tensor

Type Members

1. final class BFloat16 extends AnyVal

   

   BFloat16 represents 16-bit floating-point values.

   BFloat16 represents 16-bit floating-point values.

   This type does not actually support arithmetic directly. The expected use case is to convert to Float to perform any actual arithmetic, then convert back to a BFloat16 if needed.

   Binary representation:

   sign (1 bit) | | exponent (8 bits) | | | | mantissa (7 bits) | | | x xxxxxxxx xxxxxxx

   Value interpretation (in order of precedence, with _ wild):

   0 00000000 0000000 (positive) zero 1 00000000 0000000 negative zero _ 00000000 _ subnormal number _ 11111111 0000000 +/- infinity _ 11111111 _ not-a-number _ _ normal number

   An exponent of all 1s signals a sentinel (NaN or infinity), and all 0s signals a subnormal number. So the working "real" range of exponents we can express is [-126, +127].

   For non-zero exponents, the mantissa has an implied leading 1 bit, so 7 bits of data provide 8 bits of precision for normal numbers.

   For normal numbers:

   x = (1 - sign*2) * 2^exponent * (1 + mantissa/128)

   For subnormal numbers, the implied leading 1 bit is absent. Thus, subnormal numbers have the same exponent as the smallest normal numbers, but without an implied 1 bit.

   So for subnormal numbers:

   x = (1 - sign*2) * 2^(-127) * (mantissa/128)

2. trait Build[H, E] extends AnyRef

   

   Shapeless-powered type-class used to build literal tensors.

3. trait BuildLowPri extends AnyRef

   

4. sealed abstract class DataType extends AnyRef

   

5. final class Float16 extends AnyVal

   

   Float16 represents 16-bit floating-point values.

   Float16 represents 16-bit floating-point values.

   This type does not actually support arithmetic directly. The expected use case is to convert to Float to perform any actual arithmetic, then convert back to a Float16 if needed.

   Binary representation:

   sign (1 bit) | | exponent (5 bits) | | | | mantissa (10 bits) | | | x xxxxx xxxxxxxxxx

   Value interpretation (in order of precedence, with _ wild):

   0 00000 0000000000 (positive) zero 1 00000 0000000000 negative zero _ 00000 subnormal number _ 11111 0000000000 +/- infinity _ 11111 not-a-number _ _ normal number

   An exponent of all 1s signals a sentinel (NaN or infinity), and all 0s signals a subnormal number. So the working "real" range of exponents we can express is [-14, +15].

   For non-zero exponents, the mantissa has an implied leading 1 bit, so 10 bits of data provide 11 bits of precision for normal numbers.

   For normal numbers:

   x = (1 - sign*2) * 2^exponent * (1 + mantissa/1024)

   For subnormal numbers, the implied leading 1 bit is absent. Thus, subnormal numbers have the same exponent as the smallest normal numbers, but without an implied 1 bit.

   So for subnormal numbers:

   x = (1 - sign*2) * 2^(-14) * (mantissa/1024)

6. sealed trait OnnxFloating[A] extends OnnxNumber[A]

   

   Onnx has several types of Integral numbers

7. sealed trait OnnxIntegral[A] extends OnnxNumber[A]

   

   Onnx has several types of Integral numbers

8. sealed trait OnnxNumber[A] extends AnyRef

   

   represent Onnx's notion of a number

9. trait ScalarParser[E] extends AnyRef

   

10. sealed abstract class Shape[+A] extends AnyRef

    

11. sealed trait Storage[A] extends AnyRef

    

12. sealed abstract class StorageAllocator[A] extends AnyRef

    

13. abstract class Tensor[D <: DataType] extends AnyRef

    

14. sealed abstract class TensorException extends Exception

    

15. class TensorParser[D <: DataType] extends AnyRef

    

    Parser tensors from numpy output strings useful for testing

16. trait ToBytes[A] extends AnyRef

    

    A type class for serializing values of type A into bytes.

17. sealed trait WritableStorage[A] extends AnyRef