id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_479_0.txt |
# torch.Tensor.lgamma ¶
Tensor. lgamma ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_309_0.txt |
# torch.Tensor.multiply ¶
Tensor. multiply ( _ value _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_37_3.txt | le of Tensors. If both of them are tuples, then the Jacobian will
be a tuple of tuple of Tensors where ` Jacobian[i][j] ` will contain the
Jacobian of the ` i ` th output and ` j ` th input and will have as size the
concatenation of the sizes of the corresponding output and the corresponding
input.
Return type
... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_42_3.txt | his.
1\. Don’t use ` multiprocessing ` . Set the ` num_worker ` of [ ` DataLoader
` ](../data.html#torch.utils.data.DataLoader "torch.utils.data.DataLoader") to
zero.
2\. Share CPU tensors instead. Make sure your custom ` DataSet ` returns CPU
tensors.
[ Next  ](../c... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_10_4.txt | opyright The Linux Foundation. The PyTorch Foundation is a project of The
Linux Foundation. For web site terms of use, trademark policy and other
policies applicable to The PyTorch Foundation please see [
www.linuxfoundation.org/legal/policies/
](https://www.linuxfoundation.org/legal/policies/) . The PyTorch Foundation... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_53_7.txt | pported type can be added as mutable
> attributes. Most types can be inferred but some may need to be specified,
> see module attributes for details.
Q: I would like to trace module’s method but I keep getting this error:
` RuntimeError: Cannot insert a Tensor that requires grad as a
constant. Consider ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_307_0.txt |
# torch.bitwise_left_shift ¶
torch. bitwise_left_shift ( _ input _ , _ other _ , _ * _ , _ out =
None _ ) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the left arithmetic shift of ` input ` by ` other ` bits. The input
tensor must be of integral type. This operator suppor... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_3_0.txt | [ ](https://pytorch.org/)
* [ Get Started ](https://pytorch.org/get-started)
* Ecosystem
[ Models (Beta) Discover, publish, and reuse pre-trained models
](https://pytorch.org/hub) [ Tools & Libraries Explore the ecosystem of tools
and libraries ](https://pytorch.org/ecosystem)
* [ Mobile ](https://pytorch.... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_44_0.txt |
# torch.flatten ¶
torch. flatten ( _ input _ , _ start_dim = 0 _ , _ end_dim = -1 _ )
→ [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Flattens ` input ` by reshaping it into a one-dimensional tensor. If `
start_dim ` or ` end_dim ` are passed, only dimensions starting with `
start_... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_53.txt | ink)
* [ TanhTransform (class in torch.distributions.transforms) ](distributions.html#torch.distributions.transforms.TanhTransform)
* [ temperature() (torch.distributions.relaxed_bernoulli.RelaxedBernoulli property) ](distributions.html#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.temperature)
* [ (to... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_11_1.txt | s driving
the overall project direction with a strong bias towards PyTorch’s design
philosophy where design and code contributions are valued. Beyond the core
maintainers, there is also a slightly broader set of core developers that have
the ability to directly merge pull requests and own various parts of the core
code... | |
pytorch_torch_tensor_functions/Visualizing_PyTorch_memory_2_0.txt | ## Saving snapshots
Since the traces are just part of the snapshot, they can be pickled in the
same way to view offline later.
from pickle import dump
with open('snapshot.pickle', 'wb') as f:
dump(snapshot, f)
The file [ _memory_viz.py
](https://github.com/pytorch/pytorch/blob/master/... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_328_0.txt |
# torch.Tensor.ge ¶
Tensor. ge ( _ other _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_399_0.txt |
# torch.Tensor.copysign ¶
Tensor. copysign ( _ other _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_13_0.txt | [ ](https://pytorch.org/)
* [ Get Started ](https://pytorch.org/get-started)
* Ecosystem
[ Models (Beta) Discover, publish, and reuse pre-trained models
](https://pytorch.org/hub) [ Tools & Libraries Explore the ecosystem of tools
and libraries ](https://pytorch.org/ecosystem)
* [ Mobile ](https://pytorch.... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_8.txt | cuda.comm) ](cuda.html#torch.cuda.comm.broadcast)
* [ (in module torch.distributed) ](distributed.html#torch.distributed.broadcast)
* [ broadcast_coalesced() (in module torch.cuda.comm) ](cuda.html#torch.cuda.comm.broadcast_coalesced)
* [ broadcast_multigpu() (in module torch.distributed) ](distributed.html#tor... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_13.txt | minimum and maximum of
incoming tensors, and uses this statistic to compute the quantization
parameters.
Parameters
* **ch_axis** – Channel axis
* **dtype** – Quantized data type
* **qscheme** – Quantization scheme to be used
* **reduce_range** – Reduces the range of the quantized data type by 1 b... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_471_0.txt |
# torch.Tensor.sinh ¶
Tensor. sinh ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_39_3.txt | ensor.div() ` ](tensors.html#torch.Tensor.div "torch.Tensor.div") , [ ` torch.div() ` ](generated/torch.div.html#torch.div "torch.div") | Unifies names from inputs
[ ` Tensor.div_() ` ](tensors.html#torch.Tensor.div_ "torch.Tensor.div_") | Unifies names from inputs
[ ` Tensor.dot() ` ](tensors.html#torch.Te... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_107_0.txt |
# torch.Tensor.chalf ¶
Tensor. chalf ( _ memory_format = torch.preserve_format _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_55_4.txt | can_device_access_peer ](generated/torch.cuda.can_device_access_peer.html)
* [ torch.cuda.current_blas_handle ](generated/torch.cuda.current_blas_handle.html)
* [ torch.cuda.current_device ](generated/torch.cuda.current_device.html)
* [ torch.cuda.current_stream ](generated/torch.cuda.current_stream.html)
... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_25.txt | e
* **~Conv3d.zero_point** ( [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – scalar for the output zero point
See [ ` Conv3d ` ](generated/torch.nn.Conv3d.html#torch.nn.Conv3d
"torch.nn.Conv3d") for other attributes.
Examples:
>>> # With square kernels and equal stride
>>> m = nn.q... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_8_1.txt | kI0MUMxMUUyQUQzREIxQzRENUFFNUM5NiI+IDx4bXBNTTpEZXJpdmVkRnJvbSBzdFJlZjppbnN0YW5jZUlEPSJ4bXAuaWlkOkUxNkJENjdGQjNGMDExRTJBRDNEQjFDNEQ1QUU1Qzk2IiBzdFJlZjpkb2N1bWVudElEPSJ4bXAuZGlkOkUxNkJENjgwQjNGMDExRTJBRDNEQjFDNEQ1QUU1Qzk2Ii8+IDwvcmRmOkRlc2NyaXB0aW9uPiA8L3JkZjpSREY+IDwveDp4bXBtZXRhPiA8P3hwYWNrZXQgZW5kPSJyIj8+hfPRaQAAB6lJR... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_311_0.txt |
# torch.Tensor.sub ¶
Tensor. sub ( _ other _ , _ * _ , _ alpha = 1 _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_0.txt | [ ](https://pytorch.org/)
* [ Get Started ](https://pytorch.org/get-started)
* Ecosystem
[ Models (Beta) Discover, publish, and reuse pre-trained models
](https://pytorch.org/hub) [ Tools & Libraries Explore the ecosystem of tools
and libraries ](https://pytorch.org/ecosystem)
* [ Mobile ](https://pytorch.... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_16.txt | If ` input ` is 2D of shape (B, N) ,
>
> it will be treated as ` B ` bags (sequences) each of fixed length ` N ` ,
> and this will return ` B ` values aggregated in a way depending on the `
> mode ` . ` offsets ` is ignored and required to be ` None ` in this case.
>
> * If ` input ` is 1D of shape (N) ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_276_0.txt |
# torch.Tensor.true_divide_ ¶
Tensor. true_divide_ ( _ value _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
In-place version of ` true_divide_() `
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_4.txt | distributions.exp_family.ExponentialFamily `
Beta distribution parameterized by ` concentration1 ` and ` concentration0
` .
Example:
>>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
>>> m.sample() # Beta distributed with concentration concentration1 and concentration0
tensor([ 0.1046... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_481_0.txt |
# torch.Tensor.ceil ¶
Tensor. ceil ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_74_0.txt |
# torch.Tensor.acos ¶
Tensor. acos ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_406_0.txt |
# torch.arcsin ¶
torch. arcsin ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.asin() ` ](torch.asin.html#torch.asin "torch.asin") .
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_12.txt | n.InstanceNorm3d.html#torch.nn.InstanceNorm3d
"torch.nn.InstanceNorm3d") for details.
### layer_norm ¶
` torch.nn.functional. ` ` layer_norm ` ( _input_ , _normalized_shape_ ,
_weight=None_ , _bias=None_ , _eps=1e-05_ ) [ [source]
](_modules/torch/nn/functional.html#layer_norm) ¶
Applies Layer Normalizatio... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_67_4.txt | using a [ theme
](https://github.com/rtfd/sphinx_rtd_theme) provided by [ Read the Docs
](https://readthedocs.org) .
* torch.hub
* Publishing models
* How to implement an entrypoint?
* Important Notice
* Loading models from Hub
* Running a loaded model:
* Where are my downloade... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_62_0.txt |
# torch.mm ¶
torch. mm ( _ input _ , _ mat2 _ , _ * _ , _ out = None _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Performs a matrix multiplication of the matrices ` input ` and ` mat2 ` .
If ` input ` is a ( n × m ) (n \times m) ( n × m ) tensor, ` mat2
` is a ... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_1.txt | the operations on tensors with integers rather than
floating point values. This allows for a more compact model representation and
the use of high performance vectorized operations on many hardware platforms.
PyTorch supports INT8 quantization compared to typical FP32 models allowing
for a 4x reduction in the model siz... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_62_11.txt | orted tensors should only be GPU
tensors
Parameters
* **list** ( _tensor_ ) – List of input and output tensors of the collective. The function operates in-place and requires that each tensor to be a GPU tensor on different GPUs. You also need to make sure that ` len(tensor_list) ` is the same for all the dis... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_59_9.txt | rch/utils/data/sampler.html#SubsetRandomSampler) ¶
Samples elements randomly from a given list of indices, without replacement.
Parameters
* **indices** ( _sequence_ ) – a sequence of indices
* **generator** ( [ _Generator_ ](generated/torch.Generator.html#torch.Generator "torch.Generator") ) – Gen... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_351_0.txt |
# torch.Tensor.uniform_ ¶
Tensor. uniform_ ( _ from=0 _ , _ to=1 _ , _ * _ , _ generator=None _ )
→ [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Fills ` self ` tensor with numbers sampled from the continuous uniform
distribution:
f ( x ) = 1 to − from f(x) = \dfrac{1}{\text{t... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_298_0.txt |
# torch.Tensor.as_subclass ¶
Tensor. as_subclass ( _ cls _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Makes a ` cls ` instance with the same data pointer as ` self ` . Changes in
the output mirror changes in ` self ` , and the output stays attached to the
autograd graph. ` cls ` ... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_0.txt | [ ](https://pytorch.org/)
* [ Get Started ](https://pytorch.org/get-started)
* Ecosystem
[ Models (Beta) Discover, publish, and reuse pre-trained models
](https://pytorch.org/hub) [ Tools & Libraries Explore the ecosystem of tools
and libraries ](https://pytorch.org/ecosystem)
* [ Mobile ](https://pytorch.... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_60_14.txt | [  Previous
](quantization.html "Quantization")
* * *
© Copyright 2019, Torch Contributors.
Built with [ Sphinx ](http://sphinx-doc.org/) using a [ theme
](https://github.com/rtfd/sphinx_rtd_theme) provided by [ Read the Docs
](https://readthedocs.org) .
* Distributed ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_173_0.txt |
# torch.Tensor.detach ¶
Tensor. detach ( ) ¶
Returns a new Tensor, detached from the current graph.
The result will never require gradient.
This method also affects forward mode AD gradients and the result will never
have forward mode AD gradients.
Note
Returned Tensor shares the same storage with th... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_24.txt | ` greater_than ` ¶
alias of ` torch.distributions.constraints._GreaterThan `
` torch.distributions.constraints. ` ` greater_than_eq ` ¶
alias of ` torch.distributions.constraints._GreaterThanEq `
` torch.distributions.constraints. ` ` less_than ` ¶
alias of ` torch.distributions.constraints._Le... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_53_9.txt | best suited for
simple record-like types (think a ` NamedTuple ` with methods attached).
Everything in a user defined [ TorchScript Class ](torchscript-class) is
exported by default, functions can be decorated with [ ` @torch.jit.ignore `
](generated/torch.jit.ignore.html#torch.jit.ignore "torch.jit.ignore") if
need... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_4.txt | eLU
* ` ConvBnReLU2d ` — Conv2d + BatchNorm + ReLU
* ` ConvReLU1d ` — Conv1d + ReLU
* ` ConvReLU2d ` — Conv2d + ReLU
* ` ConvReLU3d ` — Conv3d + ReLU
* ` LinearReLU ` — Linear + ReLU
* ` torch.nn.intrinsic.qat ` — versions of layers for quantization-aware training: * ` ConvBn2d ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_466_0.txt |
# torch.Tensor.atan ¶
Tensor. atan ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_289_0.txt |
# torch.Tensor.char ¶
Tensor. char ( _ memory_format = torch.preserve_format _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_15.txt | 3618],
[ 0.4161, 0.2419, 0.7383]],
[[ 0.6246, 0.9751, 0.3618],
[ 0.0237, 0.7794, 0.0528],
[ 0.9666, 0.7761, 0.6108],
[ 0.3385, 0.8612, 0.1867]]])
>>> # example with padding_idx
>>> weights = torch.rand(10, 3)
>>> weights[0, ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_524_0.txt |
# torch.Tensor.dim ¶
Tensor. dim ( ) → [ int
](https://docs.python.org/3/library/functions.html#int "\(in Python v3.12\)")
¶
Returns the number of dimensions of ` self ` tensor.
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_53_2.txt | t.load "torch.jit.load") (f[, map_location, _extra_files]) | Load a [ ` ScriptModule ` ](generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule "torch.jit.ScriptModule") or [ ` ScriptFunction ` ](generated/torch.jit.ScriptFunction.html#torch.jit.ScriptFunction "torch.jit.ScriptFunction") previously saved with... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_50_1.txt | ipt C++ API ¶
[ TorchScript ](https://pytorch.org/docs/stable/jit.html) allows PyTorch
models defined in Python to be serialized and then loaded and run in C++
capturing the model code via compilation or tracing its execution. You can
learn more in the [ Loading a TorchScript Model in C++ tutorial
](https://pytorch.o... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_10_1.txt | eeplearningexamples_gpunet/)
[
* HiFi GAN
* * 
* *
The HiFi GAN model for generating waveforms from mel spectrograms
](/hub/nvidia_deeplearningexamples_hifigan/)
[
* Once-for-All
* * 
* *... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_208_0.txt |
# torch.var ¶
torch. var ( _ input _ , _ dim = None _ , _ * _ , _ correction = 1
_ , _ keepdim = False _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Calculates the variance over the dimensions specified by ` dim ` . ` dim `
can be a single dimension, lis... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_362_0.txt |
# torch.asinh ¶
torch. asinh ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a new tensor with the inverse hyperbolic sine of the elements of `
input ` .
out i = sinh − 1 ( input i ) \text{out}_{i} =
\sinh^{-1}(\text{input}_{... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_8.txt | [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") _or_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – degrees of freedom parameter 2
` arg_constraints ` _= {'df1': GreaterThan(lower_bound=0.0), 'df2':
GreaterThan(lower_bound=0.0)}_ ¶
` expand ` ( _batch_shape... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_31.txt | butions.negative_binomial.NegativeBinomial attribute) ](distributions.html#torch.distributions.negative_binomial.NegativeBinomial.logits)
* [ (torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli attribute) ](distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.logits)
* [ logits(... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_462_0.txt |
# torch.Tensor.dequantize ¶
Tensor. dequantize ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Given a quantized Tensor, dequantize it and return the dequantized float
Tensor.
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_28_1.txt | n PyTorch, small mistakes can
rapidly cause your program to use up all of your GPU; fortunately, the fixes
in these cases are often simple. Here are a few common things to check:
**Don’t accumulate history across your training loop.** By default,
computations involving variables that require gradients will keep histor... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_14.txt |
* ` fake_quant_enable ` controls the application of fake quantization on tensors, note that statistics can still be updated.
* ` observer_enable ` controls statistics collection on tensors
* ` dtype ` specifies the quantized dtype that is being emulated with fake-quantization,
allowable values are ... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_11.txt | r([1.0]))
>>> m.sample() # Laplace distributed with loc=0, scale=1
tensor([ 0.1046])
Parameters
* **loc** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") _or_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – mean of the distribution
* **sc... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_32_0.txt |
# torch.Tensor.logical_and ¶
Tensor. logical_and ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_293_0.txt |
# torch.Tensor.addmv ¶
Tensor. addmv ( _ mat _ , _ vec _ , _ * _ , _ beta = 1 _ , _ alpha =
1 _ ) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_446_0.txt |
# torch.Tensor.normal_ ¶
Tensor. normal_ ( _ mean = 0 _ , _ std = 1 _ , _ * _ , _ generator
= None _ ) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Fills ` self ` tensor with elements samples from the normal distribution
parameterized by [ ` mean ` ](torch.mean.html#torch.mean... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_15.txt | q(lower_bound=0)}_ ¶
` expand ` ( _batch_shape_ , __instance=None_ ) [ [source]
](_modules/torch/distributions/negative_binomial.html#NegativeBinomial.expand)
¶
` log_prob ` ( _value_ ) [ [source]
](_modules/torch/distributions/negative_binomial.html#NegativeBinomial.log_prob)
¶
` logits ` [ [s... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_52_3.txt | ml#torch.nn.ConstantPad3d "torch.nn.ConstantPad3d") | Pads the input tensor boundaries with a constant value.
## Non-linear Activations (weighted sum, nonlinearity) ¶
[ ` nn.ELU ` ](generated/torch.nn.ELU.html#torch.nn.ELU "torch.nn.ELU") | Applies the element-wise function:
---|---
[ ` nn.Hardshrink ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_158_0.txt |
# torch.Tensor.logit ¶
Tensor. logit ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_8.txt | ( 0 , x ) \+ min (
0 , α ∗ ( exp ( x ) − 1 ) ) \text{ELU}(x) = \max(0,x) +
\min(0, \alpha * (\exp(x) - 1)) ELU ( x ) = max ( 0 , x ) \+ min
( 0 , α ∗ ( exp ( x ) − 1 ) ) .
See [ ` ELU ` ](generated/torch.nn.ELU.html#torch.nn.ELU "torch.nn.ELU") for
more details... | |
pytorch_torch_tensor_functions/Visualizing_PyTorch_memory_6_0.txt | ## Zach's Blog
* Zach's Blog
* [ ](mailto:)
* [ zdevito ](https://github.com/zdevito)
* [ Zachary_DeVito ](https://www.twitter.com/Zachary_DeVito)
Technical guides for various topics related to machine learning and CUDA
programming.
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_453_0.txt |
# torch.Tensor.bool ¶
Tensor. bool ( _ memory_format = torch.preserve_format _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_2.txt | uated at value .
Parameters
**value** ( [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) –
` entropy ` ( ) [ [source]
](_modules/torch/distributions/distribution.html#Distribution.entropy) ¶
Returns entropy of distribution, batched over batch_shape.
Returns
Tensor of shape batch_shap... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_415_0.txt |
# torch.nanmedian ¶
torch. nanmedian ( _ input _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns the median of the values in ` input ` , ignoring ` NaN ` values.
This function is identical to [ ` torch.median() `
](torch.median.html#torch.median "torch.median") when there are ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_3_0.txt |
# torch.copysign ¶
torch. copysign ( _ input _ , _ other _ , _ * _ , _ out = None _ ) →
[ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Create a new floating-point tensor with the magnitude of ` input ` and the
sign of ` other ` , elementwise.
out i = { − ∣ input i ∣ if othe... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_37_6.txt | , grad_fn=<SumBackward0>),
tensor([[1.0689, 1.2431],
[3.0989, 4.4456]], grad_fn=<MulBackward0>))
>>> def pow_adder_reducer(x, y):
... return (2 * x.pow(2) + 3 * y.pow(2)).sum()
>>> inputs = (torch.rand(2), torch.rand(2))
>>> v = (torch.zeros(2), torch.ones(2))
>>> vhp(pow_adder_r... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_41_0.txt |
# torch.vdot ¶
torch. vdot ( _ input _ , _ other _ , _ * _ , _ out = None _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the dot product of two 1D vectors along a dimension.
In symbols, this function computes
∑ i = 1 n x i ‾ y i . \sum_{i=1}^n \overline{x_i}... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_38.txt | rnoulli.LogitRelaxedBernoulli.param_shape)
* [ Parameter (class in torch.nn.parameter) ](generated/torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter)
* [ ParameterDict (class in torch.nn) ](generated/torch.nn.ParameterDict.html#torch.nn.ParameterDict)
* [ ParameterList (class in torch.nn) ](generated... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_46_6.txt | * ( [ _int_ ](https://docs.python.org/3/library/functions.html#int "\(in Python v3.8\)") ) – Number of thresholds used to draw the curve.
* **walltime** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") ) – Optional override default walltime (time.time()) seconds after epoch... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_485_0.txt |
# torch.diagonal_scatter ¶
torch. diagonal_scatter ( _ input _ , _ src _ , _ offset = 0 _ , _
dim1 = 0 _ , _ dim2 = 1 _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Embeds the values of the ` src ` tensor into ` input ` along the diagonal
elements of ` input ` , with resp... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_181_0.txt |
# torch.Tensor.to_sparse ¶
Tensor. to_sparse ( _ sparseDims _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a sparse copy of the tensor. PyTorch supports sparse tensors in [
coordinate format ](../sparse.html#sparse-coo-docs) .
Parameters
**sparseDims** ( [ _int_
](http... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_240_0.txt |
# torch.as_tensor ¶
torch. as_tensor ( _ data _ , _ dtype = None _ , _ device = None _ )
→ [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Converts ` data ` into a tensor, sharing data and preserving autograd history
if possible.
If ` data ` is already a tensor with the requested dty... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_312_0.txt |
# torch.renorm ¶
torch. renorm ( _ input _ , _ p _ , _ dim _ , _ maxnorm _ , _ * _ , _
out = None _ ) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor")
¶
Returns a tensor where each sub-tensor of ` input ` along dimension ` dim `
is normalized such that the p -norm of the sub-tensor... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_90_0.txt |
# torch.Tensor.type_as ¶
Tensor. type_as ( _ tensor _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns this tensor cast to the type of the given tensor.
This is a no-op if the tensor is already of the correct type. This is
equivalent to ` self.type(tensor.type()) `
Parameters
... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_422_0.txt |
# torch.Tensor.addcmul ¶
Tensor. addcmul ( _ tensor1 _ , _ tensor2 _ , _ * _ , _ value = 1 _ )
→ [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_202_0.txt |
# torch.Tensor.logical_not ¶
Tensor. logical_not ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_49_24.txt | ts indices
Example:
>>> nnz = 5
>>> dims = [5, 5, 2, 2]
>>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
>>> V = torch.randn(nnz, dims[2], dims[3])
>>> size = torch.Size(dims)
>>> S = torch.... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_56_1.txt | ions.
For example, to get a view of an existing tensor ` t ` , you can call `
t.view(...) ` .
>>> t = torch.rand(4, 4)
>>> b = t.view(2, 8)
>>> t.storage().data_ptr() == b.storage().data_ptr() # `t` and `b` share the same underlying data.
True
# Modifying view tensor changes base tens... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_1.txt | nsforms.Normalize.__call__)
* [ (torchvision.transforms.ToPILImage method) ](torchvision/transforms.html#torchvision.transforms.ToPILImage.__call__)
* [ (torchvision.transforms.ToTensor method) ](torchvision/transforms.html#torchvision.transforms.ToTensor.__call__)
* [ __getitem__() (torchvision.datasets.CIFA... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_152_0.txt |
# torch.Tensor.addbmm ¶
Tensor. addbmm ( _ batch1 _ , _ batch2 _ , _ * _ , _ beta = 1 _ , _
alpha = 1 _ ) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor")
¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_42.txt | ributed.reduce_scatter)
* [ reduce_scatter_multigpu() (in module torch.distributed) ](distributed.html#torch.distributed.reduce_scatter_multigpu)
* [ ReduceLROnPlateau (class in torch.optim.lr_scheduler) ](optim.html#torch.optim.lr_scheduler.ReduceLROnPlateau)
* [ ReduceOp (class in torch.distributed) ](distribut... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_60_1.txt | ning
APIs in the RPC package are stable. There are multiple ongoing work items to
improve performance and error handling, which will ship in future releases.
Note
Please refer to [ PyTorch Distributed Overview
](https://pytorch.org/tutorials/beginner/dist_overview.html) for a brief
introduction to all features relat... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_329_0.txt |
# torch.Tensor.exponential_ ¶
Tensor. exponential_ ( _ lambd = 1 _ , _ * _ , _ generator = None _
) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Fills ` self ` tensor with elements drawn from the PDF (probability density
function):
f ( x ) = λ e − λ x , x > 0 f(x) =... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_6.txt | g_prob ` ( _value_ ) [ [source]
](_modules/torch/distributions/cauchy.html#Cauchy.log_prob) ¶
_property_ ` mean ` ¶
` rsample ` ( _sample_shape=torch.Size([])_ ) [ [source]
](_modules/torch/distributions/cauchy.html#Cauchy.rsample) ¶
` support ` _= Real()_ ¶
_property_ ` variance ` ¶
... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_14_0.txt |
# torch.dot ¶
torch. dot ( _ input _ , _ other _ , _ * _ , _ out = None _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the dot product of two 1D tensors.
Note
Unlike NumPy’s dot, torch.dot intentionally only supports computing the dot
product of two 1D tensors with the... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_21.txt | hon v3.8\)") _,_ [ _int_ ](https://docs.python.org/3/library/functions.html#int "\(in Python v3.8\)") _]_ ) – output spatial size.
* **scale_factor** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") _or_ _Tuple_ _[_ [ _float_ ](https://docs.python.org/3/library/functions.ht... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_270_0.txt |
# torch.Tensor.arctan2_ ¶
Tensor. arctan2_ ( ) ¶
atan2_(other) -> Tensor
In-place version of [ ` arctan2() `
](torch.Tensor.arctan2.html#torch.Tensor.arctan2 "torch.Tensor.arctan2")
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_151_0.txt |
# torch.fix ¶
torch. fix ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.trunc() ` ](torch.trunc.html#torch.trunc "torch.trunc")
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_43_2.txt | h a model in various segments and checkpoint each
segment. All segments except the last will run in [ ` torch.no_grad() `
](generated/torch.no_grad.html#torch.no_grad "torch.no_grad") manner, i.e.,
not storing the intermediate activations. The inputs of each checkpointed
segment will be saved for re-running the segmen... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_19_0.txt |
# torch.xlogy ¶
torch. xlogy ( _ input _ , _ other _ , _ * _ , _ out = None _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.special.xlogy() ` ](../special.html#torch.special.xlogy
"torch.special.xlogy") .
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_286_0.txt |
# torch.Tensor.clip_ ¶
Tensor. clip_ ( _ min = None _ , _ max = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` clamp_() ` ](torch.Tensor.clamp_.html#torch.Tensor.clamp_
"torch.Tensor.clamp_") .
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_271_0.txt |
# torch.Tensor.arcsinh ¶
Tensor. arcsinh ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_12_0.txt |
# torch.Tensor.bfloat16 ¶
Tensor. bfloat16 ( _ memory_format = torch.preserve_format _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
|
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