[WIP] Add part of the Keras API

build.sbt

@@ -1,8 +1,9 @@
 import sbtcrossproject.CrossPlugin.autoImport.{crossProject, CrossType}
+import scala.sys.process._
 
 organization in ThisBuild := "me.shadaj"
 
-scalaVersion in ThisBuild := "2.12.8"
+scalaVersion in ThisBuild := "2.12.10"
 
 addCommandAlias(
   "publishSignedAll",
@@ -27,13 +28,14 @@ lazy val scalaPyTensorFlowCross = crossProject(JVMPlatform, NativePlatform)
   .in(file("."))
   .settings(
     name := "scalapy-tensorflow",
-    libraryDependencies += "me.shadaj" %%% "scalapy-core" % "0.3.0",
-    libraryDependencies += "me.shadaj" %%% "scalapy-numpy" % "0.1.0+3-046d1d67",
+    libraryDependencies += "me.shadaj" %%% "scalapy-core" % "0.3.0+17-2bfe86de",
+    // TODO: update to released scalapy-numpy
+    libraryDependencies += "me.shadaj" %%% "scalapy-numpy" % "0.1.0+5-ad550211+20200401-1343",
     libraryDependencies += "org.scalatest" %%% "scalatest" % "3.1.0-SNAP8" % Test
   ).jvmSettings(
     libraryDependencies += "org.scalacheck" %% "scalacheck" % "1.14.0" % Test,
     fork in Test := true,
-    javaOptions in Test += s"-Djava.library.path=${sys.env.getOrElse("JEP_PATH", "/usr/local/lib/python3.7/site-packages/jep")}"
+      javaOptions in Test += s"-Djna.library.path=${"python3-config --prefix".!!.trim}/lib"
   ).nativeSettings(
     scalaVersion := "2.11.12",
     libraryDependencies += "com.github.lolgab" %%% "scalacheck" % "1.14.1" % Test,

project/build.properties

@@ -1 +1 @@
-sbt.version=1.2.8
+sbt.version=1.3.3

src/main/scala/me/shadaj/scalapy/tensorflow/keras/Keras.scala

@@ -0,0 +1,21 @@
+package me.shadaj.scalapy.tensorflow.keras
+
+import me.shadaj.scalapy.py
+import me.shadaj.scalapy.tensorflow.keras.backend.Backend
+import me.shadaj.scalapy.tensorflow.keras.datasets.Datasets
+import me.shadaj.scalapy.tensorflow.keras.models.Models
+import me.shadaj.scalapy.tensorflow.keras.optimizers.Optimizers
+import me.shadaj.scalapy.tensorflow.keras.utils.Utils
+import me.shadaj.scalapy.tensorflow.keras.losses.Losses
+import me.shadaj.scalapy.tensorflow.keras.layers.Layers
+
+
+@py.native trait Keras extends py.Object {
+  def models: Models = py.native
+  def datasets: Datasets = py.native
+  def backend: Backend = py.native
+  def utils: Utils = py.native
+  def optimizers: Optimizers = py.native
+  def losses: Losses = py.native
+  def layers: Layers = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/backend/Backend.scala

@@ -0,0 +1,8 @@
+package me.shadaj.scalapy.tensorflow.keras.backend
+
+import me.shadaj.scalapy.py
+
+@py.native trait Backend extends py.Object {
+ def image_data_format(): String = py.native
+}
+

src/main/scala/me/shadaj/scalapy/tensorflow/keras/datasets/Datasets.scala

@@ -0,0 +1,7 @@
+package me.shadaj.scalapy.tensorflow.keras.datasets
+
+import me.shadaj.scalapy.py
+
+@py.native trait Datasets extends py.Object {
+  def mnist: Mnist = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/datasets/Mnist.scala

@@ -0,0 +1,8 @@
+package me.shadaj.scalapy.tensorflow.keras.datasets
+
+import me.shadaj.scalapy.py
+import me.shadaj.scalapy.numpy.NDArray
+
+@py.native trait Mnist extends py.Object {
+  def load_data(path: String = "mnist.npz"): ((NDArray[Long], NDArray[Long]), (NDArray[Long], NDArray[Long])) = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/layers/Conv2D.scala

@@ -0,0 +1,23 @@
+package me.shadaj.scalapy.tensorflow.keras
+package layers
+
+import me.shadaj.scalapy.py
+
+@py.native trait Conv2D extends Layer {
+  def filters: Int = py.native
+  def kernel_size: py.|[Int, (Int, Int)] = py.native
+  def strides: py.|[Int, (Int, Int)] = py.native
+  def padding: String = py.native
+  def data_format: py.NoneOr[String] = py.native
+  def dilation_rate: py.|[Int, (Int, Int)] = py.native
+  def activation: py.NoneOr[String] = py.native
+  def use_bias: Boolean = py.native
+  def kernel_initializer: String = py.native
+  def bias_initializer: String = py.native
+  def kernel_regularizer: py.NoneOr[String] = py.native
+  def bias_regularizer: py.NoneOr[String] = py.native
+  def activity_regularizer: py.NoneOr[String] = py.native
+  def kernel_constraint: py.NoneOr[String] = py.native
+  def bias_constraint: py.NoneOr[String] = py.native
+  def input_shape: py.NoneOr[(Int, Int, Int)] = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/layers/Dense.scala

@@ -0,0 +1,17 @@
+package me.shadaj.scalapy.tensorflow.keras
+package layers
+
+import me.shadaj.scalapy.py
+
+@py.native trait Dense extends Layer {
+  def units: Int = py.native
+  def activation: py.NoneOr[String] = py.native
+  def use_bias: Boolean = py.native
+  def kernel_initializer: String = py.native
+  def bias_initializer: String = py.native
+  def kernel_regularizer: py.NoneOr[String] = py.native
+  def bias_regularizer: py.NoneOr[String] = py.native
+  def activity_regularizer: py.NoneOr[String] = py.native
+  def kernel_constraint: py.NoneOr[String] = py.native
+  def bias_constraint: py.NoneOr[String] = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/layers/Dropout.scala

@@ -0,0 +1,11 @@
+package me.shadaj.scalapy.tensorflow.keras
+package layers
+
+import me.shadaj.scalapy.py
+import me.shadaj.scalapy.tensorflow.Tensor
+
+@py.native trait Dropout extends Layer {
+  def rate: Double = py.native
+  def noise_shape: py.NoneOr[Tensor] = py.native
+  def seed: py.NoneOr[Int] = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/layers/Flatten.scala

@@ -0,0 +1,7 @@
+package me.shadaj.scalapy.tensorflow.keras.layers
+
+import me.shadaj.scalapy.py
+
+@py.native trait Flatten extends Layer {
+  def data_format: py.NoneOr[String] = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/layers/Layer.scala

@@ -0,0 +1,5 @@
+package me.shadaj.scalapy.tensorflow.keras.layers
+
+import me.shadaj.scalapy.py
+
+@py.native trait Layer extends py.Object

src/main/scala/me/shadaj/scalapy/tensorflow/keras/layers/Layers.scala

@@ -0,0 +1,49 @@
+package me.shadaj.scalapy.tensorflow.keras.layers
+
+import me.shadaj.scalapy.py
+import me.shadaj.scalapy.tensorflow.Tensor
+
+@py.native trait Layers extends py.Object {
+  def Conv2D(filters: Int,
+             kernel_size: py.|[Int, (Int, Int)],
+             strides: py.|[Int, (Int, Int)] = (1, 1),
+             padding: String = "valid",
+             data_format: py.NoneOr[String] = py.None,
+             dilation_rate: py.|[Int, (Int, Int)] = (1, 1),
+             activation: py.NoneOr[String] = py.None,
+             use_bias: Boolean = true,
+             kernel_initializer: String = "glorot_uniform",
+             bias_initializer: String = "zeros",
+             kernel_regularizer: py.NoneOr[String] = py.None,
+             bias_regularizer: py.NoneOr[String] = py.None,
+             activity_regularizer: py.NoneOr[String] = py.None,
+             kernel_constraint: py.NoneOr[String] = py.None,
+             bias_constraint: py.NoneOr[String] = py.None,
+             input_shape: py.NoneOr[(Int, Int, Int)] = py.None
+            ): Conv2D = py.nativeNamed
+
+  def Dropout(rate: Double,
+              noise_shape: py.NoneOr[Tensor] = py.None,
+              seed: py.NoneOr[Int] = py.None
+             ): Dropout = py.nativeNamed
+
+  def MaxPooling2D(pool_size: (Int, Int),
+                   strides: py.NoneOr[py.|[Int, (Int, Int)]] = py.None,
+                   padding: String = "valid",
+                   data_format: py.NoneOr[String] = py.None
+                  ): MaxPooling2D = py.nativeNamed
+
+  def Flatten(data_format: py.NoneOr[String] = py.None): Flatten = py.nativeNamed
+
+  def Dense(units: Int,
+            activation: py.NoneOr[String] = py.None,
+            use_bias: Boolean = true,
+            kernel_initializer: String = "glorot_uniform",
+            bias_initializer: String = "zeros",
+            kernel_regularizer: py.NoneOr[String] = py.None,
+            bias_regularizer: py.NoneOr[String] = py.None,
+            activity_regularizer: py.NoneOr[String] = py.None,
+            kernel_constraint: py.NoneOr[String] = py.None,
+            bias_constraint: py.NoneOr[String] = py.None
+           ): Dense = py.nativeNamed
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/layers/MaxPooling2D.scala

@@ -0,0 +1,11 @@
+package me.shadaj.scalapy.tensorflow.keras.layers
+
+import me.shadaj.scalapy.tensorflow.Tensor
+import me.shadaj.scalapy.py
+
+@py.native trait MaxPooling2D extends Layer {
+  def pool_size: (Int, Int) = py.native
+  def strides: py.NoneOr[py.|[Int, (Int, Int)]] = py.native
+  def padding: String = py.native
+  def data_format: py.NoneOr[String] = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/layers/package.scala

@@ -0,0 +1,30 @@
+package me.shadaj.scalapy.tensorflow.keras
+
+import scala.util.control.NonFatal
+import me.shadaj.scalapy.py.{PyValue, Reader}
+import me.shadaj.scalapy.py
+
+package object layers {
+  implicit val tupleReader: Reader[py.|[Int, (Int, Int)]] = new Reader[py.|[Int, (Int, Int)]] {
+    override def read(v: PyValue): py.|[Int, (Int, Int)] = {
+      try {
+        v.getLong.toInt
+      } catch {
+        case NonFatal(_) =>
+          val tuple = v.getTuple
+          (tuple(0).getLong.toInt, tuple(1).getLong.toInt)
+      }
+    }
+  }
+
+  implicit def noneReader[T](implicit reader: Reader[T]): Reader[py.NoneOr[T]] = new Reader[py.NoneOr[T]] {
+    override def read(v: PyValue): py.NoneOr[T] = {
+      try {
+        reader.read(v)
+      } catch {
+        // TODO: read None
+        case NonFatal(_) => py.None
+      }
+    }
+  }
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/losses/Losses.scala

@@ -0,0 +1,8 @@
+package me.shadaj.scalapy.tensorflow.keras.losses
+
+import me.shadaj.scalapy.py
+import me.shadaj.scalapy.py.PyFunction
+
+@py.native trait Losses extends py.Object {
+  def categorical_crossentropy: PyFunction = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/models/Models.scala

@@ -0,0 +1,7 @@
+package me.shadaj.scalapy.tensorflow.keras.models
+
+import me.shadaj.scalapy.py
+
+@py.native trait Models extends py.Object {
+  def Sequential(): Sequential = py.native
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/models/Sequential.scala

@@ -0,0 +1,37 @@
+package me.shadaj.scalapy.tensorflow.keras
+package models
+
+import me.shadaj.scalapy.py
+import me.shadaj.scalapy.tensorflow.keras.layers.Layer
+import me.shadaj.scalapy.numpy.NDArray
+import me.shadaj.scalapy.py.PyFunction
+import me.shadaj.scalapy.tensorflow.keras.optimizers.Optimizer
+
+@py.native trait Sequential extends py.Object {
+  private val origDynamic = this.as[py.Dynamic]
+
+  def add(layer: Layer): Unit = origDynamic.add(layer = layer).as[Unit]
+
+  def compile(optimizer: py.|[String, Optimizer] = "rmsprop",
+              loss: py.NoneOr[PyFunction] = py.None,
+              metrics: Seq[String] = Seq.empty,
+              loss_weights: Seq[(Double, Double)] = Seq.empty,
+              sample_weight_mode: py.NoneOr[String] = py.None,
+              weighted_metrics: Seq[String] = Seq.empty,
+              target_tensors: py.NoneOr[String] = py.None
+             ) = origDynamic.compile(optimizer = optimizer, loss = loss, metrics = metrics, loss_weights = loss_weights, sample_weight_mode = sample_weight_mode, weighted_metrics = weighted_metrics, target_tensors = target_tensors).as[Unit]
+
+  def fit(x: NDArray[Long],
+          y: NDArray[Long],
+          batch_size: py.NoneOr[Int] = py.None,
+          epochs: Int = 1,
+          verbose: Int = 1,
+          validation_data: py.NoneOr[(NDArray[Long], NDArray[Long])] = py.None
+         ): Unit = origDynamic.fit(x = x, y = y, batch_size = batch_size, epochs = epochs, verbose = verbose, validation_data = validation_data).as[Unit]
+
+  def evaluate(x: NDArray[Long],
+               y: NDArray[Long],
+               batch_size: py.NoneOr[Int] = py.None,
+               verbose: Int = 1
+              ): Seq[Double] = origDynamic.evaluate(x = x, y = y, batch_size = batch_size, verbose = verbose).as[Seq[Double]]
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/optimizers/Optimizers.scala

@@ -0,0 +1,18 @@
+package me.shadaj.scalapy.tensorflow.keras.optimizers
+
+import me.shadaj.scalapy.py
+import me.shadaj.scalapy.py.{PyValue, Reader}
+
+@py.native trait Optimizers extends py.Object {
+  def Adadelta(): Adadelta = py.native
+}
+
+@py.native trait Optimizer extends py.Object
+
+@py.native class Adadelta(val value: PyValue) extends Optimizer
+
+object Adadelta {
+  implicit val reader: Reader[Adadelta] = new Reader[Adadelta] {
+    override def read(v: PyValue): Adadelta = new Adadelta(v)
+  }
+}

src/main/scala/me/shadaj/scalapy/tensorflow/keras/utils/Utils.scala

@@ -0,0 +1,8 @@
+package me.shadaj.scalapy.tensorflow.keras.utils
+
+import me.shadaj.scalapy.py
+import me.shadaj.scalapy.numpy.NDArray
+
+@py.native trait Utils extends py.Object {
+  def to_categorical(y: NDArray[Long], num_classes: py.NoneOr[Long] = py.None, dtype: String = "float32"): NDArray[Long] = py.native
+}