AutoGrad

Value Members

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. val EPSILON: Double

   

5. def abs[T](x: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

   

   Element-wise absolute value.

   Element-wise absolute value.

   x

   A variable.

   returns

   A variable.

6. final def asInstanceOf[T0]: T0

   

   Definition Classes

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7. def batchDot[T](x: Variable[T], y: Variable[T], axes: List[Int], normalize: Boolean = false)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

   

   Operator that computes a dot product between samples in two tensors.

   Operator that computes a dot product between samples in two tensors.

   x

   A variable.

   y

   A variable.

   axes

   Axes along which to perform multiplication.

   normalize

   Whether to L2-normalize samples along the dot product axis before taking the dot product. If set to True, then the output of the dot product is the cosine proximity between the two samples.

8. def clip[T](x: Variable[T], min: Double, max: Double)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

   

   Element-wise value clipping.

   Element-wise value clipping.

   x

   A variable.

   min

   Double.

   max

   Double.

   returns

   A variable.

9. def clone(): AnyRef

   

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   protected[java.lang]

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10. def contiguous[T](input: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Turn the output and grad to be contiguous for the input Variable

11. def epsilon[T]()(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Double

    

    Define the value of epsilon.

    Define the value of epsilon.

    returns

    A value of type Double.

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

    

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13. def equals(arg0: Any): Boolean

    

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14. def erf[T](x: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Computes the error function(Gauss error function) of each element.

    Computes the error function(Gauss error function) of each element.

    x

    A variable.

    returns

    A variable.

15. def exp[T](x: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Element-wise exponential.

    Element-wise exponential.

    x

    A variable.

    returns

    A variable.

16. def expandDims[T](x: Variable[T], axis: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Adds a 1-sized dimension at index "axis".

    Adds a 1-sized dimension at index "axis". The axis is 0 based and if you set the axis to 0, you would change the batch dim.

    axis

    Position where to add a new axis.

17. def finalize(): Unit

    

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    protected[java.lang]

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

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

    

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19. def hashCode(): Int

    

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20. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

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21. def l2Normalize[T](x: Variable[T], axis: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Normalizes a tensor wrt the L2 norm alongside the specified axis.

    Normalizes a tensor wrt the L2 norm alongside the specified axis.

    x

    A variable.

    axis

    Axis along which to perform multiplication.

22. def log[T](x: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Element-wise log.

    Element-wise log.

    x

    A variable.

    returns

    A variable.

23. def maximum[T](x: Variable[T], y: Double)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Element-wise maximum of two variables

    Element-wise maximum of two variables

    x

    A variable.

    y

    Double

    returns

    A variable.

24. def maximum[T](x: Variable[T], y: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Element-wise maximum of two variables

    Element-wise maximum of two variables

    x

    A variable.

    y

    A variable.

    returns

    A variable.

25. def mean[T](x: Variable[T], axis: Int = 0, keepDims: Boolean = false)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Mean of a tensor, alongside the specified axis.

    Mean of a tensor, alongside the specified axis.

    axis

    axis to compute the mean. 0-based indexed.

    keepDims

    A boolean, whether to keep the dimensions or not. If keepDims is False, the rank of the tensor is reduced by 1. If keepDims is True, the reduced dimensions are retained with length 1.

    returns

    A tensor with the mean of elements of x.

26. def mm[T](x: Variable[T], y: Variable[T], axes: List[Int] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Module to perform matrix multiplication on two mini-batch inputs, producing a mini-batch.

    Module to perform matrix multiplication on two mini-batch inputs, producing a mini-batch.

    x

    A variable.

    y

    A variable.

    axes

    Axes along which to perform multiplication.

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

    

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28. final def notify(): Unit

    

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29. final def notifyAll(): Unit

    

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30. def pow[T](x: Variable[T], a: Double)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Element-wise exponentiation.

    Element-wise exponentiation.

    x

    A variable.

    a

    Double.

    returns

    A variable.

31. def softplus[T](x: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Softplus of a variable.

    Softplus of a variable.

    x

    A variable.

    returns

    A variable.

32. def softsign[T](x: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Softsign of a variable.

    Softsign of a variable.

    x

    A variable.

    returns

    A variable.

33. def sqrt[T](x: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Element-wise square root.

    Element-wise square root.

    x

    A variable.

    returns

    A variable.

34. def square[T](x: Variable[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Element-wise square.

    Element-wise square.

    x

    A variable.

    returns

    A variable.

35. def stack[T](inputs: List[Variable[T]], axis: Int = 1)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Stacks a list of rank R tensors into a rank R+1 tensor.

    Stacks a list of rank R tensors into a rank R+1 tensor.

    axis

    axis along which to perform stacking.

36. def sum[T](x: Variable[T], axis: Int = 0, keepDims: Boolean = false)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

    

    Sum of the values in a variable, alongside the specified axis.

    Sum of the values in a variable, alongside the specified axis.

    x

    A variable.

    axis

    axis to compute the mean. 0-based indexed.

    keepDims

    A boolean, whether to keep the dimensions or not. If keepDims is False, the rank of the variable is reduced by 1. If keepDims is True, the reduced dimensions are retained with length 1.

    returns

    A variable with the mean of elements of x.

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

    

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38. def toString(): String

    

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39. final def wait(): Unit

    

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

    

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

    

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