forked from kscience/kmath
First tensorflow test.
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c583320051
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@ -7,6 +7,7 @@ description = "Google tensorflow connector"
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dependencies {
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api(project(":kmath-tensors"))
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api("org.tensorflow:tensorflow-core-api:0.3.3")
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testImplementation("org.tensorflow:tensorflow-core-platform:0.3.3")
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}
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readme {
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@ -5,6 +5,7 @@ import org.tensorflow.Output
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import org.tensorflow.ndarray.NdArray
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import org.tensorflow.op.core.Constant
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import org.tensorflow.types.TFloat64
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import space.kscience.kmath.expressions.Symbol
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import space.kscience.kmath.misc.PerformancePitfall
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import space.kscience.kmath.nd.DefaultStrides
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import space.kscience.kmath.nd.Shape
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@ -13,20 +14,22 @@ import space.kscience.kmath.operations.DoubleField
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public class DoubleTensorFlowOutput(
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graph: Graph,
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output: Output<TFloat64>
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output: Output<TFloat64>,
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) : TensorFlowOutput<Double, TFloat64>(graph, output) {
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override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Double> = output.asTensor()
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override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Double> = this as TFloat64
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}
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public class DoubleTensorFlowAlgebra internal constructor(
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graph: Graph
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graph: Graph,
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) : TensorFlowAlgebra<Double, TFloat64, DoubleField>(graph) {
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override val elementAlgebra: DoubleField get() = DoubleField
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override fun structureND(
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shape: Shape,
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initializer: DoubleField.(IntArray) -> Double
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initializer: DoubleField.(IntArray) -> Double,
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): StructureND<Double> {
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val res = TFloat64.tensorOf(org.tensorflow.ndarray.Shape.of(*shape.toLongArray())) { array ->
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DefaultStrides(shape).forEach { index ->
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@ -53,4 +56,20 @@ public class DoubleTensorFlowAlgebra internal constructor(
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override fun Output<TFloat64>.wrap(): TensorFlowOutput<Double, TFloat64> = DoubleTensorFlowOutput(graph, this)
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override fun const(value: Double): Constant<TFloat64> = ops.constant(value)
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}
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public fun DoubleField.produceWithTF(
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block: DoubleTensorFlowAlgebra.() -> StructureND<Double>,
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): StructureND<Double> = Graph().use { graph ->
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val scope = DoubleTensorFlowAlgebra(graph)
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scope.export(scope.block())
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}
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public fun DoubleField.produceMapWithTF(
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block: DoubleTensorFlowAlgebra.() -> Map<Symbol, StructureND<Double>>,
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): Map<Symbol, StructureND<Double>> = Graph().use { graph ->
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val scope = DoubleTensorFlowAlgebra(graph)
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scope.block().mapValues { scope.export(it.value) }
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}
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@ -0,0 +1,21 @@
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package space.kscience.kmath.tensorflow
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import org.tensorflow.Graph
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import org.tensorflow.Output
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import org.tensorflow.ndarray.NdArray
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import org.tensorflow.types.TInt32
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import org.tensorflow.types.TInt64
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public class IntTensorFlowOutput(
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graph: Graph,
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output: Output<TInt32>,
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) : TensorFlowOutput<Int, TInt32>(graph, output) {
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override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Int> = this as TInt32
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}
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public class LongTensorFlowOutput(
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graph: Graph,
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output: Output<TInt64>,
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) : TensorFlowOutput<Long, TInt64>(graph, output) {
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override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Long> = this as TInt64
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}
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@ -8,8 +8,13 @@ import org.tensorflow.Session
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import org.tensorflow.ndarray.NdArray
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import org.tensorflow.op.Ops
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import org.tensorflow.op.core.Constant
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import org.tensorflow.op.core.Max
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import org.tensorflow.op.core.Min
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import org.tensorflow.op.core.Sum
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import org.tensorflow.types.TInt32
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import org.tensorflow.types.family.TType
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import space.kscience.kmath.misc.PerformancePitfall
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.nd.Shape
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.operations.Ring
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@ -38,8 +43,8 @@ public value class TensorFlowArray<T>(public val tensor: NdArray<T>) : Tensor<T>
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}
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public abstract class TensorFlowOutput<T, TT : TType>(
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private val graph: Graph,
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output: Output<TT>
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protected val graph: Graph,
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output: Output<TT>,
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) : TensorFlowTensor<T> {
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public var output: Output<TT> = output
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@ -49,9 +54,10 @@ public abstract class TensorFlowOutput<T, TT : TType>(
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protected abstract fun org.tensorflow.Tensor.actualizeTensor(): NdArray<T>
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private val actualTensor by lazy {
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val session = Session(graph)
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TensorFlowArray(session.runner().fetch(output).run().first().actualizeTensor())
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internal val actualTensor by lazy {
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Session(graph).use { session ->
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TensorFlowArray(session.runner().fetch(output).run().first().actualizeTensor())
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}
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}
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override fun get(index: IntArray): T = actualTensor[index]
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@ -66,9 +72,9 @@ public abstract class TensorFlowOutput<T, TT : TType>(
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}
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public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal constructor(
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protected val graph: Graph
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) : TensorAlgebra<T,A> {
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public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal constructor(
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protected val graph: Graph,
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) : TensorAlgebra<T, A> {
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protected val ops: Ops by lazy { Ops.create(graph) }
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@ -83,7 +89,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
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private inline fun StructureND<T>.biOp(
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other: StructureND<T>,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): TensorFlowOutput<T, TT> {
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val left = asTensorFlow().output
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val right = other.asTensorFlow().output
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@ -92,7 +98,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
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private inline fun T.biOp(
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other: StructureND<T>,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): TensorFlowOutput<T, TT> {
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val left = const(this)
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val right = other.asTensorFlow().output
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@ -101,7 +107,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
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private inline fun StructureND<T>.biOp(
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value: T,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): TensorFlowOutput<T, TT> {
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val left = asTensorFlow().output
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val right = const(value)
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@ -110,7 +116,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
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private inline fun Tensor<T>.inPlaceOp(
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other: StructureND<T>,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): Unit {
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val origin = asTensorFlow()
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val left = origin.output
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@ -120,7 +126,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
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private inline fun Tensor<T>.inPlaceOp(
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value: T,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): Unit {
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val origin = asTensorFlow()
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val left = origin.output
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@ -128,8 +134,8 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
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origin.output = operation(left, right).asOutput()
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}
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private inline fun unOp(value: StructureND<T>, operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
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operation(value.asTensorFlow().output).asOutput().wrap()
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private inline fun StructureND<T>.unOp(operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
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operation(asTensorFlow().output).asOutput().wrap()
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override fun T.plus(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::add)
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@ -149,67 +155,79 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
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override fun Tensor<T>.minusAssign(value: T): Unit = inPlaceOp(value, ops.math::sub)
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override fun Tensor<T>.minusAssign(other: StructureND<T>): Unit = inPlaceOp(other, ops.math::sub)
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override fun Tensor<T>.minusAssign(arg: StructureND<T>): Unit = inPlaceOp(arg, ops.math::sub)
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override fun T.times(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
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override fun StructureND<T>.times(arg: T): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
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override fun StructureND<T>.times(other: StructureND<T>): TensorFlowOutput<T, TT> = biOp(other, ops.math::mul)
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override fun StructureND<T>.times(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
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override fun Tensor<T>.timesAssign(value: T): Unit = inPlaceOp(value, ops.math::mul)
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override fun Tensor<T>.timesAssign(arg: StructureND<T>): Unit = inPlaceOp(arg, ops.math::mul)
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override fun StructureND<T>.unaryMinus(): TensorFlowOutput<T, TT> = unOp(this, ops.math::neg)
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override fun StructureND<T>.unaryMinus(): TensorFlowOutput<T, TT> = unOp(ops.math::neg)
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override fun StructureND<T>.get(i: Int): Tensor<T> {
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override fun Tensor<T>.get(i: Int): Tensor<T> = unOp {
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TODO("Not yet implemented")
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}
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override fun StructureND<T>.transpose(i: Int, j: Int): Tensor<T> = unOp(this) {
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override fun Tensor<T>.transpose(i: Int, j: Int): Tensor<T> = unOp {
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ops.linalg.transpose(it, ops.constant(intArrayOf(i, j)))
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}
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override fun Tensor<T>.view(shape: IntArray): Tensor<T> {
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TODO("Not yet implemented")
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override fun Tensor<T>.view(shape: IntArray): Tensor<T> = unOp {
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ops.reshape(it, ops.constant(shape))
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}
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override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> {
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TODO("Not yet implemented")
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override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> = biOp(other) { l, r ->
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ops.reshape(l, ops.shape(r))
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}
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override fun StructureND<T>.dot(other: StructureND<T>): TensorFlowOutput<T, TT> = biOp(other) { l, r ->
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ops.linalg.matMul(l, r)
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}
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override fun diagonalEmbedding(diagonalEntries: Tensor<T>, offset: Int, dim1: Int, dim2: Int): Tensor<T> = ops.run {
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TODO("Not yet implemented")
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override fun diagonalEmbedding(
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diagonalEntries: Tensor<T>,
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offset: Int,
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dim1: Int,
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dim2: Int,
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): TensorFlowOutput<T, TT> = diagonalEntries.unOp {
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TODO()
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}
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override fun StructureND<T>.sum(): T = TODO("Not yet implemented")
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override fun StructureND<T>.sum(): T = unOp {
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ops.sum(it, ops.constant(intArrayOf()))
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}.value()
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override fun StructureND<T>.sum(dim: Int, keepDim: Boolean): Tensor<T> {
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TODO("Not yet implemented")
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override fun StructureND<T>.sum(dim: Int, keepDim: Boolean): TensorFlowOutput<T, TT> = unOp {
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ops.sum(it, ops.constant(dim), Sum.keepDims(keepDim))
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}
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override fun StructureND<T>.min(): T {
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TODO("Not yet implemented")
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override fun StructureND<T>.min(): T = unOp {
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ops.min(it, ops.constant(intArrayOf()))
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}.value()
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override fun StructureND<T>.min(dim: Int, keepDim: Boolean): Tensor<T> = unOp {
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ops.min(it, ops.constant(dim), Min.keepDims(keepDim))
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}
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override fun StructureND<T>.min(dim: Int, keepDim: Boolean): Tensor<T> {
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TODO("Not yet implemented")
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override fun StructureND<T>.max(): T = unOp {
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ops.max(it, ops.constant(intArrayOf()))
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}.value()
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override fun StructureND<T>.max(dim: Int, keepDim: Boolean): Tensor<T> = unOp {
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ops.max(it, ops.constant(dim), Max.keepDims(keepDim))
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}
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override fun StructureND<T>.max(): T {
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TODO("Not yet implemented")
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}
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override fun StructureND<T>.argMax(dim: Int, keepDim: Boolean): Tensor<Int> = IntTensorFlowOutput(
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graph,
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ops.math.argMax(asTensorFlow().output, ops.constant(dim), TInt32::class.java).output()
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).actualTensor
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override fun StructureND<T>.max(dim: Int, keepDim: Boolean): Tensor<T> {
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TODO("Not yet implemented")
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}
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override fun StructureND<T>.argMax(dim: Int, keepDim: Boolean): Tensor<T> {
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TODO("Not yet implemented")
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}
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@OptIn(UnstableKMathAPI::class)
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override fun export(arg: StructureND<T>): StructureND<T> =
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if (arg is TensorFlowOutput<T, *>) arg.actualTensor else arg
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}
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@ -0,0 +1,19 @@
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package space.kscience.kmath.tensorflow
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import org.junit.jupiter.api.Test
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.nd.structureND
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import space.kscience.kmath.operations.DoubleField
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class DoubleTensorFlowOps {
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@Test
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fun basicOps() {
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val res = DoubleField.produceWithTF {
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val initial = structureND(2, 2) { 1.0 }
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initial + (initial * 2.0)
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}
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println(StructureND.toString(res))
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}
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}
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@ -3,6 +3,8 @@
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* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
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*/
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@file:OptIn(PerformancePitfall::class)
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package space.kscience.kmath.viktor
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import org.jetbrains.bio.viktor.F64Array
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