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object NN extends NN

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Type Members

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

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  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]
    Attributes
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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[lang]
    Definition Classes
    AnyRef
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
  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(scala.this.throws.<init>$default$1[IllegalArgumentException])
  16. def dynamicDropout[T, I](input: Output[T], keepProbability: Output[T], scaleOutput: Boolean = true, noiseShape: Output[I] = null, seed: Option[Int] = None, name: String = "Dropout")(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Output[T]
    Definition Classes
    NN
  17. def elu[T, OL[A] <: OutputLike[A]](input: OL[T], name: String = "ELU")(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], ev: Aux[OL, T]): OL[T]
    Definition Classes
    NN
  18. def eluGradient[T](op: Op[Output[T], Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Output[T]
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    Definition Classes
    NN
  19. def eluHessian[T](op: Op[(Output[T], Output[T]), Output[T]], outputGradient: Output[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): (Output[T], Output[T])
    Attributes
    protected
    Definition Classes
    NN
  20. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  22. def fusedBatchNormalization[T](x: Output[T], scale: Output[Float], offset: Output[Float], mean: Option[Output[Float]] = None, variance: Option[Output[Float]] = None, epsilon: Float = 0.0001f, dataFormat: CNNDataFormat = NWCFormat, isTraining: Boolean = true, name: String = "FusedBatchNormalization")(implicit arg0: core.types.TF[T], arg1: core.types.IsDecimal[T]): (Output[T], Output[Float], Output[Float], Output[Float], Output[Float])
    Definition Classes
    NN
    Annotations
    @throws(scala.this.throws.<init>$default$1[IllegalArgumentException])
  23. 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])
    Attributes
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    Definition Classes
    NN
  24. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
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    @native() @HotSpotIntrinsicCandidate()
  25. def hashCode(): Int
    Definition Classes
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    Annotations
    @native() @HotSpotIntrinsicCandidate()
  26. 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
  27. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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]
    Attributes
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    Definition Classes
    NN
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. final def ne(arg0: AnyRef): Boolean
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  45. final def notify(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  46. final def notifyAll(): Unit
    Definition Classes
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    @native() @HotSpotIntrinsicCandidate()
  47. 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
  48. 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
  49. 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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  50. 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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    Definition Classes
    NN
  51. 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
  52. 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
  53. 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
  54. 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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    NN
  55. 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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  56. 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(scala.this.throws.<init>$default$1[org.platanios.tensorflow.api.core.exception.InvalidShapeException])
  57. 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
  58. 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
  59. 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
  60. 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])
    Attributes
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    NN
  61. 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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  62. 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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  63. 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
  64. 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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  65. 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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  66. 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
  67. 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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  68. 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
    NN
  69. 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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  70. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
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  71. def toString(): String
    Definition Classes
    AnyRef → Any
  72. 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
  73. 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
  74. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  75. final def wait(arg0: Long): Unit
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    @throws(classOf[java.lang.InterruptedException]) @native()
  76. final def wait(): Unit
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    @throws(classOf[java.lang.InterruptedException])
  77. object CNNDataFormat
  78. object ConvPaddingMode
  79. case object NCWFormat extends CNNDataFormat with Product with Serializable
  80. case object NWCFormat extends CNNDataFormat with Product with Serializable
  81. case object SameConvPadding extends ConvPaddingMode with Product with Serializable
  82. case object ValidConvPadding extends ConvPaddingMode with Product with Serializable

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  1. def finalize(): Unit
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    (Since version ) see corresponding Javadoc for more information.

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