rust/candle-coursera-ml
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Vishal Patil
2025-01-16 21:50:06 -05:00
parent 636447a18a
commit 89514173a6
1 changed files with 5 additions and 15 deletions
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20
neural-networks-101/src/main.rs
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@@ -1,6 +1,6 @@
use anyhow::Result;
use candle_core::{DType, Device, Tensor, D};
use candle_nn::{loss, ops, Linear, Module, Optimizer, VarBuilder, VarMap};
use candle_nn::{loss, Linear, Module, Optimizer, VarBuilder, VarMap};
use clap::Parser;
use rand::Rng;
use std::f64::consts::PI;
@@ -8,7 +8,7 @@ use std::rc::Rc;
const INPUT_COUNT: usize = 3;
const LAYER1_COUNT: usize = 100;
const LAYER2_COUNT: usize = 100;
const LAYER2_COUNT: usize = 50;
const OUTPUT_COUNT: usize = 1;
struct Dataset {
@@ -122,7 +122,7 @@ fn main() -> Result<()> {
let test_images = dataset.test_data.to_device(&device)?;
let test_labels = dataset
.test_labels
.to_dtype(DType::U32)?
.to_dtype(DType::F32)?
.to_device(&device)?;
for epoch in 1..args.epochs {
@@ -131,19 +131,9 @@ fn main() -> Result<()> {
sgd.backward_step(&loss)?;
let test_logits = model.forward(&test_images)?;
let sum_ok = test_logits
.argmax(D::Minus1)?
.eq(&test_labels)?
.to_dtype(DType::F32)?
.sum_all()?
.to_scalar::<f32>()?;
let test_accuracy = sum_ok / test_labels.dims1()? as f32;
let test_loss = loss::mse(&test_logits.squeeze(1)?, &test_labels)?;
if args.progress && epoch % 100 == 0 {
println!(
"{epoch:4} train loss: {:8.5} test acc: {:5.2}%",
loss.to_scalar::<f32>()?,
100. * test_accuracy
);
println!("{epoch:4} test loss: {:8.5}", test_loss.to_scalar::<f32>()?);
}
}