Made properties public (#538) (#553)

(cherry picked from commit b4450a3)

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/optimizer/AdaDelta.kt

@@ -46,9 +46,9 @@ private const val ACCUMULATOR_UPDATE = "accum_update"
  * @property [epsilon] Float >= 0. Fuzz factor.
  */
 public class AdaDelta(
-    private val learningRate: Float = 0.1f,
-    private val rho: Float = 0.95f,
-    private val epsilon: Float = 1e-8f,
+    public val learningRate: Float = 0.1f,
+    public val rho: Float = 0.95f,
+    public val epsilon: Float = 1e-8f,
     clipGradient: ClipGradientAction = NoClipGradient()
 ) : Optimizer(clipGradient) {
     private lateinit var epsilonConstant: Constant<Float>

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/optimizer/AdaGrad.kt

@@ -40,8 +40,8 @@ private const val ACCUMULATOR = "accumulator"
  * @property [initialAccumulatorValue] Decay: Float >= 0. Learning rate decay over each update.
  */
 public class AdaGrad(
-    private val learningRate: Float = 0.1f,
-    private val initialAccumulatorValue: Float = 0.01f,
+    public val learningRate: Float = 0.1f,
+    public val initialAccumulatorValue: Float = 0.01f,
     clipGradient: ClipGradientAction = NoClipGradient()
 ) : Optimizer(clipGradient) {
     private lateinit var initialAccumulatorValueConstant: Constant<Float>

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/optimizer/AdaGradDA.kt

@@ -46,10 +46,10 @@ private const val SQUARED_ACCUMULATOR = "gradient_squared_accumulator"
  * @property [l2Strength] A float value, must be greater than or equal to zero.
  */
 public class AdaGradDA(
-    private val learningRate: Float = 0.1f,
-    private val initialAccumulatorValue: Float = 0.01f,
-    private val l1Strength: Float = 0.01f,
-    private val l2Strength: Float = 0.01f,
+    public val learningRate: Float = 0.1f,
+    public val initialAccumulatorValue: Float = 0.01f,
+    public val l1Strength: Float = 0.01f,
+    public val l2Strength: Float = 0.01f,
     clipGradient: ClipGradientAction = NoClipGradient()
 ) : Optimizer(clipGradient) {
     private lateinit var learningRateConst: Constant<Float>

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/optimizer/Adam.kt

@@ -45,11 +45,11 @@ private val SECOND_BETA_POWER_NAME = defaultOptimizerVariableName("beta2_power")
  * @property [epsilon] Float >= 0. Fuzz factor.
  */
 public class Adam(
-    private val learningRate: Float = 0.001f,
-    private val beta1: Float = 0.9f,
-    private val beta2: Float = 0.999f,
-    private val epsilon: Float = 1e-07f,
-    private val useNesterov: Boolean = false,
+    public val learningRate: Float = 0.001f,
+    public val beta1: Float = 0.9f,
+    public val beta2: Float = 0.999f,
+    public val epsilon: Float = 1e-07f,
+    public val useNesterov: Boolean = false,
     clipGradient: ClipGradientAction = NoClipGradient()
 ) : Optimizer(clipGradient) {
 

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/optimizer/Adamax.kt

@@ -46,10 +46,10 @@ private val FIRST_BETA_POWER_NAME = defaultOptimizerVariableName("beta1_power")
  * @property [epsilon] Float >= 0. Fuzz factor.
  */
 public class Adamax(
-    private val learningRate: Float = 0.001f,
-    private val beta1: Float = 0.9f,
-    private val beta2: Float = 0.999f,
-    private val epsilon: Float = 1e-07f,
+    public val learningRate: Float = 0.001f,
+    public val beta1: Float = 0.9f,
+    public val beta2: Float = 0.999f,
+    public val epsilon: Float = 1e-07f,
     clipGradient: ClipGradientAction = NoClipGradient()
 ) : Optimizer(clipGradient) {
 

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/optimizer/Ftrl.kt

@@ -52,12 +52,12 @@ private const val LINEAR_ACCUMULATOR = "linear_accumulator"
  * When input is sparse shrinkage will only happen on the active weights.
  */
 public class Ftrl(
-    private val learningRate: Float = 0.001f,
-    private val l1RegularizationStrength: Float = 0.0f,
-    private val l2RegularizationStrength: Float = 0.0f,
-    private val learningRatePower: Float = -0.5f,
-    private val l2ShrinkageRegularizationStrength: Float = 0.0f,
-    private var initialAccumulatorValue: Float = 0.0f,
+    public val learningRate: Float = 0.001f,
+    public val l1RegularizationStrength: Float = 0.0f,
+    public val l2RegularizationStrength: Float = 0.0f,
+    public val learningRatePower: Float = -0.5f,
+    public val l2ShrinkageRegularizationStrength: Float = 0.0f,
+    public var initialAccumulatorValue: Float = 0.0f,
     clipGradient: ClipGradientAction = NoClipGradient()
 ) : Optimizer(clipGradient) {
     /**  */

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/optimizer/Momentum.kt

@@ -25,9 +25,9 @@ private const val MOMENTUM = "momentum"
  * @property [useNesterov] If true, applies Nesterov momentum.
  */
 public class Momentum(
-    private val learningRate: Float = 0.001f,
-    private val momentum: Float = 0.99f,
-    private val useNesterov: Boolean = true,
+    public val learningRate: Float = 0.001f,
+    public val momentum: Float = 0.99f,
+    public val useNesterov: Boolean = true,
     clipGradient: ClipGradientAction = NoClipGradient()
 ) : Optimizer(clipGradient) {
     private lateinit var momentumConst: Constant<Float>

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/optimizer/RMSProp.kt

@@ -31,11 +31,11 @@ private const val MOMENTUM = "momentum"
  * @property [centered] Centered or not.
  */
 public class RMSProp(
-    private val learningRate: Float = 0.001f,
-    private val decay: Float = 0.9f,
-    private val momentum: Float = 0.0f,
-    private val epsilon: Float = 1e-10f,
-    private val centered: Boolean = false,
+    public val learningRate: Float = 0.001f,
+    public val decay: Float = 0.9f,
+    public val momentum: Float = 0.0f,
+    public val epsilon: Float = 1e-10f,
+    public val centered: Boolean = false,
     clipGradient: ClipGradientAction = NoClipGradient()
 ) : Optimizer(clipGradient) {
 

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/optimizer/SGD.kt

@@ -19,7 +19,7 @@ import org.tensorflow.op.train.ApplyGradientDescent
  * NOTE: It's not an equivalent for `keras.sgd`, it is a pure SGD with simple 'variable' update by subtracting 'alpha' * 'delta' from it.
  */
 public class SGD(
-    private var learningRate: Float = 0.2f,
+    public var learningRate: Float = 0.2f,
     clipGradient: ClipGradientAction = NoClipGradient()
 ) : Optimizer(clipGradient) {