torch.nn

These are the basic building block for graphs

Parameter

A kind of Tensor that is to be considered a module parameter.

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.

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.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.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.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 element-wise function:
nn.Hardshrink	Applies the hard shrinkage function element-wise:
nn.Hardsigmoid	Applies the element-wise function:
nn.Hardtanh	Applies the HardTanh function element-wise
nn.Hardswish	Applies the hardswish function, element-wise, as described in the paper:
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.
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.SiLU	Applies the silu function, element-wise.
nn.Sigmoid	Applies the element-wise function:
nn.Softplus	Applies the element-wise function:
nn.Softshrink	Applies the soft shrinkage function elementwise:
nn.Softsign	Applies the element-wise function:
nn.Tanh	Applies the element-wise function:
nn.Tanhshrink	Applies the element-wise function:
nn.Threshold	Thresholds each element of the input Tensor.

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 (a mini-batch of 1D inputs with optional additional channel dimension) 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.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 3D input (a mini-batch of 1D inputs with optional additional channel dimension) 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.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: $y = x_1^T A x_2 + b$

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.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.

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 batchwise pairwise distance between vectors $v_1$ , $v_2$ using the p-norm:

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 combines nn.LogSoftmax() and nn.NLLLoss() in one single class.
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.KLDivLoss	The `Kullback-Leibler divergence`_ Loss
nn.BCELoss	Creates a criterion that measures the Binary Cross Entropy between the target and the output:
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 Tensors, and a label 1D mini-batch 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.SmoothL1Loss	Creates a criterion that uses a squared term if the absolute element-wise error falls below 1 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 $0$ .

Vision Layers

nn.PixelShuffle	Rearranges elements in a tensor of shape $(*, C \times r^2, H, W)$ to a tensor of shape $(*, C, H \times r, W \times r)$ .
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.

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 L``n``-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.

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.

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.