NNOps

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

4. def addBias(bias: Output, cNNDataFormat: NN.CNNDataFormat = CNNDataFormat.default): Output

   

   $OpDocNNAddBias

   $OpDocNNAddBias

   bias

   Bias tensor that must be one-dimensional (i.e., it must have rank 1).

   cNNDataFormat

   Data format of the input and output tensors. With the default format NWCFormat, the bias tensor will be added to the last dimension of the value tensor. Alternatively, the format could be NCWFormat, and the bias tensor would be added to the third-to-last dimension.

   returns

   Created op output.

5. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

6. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

7. def conv2D(filter: Output, stride1: Long, stride2: Long, padding: ConvPaddingMode, dataFormat: NN.CNNDataFormat = CNNDataFormat.default, dilations: (Int, Int, Int, Int) = (1, 1, 1, 1), useCuDNNOnGPU: Boolean = true, name: String = "Conv2D"): Output

   

   $OpDocNNConv2D

   $OpDocNNConv2D

   filter

   4-D tensor with shape [filterHeight, filterWidth, inChannels, outChannels].

   stride1

   Stride of the sliding window along the second dimension of this tensor.

   stride2

   Stride of the sliding window along the third dimension of this tensor.

   padding

   Padding mode to use.

   dataFormat

   Format of the input and output data.

   dilations

   The dilation factor for each dimension of input. If set to k > 1, there will be k - 1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of dataFormat. Dilations in the batch and depth dimensions must be set to 1.

   useCuDNNOnGPU

   Boolean value indicating whether or not to use CuDNN for the created op, if its placed on a GPU, as opposed to the TensorFlow implementation.

   name

   Name for the created op.

   returns

   Created op output, which is a 4-D tensor whose dimension order depends on the value of dataFormat.

8. def crelu: Output

   

   $OpDocNNCrelu

   $OpDocNNCrelu

   returns

   Created op output.

9. def dropout(keepProbability: Float, scaleOutput: Boolean = true, noiseShape: Output = null, seed: Option[Int] = None): Output

   

   $OpDocNNDropout

   $OpDocNNDropout

   keepProbability

   Probability (i.e., number in the interval (0, 1]) that each element is kept.

   scaleOutput

   If true, the outputs will be divided by the keep probability.

   noiseShape

   INT32 rank-1 tensor representing the shape for the randomly generated keep/drop flags.

   seed

   Optional random seed, used to generate a random seed pair for the random number generator, when combined with the graph-level seed.

   returns

   Created op output that has the same shape as input.

10. def dynamicDropout(keepProbability: Output, scaleOutput: Boolean = true, noiseShape: Output = null, seed: Option[Int] = None): Output

    

    $OpDocNNDropout

    $OpDocNNDropout

    keepProbability

    Probability (i.e., number in the interval (0, 1]) that each element is kept.

    scaleOutput

    If true, the outputs will be divided by the keep probability.

    noiseShape

    INT32 rank-1 tensor representing the shape for the randomly generated keep/drop flags.

    seed

    Optional random seed, used to generate a random seed pair for the random number generator, when combined with the graph-level seed.

    returns

    Created op output that has the same shape as input.

11. def elu: Output

    

    $OpDocNNElu

    $OpDocNNElu

    returns

    Created op output.

12. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

13. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

14. final def getClass(): Class[_]

    

    Definition Classes

    AnyRef → Any

15. def inTopK(targets: Output, k: Output): Output

    

    $OpDocNNInTopK

    $OpDocNNInTopK

    targets

    INT32 or INT64 tensor containing the targets.

    k

    Scalar INT32 or INT64 tensor containing the number of top elements to look at.

    returns

    Created op output.

16. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

17. def l2Normalize(axes: Output, epsilon: Float = 1e-12f): Output

    

    $OpDocNNL2Normalize

    $OpDocNNL2Normalize

    axes

    Tensor containing the axes along which to normalize.

    epsilon

    Lower bound value for the norm. The created op will use sqrt(epsilon) as the divisor, if norm < sqrt(epsilon).

    returns

    Created op output.

18. def linear(weights: Output, bias: Output): Output

    

    $OpDocNNLinear

    $OpDocNNLinear

    weights

    Weights tensor.

    bias

    Bias tensor.

    returns

    Created op output.

19. def localResponseNormalization(depthRadius: Int = 5, bias: Float = 1.0f, alpha: Float = 1.0f, beta: Float = 0.5f, name: String = "LocalResponseNormalization"): Output

    

    $OpDocNNLocalResponseNormalization

    $OpDocNNLocalResponseNormalization

    depthRadius

    Half-width of the 1-D normalization window.

    bias

    Offset (usually positive to avoid dividing by 0).

    alpha

    Scale factor (usually positive).

    beta

    Exponent.

    name

    Name for the created op.

    returns

    Created op output.

20. def logSoftmax(axis: Int = 1): Output

    

    $OpDocNNLogSoftmax

    $OpDocNNLogSoftmax

    axis

    Axis along which to perform the log-softmax. Defaults to -1 denoting the last axis.

    returns

    Created op output.

21. def lrn(depthRadius: Int = 5, bias: Float = 1.0f, alpha: Float = 1.0f, beta: Float = 0.5f, name: String = "LRN"): Output

    

    $OpDocNNLocalResponseNormalization

    $OpDocNNLocalResponseNormalization

    depthRadius

    Half-width of the 1-D normalization window.

    bias

    Offset (usually positive to avoid dividing by 0).

    alpha

    Scale factor (usually positive).

    beta

    Exponent.

    name

    Name for the created op.

    returns

    Created op output.

22. def maxPool(windowSize: Seq[Long], stride1: Long, stride2: Long, padding: ConvPaddingMode, dataFormat: NN.CNNDataFormat = CNNDataFormat.default, name: String = "MaxPool"): Output

    

    $OpDocNNMaxPool

    $OpDocNNMaxPool

    windowSize

    The size of the pooling window for each dimension of the input tensor.

    stride1

    Stride of the sliding window along the second dimension of input.

    stride2

    Stride of the sliding window along the third dimension of input.

    padding

    Padding mode to use.

    dataFormat

    Format of the input and output data.

    name

    Name for the created op.

    returns

    Created op output, which is a 4-D tensor whose dimension order depends on the value of dataFormat.

23. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

24. final def notify(): Unit

    

    Definition Classes

    AnyRef

25. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

26. val output: Output

    

27. def relu(alpha: Float = 0.0f): Output

    

    $OpDocNNRelu

    $OpDocNNRelu

    alpha

    Slope of the negative section, also known as leakage parameter. If other than 0.0f, the negative part will be equal to alpha * x instead of 0. Defaults to 0.

    returns

    Created op output.

28. def relu6: Output

    

    $OpDocNNRelu6

    $OpDocNNRelu6

    returns

    Created op output.

29. def selu: Output

    

    $OpDocNNSelu

    $OpDocNNSelu

    returns

    Created op output.

30. def softmax(axis: Int = 1): Output

    

    $OpDocNNSoftmax

    $OpDocNNSoftmax

    axis

    Axis along which to perform the softmax. Defaults to -1 denoting the last axis.

    returns

    Created op output.

31. def softplus: Output

    

    $OpDocNNSoftplus

    $OpDocNNSoftplus

    returns

    Created op output.

32. def softsign: Output

    

    $OpDocNNSoftsign

    $OpDocNNSoftsign

    returns

    Created op output.

33. final def synchronized[T0](arg0: ⇒ T0): T0

    

    Definition Classes

    AnyRef

34. def topK(k: Output = 1, sorted: Boolean = true): (Output, Output)

    

    $OpDocNNTopK

    $OpDocNNTopK

    k

    Scalar INT32 tensor containing the number of top elements to look for along the last axis of input.

    sorted

    If true, the resulting k elements will be sorted by their values in descending order.

    returns

    Tuple containing the created op outputs: (i) values: the k largest elements along each last dimensional slice, and (ii) indices: the indices of values within the last axis of input.

35. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

36. final def wait(arg0: Long, arg1: Int): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

37. final def wait(arg0: Long): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )