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42 lines
1.2 KiB
Rust
42 lines
1.2 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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