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object NN extends NN

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  1. sealed trait CNNDataFormat extends AnyRef
  2. sealed trait ConvPaddingMode extends AnyRef

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. def addBias[T](value: Output[T], bias: Output[T], cNNDataFormat: CNNDataFormat = CNNDataFormat.default, name: String = "AddBias")(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Output[T]
    Definition Classes
    NN
  5. def addBiasGradient[T](op: Op[(Output[T], Output[T]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): (Output[T], Output[T])
    Attributes
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    Definition Classes
    NN
  6. def addBiasHessian[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Output[T]
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    NN
  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. def batchNormalization[T](x: Output[T], mean: Output[T], variance: Output[T], offset: Option[Output[T]] = None, scale: Option[Output[T]] = None, epsilon: Output[T], name: String = "BatchNormalization")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  9. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @native() @throws( ... )
  10. def conv2D[T](input: Output[T], filter: Output[T], stride1: Long, stride2: Long, padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default, dilations: (Int, Int, Int, Int) = (1, 1, 1, 1), useCuDNNOnGPU: Boolean = true, name: String = "Conv2D")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  11. def conv2DBackpropFilter[T](input: Output[T], filterSizes: Output[Int], outputGradient: Output[T], stride1: Long, stride2: Long, padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default, dilations: (Int, Int, Int, Int) = (1, 1, 1, 1), useCuDNNOnGPU: Boolean = true, name: String = "Conv2DBackpropFilter")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  12. def conv2DBackpropInput[T](inputSizes: Output[Int], filter: Output[T], outputGradient: Output[T], stride1: Long, stride2: Long, padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default, dilations: (Int, Int, Int, Int) = (1, 1, 1, 1), useCuDNNOnGPU: Boolean = true, name: String = "Conv2DBackpropInput")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  13. def conv2DGradient[T](op: Op[(Output[T], Output[T]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): (Output[T], Output[T])
    Attributes
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    Definition Classes
    NN
  14. def crelu[T](input: Output[T], axis: Output[Int] = -1, name: String = "CReLU")(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Output[T]
    Definition Classes
    NN
  15. def dropout[T, I](input: Output[T], keepProbability: Float, scaleOutput: Boolean = true, noiseShape: Output[I] = null, seed: Option[Int] = None, name: String = "Dropout")(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Output[T]
    Definition Classes
    NN
    Annotations
    @throws( ... )
  16. def dynamicDropout[T, I](input: Output[T], keepProbability: Output[T], scaleOutput: Boolean = true, noiseShape: Output[I] = null, seed: Option[Int] = None, name: String = "Dropout")(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Output[T]
    Definition Classes
    NN
  17. def elu[T, OL[A] <: OutputLike[A]](input: OL[T], name: String = "ELU")(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], ev: Aux[OL, T]): OL[T]
    Definition Classes
    NN
  18. def eluGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Output[T]
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    Definition Classes
    NN
  19. def eluHessian[T](op: Op[(Output[T], Output[T]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): (Output[T], Output[T])
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    Definition Classes
    NN
  20. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  22. def finalize(): Unit
    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  23. def fusedBatchNormalization[T](x: Output[T], scale: Output[Float], offset: Output[Float], mean: Option[Output[Float]] = None, variance: Option[Output[Float]] = None, epsilon: Float = 0.0001f, dataFormat: CNNDataFormat = NWCFormat, isTraining: Boolean = true, name: String = "FusedBatchNormalization")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): (Output[T], Output[Float], Output[Float], Output[Float], Output[Float])
    Definition Classes
    NN
    Annotations
    @throws( ... )
  24. def fusedBatchNormalizationGradient[T](op: Op[(Output[T], Output[Float], Output[Float], Output[Float], Output[Float]), (Output[T], Output[Float], Output[Float], Output[Float], Output[Float])], outputGradient: (Output[T], Output[Float], Output[Float], Output[Float], Output[Float]))(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): (Output[T], Output[Float], Output[Float], Output[Float], Output[Float])
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    Definition Classes
    NN
  25. final def getClass(): Class[_]
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    @native()
  26. def hashCode(): Int
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    @native()
  27. def inTopK[I](predictions: Output[Float], targets: Output[I], k: Output[I], name: String = "InTopK")(implicit arg0: core.types.TF[I], arg1: core.types.IsIntOrLong[I]): Output[Boolean]
    Definition Classes
    NN
  28. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  29. def l2Loss[T](input: Output[T], name: String = "L2Loss")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  30. def l2LossGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
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    NN
  31. def l2Normalize[T, I](x: Output[T], axes: Output[I], epsilon: Float = 1e-12f, name: String = "L2Normalize")(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Output[T]
    Definition Classes
    NN
  32. def linear[T](x: Output[T], weights: Output[T], bias: Output[T] = null, name: String = "Linear")(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Output[T]
    Definition Classes
    NN
  33. def localResponseNormalization[T](input: Output[T], depthRadius: Int = 5, bias: Float = 1.0f, alpha: Float = 1.0f, beta: Float = 0.5f, name: String = "LocalResponseNormalization")(implicit arg0: core.types.TF[T], arg1: core.types.IsTruncatedHalfOrHalfOrFloat[T]): Output[T]
    Definition Classes
    NN
  34. def logPoissonLoss[T](logPredictions: Output[T], targets: Output[T], computeFullLoss: Boolean = false, name: String = "LogPoissonLoss")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  35. def logSoftmax[T](logits: Output[T], axis: Int = -1, name: String = "LogSoftmax")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  36. def logSoftmaxGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
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    NN
  37. def lrn[T](input: Output[T], depthRadius: Int = 5, bias: Float = 1.0f, alpha: Float = 1.0f, beta: Float = 0.5f, name: String = "LRN")(implicit arg0: core.types.TF[T], arg1: core.types.IsTruncatedHalfOrHalfOrFloat[T]): Output[T]
    Definition Classes
    NN
  38. def lrnGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsTruncatedHalfOrHalfOrFloat[T]): Output[T]
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    NN
  39. def maxPool[T](input: Output[T], windowSize: Output[Int], strides: Output[Int], padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default, name: String = "MaxPool")(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Output[T]
    Definition Classes
    NN
  40. def maxPoolGrad[T](originalInput: Output[T], originalOutput: Output[T], outputGradient: Output[T], windowSize: Output[Int], strides: Output[Int], padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default, name: String = "MaxPoolGrad")(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Output[T]
    Definition Classes
    NN
  41. def maxPoolGradGrad[T](originalInput: Output[T], originalOutput: Output[T], outputGradient: Output[T], windowSize: Output[Int], strides: Output[Int], padding: ConvPaddingMode, dataFormat: CNNDataFormat = CNNDataFormat.default, name: String = "MaxPoolGradGrad")(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Output[T]
    Definition Classes
    NN
  42. def maxPoolGradient[T](op: Op[(Output[T], Output[Int], Output[Int]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): (Output[T], Output[Int], Output[Int])
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    NN
  43. def maxPoolHessian[T](op: Op[(Output[T], Output[T], Output[T], Output[Int], Output[Int]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): (Output[T], Output[T], Output[T], Output[Int], Output[Int])
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    NN
  44. def maxPoolHessianGradient[T](op: Op[(Output[T], Output[T], Output[T], Output[Int], Output[Int]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): (Output[T], Output[T], Output[T], Output[Int], Output[Int])
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    NN
  45. final def ne(arg0: AnyRef): Boolean
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  46. final def notify(): Unit
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    @native()
  47. final def notifyAll(): Unit
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    @native()
  48. def relu[T](input: Output[T], alpha: Float = 0.0f, name: String = "ReLU")(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Output[T]
    Definition Classes
    NN
  49. def relu6[T, OL[A] <: OutputLike[A]](input: OL[T], name: String = "ReLU6")(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], ev: Aux[OL, T]): OL[T]
    Definition Classes
    NN
  50. def relu6Gradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Output[T]
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    NN
  51. def relu6Hessian[T](op: Op[(Output[T], Output[T]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): (Output[T], Output[T])
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    NN
  52. def reluGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Output[T]
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    NN
  53. def reluHessian[T](op: Op[(Output[T], Output[T]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): (Output[T], Output[T])
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    NN
  54. def selu[T, OL[A] <: OutputLike[A]](input: OL[T], name: String = "SELU")(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], ev: Aux[OL, T]): OL[T]
    Definition Classes
    NN
  55. def seluGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Output[T]
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  56. def seluHessian[T](op: Op[(Output[T], Output[T]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): (Output[T], Output[T])
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  57. def sequenceLoss[T, L](logits: Output[T], labels: Output[L], lossFn: (Output[T], Output[L]) ⇒ Output[T], weights: Output[T] = null, averageAcrossTimeSteps: Boolean = true, averageAcrossBatch: Boolean = true, name: String = "SequenceLoss")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T], arg2: core.types.TF[L]): Output[T]
    Definition Classes
    NN
    Annotations
    @throws( ... )
  58. def sigmoidCrossEntropy[T](logits: Output[T], labels: Output[T], weights: Output[T] = null, name: String = "SigmoidCrossEntropy")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  59. def softmax[T](logits: Output[T], axis: Int = -1, name: String = "Softmax")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  60. def softmaxCrossEntropy[T](logits: Output[T], labels: Output[T], axis: Int = -1, name: String = "SoftmaxCrossEntropy")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
    Definition Classes
    NN
  61. def softmaxCrossEntropyGradient[T](op: Op[(Output[T], Output[T]), (Output[T], Output[T])], outputGradient: (Output[T], Output[T]))(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): (Output[T], Output[T])
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    NN
  62. def softmaxGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
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    NN
  63. def softmaxHelper[T](logits: Output[T], opType: String, axis: Int = -1, name: String = "Softmax")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
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    NN
  64. def softplus[T, OL[A] <: OutputLike[A]](input: OL[T], name: String = "Softplus")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T], ev: Aux[OL, T]): OL[T]
    Definition Classes
    NN
  65. def softplusGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
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  66. def softplusHessian[T](op: Op[(Output[T], Output[T]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): (Output[T], Output[T])
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  67. def softsign[T, OL[A] <: OutputLike[A]](input: OL[T], name: String = "Softsign")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T], ev: Aux[OL, T]): OL[T]
    Definition Classes
    NN
  68. def softsignGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): Output[T]
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  69. def sparseSoftmaxCrossEntropy[T, I](logits: Output[T], labels: Output[I], axis: Int = -1, name: String = "SparseSoftmaxCrossEntropy")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Output[T]
    Definition Classes
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  70. def sparseSoftmaxCrossEntropyGradient[T, I](op: Op[(Output[T], Output[I]), (Output[T], Output[T])], outputGradient: (Output[T], Output[T]))(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): (Output[T], Output[I])
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  71. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  72. def toString(): String
    Definition Classes
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  73. def topK[T](input: Output[T], k: Output[Int], sorted: Boolean = true, name: String = "TopK")(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): (Output[T], Output[Int])
    Definition Classes
    NN
  74. def topKGradient[T](op: Op[(Output[T], Output[Int]), (Output[T], Output[Int])], outputGradient: (Output[T], Output[Int]))(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): (Output[T], Output[Int])
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    NN
  75. final def wait(): Unit
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    @throws( ... )
  76. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  77. final def wait(arg0: Long): Unit
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    @native() @throws( ... )
  78. object CNNDataFormat
  79. object ConvPaddingMode
  80. object NCWFormat extends CNNDataFormat with Product with Serializable
  81. object NWCFormat extends CNNDataFormat with Product with Serializable
  82. object SameConvPadding extends ConvPaddingMode with Product with Serializable
  83. object ValidConvPadding extends ConvPaddingMode with Product with Serializable

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