mirror of
https://github.com/huggingface/candle.git
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86e1803191
* add bce with logit loss * add bce with logit loss * remove imports * fix tiny bug * add test documentation and refactor function * fix test cases and formatting
89 lines
2.4 KiB
Rust
89 lines
2.4 KiB
Rust
#[cfg(feature = "mkl")]
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extern crate intel_mkl_src;
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#[cfg(feature = "accelerate")]
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extern crate accelerate_src;
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use candle::test_utils::to_vec0_round;
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use candle::{Device, Result, Tensor};
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/* Equivalent python code:
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import torch
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import torch.nn.functional as F
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input = torch.tensor([
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[ 1.1050, 0.3013, -1.5394, -2.1528, -0.8634],
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[ 1.0730, -0.9419, -0.1670, -0.6582, 0.5061],
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[ 0.8318, 1.1154, -0.3610, 0.5351, 1.0830]])
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target = torch.tensor([1, 0, 4])
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print(F.nll_loss(F.log_softmax(input, dim=1), target))
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print(F.cross_entropy(input, target))
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*/
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#[test]
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fn nll_and_cross_entropy() -> Result<()> {
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let cpu = Device::Cpu;
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let input = Tensor::new(
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&[
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[1.1050f32, 0.3013, -1.5394, -2.1528, -0.8634],
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[1.0730, -0.9419, -0.1670, -0.6582, 0.5061],
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[0.8318, 1.1154, -0.3610, 0.5351, 1.0830],
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],
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&cpu,
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)?;
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let target = Tensor::new(&[1u32, 0, 4], &cpu)?;
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let log_softmax = candle_nn::ops::log_softmax(&input, 1)?;
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let loss = candle_nn::loss::nll(&log_softmax, &target)?;
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assert_eq!(to_vec0_round(&loss, 4)?, 1.1312);
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let loss = candle_nn::loss::cross_entropy(&input, &target)?;
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assert_eq!(to_vec0_round(&loss, 4)?, 1.1312);
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Ok(())
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}
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/* Equivalent python code:
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import torch
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import torch.nn.functional as F
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inp = torch.Tensor([[ 2.3611, -0.8813, -0.5006, -0.2178],
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[ 0.0419, 0.0763, -1.0457, -1.6692],
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[-1.0494, 0.8111, 1.5723, 1.2315],
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[ 1.3081, 0.6641, 1.1802, -0.2547],
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[ 0.5292, 0.7636, 0.3692, -0.8318]])
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target = torch.Tensor([[0., 1., 0., 0.],
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[0., 1., 0., 0.],
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[0., 0., 0., 1.],
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[1., 0., 0., 0.],
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[0., 0., 1., 0.]])
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print(F.binary_cross_entropy_with_logits(inp, target))
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*/
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#[test]
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fn binary_cross_entropy_with_logit() -> Result<()> {
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let cpu = Device::Cpu;
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let inp = [
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[2.3611f32, -0.8813, -0.5006, -0.2178],
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[0.0419, 0.0763, -1.0457, -1.6692],
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[-1.0494, 0.8111, 1.5723, 1.2315],
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[1.3081, 0.6641, 1.1802, -0.2547],
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[0.5292, 0.7636, 0.3692, -0.8318],
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];
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let target = [
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[0.0f32, 1., 0., 0.],
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[0., 1., 0., 0.],
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[0., 0., 0., 1.],
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[1., 0., 0., 0.],
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[0., 0., 1., 0.],
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];
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let inp = Tensor::new(&inp, &cpu)?;
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let target = Tensor::new(&target, &cpu)?;
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let loss = candle_nn::loss::binary_cross_entropy_with_logit(&inp, &target)?;
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assert_eq!(to_vec0_round(&loss, 4)?, 0.8224);
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Ok(())
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}
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