module_full_backward_hook	Registers a backward hook common to all the modules.

Convolution Layers

nn.Conv1d	Applies a 1D convolution over an input signal composed of several input planes.
nn.Conv2d	Applies a 2D convolution over an input signal composed of several input planes.
nn.Conv3d	Applies a 3D convolution over an input signal composed of several input planes.
nn.ConvTranspose1d	Applies a 1D transposed convolution operator over an input image composed of several input planes.
nn.ConvTranspose2d	Applies a 2D transposed convolution operator over an input image composed of several input planes.
nn.ConvTranspose3d	Applies a 3D transposed convolution operator over an input image composed of several input planes.
nn.LazyConv1d	A torch.nn.Conv1d module with lazy initialization of the in_channels argument of the Conv1d that is inferred from the input.size(1).
nn.LazyConv2d	A torch.nn.Conv2d module with lazy initialization of the in_channels argument of the Conv2d that is inferred from the input.size(1).
nn.LazyConv3d	A torch.nn.Conv3d module with lazy initialization of the in_channels argument of the Conv3d that is inferred from the input.size(1).
nn.LazyConvTranspose1d	A torch.nn.ConvTranspose1d module with lazy initialization of the in_channels argument of the ConvTranspose1d that is inferred from the input.size(1).
nn.LazyConvTranspose2d	A torch.nn.ConvTranspose2d module with lazy initialization of the in_channels argument of the ConvTranspose2d that is inferred from the input.size(1).
nn.LazyConvTranspose3d	A torch.nn.ConvTranspose3d module with lazy initialization of the in_channels argument of the ConvTranspose3d that is inferred from the input.size(1).
nn.Unfold	Extracts sliding local blocks from a batched input tensor.
nn.Fold	Combines an array of sliding local blocks into a large containing tensor.

Pooling layers

nn.MaxPool1d	Applies a 1D max pooling over an input signal composed of several input planes.
nn.MaxPool2d	Applies a 2D max pooling over an input signal composed of several input planes.
nn.MaxPool3d	Applies a 3D max pooling over an input signal composed of several input planes.
nn.MaxUnpool1d	Computes a partial inverse of MaxPool1d.
nn.MaxUnpool2d	Computes a partial inverse of MaxPool2d.
nn.MaxUnpool3d	Computes a partial inverse of MaxPool3d.
nn.AvgPool1d	Applies a 1D average pooling over an input signal composed of several input planes.
nn.AvgPool2d	Applies a 2D average pooling over an input signal composed of several input planes.
nn.AvgPool3d	Applies a 3D average pooling over an input signal composed of several input planes.
nn.FractionalMaxPool2d	Applies a 2D fractional max pooling over an input signal composed of several input planes.
nn.FractionalMaxPool3d	Applies a 3D fractional max pooling over an input signal composed of several input planes.
nn.LPPool1d	Applies a 1D power-average pooling over an input signal composed of several input planes.
nn.LPPool2d	Applies a 2D power-average pooling over an input signal composed of several input planes.
nn.AdaptiveMaxPool1d	Applies a 1D adaptive max pooling over an input signal composed of several input planes.
nn.AdaptiveMaxPool2d	Applies a 2D adaptive max pooling over an input signal composed of several input planes.
nn.AdaptiveMaxPool3d	Applies a 3D adaptive max pooling over an input signal composed of several input planes.
nn.AdaptiveAvgPool1d	Applies a 1D adaptive average pooling over an input signal composed of several input planes.
nn.AdaptiveAvgPool2d	Applies a 2D adaptive average pooling over an input signal composed of several input planes.
nn.AdaptiveAvgPool3d	Applies a 3D adaptive average pooling over an input signal composed of several input planes.

Padding Layers

nn.ReflectionPad1d	Pads the input tensor using the reflection of the input boundary.
nn.ReflectionPad2d	Pads the input tensor using the reflection of the input boundary.
nn.ReflectionPad3d	Pads the input tensor using the reflection of the input boundary.
nn.ReplicationPad1d	Pads the input tensor using replication of the input boundary.
nn.ReplicationPad2d	Pads the input tensor using replication of the input boundary.
nn.ReplicationPad3d	Pads the input tensor using replication of the input boundary.
nn.ZeroPad2d	Pads the input tensor boundaries with zero.
nn.ConstantPad1d	Pads the input tensor boundaries with a constant value.
nn.ConstantPad2d	Pads the input tensor boundaries with a constant value.
nn.ConstantPad3d	Pads the input tensor boundaries with a constant value.

Non-linear Activations (weighted sum, nonlinearity)

nn.ELU	Applies the Exponential Linear Unit (ELU) function, element-wise, as described in the paper: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).
nn.Hardshrink	Applies the Hard Shrinkage (Hardshrink) function element-wise.
nn.Hardsigmoid	Applies the Hardsigmoid function element-wise.
nn.Hardtanh	Applies the HardTanh function element-wise.
nn.Hardswish	Applies the Hardswish function, element-wise, as described in the paper: Searching for MobileNetV3.
nn.LeakyReLU	Applies the element-wise function:
nn.LogSigmoid	Applies the element-wise function:
nn.MultiheadAttention	Allows the model to jointly attend to information from different representation subspaces as described in the paper: Attention Is All You Need.
nn.PReLU	Applies the element-wise function:
nn.ReLU	Applies the rectified linear unit function element-wise:
nn.ReLU6	Applies the element-wise function:
nn.RReLU	Applies the randomized leaky rectified liner unit function, element-wise, as described in the paper:
nn.SELU	Applied element-wise, as:
nn.CELU	Applies the element-wise function:
nn.GELU	Applies the Gaussian Error Linear Units function:
nn.Sigmoid	Applies the element-wise function:
nn.SiLU	Applies the Sigmoid Linear Unit (SiLU) function, element-wise.
nn.Mish	Applies the Mish function, element-wise.
nn.Softplus	Applies the Softplus function $\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))$ element-wise.
nn.Softshrink	Applies the soft shrinkage function elementwise:
nn.Softsign	Applies the element-wise function:
nn.Tanh	Applies the Hyperbolic Tangent (Tanh) function element-wise.
nn.Tanhshrink	Applies the element-wise function:
nn.Threshold	Thresholds each element of the input Tensor.
nn.GLU	Applies the gated linear unit function ${GLU}(a, b)= a \otimes \sigma(b)$ where $a$ is the first half of the input matrices and $b$ is the second half.

Non-linear Activations (other)

nn.Softmin	Applies the Softmin function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0, 1] and sum to 1.
nn.Softmax	Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0,1] and sum to 1.
nn.Softmax2d	Applies SoftMax over features to each spatial location.
nn.LogSoftmax	Applies the $\log(\text{Softmax}(x))$ function to an n-dimensional input Tensor.
nn.AdaptiveLogSoftmaxWithLoss	Efficient softmax approximation as described in Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou.

Normalization Layers

nn.BatchNorm1d	Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
nn.BatchNorm2d	Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
nn.BatchNorm3d	Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
nn.LazyBatchNorm1d	A torch.nn.BatchNorm1d module with lazy initialization of the num_features argument of the BatchNorm1d that is inferred from the input.size(1).
nn.LazyBatchNorm2d	A torch.nn.BatchNorm2d module with lazy initialization of the num_features argument of the BatchNorm2d that is inferred from the input.size(1).
nn.LazyBatchNorm3d	A torch.nn.BatchNorm3d module with lazy initialization of the num_features argument of the BatchNorm3d that is inferred from the input.size(1).
nn.GroupNorm	Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization
nn.SyncBatchNorm	Applies Batch Normalization over a N-Dimensional input (a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
nn.InstanceNorm1d	Applies Instance Normalization over a 2D (unbatched) or 3D (batched) input as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.
nn.InstanceNorm2d	Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.
nn.InstanceNorm3d	Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.
nn.LazyInstanceNorm1d	A torch.nn.InstanceNorm1d module with lazy initialization of the num_features argument of the InstanceNorm1d that is inferred from the input.size(1).
nn.LazyInstanceNorm2d	A torch.nn.InstanceNorm2d module with lazy initialization of the num_features argument of the InstanceNorm2d that is inferred from the input.size(1).
nn.LazyInstanceNorm3d	A torch.nn.InstanceNorm3d module with lazy initialization of the num_features argument of the InstanceNorm3d that is inferred from the input.size(1).
nn.LayerNorm	Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization
nn.LocalResponseNorm	Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension.

Recurrent Layers

nn.RNNBase
nn.RNN	Applies a multi-layer Elman RNN with $\tanh$ or $\text{ReLU}$ non-linearity to an input sequence.
nn.LSTM	Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.
nn.GRU	Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
nn.RNNCell	An Elman RNN cell with tanh or ReLU non-linearity.
nn.LSTMCell	A long short-term memory (LSTM) cell.
nn.GRUCell	A gated recurrent unit (GRU) cell

Transformer Layers

nn.Transformer	A transformer model.
nn.TransformerEncoder	TransformerEncoder is a stack of N encoder layers.
nn.TransformerDecoder	TransformerDecoder is a stack of N decoder layers
nn.TransformerEncoderLayer	TransformerEncoderLayer is made up of self-attn and feedforward network.
nn.TransformerDecoderLayer	TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.

Linear Layers

nn.Identity	A placeholder identity operator that is argument-insensitive.
nn.Linear	Applies a linear transformation to the incoming data: $y = xA^T + b$
nn.Bilinear	Applies a bilinear transformation to the incoming data: $y = x_1^T A x_2 + b$
nn.LazyLinear	A torch.nn.Linear module where in_features is inferred.

Dropout Layers

nn.Dropout	During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution.
nn.Dropout1d	Randomly zero out entire channels (a channel is a 1D feature map, e.g., the $j$-th channel of the $i$-th sample in the batched input is a 1D tensor $\text{input}[i, j]$).
nn.Dropout2d	Randomly zero out entire channels (a channel is a 2D feature map, e.g., the $j$-th channel of the $i$-th sample in the batched input is a 2D tensor $\text{input}[i, j]$).
nn.Dropout3d	Randomly zero out entire channels (a channel is a 3D feature map, e.g., the $j$-th channel of the $i$-th sample in the batched input is a 3D tensor $\text{input}[i, j]$).
nn.AlphaDropout	Applies Alpha Dropout over the input.
nn.FeatureAlphaDropout	Randomly masks out entire channels (a channel is a feature map, e.g.

Sparse Layers

nn.Embedding	A simple lookup table that stores embeddings of a fixed dictionary and size.
nn.EmbeddingBag	Computes sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.

Distance Functions

nn.CosineSimilarity	Returns cosine similarity between $x_1$ and $x_2$, computed along dim.
nn.PairwiseDistance	Computes the pairwise distance between input vectors, or between columns of input matrices.

Loss Functions

nn.L1Loss	Creates a criterion that measures the mean absolute error (MAE) between each element in the input $x$ and target $y$.
nn.MSELoss	Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input $x$ and target $y$.
nn.CrossEntropyLoss	This criterion computes the cross entropy loss between input logits and target.
nn.CTCLoss	The Connectionist Temporal Classification loss.
nn.NLLLoss	The negative log likelihood loss.
nn.PoissonNLLLoss	Negative log likelihood loss with Poisson distribution of target.
nn.GaussianNLLLoss	Gaussian negative log likelihood loss.
nn.KLDivLoss	The Kullback-Leibler divergence loss.
nn.BCELoss	Creates a criterion that measures the Binary Cross Entropy between the target and the input probabilities:
nn.BCEWithLogitsLoss	This loss combines a Sigmoid layer and the BCELoss in one single class.
nn.MarginRankingLoss	Creates a criterion that measures the loss given inputs $x1$, $x2$, two 1D mini-batch or 0D Tensors, and a label 1D mini-batch or 0D Tensor $y$ (containing 1 or -1).
nn.HingeEmbeddingLoss	Measures the loss given an input tensor $x$ and a labels tensor $y$ (containing 1 or -1).
nn.MultiLabelMarginLoss	Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input $x$ (a 2D mini-batch Tensor) and output $y$ (which is a 2D Tensor of target class indices).
nn.HuberLoss	Creates a criterion that uses a squared term if the absolute element-wise error falls below delta and a delta-scaled L1 term otherwise.
nn.SmoothL1Loss	Creates a criterion that uses a squared term if the absolute element-wise error falls below beta and an L1 term otherwise.
nn.SoftMarginLoss	Creates a criterion that optimizes a two-class classification logistic loss between input tensor $x$ and target tensor $y$ (containing 1 or -1).
nn.MultiLabelSoftMarginLoss	Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input $x$ and target $y$ of size $(N, C)$.
nn.CosineEmbeddingLoss	Creates a criterion that measures the loss given input tensors $x_1$, $x_2$ and a Tensor label $y$ with values 1 or -1.
nn.MultiMarginLoss	Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input $x$ (a 2D mini-batch Tensor) and output $y$ (which is a 1D tensor of target class indices, $0 \leq y \leq \text{x.size}(1)-1$):
nn.TripletMarginLoss	Creates a criterion that measures the triplet loss given an input tensors $x1$, $x2$, $x3$ and a margin with a value greater than $0$.
nn.TripletMarginWithDistanceLoss	Creates a criterion that measures the triplet loss given input tensors $a$, $p$, and $n$ (representing anchor, positive, and negative examples, respectively), and a nonnegative, real-valued function ("distance function") used to compute the relationship between the anchor and positive example ("positive distance") and the anchor and negative example ("negative distance").

Vision Layers

nn.PixelShuffle	Rearranges elements in a tensor of shape $(*, C \times r^2, H, W)$ to a tensor of shape $(*, C, H \times r, W \times r)$, where r is an upscale factor.
nn.PixelUnshuffle	Reverses the PixelShuffle operation by rearranging elements in a tensor of shape $(*, C, H \times r, W \times r)$ to a tensor of shape $(*, C \times r^2, H, W)$, where r is a downscale factor.
nn.Upsample	Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
nn.UpsamplingNearest2d	Applies a 2D nearest neighbor upsampling to an input signal composed of several input channels.
nn.UpsamplingBilinear2d	Applies a 2D bilinear upsampling to an input signal composed of several input channels.

Shuffle Layers

nn.ChannelShuffle

Divide the channels in a tensor of shape $(*, C , H, W)$ into g groups and rearrange them as $(*, C \frac g, g, H, W)$, while keeping the original tensor shape.

DataParallel Layers (multi-GPU, distributed)

nn.DataParallel	Implements data parallelism at the module level.
nn.parallel.DistributedDataParallel	Implements distributed data parallelism that is based on torch.distributed package at the module level.

Utilities

From the torch.nn.utils module

clip_grad_norm_	Clips gradient norm of an iterable of parameters.
clip_grad_value_	Clips gradient of an iterable of parameters at specified value.
parameters_to_vector	Convert parameters to one vector
vector_to_parameters	Convert one vector to the parameters
prune.BasePruningMethod	Abstract base class for creation of new pruning techniques.

prune.PruningContainer	Container holding a sequence of pruning methods for iterative pruning.
prune.Identity	Utility pruning method that does not prune any units but generates the pruning parametrization with a mask of ones.
prune.RandomUnstructured	Prune (currently unpruned) units in a tensor at random.
prune.L1Unstructured	Prune (currently unpruned) units in a tensor by zeroing out the ones with the lowest L1-norm.
prune.RandomStructured	Prune entire (currently unpruned) channels in a tensor at random.
prune.LnStructured	Prune entire (currently unpruned) channels in a tensor based on their Ln-norm.
prune.CustomFromMask
prune.identity	Applies pruning reparametrization to the tensor corresponding to the parameter called name in module without actually pruning any units.
prune.random_unstructured	Prunes tensor corresponding to parameter called name in module by removing the specified amount of (currently unpruned) units selected at random.
prune.l1_unstructured	Prunes tensor corresponding to parameter called name in module by removing the specified amount of (currently unpruned) units with the lowest L1-norm.
prune.random_structured	Prunes tensor corresponding to parameter called name in module by removing the specified amount of (currently unpruned) channels along the specified dim selected at random.
prune.ln_structured	Prunes tensor corresponding to parameter called name in module by removing the specified amount of (currently unpruned) channels along the specified dim with the lowest Ln-norm.
prune.global_unstructured	Globally prunes tensors corresponding to all parameters in parameters by applying the specified pruning_method.
prune.custom_from_mask	Prunes tensor corresponding to parameter called name in module by applying the pre-computed mask in mask.
prune.remove	Removes the pruning reparameterization from a module and the pruning method from the forward hook.
prune.is_pruned	Check whether module is pruned by looking for forward_pre_hooks in its modules that inherit from the BasePruningMethod.
weight_norm	Applies weight normalization to a parameter in the given module.
remove_weight_norm	Removes the weight normalization reparameterization from a module.
spectral_norm	Applies spectral normalization to a parameter in the given module.
remove_spectral_norm	Removes the spectral normalization reparameterization from a module.
skip_init	Given a module class object and args / kwargs, instantiates the module without initializing parameters / buffers.

Parametrizations implemented using the new parametrization functionality in torch.nn.utils.parameterize.register_parametrization().

parametrizations.orthogonal	Applies an orthogonal or unitary parametrization to a matrix or a batch of matrices.
parametrizations.spectral_norm	Applies spectral normalization to a parameter in the given module.

Utility functions to parametrize Tensors on existing Modules. Note that these functions can be used to parametrize a given Parameter or Buffer given a specific function that maps from an input space to the parametrized space. They are not parameterizations that would transform an object into a parameter. See the Parametrizations tutorial for more information on how to implement your own parametrizations.

parametrize.register_parametrization	Adds a parametrization to a tensor in a module.
parametrize.remove_parametrizations	Removes the parametrizations on a tensor in a module.
parametrize.cached	Context manager that enables the caching system within parametrizations registered with register_parametrization().
parametrize.is_parametrized	Returns True if module has an active parametrization.

parametrize.ParametrizationList

A sequential container that holds and manages the original or original0, original1, .

Utility functions to calls a given Module in a stateless manner.

stateless.functional_call

Performs a functional call on the module by replacing the module parameters and buffers with the provided ones.

Utility functions in other modules

nn.utils.rnn.PackedSequence	Holds the data and list of batch_sizes of a packed sequence.
nn.utils.rnn.pack_padded_sequence	Packs a Tensor containing padded sequences of variable length.
nn.utils.rnn.pad_packed_sequence	Pads a packed batch of variable length sequences.
nn.utils.rnn.pad_sequence	Pad a list of variable length Tensors with padding_value
nn.utils.rnn.pack_sequence	Packs a list of variable length Tensors

nn.Flatten	Flattens a contiguous range of dims into a tensor.
nn.Unflatten	Unflattens a tensor dim expanding it to a desired shape.

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 Quantization documentation.

Lazy Modules Initialization

nn.modules.lazy.LazyModuleMixin

A mixin for modules that lazily initialize parameters, also known as "lazy modules."