@Namespace(value="torch::nn") @NoOffset @Properties(inherit=torch.class) public class CosineEmbeddingLossImpl extends CosineEmbeddingLossImplCloneable
input1
, input2
, and a Tensor
label target
with values 1 or
-1. This is used for measuring whether two inputs are similar or
dissimilar, using the cosine distance, and is typically used for learning
nonlinear embeddings or semi-supervised learning.
See https://pytorch.org/docs/master/nn.html#torch.nn.CosineEmbeddingLoss to
learn about the exact behavior of this module.
See the documentation for torch::nn::CosineEmbeddingLossOptions
class to
learn what constructor arguments are supported for this module.
Example:
CosineEmbeddingLoss model(CosineEmbeddingLossOptions().margin(0.5));
Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter
Constructor and Description |
---|
CosineEmbeddingLossImpl() |
CosineEmbeddingLossImpl(CosineEmbeddingLossOptions options_) |
CosineEmbeddingLossImpl(Module pointer)
Downcast constructor.
|
CosineEmbeddingLossImpl(Pointer p)
Pointer cast constructor.
|
Modifier and Type | Method and Description |
---|---|
Tensor |
forward(Tensor input1,
Tensor input2,
Tensor target) |
CosineEmbeddingLossOptions |
options()
The options with which this
Module was constructed. |
CosineEmbeddingLossImpl |
options(CosineEmbeddingLossOptions setter) |
void |
pretty_print(Pointer stream)
Pretty prints the
CosineEmbeddingLoss module into the given stream . |
void |
reset()
reset() must perform initialization of all members with reference
semantics, most importantly parameters, buffers and submodules. |
asModule, asModule, clone, clone
apply, apply, apply, apply, apply, apply, apply, apply, buffers, buffers, children, eval, is_serializable, is_training, load, modules, modules, name, named_buffers, named_buffers, named_children, named_modules, named_modules, named_modules, named_parameters, named_parameters, parameters, parameters, register_buffer, register_buffer, register_module, register_module, register_parameter, register_parameter, register_parameter, register_parameter, save, shiftLeft, to, to, to, train, unregister_module, unregister_module, zero_grad
address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, getDirectBufferAddress, getPointer, getPointer, getPointer, getPointer, hashCode, interruptDeallocatorThread, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetAddress, offsetof, offsetof, parseBytes, physicalBytes, physicalBytesInaccurate, position, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, sizeof, toString, totalBytes, totalCount, totalPhysicalBytes, withDeallocator, zero
public CosineEmbeddingLossImpl(Pointer p)
Pointer(Pointer)
.public CosineEmbeddingLossImpl(Module pointer)
public CosineEmbeddingLossImpl(@ByVal(nullValue="torch::nn::CosineEmbeddingLossOptions{}") CosineEmbeddingLossOptions options_)
public CosineEmbeddingLossImpl()
public void reset()
CosineEmbeddingLossImplCloneable
reset()
must perform initialization of all members with reference
semantics, most importantly parameters, buffers and submodules.reset
in class CosineEmbeddingLossImplCloneable
public void pretty_print(@Cast(value="std::ostream*") @ByRef Pointer stream)
CosineEmbeddingLoss
module into the given stream
.pretty_print
in class Module
@ByVal public Tensor forward(@Const @ByRef Tensor input1, @Const @ByRef Tensor input2, @Const @ByRef Tensor target)
@ByRef public CosineEmbeddingLossOptions options()
Module
was constructed.public CosineEmbeddingLossImpl options(CosineEmbeddingLossOptions setter)
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