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trait NN extends AnyRef

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NN
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  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  3. final def ==(arg0: Any): Boolean
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  4. def addBias[T](value: Tensor[T], bias: Tensor[T], cNNDataFormat: CNNDataFormat = CNNDataFormat.default)(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[T]
  5. final def asInstanceOf[T0]: T0
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  6. def clone(): AnyRef
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
  7. def conv2D[T](input: Tensor[T], filter: Tensor[T], stride1: Long, stride2: Long, padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default, dilations: (Int, Int, Int, Int) = (1, 1, 1, 1), useCuDNNOnGPU: Boolean = true)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Tensor[T]
  8. def conv2DBackpropFilter[T](input: Tensor[T], filterSizes: Tensor[Int], outputGradient: Tensor[T], stride1: Long, stride2: Long, padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default, dilations: (Int, Int, Int, Int) = (1, 1, 1, 1), useCuDNNOnGPU: Boolean = true)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Tensor[T]
  9. def conv2DBackpropInput[T](inputSizes: Tensor[Int], filter: Tensor[T], outputGradient: Tensor[T], stride1: Long, stride2: Long, padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default, dilations: (Int, Int, Int, Int) = (1, 1, 1, 1), useCuDNNOnGPU: Boolean = true)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Tensor[T]
  10. def crelu[T](x: Tensor[T], axis: Tensor[Int] = -1)(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Tensor[T]
  11. def dropout[T, I](input: Tensor[T], keepProbability: Float, scaleOutput: Boolean = true, noiseShape: Tensor[I] = null, seed: Option[Int] = None)(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Tensor[T]
  12. def elu[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], ev: Aux[TL, T]): TL[T]
  13. final def eq(arg0: AnyRef): Boolean
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  14. def equals(arg0: AnyRef): Boolean
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  15. final def getClass(): Class[_ <: AnyRef]
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  16. def hashCode(): Int
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  17. def inTopK[I](predictions: Tensor[Float], targets: Tensor[I], k: Tensor[I])(implicit arg0: core.types.TF[I], arg1: core.types.IsIntOrLong[I]): Tensor[Boolean]
  18. final def isInstanceOf[T0]: Boolean
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  19. def l2Loss[T](input: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Tensor[T]
  20. def l2Normalize[T](x: Tensor[T], axes: Tensor[Int], epsilon: Float = 1e-12f)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  21. def linear[T](x: Tensor[T], weights: Tensor[T], bias: Tensor[T] = null)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  22. def localResponseNormalization[T](input: Tensor[T], depthRadius: Int = 5, bias: Float = 1.0f, alpha: Float = 1.0f, beta: Float = 0.5f)(implicit arg0: core.types.TF[T], arg1: core.types.IsTruncatedHalfOrHalfOrFloat[T]): Tensor[T]
  23. def logPoissonLoss[T](logPredictions: Tensor[T], targets: Tensor[T], computeFullLoss: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Tensor[T]
  24. def logSoftmax[T](logits: Tensor[T], axis: Int = -1)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Tensor[T]
  25. def lrn[T](input: Tensor[T], depthRadius: Int = 5, bias: Float = 1.0f, alpha: Float = 1.0f, beta: Float = 0.5f)(implicit arg0: core.types.TF[T], arg1: core.types.IsTruncatedHalfOrHalfOrFloat[T]): Tensor[T]
  26. def maxPool[T](input: Tensor[T], windowSize: Seq[Int], stride1: Int, stride2: Int, padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default)(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[T]
  27. def maxPoolGrad[T](originalInput: Tensor[T], originalOutput: Tensor[T], outputGradient: Tensor[T], windowSize: Seq[Int], stride1: Int, stride2: Int, padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default)(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[T]
  28. def maxPoolGradGrad[T](originalInput: Tensor[T], originalOutput: Tensor[T], outputGradient: Tensor[T], windowSize: Seq[Int], stride1: Int, stride2: Int, padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default)(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[T]
  29. final def ne(arg0: AnyRef): Boolean
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  30. final def notify(): Unit
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  31. final def notifyAll(): Unit
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  32. def relu[T](x: Tensor[T], alpha: Float = 0.0f)(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Tensor[T]
  33. def relu6[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], ev: Aux[TL, T]): TL[T]
  34. def selu[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], ev: Aux[TL, T]): TL[T]
  35. def sequenceLoss[T, I](logits: Tensor[T], labels: Tensor[I], weights: Tensor[T] = null, averageAcrossTimeSteps: Boolean = true, averageAcrossBatch: Boolean = true, lossFn: (Tensor[T], Tensor[I]) => Tensor[T] = null)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
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    @throws(scala.this.throws.<init>$default$1[org.platanios.tensorflow.api.core.exception.InvalidShapeException])
  36. def sigmoidCrossEntropy[T](logits: Tensor[T], labels: Tensor[T], weights: Tensor[T] = null)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Tensor[T]
  37. def softmax[T](logits: Tensor[T], axis: Int = -1)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Tensor[T]
  38. def softmaxCrossEntropy[T](logits: Tensor[T], labels: Tensor[T], axis: Int = -1)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Tensor[T]
  39. def softplus[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], ev: Aux[TL, T]): TL[T]
  40. def softsign[T](input: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Tensor[T]
  41. def sparseSoftmaxCrossEntropy[T, I](logits: Tensor[T], labels: Tensor[I], axis: Int = -1)(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
  42. final def synchronized[T0](arg0: => T0): T0
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  43. def toString(): String
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  44. def topK[T](input: Tensor[T], k: Tensor[Int] = 1, sorted: Boolean = true)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): (Tensor[T], Tensor[Int])
  45. final def wait(arg0: Long, arg1: Int): Unit
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  46. final def wait(arg0: Long): Unit
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  47. final def wait(): Unit
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