@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, cloneapply, 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_gradaddress, 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, zeropublic CosineEmbeddingLossImpl(Pointer p)
Pointer(Pointer).public CosineEmbeddingLossImpl(Module pointer)
public CosineEmbeddingLossImpl(@ByVal(nullValue="torch::nn::CosineEmbeddingLossOptions{}") CosineEmbeddingLossOptions options_)
public CosineEmbeddingLossImpl()
public void reset()
CosineEmbeddingLossImplCloneablereset() must perform initialization of all members with reference
semantics, most importantly parameters, buffers and submodules.reset in class CosineEmbeddingLossImplCloneablepublic 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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