Remove nn package.

.github/workflows/ci.yml

@@ -47,9 +47,6 @@ jobs:
       - name: Compile core module
         run: sbt "project core" compile
 
-      - name: Compile nn module
-        run: sbt "project nn" compile
-
       - name: Compile examples module
         run: sbt "project examples" compile
 

AGENTS.md

@@ -837,7 +837,7 @@ val jacFwd = Autodiff.jacFwd(linearMap)
 
 ```scala
 import dimwit.*
-import nn.{GradientDescent, GradientOptimizer}
+import dimwit.optimizer.{GradientDescent, GradientOptimizer}
 import dimwit.random.Random
 
 trait Feature derives Label
@@ -884,7 +884,7 @@ val trained = optimizer.iterate(initModelParams)(gradFunc)
 ### Lion Optimizer
 
 ```scala
-import nn.Lion
+import dimwit.optimizer.Lion
 
 // Lion optimizer with momentum
 val lionOptimizer = Lion(learningRate = Tensor0(1e-3f), beta1 = Tensor0(0.9f), beta2 = Tensor0(0.99f), weightDecay = Tensor0(0.0f))

build.sbt

@@ -11,7 +11,7 @@ ThisBuild / resolvers += "Sonatype OSS Snapshots" at "https://oss.sonatype.org/c
 addCommandAlias("testAndCoverage", "; clean; coverage; test; coverageReport")
 
 lazy val root = (project in file("."))
-  .aggregate(core, nn, examples)
+  .aggregate(core, examples)
   .settings(
     name := "dimwit-root"
   )
@@ -43,16 +43,9 @@ lazy val core = (project in file("core"))
     Compile / packageDoc / publishArtifact := true
   )
 
-lazy val nn = (project in file("nn"))
-  .settings(
-    name := "dimwit-nn"
-  )
-  .dependsOn(core)
-
 // Examples subproject
 lazy val examples = (project in file("examples"))
   .dependsOn(core)
-  .dependsOn(nn)
   .settings(
     name := "dimwit-examples",
     // Examples use the same Scala version and dependencies as main project
@@ -81,7 +74,7 @@ lazy val examples = (project in file("examples"))
 // Processes files in /mdocs that need to be copied to the root (e.g. README.md)
 lazy val docsRoot = (project in file(".dimwit-docs-root"))
   .enablePlugins(MdocPlugin)
-  .dependsOn(core, nn)
+  .dependsOn(core)
   .settings(
     name := "dimwit-docs-root",
     mdocIn := (ThisBuild / baseDirectory).value / "mdocs",
@@ -98,7 +91,7 @@ lazy val docsRoot = (project in file(".dimwit-docs-root"))
 // Processes all other docs in /mdocs/docs/ → output to docs/
 lazy val docs = (project in file(".dimwit-docs"))
   .enablePlugins(MdocPlugin)
-  .dependsOn(core, nn)
+  .dependsOn(core)
   .settings(
     name := "dimwit-docs",
     mdocIn := (ThisBuild / baseDirectory).value / "mdocs/docs",

nn/src/main/scala/nn/Activation.scala → core/src/main/scala/dimwit/nn/ActivationFunctions.scala

@@ -1,6 +1,7 @@
-package nn
+package dimwit.nn
 
-import dimwit.*
+import dimwit.tensor.*
+import dimwit.tensor.TensorOps.IsFloating
 import dimwit.jax.Jax
 import dimwit.python.PyBridge.{liftPyTensor, toPyTensor}
 

core/src/main/scala/dimwit/nn/package.scala

@@ -0,0 +1 @@
+

nn/src/main/scala/nn/GradientOptimizer.scala → core/src/main/scala/dimwit/optimizer/GradientOptimizer.scala

@@ -1,4 +1,4 @@
-package nn
+package dimwit.optimizer
 
 import dimwit.*
 import dimwit.autodiff.FloatTree.ops.*

docs/quickstart.md

@@ -12,7 +12,7 @@ Before we start exploring the features of DimWit, let's look at a simple example
 // main imports for basic tensor operations and automatic differentiation
 import dimwit.*
 import dimwit.Autodiff.grad // TODO replace with cleaner import after PR is merged
-import nn.GradientDescent // TODO replace with cleaner import after refactoring 
+import dimwit.optimizer.GradientDescent // TODO replace with cleaner import after refactoring 
 
 // labels for tensor axes
 trait Batch derives Label

examples/src/main/scala/basic/LogisticRegression.scala

@@ -3,8 +3,8 @@ package examples.basic
 import dimwit.*
 import dimwit.Conversions.given
 import dimwit.autodiff.*
-import nn.*
-import nn.ActivationFunctions.{sigmoid, relu}
+import dimwit.optimizer.GradientDescent
+import dimwit.nn.ActivationFunctions.{sigmoid, relu}
 import dimwit.random.Random
 import dimwit.stats.Normal