Package: torch 0.13.0.9001
torch: Tensors and Neural Networks with 'GPU' Acceleration
Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.
Authors:
torch_0.13.0.9001.tar.gz
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torch.pdf |torch.html✨
torch/json (API)
NEWS
# Install 'torch' in R: |
install.packages('torch', repos = c('https://mlverse.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mlverse/torch/issues
Last updated 21 days agofrom:9da75b29ac. Checks:OK: 5 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 15 2024 |
R-4.5-win-x86_64 | NOTE | Nov 15 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 15 2024 |
R-4.4-win-x86_64 | NOTE | Nov 15 2024 |
R-4.4-mac-x86_64 | OK | Nov 15 2024 |
R-4.4-mac-aarch64 | OK | Nov 15 2024 |
R-4.3-win-x86_64 | NOTE | Nov 15 2024 |
R-4.3-mac-x86_64 | OK | Nov 15 2024 |
R-4.3-mac-aarch64 | OK | Nov 15 2024 |
Exports:%>%as_arrayas_iteratorautograd_backwardautograd_functionautograd_gradautograd_set_grad_modebackends_cudnn_is_availablebackends_cudnn_versionbackends_mkl_is_availablebackends_mkldnn_is_availablebackends_mps_is_availablebackends_openmp_is_availablebuffer_from_torch_tensorcall_torch_functionclone_modulecontrib_sort_verticescuda_amp_grad_scalercuda_current_devicecuda_device_countcuda_empty_cachecuda_get_device_capabilitycuda_get_rng_statecuda_is_availablecuda_memory_statscuda_memory_summarycuda_runtime_versioncuda_set_rng_statecuda_synchronizedataloaderdataloader_make_iterdataloader_nextdatasetdataset_subsetdistr_bernoullidistr_categoricaldistr_chi2distr_gammadistr_mixture_same_familydistr_multivariate_normaldistr_normaldistr_poissonenumerateget_install_libs_urlinstall_torchinstall_torch_from_fileis_dataloaderis_nn_bufferis_nn_moduleis_nn_parameteris_optimizeris_torch_deviceis_torch_dtypeis_torch_layoutis_torch_memory_formatis_torch_qschemeis_undefined_tensoriterable_datasetjit_compilejit_loadjit_opsjit_savejit_save_for_mobilejit_scalarjit_tracejit_trace_modulejit_tuplelinalg_choleskylinalg_cholesky_exlinalg_condlinalg_detlinalg_eiglinalg_eighlinalg_eigvalslinalg_eigvalshlinalg_householder_productlinalg_invlinalg_inv_exlinalg_lstsqlinalg_matrix_normlinalg_matrix_powerlinalg_matrix_ranklinalg_multi_dotlinalg_normlinalg_pinvlinalg_qrlinalg_slogdetlinalg_solvelinalg_solve_triangularlinalg_svdlinalg_svdvalslinalg_tensorinvlinalg_tensorsolvelinalg_vector_normload_state_dictlocal_autocastlocal_devicelocal_enable_gradlocal_no_gradlocal_torch_manual_seedlooplr_cosine_annealinglr_lambdalr_multiplicativelr_one_cyclelr_reduce_on_plateaulr_schedulerlr_stepnn_adaptive_avg_pool1dnn_adaptive_avg_pool2dnn_adaptive_avg_pool3dnn_adaptive_log_softmax_with_lossnn_adaptive_max_pool1dnn_adaptive_max_pool2dnn_adaptive_max_pool3dnn_avg_pool1dnn_avg_pool2dnn_avg_pool3dnn_batch_norm1dnn_batch_norm2dnn_batch_norm3dnn_bce_lossnn_bce_with_logits_lossnn_bilinearnn_buffernn_celunn_contrib_sparsemaxnn_conv_transpose1dnn_conv_transpose2dnn_conv_transpose3dnn_conv1dnn_conv2dnn_conv3dnn_cosine_embedding_lossnn_cross_entropy_lossnn_ctc_lossnn_dropoutnn_dropout2dnn_dropout3dnn_elunn_embeddingnn_embedding_bagnn_flattennn_fractional_max_pool2dnn_fractional_max_pool3dnn_gelunn_glunn_group_normnn_grunn_hardshrinknn_hardsigmoidnn_hardswishnn_hardtanhnn_hinge_embedding_lossnn_identitynn_init_calculate_gainnn_init_constant_nn_init_dirac_nn_init_eye_nn_init_kaiming_normal_nn_init_kaiming_uniform_nn_init_normal_nn_init_ones_nn_init_orthogonal_nn_init_sparse_nn_init_trunc_normal_nn_init_uniform_nn_init_xavier_normal_nn_init_xavier_uniform_nn_init_zeros_nn_kl_div_lossnn_l1_lossnn_layer_normnn_leaky_relunn_linearnn_log_sigmoidnn_log_softmaxnn_lp_pool1dnn_lp_pool2dnn_lstmnn_margin_ranking_lossnn_max_pool1dnn_max_pool2dnn_max_pool3dnn_max_unpool1dnn_max_unpool2dnn_max_unpool3dnn_modulenn_module_dictnn_module_listnn_mse_lossnn_multi_margin_lossnn_multihead_attentionnn_multilabel_margin_lossnn_multilabel_soft_margin_lossnn_nll_lossnn_pairwise_distancenn_parameternn_poisson_nll_lossnn_prelunn_prune_headnn_relunn_relu6nn_rnnnn_rrelunn_selunn_sequentialnn_sigmoidnn_silunn_smooth_l1_lossnn_soft_margin_lossnn_softmaxnn_softmax2dnn_softminnn_softplusnn_softshrinknn_softsignnn_tanhnn_tanhshrinknn_thresholdnn_triplet_margin_lossnn_triplet_margin_with_distance_lossnn_unflattennn_upsamplenn_utils_clip_grad_norm_nn_utils_clip_grad_value_nn_utils_rnn_pack_padded_sequencenn_utils_rnn_pack_sequencenn_utils_rnn_pad_packed_sequencenn_utils_rnn_pad_sequencenn_utils_weight_normnnf_adaptive_avg_pool1dnnf_adaptive_avg_pool2dnnf_adaptive_avg_pool3dnnf_adaptive_max_pool1dnnf_adaptive_max_pool2dnnf_adaptive_max_pool3dnnf_affine_gridnnf_alpha_dropoutnnf_avg_pool1dnnf_avg_pool2dnnf_avg_pool3dnnf_batch_normnnf_bilinearnnf_binary_cross_entropynnf_binary_cross_entropy_with_logitsnnf_celunnf_celu_nnf_contrib_sparsemaxnnf_conv_tbcnnf_conv_transpose1dnnf_conv_transpose2dnnf_conv_transpose3dnnf_conv1dnnf_conv2dnnf_conv3dnnf_cosine_embedding_lossnnf_cosine_similaritynnf_cross_entropynnf_ctc_lossnnf_dropoutnnf_dropout2dnnf_dropout3dnnf_elunnf_elu_nnf_embeddingnnf_embedding_bagnnf_foldnnf_fractional_max_pool2dnnf_fractional_max_pool3dnnf_gelunnf_glunnf_grid_samplennf_group_normnnf_gumbel_softmaxnnf_hardshrinknnf_hardsigmoidnnf_hardswishnnf_hardtanhnnf_hardtanh_nnf_hinge_embedding_lossnnf_instance_normnnf_interpolatennf_kl_divnnf_l1_lossnnf_layer_normnnf_leaky_relunnf_linearnnf_local_response_normnnf_log_softmaxnnf_logsigmoidnnf_lp_pool1dnnf_lp_pool2dnnf_margin_ranking_lossnnf_max_pool1dnnf_max_pool2dnnf_max_pool3dnnf_max_unpool1dnnf_max_unpool2dnnf_max_unpool3dnnf_mse_lossnnf_multi_head_attention_forwardnnf_multi_margin_lossnnf_multilabel_margin_lossnnf_multilabel_soft_margin_lossnnf_nll_lossnnf_normalizennf_one_hotnnf_padnnf_pairwise_distancennf_pdistnnf_pixel_shufflennf_poisson_nll_lossnnf_prelunnf_relunnf_relu_nnf_relu6nnf_rrelunnf_rrelu_nnf_selunnf_selu_nnf_sigmoidnnf_silunnf_smooth_l1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Dependencies:bitbit64callrclicolorspacecorodescfarvergluejsonlitelabelinglifecyclemagrittrmunsellprocessxpsR6RColorBrewerRcpprlangsafetensorsscalesviridisLitewithr
Creating tensors
Rendered fromtensor-creation.Rmd
usingknitr::rmarkdown
on Nov 15 2024.Last update: 2022-02-04
Started: 2020-06-27
Distributions
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usingknitr::rmarkdown
on Nov 15 2024.Last update: 2022-02-04
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Extending Autograd
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usingknitr::rmarkdown
on Nov 15 2024.Last update: 2022-02-04
Started: 2020-04-29
Indexing tensors
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usingknitr::rmarkdown
on Nov 15 2024.Last update: 2023-08-10
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Installation
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usingknitr::rmarkdown
on Nov 15 2024.Last update: 2024-07-02
Started: 2020-10-08
Loading data
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usingknitr::rmarkdown
on Nov 15 2024.Last update: 2022-02-15
Started: 2020-07-01
Python to R
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usingknitr::rmarkdown
on Nov 15 2024.Last update: 2022-02-25
Started: 2021-02-11
Serialization
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usingknitr::rmarkdown
on Nov 15 2024.Last update: 2023-08-08
Started: 2020-09-24
TorchScript
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usingknitr::rmarkdown
on Nov 15 2024.Last update: 2022-02-04
Started: 2021-07-01
Using autograd
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usingknitr::rmarkdown
on Nov 15 2024.Last update: 2022-02-04
Started: 2020-04-29