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abs acos acosh AdaptivePool1dShape AdaptivePool2dShape add addbmm AddbmmOptions addcdiv AddcdivOptions addcmul AddcmulOptions addmm AddmmOptions addmv AddmvOptions addr AddrOptions adjoint all allclose AllcloseOptions AlphaBetaOptions amax amin aminmax AminmaxOptions angle any applyOut arange are_deterministic_algorithms_enabled argmax argmin argsort argwhere as_strided as_tensor asin asinh AssertNoShapeError AssertNotError AsStridedOptions At at_error_index_out_of_bounds atan atan2 atanh atleast_1d atleast_2d atleast_3d AtShape autocast_decrement_nesting autocast_increment_nesting autograd_gradient_mismatch_error autograd_not_registered_error AutogradConfig AutogradDevice AutogradDType AutogradEntry AutogradHandle AutogradHandleImpl AxesRecord BackwardFn baddbmm BaddbmmOptions bartlett_window BaseKernelConfig batch_dimensions_do_not_match_error bernoulli BernoulliOptions BinaryBackwardFn BinaryBroadcastResult BinaryDType BinaryKernelConfigCPU BinaryKernelCPU BinaryOpConfig 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binary_cross_entropy_with_logits

torch.nn.functional.binary_cross_entropy_with_logits

function binary_cross_entropy_with_logits(input: Tensor, target: Tensor, options?: BinaryCrossEntropyWithLogitsFunctionalOptions): AnyTensor

Binary cross entropy with logits loss. More numerically stable than sigmoid + binary_cross_entropy.

Parameters

inputTensor: Tensor of logits (unbounded)
targetTensor: Tensor of binary targets (0 or 1)
optionsBinaryCrossEntropyWithLogitsFunctionalOptionsoptional

Returns

AnyTensor

See Also

PyTorch torch.nn.functional.binary_cross_entropy_with_logits

binary_cross_entropy

BinaryCrossEntropyFunctionalOptions