Function reference • torch

Tensor creation utilities

torch_empty(): Empty

torch_arange(): Arange

torch_eye(): Eye

torch_full(): Full

torch_linspace(): Linspace

torch_logspace(): Logspace

torch_ones(): Ones

torch_rand(): Rand

torch_randint(): Randint

torch_randn(): Randn

torch_randperm(): Randperm

torch_zeros(): Zeros

torch_empty_like(): Empty_like

torch_full_like(): Full_like

torch_ones_like(): Ones_like

torch_rand_like(): Rand_like

torch_randint_like(): Randint_like

torch_randn_like(): Randn_like

torch_zeros_like(): Zeros_like

as_array(): Converts to array

torch_tensor_from_buffer() buffer_from_torch_tensor(): Creates a tensor from a buffer of memory

Tensor attributes

torch_set_default_dtype() torch_get_default_dtype(): Gets and sets the default floating point dtype.

is_torch_device(): Checks if object is a device

is_torch_dtype(): Check if object is a torch data type

torch_float32() torch_float() torch_float64() torch_double() torch_cfloat32() torch_chalf() torch_cfloat() torch_cfloat64() torch_cdouble() torch_cfloat128() torch_float16() torch_half() torch_uint8() torch_int8() torch_int16() torch_short() torch_int32() torch_int() torch_int64() torch_long() torch_bool() torch_quint8() torch_qint8() torch_qint32(): Torch data types

torch_finfo(): Floating point type info

torch_iinfo(): Integer type info

torch_per_channel_affine() torch_per_tensor_affine() torch_per_channel_symmetric() torch_per_tensor_symmetric(): Creates the corresponding Scheme object

torch_reduction_sum() torch_reduction_mean() torch_reduction_none(): Creates the reduction objet

is_torch_layout(): Check if an object is a torch layout.

is_torch_memory_format(): Check if an object is a memory format

is_torch_qscheme(): Checks if an object is a QScheme

is_undefined_tensor(): Checks if a tensor is undefined

Serialization

load_state_dict(): Load a state dict file

torch_load(): Loads a saved object

torch_save(): Saves an object to a disk file.

torch_serialize(): Serialize a torch object returning a raw object

clone_module(): Clone a torch module.

Mathematical operations on tensors

torch_set_num_threads() torch_set_num_interop_threads() torch_get_num_interop_threads() torch_get_num_threads(): Number of threads

torch_abs(): Abs

torch_absolute(): Absolute

torch_acos(): Acos

torch_acosh(): Acosh

torch_adaptive_avg_pool1d(): Adaptive_avg_pool1d

torch_add(): Add

torch_addbmm(): Addbmm

torch_addcdiv(): Addcdiv

torch_addcmul(): Addcmul

torch_addmm(): Addmm

torch_addmv(): Addmv

torch_addr(): Addr

torch_allclose(): Allclose

torch_amax(): Amax

torch_amin(): Amin

torch_angle(): Angle

torch_arccos(): Arccos

torch_arccosh(): Arccosh

torch_arcsin(): Arcsin

torch_arcsinh(): Arcsinh

torch_arctan(): Arctan

torch_arctanh(): Arctanh

torch_argmax: Argmax

torch_argmin: Argmin

torch_argsort(): Argsort

torch_as_strided(): As_strided

torch_asin(): Asin

torch_asinh(): Asinh

torch_atan(): Atan

torch_atan2(): Atan2

torch_atanh(): Atanh

torch_atleast_1d(): Atleast_1d

torch_atleast_2d(): Atleast_2d

torch_atleast_3d(): Atleast_3d

torch_avg_pool1d(): Avg_pool1d

torch_baddbmm(): Baddbmm

torch_bartlett_window(): Bartlett_window

torch_bernoulli(): Bernoulli

torch_bincount: Bincount

torch_bitwise_and(): Bitwise_and

torch_bitwise_not(): Bitwise_not

torch_bitwise_or(): Bitwise_or

torch_bitwise_xor(): Bitwise_xor

torch_blackman_window(): Blackman_window

torch_block_diag(): Block_diag

torch_bmm(): Bmm

torch_broadcast_tensors(): Broadcast_tensors

torch_bucketize(): Bucketize

torch_can_cast(): Can_cast

torch_cartesian_prod(): Cartesian_prod

torch_cat(): Cat

torch_cdist(): Cdist

torch_ceil(): Ceil

torch_celu(): Celu

torch_celu_(): Celu_

torch_chain_matmul(): Chain_matmul

torch_channel_shuffle(): Channel_shuffle

torch_cholesky(): Cholesky

torch_cholesky_inverse(): Cholesky_inverse

torch_cholesky_solve(): Cholesky_solve

torch_chunk(): Chunk

torch_clamp(): Clamp

torch_clip(): Clip

torch_clone(): Clone

torch_combinations(): Combinations

torch_complex(): Complex

torch_conj(): Conj

torch_conv1d(): Conv1d

torch_conv2d(): Conv2d

torch_conv3d(): Conv3d

torch_conv_tbc(): Conv_tbc

torch_conv_transpose1d(): Conv_transpose1d

torch_conv_transpose2d(): Conv_transpose2d

torch_conv_transpose3d(): Conv_transpose3d

torch_cos(): Cos

torch_cosh(): Cosh

torch_cosine_similarity(): Cosine_similarity

torch_count_nonzero(): Count_nonzero

torch_cross(): Cross

torch_cummax(): Cummax

torch_cummin(): Cummin

torch_cumprod(): Cumprod

torch_cumsum(): Cumsum

torch_deg2rad(): Deg2rad

torch_dequantize(): Dequantize

torch_det(): Det

torch_device(): Create a Device object

torch_diag(): Diag

torch_diag_embed(): Diag_embed

torch_diagflat(): Diagflat

torch_diagonal(): Diagonal

torch_diff(): Computes the n-th forward difference along the given dimension.

torch_digamma(): Digamma

torch_dist(): Dist

torch_div(): Div

torch_divide(): Divide

torch_dot(): Dot

torch_dstack(): Dstack

torch_eig: Eig

torch_einsum(): Einsum

torch_empty_strided(): Empty_strided

torch_eq(): Eq

torch_equal(): Equal

torch_erf(): Erf

torch_erfc(): Erfc

torch_erfinv(): Erfinv

torch_exp(): Exp

torch_exp2(): Exp2

torch_expm1(): Expm1

torch_fft_fft(): Fft

torch_fft_fftfreq(): fftfreq

torch_fft_ifft(): Ifft

torch_fft_irfft(): Irfft

torch_fft_rfft(): Rfft

torch_fix(): Fix

torch_flatten(): Flatten

torch_flip(): Flip

torch_fliplr(): Fliplr

torch_flipud(): Flipud

torch_floor(): Floor

torch_floor_divide(): Floor_divide

torch_fmod(): Fmod

torch_frac(): Frac

torch_gather(): Gather

torch_gcd(): Gcd

torch_ge(): Ge

torch_generator(): Create a Generator object

torch_geqrf(): Geqrf

torch_ger(): Ger

torch_get_rng_state() torch_set_rng_state() cuda_get_rng_state() cuda_set_rng_state(): RNG state management

torch_greater(): Greater

torch_greater_equal(): Greater_equal

torch_gt(): Gt

torch_hamming_window(): Hamming_window

torch_hann_window(): Hann_window

torch_heaviside(): Heaviside

torch_histc(): Histc

torch_hstack(): Hstack

torch_hypot(): Hypot

torch_i0(): I0

torch_imag(): Imag

torch_index(): Index torch tensors

torch_index_put(): Modify values selected by indices.

torch_index_put_(): In-place version of torch_index_put.

torch_index_select(): Index_select

torch_install_path(): A simple exported version of install_path Returns the torch installation path.

torch_inverse(): Inverse

torch_is_complex(): Is_complex

torch_is_floating_point(): Is_floating_point

torch_is_installed(): Verifies if torch is installed

torch_is_nonzero(): Is_nonzero

torch_isclose(): Isclose

torch_isfinite(): Isfinite

torch_isinf(): Isinf

torch_isnan(): Isnan

torch_isneginf(): Isneginf

torch_isposinf(): Isposinf

torch_isreal(): Isreal

torch_istft(): Istft

torch_kaiser_window(): Kaiser_window

torch_kron(): Kronecker product

torch_kthvalue(): Kthvalue

torch_strided() torch_sparse_coo(): Creates the corresponding layout

torch_lcm(): Lcm

torch_le(): Le

torch_lerp(): Lerp

torch_less(): Less

torch_less_equal(): Less_equal

torch_lgamma(): Lgamma

torch_log(): Log

torch_log10(): Log10

torch_log1p(): Log1p

torch_log2(): Log2

torch_logaddexp(): Logaddexp

torch_logaddexp2(): Logaddexp2

torch_logcumsumexp(): Logcumsumexp

torch_logdet(): Logdet

torch_logical_and(): Logical_and

torch_logical_not: Logical_not

torch_logical_or(): Logical_or

torch_logical_xor(): Logical_xor

torch_logit(): Logit

torch_logsumexp(): Logsumexp

torch_lstsq: Lstsq

torch_lt(): Lt

torch_lu(): LU

torch_lu_solve(): Lu_solve

torch_lu_unpack(): Lu_unpack

torch_manual_seed() local_torch_manual_seed() with_torch_manual_seed(): Sets the seed for generating random numbers.

torch_masked_select(): Masked_select

torch_matmul(): Matmul

torch_matrix_exp(): Matrix_exp

torch_matrix_power(): Matrix_power

torch_matrix_rank: Matrix_rank

torch_max: Max

torch_maximum(): Maximum

torch_mean(): Mean

torch_median(): Median

torch_contiguous_format() torch_preserve_format() torch_channels_last_format(): Memory format

torch_meshgrid(): Meshgrid

torch_min: Min

torch_minimum(): Minimum

torch_mm(): Mm

torch_mode(): Mode

torch_movedim(): Movedim

torch_mul(): Mul

torch_multinomial(): Multinomial

torch_multiply(): Multiply

torch_mv(): Mv

torch_mvlgamma(): Mvlgamma

torch_nanquantile(): Nanquantile

torch_nansum(): Nansum

torch_narrow(): Narrow

torch_ne(): Ne

torch_neg(): Neg

torch_negative(): Negative

torch_nextafter(): Nextafter

torch_nonzero(): Nonzero

torch_norm(): Norm

torch_normal(): Normal

torch_not_equal(): Not_equal

torch_orgqr(): Orgqr

torch_ormqr(): Ormqr

torch_outer(): Outer

torch_pdist(): Pdist

torch_pinverse(): Pinverse

torch_pixel_shuffle(): Pixel_shuffle

torch_poisson(): Poisson

torch_polar(): Polar

torch_polygamma(): Polygamma

torch_pow(): Pow

torch_prod(): Prod

torch_promote_types(): Promote_types

torch_qr(): Qr

torch_quantile(): Quantile

torch_quantize_per_channel(): Quantize_per_channel

torch_quantize_per_tensor(): Quantize_per_tensor

torch_rad2deg(): Rad2deg

torch_range(): Range

torch_real(): Real

torch_reciprocal(): Reciprocal

torch_relu(): Relu

torch_relu_(): Relu_

torch_remainder(): Remainder

torch_renorm(): Renorm

torch_repeat_interleave(): Repeat_interleave

torch_reshape(): Reshape

torch_result_type(): Result_type

torch_roll(): Roll

torch_rot90(): Rot90

torch_round(): Round

torch_rrelu_(): Rrelu_

torch_rsqrt(): Rsqrt

torch_scalar_tensor(): Scalar tensor

torch_searchsorted(): Searchsorted

torch_selu(): Selu

torch_selu_(): Selu_

torch_sgn(): Sgn

torch_sigmoid(): Sigmoid

torch_sign(): Sign

torch_signbit(): Signbit

torch_sin(): Sin

torch_sinh(): Sinh

torch_slogdet(): Slogdet

torch_sort: Sort

torch_sparse_coo_tensor(): Sparse_coo_tensor

torch_split(): Split

torch_sqrt(): Sqrt

torch_square(): Square

torch_squeeze(): Squeeze

torch_stack(): Stack

torch_std(): Std

torch_std_mean(): Std_mean

torch_stft(): Stft

torch_sub(): Sub

torch_subtract(): Subtract

torch_sum(): Sum

torch_svd(): Svd

torch_t(): T

torch_take(): Take

torch_tan(): Tan

torch_tanh(): Tanh

torch_tensor(): Converts R objects to a torch tensor

torch_tensor_from_buffer() buffer_from_torch_tensor(): Creates a tensor from a buffer of memory

torch_tensordot(): Tensordot

torch_threshold_(): Threshold_

torch_topk(): Topk

torch_trace(): Trace

torch_transpose(): Transpose

torch_trapz(): Trapz

torch_triangular_solve(): Triangular_solve

torch_tril(): Tril

torch_tril_indices(): Tril_indices

torch_triu(): Triu

torch_triu_indices(): Triu_indices

torch_true_divide(): TRUE_divide

torch_trunc(): Trunc

torch_unbind(): Unbind

torch_unique_consecutive(): Unique_consecutive

torch_unsafe_chunk(): Unsafe_chunk

torch_unsafe_split(): Unsafe_split

torch_unsqueeze(): Unsqueeze

torch_vander(): Vander

torch_var(): Var

torch_var_mean(): Var_mean

torch_vdot(): Vdot

torch_view_as_complex(): View_as_complex

torch_view_as_real(): View_as_real

torch_vstack(): Vstack

torch_where(): Where

broadcast_all(): Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules:

Neural network modules

nn_adaptive_avg_pool1d(): Applies a 1D adaptive average pooling over an input signal composed of several input planes.

nn_adaptive_avg_pool2d(): Applies a 2D adaptive average pooling over an input signal composed of several input planes.

nn_adaptive_avg_pool3d(): Applies a 3D adaptive average pooling over an input signal composed of several input planes.

nn_adaptive_log_softmax_with_loss(): AdaptiveLogSoftmaxWithLoss module

nn_adaptive_max_pool1d(): Applies a 1D adaptive max pooling over an input signal composed of several input planes.

nn_adaptive_max_pool2d(): Applies a 2D adaptive max pooling over an input signal composed of several input planes.

nn_adaptive_max_pool3d(): Applies a 3D adaptive max pooling over an input signal composed of several input planes.

nn_avg_pool1d(): Applies a 1D average pooling over an input signal composed of several input planes.

nn_avg_pool2d(): Applies a 2D average pooling over an input signal composed of several input planes.

nn_avg_pool3d(): Applies a 3D average pooling over an input signal composed of several input planes.

nn_batch_norm1d(): BatchNorm1D module

nn_batch_norm2d(): BatchNorm2D

nn_batch_norm3d(): BatchNorm3D

nn_bce_loss(): Binary cross entropy loss

nn_bce_with_logits_loss(): BCE with logits loss

nn_bilinear(): Bilinear module

nn_buffer(): Creates a nn_buffer

nn_celu(): CELU module

nn_contrib_sparsemax(): Sparsemax activation

nn_conv1d(): Conv1D module

nn_conv2d(): Conv2D module

nn_conv3d(): Conv3D module

nn_conv_transpose1d(): ConvTranspose1D

nn_conv_transpose2d(): ConvTranpose2D module

nn_conv_transpose3d(): ConvTranpose3D module

nn_cosine_embedding_loss(): Cosine embedding loss

nn_cross_entropy_loss(): CrossEntropyLoss module

nn_ctc_loss(): The Connectionist Temporal Classification loss.

nn_dropout(): Dropout module

nn_dropout2d(): Dropout2D module

nn_dropout3d(): Dropout3D module

nn_elu(): ELU module

nn_embedding(): Embedding module

nn_embedding_bag(): Embedding bag module

nn_flatten(): Flattens a contiguous range of dims into a tensor.

nn_fractional_max_pool2d(): Applies a 2D fractional max pooling over an input signal composed of several input planes.

nn_fractional_max_pool3d(): Applies a 3D fractional max pooling over an input signal composed of several input planes.

nn_gelu(): GELU module

nn_glu(): GLU module

nn_group_norm(): Group normalization

nn_gru(): Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.

nn_hardshrink(): Hardshwink module

nn_hardsigmoid(): Hardsigmoid module

nn_hardswish(): Hardswish module

nn_hardtanh(): Hardtanh module

nn_hinge_embedding_loss(): Hinge embedding loss

nn_identity(): Identity module

nn_init_calculate_gain(): Calculate gain

nn_init_constant_(): Constant initialization

nn_init_dirac_(): Dirac initialization

nn_init_eye_(): Eye initialization

nn_init_kaiming_normal_(): Kaiming normal initialization

nn_init_kaiming_uniform_(): Kaiming uniform initialization

nn_init_normal_(): Normal initialization

nn_init_ones_(): Ones initialization

nn_init_orthogonal_(): Orthogonal initialization

nn_init_sparse_(): Sparse initialization

nn_init_trunc_normal_(): Truncated normal initialization

nn_init_uniform_(): Uniform initialization

nn_init_xavier_normal_(): Xavier normal initialization

nn_init_xavier_uniform_(): Xavier uniform initialization

nn_init_zeros_(): Zeros initialization

nn_kl_div_loss(): Kullback-Leibler divergence loss

nn_l1_loss(): L1 loss

nn_layer_norm(): Layer normalization

nn_leaky_relu(): LeakyReLU module

nn_linear(): Linear module

nn_log_sigmoid(): LogSigmoid module

nn_log_softmax(): LogSoftmax module

nn_lp_pool1d(): Applies a 1D power-average pooling over an input signal composed of several input planes.

nn_lp_pool2d(): Applies a 2D power-average pooling over an input signal composed of several input planes.

nn_lstm(): Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.

nn_margin_ranking_loss(): Margin ranking loss

nn_max_pool1d(): MaxPool1D module

nn_max_pool2d(): MaxPool2D module

nn_max_pool3d(): Applies a 3D max pooling over an input signal composed of several input planes.

nn_max_unpool1d(): Computes a partial inverse of MaxPool1d.

nn_max_unpool2d(): Computes a partial inverse of MaxPool2d.

nn_max_unpool3d(): Computes a partial inverse of MaxPool3d.

nn_module(): Base class for all neural network modules.

nn_module_dict(): Container that allows named values

nn_module_list(): Holds submodules in a list.

nn_mse_loss(): MSE loss

nn_multi_margin_loss(): Multi margin loss

nn_multihead_attention(): MultiHead attention

nn_multilabel_margin_loss(): Multilabel margin loss

nn_multilabel_soft_margin_loss(): Multi label soft margin loss

nn_nll_loss(): Nll loss

nn_pairwise_distance(): Pairwise distance

nn_parameter(): Creates an nn_parameter

nn_poisson_nll_loss(): Poisson NLL loss

nn_prelu(): PReLU module

nn_prune_head(): Prune top layer(s) of a network

nn_relu(): ReLU module

nn_relu6(): ReLu6 module

nn_rnn(): RNN module

nn_rrelu(): RReLU module

nn_selu(): SELU module

nn_sequential(): A sequential container

nn_sigmoid(): Sigmoid module

nn_silu(): Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function.

nn_smooth_l1_loss(): Smooth L1 loss

nn_soft_margin_loss(): Soft margin loss

nn_softmax(): Softmax module

nn_softmax2d(): Softmax2d module

nn_softmin(): Softmin

nn_softplus(): Softplus module

nn_softshrink(): Softshrink module

nn_softsign(): Softsign module

nn_tanh(): Tanh module

nn_tanhshrink(): Tanhshrink module

nn_threshold(): Threshold module

nn_triplet_margin_loss(): Triplet margin loss

nn_triplet_margin_with_distance_loss(): Triplet margin with distance loss

nn_unflatten(): Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential.

nn_upsample(): Upsample module

nn_utils_clip_grad_norm_(): Clips gradient norm of an iterable of parameters.

nn_utils_clip_grad_value_(): Clips gradient of an iterable of parameters at specified value.

nn_utils_rnn_pack_padded_sequence(): Packs a Tensor containing padded sequences of variable length.

nn_utils_rnn_pack_sequence(): Packs a list of variable length Tensors

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_weight_norm: nn_utils_weight_norm

is_nn_module(): Checks if the object is an nn_module

is_nn_parameter(): Checks if an object is a nn_parameter

is_nn_buffer(): Checks if the object is a nn_buffer

Neural networks functional module

nnf_adaptive_avg_pool1d(): Adaptive_avg_pool1d

nnf_adaptive_avg_pool2d(): Adaptive_avg_pool2d

nnf_adaptive_avg_pool3d(): Adaptive_avg_pool3d

nnf_adaptive_max_pool1d(): Adaptive_max_pool1d

nnf_adaptive_max_pool2d(): Adaptive_max_pool2d

nnf_adaptive_max_pool3d(): Adaptive_max_pool3d

nnf_affine_grid(): Affine_grid

nnf_alpha_dropout(): Alpha_dropout

nnf_avg_pool1d(): Avg_pool1d

nnf_avg_pool2d(): Avg_pool2d

nnf_avg_pool3d(): Avg_pool3d

nnf_batch_norm(): Batch_norm

nnf_bilinear(): Bilinear

nnf_binary_cross_entropy(): Binary_cross_entropy

nnf_binary_cross_entropy_with_logits(): Binary_cross_entropy_with_logits

nnf_celu() nnf_celu_(): Celu

nnf_contrib_sparsemax(): Sparsemax

nnf_conv1d(): Conv1d

nnf_conv2d(): Conv2d

nnf_conv3d(): Conv3d

nnf_conv_tbc(): Conv_tbc

nnf_conv_transpose1d(): Conv_transpose1d

nnf_conv_transpose2d(): Conv_transpose2d

nnf_conv_transpose3d(): Conv_transpose3d

nnf_cosine_embedding_loss(): Cosine_embedding_loss

nnf_cosine_similarity(): Cosine_similarity

nnf_cross_entropy(): Cross_entropy

nnf_ctc_loss(): Ctc_loss

nnf_dropout(): Dropout

nnf_dropout2d(): Dropout2d

nnf_dropout3d(): Dropout3d

nnf_elu() nnf_elu_(): Elu

nnf_embedding(): Embedding

nnf_embedding_bag(): Embedding_bag

nnf_fold(): Fold

nnf_fractional_max_pool2d(): Fractional_max_pool2d

nnf_fractional_max_pool3d(): Fractional_max_pool3d

nnf_gelu(): Gelu

nnf_glu(): Glu

nnf_grid_sample(): Grid_sample

nnf_group_norm(): Group_norm

nnf_gumbel_softmax(): Gumbel_softmax

nnf_hardshrink(): Hardshrink

nnf_hardsigmoid(): Hardsigmoid

nnf_hardswish(): Hardswish

nnf_hardtanh() nnf_hardtanh_(): Hardtanh

nnf_hinge_embedding_loss(): Hinge_embedding_loss

nnf_instance_norm(): Instance_norm

nnf_interpolate(): Interpolate

nnf_kl_div(): Kl_div

nnf_l1_loss(): L1_loss

nnf_layer_norm(): Layer_norm

nnf_leaky_relu(): Leaky_relu

nnf_linear(): Linear

nnf_local_response_norm(): Local_response_norm

nnf_log_softmax(): Log_softmax

nnf_logsigmoid(): Logsigmoid

nnf_lp_pool1d(): Lp_pool1d

nnf_lp_pool2d(): Lp_pool2d

nnf_margin_ranking_loss(): Margin_ranking_loss

nnf_max_pool1d(): Max_pool1d

nnf_max_pool2d(): Max_pool2d

nnf_max_pool3d(): Max_pool3d

nnf_max_unpool1d(): Max_unpool1d

nnf_max_unpool2d(): Max_unpool2d

nnf_max_unpool3d(): Max_unpool3d

nnf_mse_loss(): Mse_loss

nnf_multi_head_attention_forward(): Multi head attention forward

nnf_multi_margin_loss(): Multi_margin_loss

nnf_multilabel_margin_loss(): Multilabel_margin_loss

nnf_multilabel_soft_margin_loss(): Multilabel_soft_margin_loss

nnf_nll_loss(): Nll_loss

nnf_normalize(): Normalize

nnf_one_hot(): One_hot

nnf_pad(): Pad

nnf_pairwise_distance(): Pairwise_distance

nnf_pdist(): Pdist

nnf_pixel_shuffle(): Pixel_shuffle

nnf_poisson_nll_loss(): Poisson_nll_loss

nnf_prelu(): Prelu

nnf_relu() nnf_relu_(): Relu

nnf_relu6(): Relu6

nnf_rrelu() nnf_rrelu_(): Rrelu

nnf_selu() nnf_selu_(): Selu

nnf_sigmoid(): Sigmoid

nnf_silu(): Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() for more information.

nnf_smooth_l1_loss(): Smooth_l1_loss

nnf_soft_margin_loss(): Soft_margin_loss

nnf_softmax(): Softmax

nnf_softmin(): Softmin

nnf_softplus(): Softplus

nnf_softshrink(): Softshrink

nnf_softsign(): Softsign

nnf_tanhshrink(): Tanhshrink

nnf_threshold() nnf_threshold_(): Threshold

nnf_triplet_margin_loss(): Triplet_margin_loss

nnf_triplet_margin_with_distance_loss(): Triplet margin with distance loss

nnf_unfold(): Unfold

torch_device(): Create a Device object

local_device() with_device(): Device contexts

Optimizers

optimizer(): Creates a custom optimizer

optim_adadelta(): Adadelta optimizer

optim_adagrad(): Adagrad optimizer

optim_adam(): Implements Adam algorithm.

optim_adamw(): Implements AdamW algorithm

optim_asgd(): Averaged Stochastic Gradient Descent optimizer

optim_lbfgs(): LBFGS optimizer

optim_required(): Dummy value indicating a required value.

optim_rmsprop(): RMSprop optimizer

optim_rprop(): Implements the resilient backpropagation algorithm.

optim_sgd(): SGD optimizer

is_optimizer(): Checks if the object is a torch optimizer

Learning rate schedulers

lr_cosine_annealing(): Set the learning rate of each parameter group using a cosine annealing schedule

lr_lambda(): Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.

lr_multiplicative(): Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr.

lr_one_cycle(): Once cycle learning rate

lr_reduce_on_plateau(): Reduce learning rate on plateau

lr_scheduler(): Creates learning rate schedulers

lr_step(): Step learning rate decay

Datasets

dataset(): Helper function to create an function that generates R6 instances of class dataset

dataset_subset(): Dataset Subset

iterable_dataset(): Creates an iterable dataset

dataloader(): Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.

dataloader_make_iter(): Creates an iterator from a DataLoader

dataloader_next(): Get the next element of a dataloader iterator

enumerate(): Enumerate an iterator

enumerate(<dataloader>): Enumerate an iterator

tensor_dataset(): Dataset wrapping tensors.

is_dataloader(): Checks if the object is a dataloader

sampler(): Creates a new Sampler

Distributions

Distribution: Generic R6 class representing distributions

distr_bernoulli(): Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p.

distr_categorical(): Creates a categorical distribution parameterized by either probs or logits (but not both).

distr_chi2(): Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5)

distr_gamma(): Creates a Gamma distribution parameterized by shape concentration and rate.

distr_mixture_same_family(): Mixture of components in the same family

distr_multivariate_normal(): Gaussian distribution

distr_normal(): Creates a normal (also called Gaussian) distribution parameterized by loc and scale.

distr_poisson(): Creates a Poisson distribution parameterized by rate, the rate parameter.

Constraint: Abstract base class for constraints.

Autograd

autograd_backward(): Computes the sum of gradients of given tensors w.r.t. graph leaves.

autograd_function(): Records operation history and defines formulas for differentiating ops.

autograd_grad(): Computes and returns the sum of gradients of outputs w.r.t. the inputs.

autograd_set_grad_mode(): Set grad mode

with_no_grad() local_no_grad(): Temporarily modify gradient recording.

with_enable_grad() local_enable_grad(): Enable grad

with_detect_anomaly(): Context-manager that enable anomaly detection for the autograd engine.

AutogradContext: Class representing the context.

Mixed precision

local_autocast() with_autocast() set_autocast() unset_autocast(): Autocast context manager

cuda_amp_grad_scaler(): Creates a gradient scaler

Random numbers

torch_manual_seed() local_torch_manual_seed() with_torch_manual_seed(): Sets the seed for generating random numbers.

torch_get_rng_state() torch_set_rng_state() cuda_get_rng_state() cuda_set_rng_state(): RNG state management

Linear Algebra

linalg_cholesky(): Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.

linalg_cholesky_ex(): Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.

linalg_cond(): Computes the condition number of a matrix with respect to a matrix norm.

linalg_det(): Computes the determinant of a square matrix.

linalg_eig(): Computes the eigenvalue decomposition of a square matrix if it exists.

linalg_eigh(): Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.

linalg_eigvals(): Computes the eigenvalues of a square matrix.

linalg_eigvalsh(): Computes the eigenvalues of a complex Hermitian or real symmetric matrix.

linalg_householder_product(): Computes the first n columns of a product of Householder matrices.

linalg_inv(): Computes the inverse of a square matrix if it exists.

linalg_inv_ex(): Computes the inverse of a square matrix if it is invertible.

linalg_lstsq(): Computes a solution to the least squares problem of a system of linear equations.

linalg_matrix_norm(): Computes a matrix norm.

linalg_matrix_power(): Computes the n-th power of a square matrix for an integer n.

linalg_matrix_rank(): Computes the numerical rank of a matrix.

linalg_multi_dot(): Efficiently multiplies two or more matrices

linalg_norm(): Computes a vector or matrix norm.

linalg_pinv(): Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.

linalg_qr(): Computes the QR decomposition of a matrix.

linalg_slogdet(): Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.

linalg_solve(): Computes the solution of a square system of linear equations with a unique solution.

linalg_solve_triangular(): Triangular solve

linalg_svd(): Computes the singular value decomposition (SVD) of a matrix.

linalg_svdvals(): Computes the singular values of a matrix.

linalg_tensorinv(): Computes the multiplicative inverse of torch_tensordot()

linalg_tensorsolve(): Computes the solution X to the system torch_tensordot(A, X) = B.

linalg_vector_norm(): Computes a vector norm.

Cuda utilities

cuda_amp_grad_scaler(): Creates a gradient scaler

cuda_current_device(): Returns the index of a currently selected device.

cuda_device_count(): Returns the number of GPUs available.

cuda_empty_cache(): Empty cache

cuda_get_device_capability(): Returns the major and minor CUDA capability of device

cuda_is_available(): Returns a bool indicating if CUDA is currently available.

cuda_memory_stats() cuda_memory_summary(): Returns a dictionary of CUDA memory allocator statistics for a given device.

cuda_runtime_version(): Returns the CUDA runtime version

cuda_synchronize(): Waits for all kernels in all streams on a CUDA device to complete.

torch_get_rng_state() torch_set_rng_state() cuda_get_rng_state() cuda_set_rng_state(): RNG state management

JIT

jit_compile(): Compile TorchScript code into a graph

jit_load(): Loads a script_function or script_module previously saved with jit_save

jit_ops: Enable idiomatic access to JIT operators from R.

jit_save(): Saves a script_function to a path

jit_save_for_mobile(): Saves a script_function or script_module in bytecode form, to be loaded on a mobile device

jit_scalar(): Adds the 'jit_scalar' class to the input

jit_trace(): Trace a function and return an executable script_function.

jit_trace_module(): Trace a module

jit_tuple(): Adds the 'jit_tuple' class to the input

Backends

backends_cudnn_is_available(): CuDNN is available

backends_cudnn_version(): CuDNN version

backends_mkl_is_available(): MKL is available

backends_mkldnn_is_available(): MKLDNN is available

backends_mps_is_available(): MPS is available

backends_openmp_is_available(): OpenMP is available

Installation

install_torch(): Install Torch

get_install_libs_url() install_torch_from_file(): Install Torch from files

Contrib

contrib_sort_vertices(): Contrib sort vertices