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 |
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 |
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 |
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_logsigmoid |
Logsigmoid |
nnf_log_softmax |
Log_softmax |
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_multilabel_margin_loss |
Multilabel_margin_loss |
nnf_multilabel_soft_margin_loss |
Multilabel_soft_margin_loss |
nnf_multi_head_attention_forward |
Multi head attention forward |
nnf_multi_margin_loss |
Multi_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 |
Relu |
nnf_relu6 |
Relu6 |
nnf_relu_ |
Relu |
nnf_rrelu |
Rrelu |
nnf_rrelu_ |
Rrelu |
nnf_selu |
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_softmax |
Softmax |
nnf_softmin |
Softmin |
nnf_softplus |
Softplus |
nnf_softshrink |
Softshrink |
nnf_softsign |
Softsign |
nnf_soft_margin_loss |
Soft_margin_loss |
nnf_tanhshrink |
Tanhshrink |
nnf_threshold |
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 |
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_multihead_attention |
MultiHead attention |
nn_multilabel_margin_loss |
Multilabel margin loss |
nn_multilabel_soft_margin_loss |
Multi label soft margin loss |
nn_multi_margin_loss |
Multi 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_softmax |
Softmax module |
nn_softmax2d |
Softmax2d module |
nn_softmin |
Softmin |
nn_softplus |
Softplus module |
nn_softshrink |
Softshrink module |
nn_softsign |
Softsign module |
nn_soft_margin_loss |
Soft margin loss |
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 |