LeNet on MNIST + readme update (#12)

* LeNet on MNIST + readme update

* tanh + remove device toggle

* remove device entirely

Applications/MNISTTrainer/ContentView.swift

@@ -12,9 +12,6 @@ struct ContentView: View {
     // the training loop
     @State var trainer = Trainer()
 
-    // toggle for cpu/gpu training
-    @State var cpu = true
-
     var body: some View {
         VStack {
             Spacer()
@@ -30,13 +27,10 @@ struct ContentView: View {
 
                 Button("Train") {
                     Task {
-                        try! await trainer.run(device: cpu ? .cpu : .gpu)
+                        try! await trainer.run()
                     }
                 }
 
-                Toggle("CPU", isOn: $cpu)
-                    .frame(maxWidth: 150)
-
                 Spacer()
             }
             Spacer()
@@ -50,12 +44,10 @@ class Trainer {
 
     var messages = [String]()
 
-    func run(device: Device = .cpu) async throws {
+    func run() async throws {
         // Note: this is pretty close to the code in `mnist-tool`, just
         // wrapped in an Observable to make it easy to display in SwiftUI
 
-        Device.setDefault(device: device)
-
         // download & load the training data
         let url = URL(fileURLWithPath: NSTemporaryDirectory(), isDirectory: true)
         try await download(into: url)
@@ -67,9 +59,7 @@ class Trainer {
         let testLabels = data[.init(.test, .labels)]!
 
         // create the model with random weights
-        let model = MLP(
-            layers: 2, inputDimensions: trainImages.dim(-1), hiddenDimensions: 32,
-            outputDimensions: 10)
+        let model = LeNet()
         eval(model.parameters())
 
         // the training loop

Applications/MNISTTrainer/README.md

@@ -1,13 +1,13 @@
 #  MNISTTrainer
 
-This is an example showing how to do model training on both macOS and iOS.
-This will download the MNIST training data, create a new models and train
-it.  It will show the timing per epoch and the test accuracy as it trains.
+This is an example of model training that works on both macOS and iOS.
+The example will download the MNIST training data, create a LeNet, and train
+it. It will show the epoch time and test accuracy as it trains.
 
 You will need to set the Team on the MNISTTrainer target in order to build and
 run on iOS.
 
 Some notes about the setup:
 
-- this will download test data over the network so MNISTTrainer -> Signing & Capabilities has the "Outgoing Connections (Client)" set in the App Sandbox
-- the website it connects to uses http rather than https so it has a "App Transport Security Settings" in the Info.plist
+- This will download test data over the network so MNISTTrainer -> Signing & Capabilities has the "Outgoing Connections (Client)" set in the App Sandbox
+- The website it connects to uses http rather than https so it has a "App Transport Security Settings" in the Info.plist