Add Rust MNIST codeHEADmaster

1 files changed, 54 insertions, 0 deletions

diff --git a/mnist/src/mnist_conv.rs b/mnist/src/mnist_conv.rs
new file mode 100644
index 0000000..3cb5ee1
--- /dev/null
+++ b/mnist/src/mnist_conv.rs
@@ -0,0 +1,54 @@
+// CNN model. This should rearch 99.1% accuracy.
+
+use anyhow::Result;
+use tch::{nn, nn::ModuleT, nn::OptimizerConfig, Device, Tensor};
+
+#[derive(Debug)]
+struct Net {
+    conv1: nn::Conv2D,
+    conv2: nn::Conv2D,
+    fc1: nn::Linear,
+    fc2: nn::Linear,
+}
+
+impl Net {
+    fn new(vs: &nn::Path) -> Net {
+        let conv1 = nn::conv2d(vs, 1, 32, 5, Default::default());
+        let conv2 = nn::conv2d(vs, 32, 64, 5, Default::default());
+        let fc1 = nn::linear(vs, 1024, 1024, Default::default());
+        let fc2 = nn::linear(vs, 1024, 10, Default::default());
+        Net { conv1, conv2, fc1, fc2 }
+    }
+}
+
+impl nn::ModuleT for Net {
+    fn forward_t(&self, xs: &Tensor, train: bool) -> Tensor {
+        xs.view([-1, 1, 28, 28])
+            .apply(&self.conv1)
+            .max_pool2d_default(2)
+            .apply(&self.conv2)
+            .max_pool2d_default(2)
+            .view([-1, 1024])
+            .apply(&self.fc1)
+            .relu()
+            .dropout_(0.5, train)
+            .apply(&self.fc2)
+    }
+}
+
+pub fn run() -> Result<()> {
+    let m = tch::vision::mnist::load_dir("data")?;
+    let vs = nn::VarStore::new(Device::cuda_if_available());
+    let net = Net::new(&vs.root());
+    let mut opt = nn::Adam::default().build(&vs, 1e-4)?;
+    for epoch in 1..100 {
+        for (bimages, blabels) in m.train_iter(256).shuffle().to_device(vs.device()) {
+            let loss = net.forward_t(&bimages, true).cross_entropy_for_logits(&blabels);
+            opt.backward_step(&loss);
+        }
+        let test_accuracy =
+            net.batch_accuracy_for_logits(&m.test_images, &m.test_labels, vs.device(), 1024);
+        println!("epoch: {:4} test acc: {:5.2}%", epoch, 100. * test_accuracy,);
+    }
+    Ok(())
+}