| .. role:: hidden |
| :class: hidden-section |
| |
| torch.nn.functional |
| =================== |
| |
| .. currentmodule:: torch.nn.functional |
| |
| Convolution functions |
| ---------------------------------- |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| conv1d |
| conv2d |
| conv3d |
| conv_transpose1d |
| conv_transpose2d |
| conv_transpose3d |
| unfold |
| fold |
| |
| Pooling functions |
| ---------------------------------- |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| avg_pool1d |
| avg_pool2d |
| avg_pool3d |
| max_pool1d |
| max_pool2d |
| max_pool3d |
| max_unpool1d |
| max_unpool2d |
| max_unpool3d |
| lp_pool1d |
| lp_pool2d |
| lp_pool3d |
| adaptive_max_pool1d |
| adaptive_max_pool2d |
| adaptive_max_pool3d |
| adaptive_avg_pool1d |
| adaptive_avg_pool2d |
| adaptive_avg_pool3d |
| fractional_max_pool2d |
| fractional_max_pool3d |
| |
| Attention Mechanisms |
| ------------------------------- |
| |
| The :mod:`torch.nn.attention.bias` module contains attention_biases that are designed to be used with |
| scaled_dot_product_attention. |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| scaled_dot_product_attention |
| |
| Non-linear activation functions |
| ------------------------------- |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| threshold |
| threshold_ |
| relu |
| relu_ |
| hardtanh |
| hardtanh_ |
| hardswish |
| relu6 |
| elu |
| elu_ |
| selu |
| celu |
| leaky_relu |
| leaky_relu_ |
| prelu |
| rrelu |
| rrelu_ |
| glu |
| gelu |
| logsigmoid |
| hardshrink |
| tanhshrink |
| softsign |
| softplus |
| softmin |
| softmax |
| softshrink |
| gumbel_softmax |
| log_softmax |
| tanh |
| sigmoid |
| hardsigmoid |
| silu |
| mish |
| batch_norm |
| group_norm |
| instance_norm |
| layer_norm |
| local_response_norm |
| rms_norm |
| normalize |
| |
| .. _Link 1: https://arxiv.org/abs/1611.00712 |
| .. _Link 2: https://arxiv.org/abs/1611.01144 |
| |
| Linear functions |
| ---------------- |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| linear |
| bilinear |
| |
| Dropout functions |
| ----------------- |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| dropout |
| alpha_dropout |
| feature_alpha_dropout |
| dropout1d |
| dropout2d |
| dropout3d |
| |
| Sparse functions |
| ---------------------------------- |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| embedding |
| embedding_bag |
| one_hot |
| |
| Distance functions |
| ---------------------------------- |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| pairwise_distance |
| cosine_similarity |
| pdist |
| |
| |
| Loss functions |
| -------------- |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| binary_cross_entropy |
| binary_cross_entropy_with_logits |
| poisson_nll_loss |
| cosine_embedding_loss |
| cross_entropy |
| ctc_loss |
| gaussian_nll_loss |
| hinge_embedding_loss |
| kl_div |
| l1_loss |
| mse_loss |
| margin_ranking_loss |
| multilabel_margin_loss |
| multilabel_soft_margin_loss |
| multi_margin_loss |
| nll_loss |
| huber_loss |
| smooth_l1_loss |
| soft_margin_loss |
| triplet_margin_loss |
| triplet_margin_with_distance_loss |
| |
| Vision functions |
| ---------------- |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| pixel_shuffle |
| pixel_unshuffle |
| pad |
| interpolate |
| upsample |
| upsample_nearest |
| upsample_bilinear |
| grid_sample |
| affine_grid |
| |
| DataParallel functions (multi-GPU, distributed) |
| ----------------------------------------------- |
| |
| :hidden:`data_parallel` |
| ~~~~~~~~~~~~~~~~~~~~~~~ |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| torch.nn.parallel.data_parallel |
