adaptive_avg_pool1d() | |
adaptive_avg_pool2d() | |
adaptive_avg_pool3d() | |
_adaptive_max_pool1d() | |
adaptive_max_pool1d() | |
adaptive_max_pool1d_with_indices() | |
_adaptive_max_pool2d() | |
adaptive_max_pool2d() | |
adaptive_max_pool2d_with_indices() | |
_adaptive_max_pool3d() | |
adaptive_max_pool3d() | |
adaptive_max_pool3d_with_indices() | |
_add_docstr() | |
affine_grid() | |
alpha_dropout() | |
assert_int_or_pair() | |
avg_pool1d() | |
avg_pool2d() | |
avg_pool3d() | |
batch_norm() | |
bilinear() | |
binary_cross_entropy() | |
binary_cross_entropy_with_logits() | |
boolean_dispatch() | |
BroadcastingList1 | ? |
BroadcastingList2 | ? |
BroadcastingList3 | ? |
Callable | ? |
_canonical_mask() | |
celu() | |
celu_() | |
channel_shuffle() | |
conv1d() | |
conv2d() | |
conv3d() | |
conv_tbc() | |
conv_transpose1d() | |
conv_transpose2d() | |
conv_transpose3d() | |
cosine_embedding_loss() | |
cosine_similarity() | |
cross_entropy() | |
ctc_loss() | |
dropout() | |
dropout1d() | |
dropout2d() | |
dropout3d() | |
DType | int class |
elu() | |
elu_() | |
embedding() | |
embedding_bag() | |
feature_alpha_dropout() | |
fold() | |
_fractional_max_pool2d() | |
fractional_max_pool2d() | |
fractional_max_pool2d_with_indices() | |
_fractional_max_pool3d() | |
fractional_max_pool3d() | |
fractional_max_pool3d_with_indices() | |
gaussian_nll_loss() | |
gelu() | |
_get_softmax_dim() | |
glu() | |
grad | Module |
grid_sample() | |
GRID_SAMPLE_INTERPOLATION_MODES | dict object |
GRID_SAMPLE_PADDING_MODES | dict object |
group_norm() | |
gumbel_softmax() | |
handle_torch_function() | |
hardshrink() | |
hardsigmoid() | |
hardswish() | |
hardtanh() | |
hardtanh_() | |
has_torch_function() | |
has_torch_function_unary() | |
has_torch_function_variadic() | |
hinge_embedding_loss() | |
huber_loss() | |
_infer_size() | |
_in_projection() | |
_in_projection_packed() | |
instance_norm() | |
interpolate() | |
kl_div() | |
l1_loss() | |
layer_norm() | |
leaky_relu() | |
leaky_relu_() | |
linear() | |
List | ? |
_list_with_default() | |
local_response_norm() | |
logsigmoid() | |
log_softmax() | |
lp_pool1d() | |
lp_pool2d() | |
margin_ranking_loss() | |
math | Module |
_max_pool1d() | |
max_pool1d() | |
max_pool1d_with_indices() | |
_max_pool2d() | |
max_pool2d() | |
max_pool2d_with_indices() | |
_max_pool3d() | |
max_pool3d() | |
max_pool3d_with_indices() | |
max_unpool1d() | |
max_unpool2d() | |
max_unpool3d() | |
_mha_shape_check() | |
mish() | |
mse_loss() | |
multi_head_attention_forward() | |
multilabel_margin_loss() | |
multilabel_soft_margin_loss() | |
multi_margin_loss() | |
native_channel_shuffle() | |
nll_loss() | |
_no_grad_embedding_renorm_() | |
_none_or_dtype() | |
normalize() | |
one_hot() | |
Optional | ? |
_overload() | |
pad() | |
_pair() | |
pairwise_distance() | |
pdist() | |
pixel_shuffle() | |
pixel_unshuffle() | |
poisson_nll_loss() | |
prelu() | |
_Reduction | Module |
relu() | |
relu_() | |
relu6() | |
reproducibility_notes | dict object |
rrelu() | |
rrelu_() | |
scaled_dot_product_attention() | |
selu() | |
selu_() | |
sigmoid() | |
silu() | |
_single() | |
smooth_l1_loss() | |
soft_margin_loss() | |
softmax() | |
softmin() | |
softplus() | |
softshrink() | |
softsign() | |
sparse_support_notes | dict object |
_sym_int() | |
tanh() | |
tanhshrink() | |
Tensor | ? |
tf32_notes | dict object |
_threshold() | |
threshold() | |
threshold_() | |
torch | Module |
_triple() | |
triplet_margin_loss() | |
triplet_margin_with_distance_loss() | |
Tuple | ? |
TYPE_CHECKING | bool object |
unfold() | |
Union | ? |
_unpool_output_size() | |
upsample() | |
upsample_bilinear() | |
upsample_nearest() | |
utils | Module |
_verify_batch_size() | |
_verify_spatial_size() | |
_VF | Module |
warnings | Module |