Add Rust MNIST codeHEADmaster

1 files changed, 42 insertions, 0 deletions

diff --git a/mnist/src/mnist_linear.rs b/mnist/src/mnist_linear.rs
new file mode 100644
index 0000000..d3245f7
--- /dev/null
+++ b/mnist/src/mnist_linear.rs
@@ -0,0 +1,42 @@
+// This should rearch 91.5% accuracy.
+
+use anyhow::Result;
+use tch::{kind, no_grad, vision, Kind, Tensor};
+
+const IMAGE_DIM: i64 = 784;
+const LABELS: i64 = 10;
+
+pub fn run() -> Result<()> {
+    let m = vision::mnist::load_dir("data")?;
+    println!("train-images: {:?}", m.train_images.size());
+    println!("train-labels: {:?}", m.train_labels.size());
+    println!("test-images: {:?}", m.test_images.size());
+    println!("test-labels: {:?}", m.test_labels.size());
+    let mut ws = Tensor::zeros(&[IMAGE_DIM, LABELS], kind::FLOAT_CPU).set_requires_grad(true);
+    let mut bs = Tensor::zeros(&[LABELS], kind::FLOAT_CPU).set_requires_grad(true);
+    for epoch in 1..200 {
+        let logits = m.train_images.mm(&ws) + &bs;
+        let loss = logits.log_softmax(-1, Kind::Float).nll_loss(&m.train_labels);
+        ws.zero_grad();
+        bs.zero_grad();
+        loss.backward();
+        no_grad(|| {
+            ws += ws.grad() * (-1);
+            bs += bs.grad() * (-1);
+        });
+        let test_logits = m.test_images.mm(&ws) + &bs;
+        let test_accuracy = test_logits
+            .argmax(Some(-1), false)
+            .eq_tensor(&m.test_labels)
+            .to_kind(Kind::Float)
+            .mean(Kind::Float)
+            .double_value(&[]);
+        println!(
+            "epoch: {:4} train loss: {:8.5} test acc: {:5.2}%",
+            epoch,
+            loss.double_value(&[]),
+            100. * test_accuracy
+        );
+    }
+    Ok(())
+}