tf.experimental.numpy.int32 Signed integer type, compatible with C int.
tf.experimental.numpy.int32(
    *args, **kwargs
)
 Character code: 'i'. Canonical name: np.intc. Alias on this platform: np.int32: 32-bit signed integer (-2147483648 to 2147483647). Methods all
all()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. any
any()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmax
argmax()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmin
argmin()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argsort
argsort()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. astype
astype()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. byteswap
byteswap()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. choose
choose()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. clip
clip()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. compress
compress()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. conj
conj()
 conjugate
conjugate()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. copy
copy()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumprod
cumprod()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumsum
cumsum()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. diagonal
diagonal()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dump
dump()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dumps
dumps()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. fill
fill()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. flatten
flatten()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. getfield
getfield()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. item
item()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. itemset
itemset()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. max
max()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. mean
mean()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. min
min()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. newbyteorder
newbyteorder()
 newbyteorder(new_order='S') Return a new dtype with a different byte order. Changes are also made in all fields and sub-arrays of the data type. The new_order code can be any from the following:  'S' - swap dtype from current to opposite endian   '<', 'L'- little endian '>', 'B'- big endian '=', 'N'- native order '|', 'I'- ignore (no change to byte order)  Parameters new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian. Returns new_dtype : dtype New dtype object with the given change to the byte order. nonzero
nonzero()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. prod
prod()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ptp
ptp()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. put
put()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ravel
ravel()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. repeat
repeat()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. reshape
reshape()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. resize
resize()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. round
round()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. searchsorted
searchsorted()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setfield
setfield()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setflags
setflags()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sort
sort()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. squeeze
squeeze()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. std
std()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sum
sum()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. swapaxes
swapaxes()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. take
take()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tobytes
tobytes()
 tofile
tofile()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tolist
tolist()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tostring
tostring()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. trace
trace()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. transpose
transpose()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. var
var()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. view
view()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. __abs__
__abs__()
 abs(self) __add__
__add__(
    value, /
)
 Return self+value. __and__
__and__(
    value, /
)
 Return self&value. __bool__
__bool__()
 self != 0 __eq__
__eq__(
    value, /
)
 Return self==value. __floordiv__
__floordiv__(
    value, /
)
 Return self//value. __ge__
__ge__(
    value, /
)
 Return self>=value. __getitem__
__getitem__(
    key, /
)
 Return self[key]. __gt__
__gt__(
    value, /
)
 Return self>value. __invert__
__invert__()
 ~self __le__
__le__(
    value, /
)
 Return self<=value. __lt__
__lt__(
    value, /
)
 Return self<value. __mod__
__mod__(
    value, /
)
 Return self%value. __mul__
__mul__(
    value, /
)
 Return self*value. __ne__
__ne__(
    value, /
)
 Return self!=value. __neg__
__neg__()
 -self __or__
__or__(
    value, /
)
 Return self|value. __pos__
__pos__()
 +self __pow__
__pow__(
    value, mod, /
)
 Return pow(self, value, mod). __radd__
__radd__(
    value, /
)
 Return value+self. __rand__
__rand__(
    value, /
)
 Return value&self. __rfloordiv__
__rfloordiv__(
    value, /
)
 Return value//self. __rmod__
__rmod__(
    value, /
)
 Return value%self. __rmul__
__rmul__(
    value, /
)
 Return value*self. __ror__
__ror__(
    value, /
)
 Return value|self. __rpow__
__rpow__(
    value, mod, /
)
 Return pow(value, self, mod). __rsub__
__rsub__(
    value, /
)
 Return value-self. __rtruediv__
__rtruediv__(
    value, /
)
 Return value/self. __rxor__
__rxor__(
    value, /
)
 Return value^self. __sub__
__sub__(
    value, /
)
 Return self-value. __truediv__
__truediv__(
    value, /
)
 Return self/value. __xor__
__xor__(
    value, /
)
 Return self^value.

 Class Variables
  T

  base

  data

  denominator

  dtype

  flags

  flat

  imag

  itemsize

  nbytes

  ndim

  numerator

  real

  shape

  size

  strides


tf.experimental.numpy.int64 Signed integer type, compatible with Python int anc C long.  View aliases  Main aliases
tf.experimental.numpy.int_
tf.experimental.numpy.int64(
    *args, **kwargs
)
 Character code: 'l'. Canonical name: np.int_. Alias on this platform: np.int64: 64-bit signed integer (-9223372036854775808 to 9223372036854775807). Alias on this platform: np.intp: Signed integer large enough to fit pointer, compatible with C intptr_t. Methods all
all()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. any
any()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmax
argmax()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmin
argmin()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argsort
argsort()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. astype
astype()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. byteswap
byteswap()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. choose
choose()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. clip
clip()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. compress
compress()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. conj
conj()
 conjugate
conjugate()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. copy
copy()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumprod
cumprod()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumsum
cumsum()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. diagonal
diagonal()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dump
dump()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dumps
dumps()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. fill
fill()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. flatten
flatten()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. getfield
getfield()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. item
item()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. itemset
itemset()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. max
max()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. mean
mean()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. min
min()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. newbyteorder
newbyteorder()
 newbyteorder(new_order='S') Return a new dtype with a different byte order. Changes are also made in all fields and sub-arrays of the data type. The new_order code can be any from the following:  'S' - swap dtype from current to opposite endian   '<', 'L'- little endian '>', 'B'- big endian '=', 'N'- native order '|', 'I'- ignore (no change to byte order)  Parameters new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian. Returns new_dtype : dtype New dtype object with the given change to the byte order. nonzero
nonzero()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. prod
prod()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ptp
ptp()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. put
put()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ravel
ravel()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. repeat
repeat()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. reshape
reshape()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. resize
resize()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. round
round()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. searchsorted
searchsorted()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setfield
setfield()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setflags
setflags()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sort
sort()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. squeeze
squeeze()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. std
std()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sum
sum()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. swapaxes
swapaxes()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. take
take()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tobytes
tobytes()
 tofile
tofile()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tolist
tolist()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tostring
tostring()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. trace
trace()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. transpose
transpose()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. var
var()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. view
view()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. __abs__
__abs__()
 abs(self) __add__
__add__(
    value, /
)
 Return self+value. __and__
__and__(
    value, /
)
 Return self&value. __bool__
__bool__()
 self != 0 __eq__
__eq__(
    value, /
)
 Return self==value. __floordiv__
__floordiv__(
    value, /
)
 Return self//value. __ge__
__ge__(
    value, /
)
 Return self>=value. __getitem__
__getitem__(
    key, /
)
 Return self[key]. __gt__
__gt__(
    value, /
)
 Return self>value. __invert__
__invert__()
 ~self __le__
__le__(
    value, /
)
 Return self<=value. __lt__
__lt__(
    value, /
)
 Return self<value. __mod__
__mod__(
    value, /
)
 Return self%value. __mul__
__mul__(
    value, /
)
 Return self*value. __ne__
__ne__(
    value, /
)
 Return self!=value. __neg__
__neg__()
 -self __or__
__or__(
    value, /
)
 Return self|value. __pos__
__pos__()
 +self __pow__
__pow__(
    value, mod, /
)
 Return pow(self, value, mod). __radd__
__radd__(
    value, /
)
 Return value+self. __rand__
__rand__(
    value, /
)
 Return value&self. __rfloordiv__
__rfloordiv__(
    value, /
)
 Return value//self. __rmod__
__rmod__(
    value, /
)
 Return value%self. __rmul__
__rmul__(
    value, /
)
 Return value*self. __ror__
__ror__(
    value, /
)
 Return value|self. __rpow__
__rpow__(
    value, mod, /
)
 Return pow(value, self, mod). __rsub__
__rsub__(
    value, /
)
 Return value-self. __rtruediv__
__rtruediv__(
    value, /
)
 Return value/self. __rxor__
__rxor__(
    value, /
)
 Return value^self. __sub__
__sub__(
    value, /
)
 Return self-value. __truediv__
__truediv__(
    value, /
)
 Return self/value. __xor__
__xor__(
    value, /
)
 Return self^value.

 Class Variables
  T

  base

  data

  denominator

  dtype

  flags

  flat

  imag

  itemsize

  nbytes

  ndim

  numerator

  real

  shape

  size

  strides


tf.experimental.numpy.int8 Signed integer type, compatible with C char.
tf.experimental.numpy.int8(
    *args, **kwargs
)
 Character code: 'b'. Canonical name: np.byte. Alias on this platform: np.int8: 8-bit signed integer (-128 to 127). Methods all
all()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. any
any()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmax
argmax()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmin
argmin()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argsort
argsort()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. astype
astype()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. byteswap
byteswap()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. choose
choose()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. clip
clip()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. compress
compress()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. conj
conj()
 conjugate
conjugate()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. copy
copy()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumprod
cumprod()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumsum
cumsum()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. diagonal
diagonal()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dump
dump()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dumps
dumps()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. fill
fill()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. flatten
flatten()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. getfield
getfield()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. item
item()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. itemset
itemset()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. max
max()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. mean
mean()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. min
min()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. newbyteorder
newbyteorder()
 newbyteorder(new_order='S') Return a new dtype with a different byte order. Changes are also made in all fields and sub-arrays of the data type. The new_order code can be any from the following:  'S' - swap dtype from current to opposite endian   '<', 'L'- little endian '>', 'B'- big endian '=', 'N'- native order '|', 'I'- ignore (no change to byte order)  Parameters new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian. Returns new_dtype : dtype New dtype object with the given change to the byte order. nonzero
nonzero()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. prod
prod()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ptp
ptp()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. put
put()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ravel
ravel()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. repeat
repeat()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. reshape
reshape()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. resize
resize()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. round
round()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. searchsorted
searchsorted()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setfield
setfield()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setflags
setflags()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sort
sort()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. squeeze
squeeze()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. std
std()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sum
sum()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. swapaxes
swapaxes()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. take
take()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tobytes
tobytes()
 tofile
tofile()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tolist
tolist()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tostring
tostring()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. trace
trace()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. transpose
transpose()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. var
var()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. view
view()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. __abs__
__abs__()
 abs(self) __add__
__add__(
    value, /
)
 Return self+value. __and__
__and__(
    value, /
)
 Return self&value. __bool__
__bool__()
 self != 0 __eq__
__eq__(
    value, /
)
 Return self==value. __floordiv__
__floordiv__(
    value, /
)
 Return self//value. __ge__
__ge__(
    value, /
)
 Return self>=value. __getitem__
__getitem__(
    key, /
)
 Return self[key]. __gt__
__gt__(
    value, /
)
 Return self>value. __invert__
__invert__()
 ~self __le__
__le__(
    value, /
)
 Return self<=value. __lt__
__lt__(
    value, /
)
 Return self<value. __mod__
__mod__(
    value, /
)
 Return self%value. __mul__
__mul__(
    value, /
)
 Return self*value. __ne__
__ne__(
    value, /
)
 Return self!=value. __neg__
__neg__()
 -self __or__
__or__(
    value, /
)
 Return self|value. __pos__
__pos__()
 +self __pow__
__pow__(
    value, mod, /
)
 Return pow(self, value, mod). __radd__
__radd__(
    value, /
)
 Return value+self. __rand__
__rand__(
    value, /
)
 Return value&self. __rfloordiv__
__rfloordiv__(
    value, /
)
 Return value//self. __rmod__
__rmod__(
    value, /
)
 Return value%self. __rmul__
__rmul__(
    value, /
)
 Return value*self. __ror__
__ror__(
    value, /
)
 Return value|self. __rpow__
__rpow__(
    value, mod, /
)
 Return pow(value, self, mod). __rsub__
__rsub__(
    value, /
)
 Return value-self. __rtruediv__
__rtruediv__(
    value, /
)
 Return value/self. __rxor__
__rxor__(
    value, /
)
 Return value^self. __sub__
__sub__(
    value, /
)
 Return self-value. __truediv__
__truediv__(
    value, /
)
 Return self/value. __xor__
__xor__(
    value, /
)
 Return self^value.

 Class Variables
  T

  base

  data

  denominator

  dtype

  flags

  flat

  imag

  itemsize

  nbytes

  ndim

  numerator

  real

  shape

  size

  strides


tf.experimental.numpy.isclose TensorFlow variant of NumPy's isclose.
tf.experimental.numpy.isclose(
    a, b, rtol=1e-05, atol=1e-08, equal_nan=False
)
 See the NumPy documentation for numpy.isclose.


tf.experimental.numpy.iscomplex TensorFlow variant of NumPy's iscomplex.
tf.experimental.numpy.iscomplex(
    x
)
 See the NumPy documentation for numpy.iscomplex.


tf.experimental.numpy.iscomplexobj TensorFlow variant of NumPy's iscomplexobj.
tf.experimental.numpy.iscomplexobj(
    x
)
 See the NumPy documentation for numpy.iscomplexobj.


tf.experimental.numpy.isfinite TensorFlow variant of NumPy's isfinite.
tf.experimental.numpy.isfinite(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.isfinite.


tf.experimental.numpy.isinf TensorFlow variant of NumPy's isinf.
tf.experimental.numpy.isinf(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.isinf.


tf.experimental.numpy.isnan TensorFlow variant of NumPy's isnan.
tf.experimental.numpy.isnan(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.isnan.


tf.experimental.numpy.isneginf TensorFlow variant of NumPy's isneginf.
tf.experimental.numpy.isneginf(
    x
)
 Unsupported arguments: out. See the NumPy documentation for numpy.isneginf.


tf.experimental.numpy.isposinf TensorFlow variant of NumPy's isposinf.
tf.experimental.numpy.isposinf(
    x
)
 Unsupported arguments: out. See the NumPy documentation for numpy.isposinf.


tf.experimental.numpy.isreal TensorFlow variant of NumPy's isreal.
tf.experimental.numpy.isreal(
    x
)
 See the NumPy documentation for numpy.isreal.


tf.experimental.numpy.isrealobj TensorFlow variant of NumPy's isrealobj.
tf.experimental.numpy.isrealobj(
    x
)
 See the NumPy documentation for numpy.isrealobj.


tf.experimental.numpy.isscalar TensorFlow variant of NumPy's isscalar.
tf.experimental.numpy.isscalar(
    num
)
 Unsupported arguments: element. See the NumPy documentation for numpy.isscalar.


tf.experimental.numpy.issubdtype Returns True if first argument is a typecode lower/equal in type hierarchy.
tf.experimental.numpy.issubdtype(
    arg1, arg2
)
 Parameters arg1, arg2 : dtype_like dtype or string representing a typecode. Returns out : bool See Also issubsctype, issubclass_ numpy.core.numerictypes : Overview of numpy type hierarchy. Examples >>> np.issubdtype('S1', np.string_)
True
>>> np.issubdtype(np.float64, np.float32)
False


tf.experimental.numpy.ix_ TensorFlow variant of NumPy's ix_.
tf.experimental.numpy.ix_(
    *args
)
 See the NumPy documentation for numpy.ix_.


tf.experimental.numpy.kron TensorFlow variant of NumPy's kron.
tf.experimental.numpy.kron(
    a, b
)
 See the NumPy documentation for numpy.kron.


tf.experimental.numpy.lcm TensorFlow variant of NumPy's lcm.
tf.experimental.numpy.lcm(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.lcm.


tf.experimental.numpy.less TensorFlow variant of NumPy's less.
tf.experimental.numpy.less(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.less.


tf.experimental.numpy.less_equal TensorFlow variant of NumPy's less_equal.
tf.experimental.numpy.less_equal(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.less_equal.


tf.experimental.numpy.linspace TensorFlow variant of NumPy's linspace.
tf.experimental.numpy.linspace(
    start, stop, num=50, endpoint=True, retstep=False, dtype=float, axis=0
)
 See the NumPy documentation for numpy.linspace.


tf.experimental.numpy.log TensorFlow variant of NumPy's log.
tf.experimental.numpy.log(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.log.


tf.experimental.numpy.log10 TensorFlow variant of NumPy's log10.
tf.experimental.numpy.log10(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.log10.


tf.experimental.numpy.log1p TensorFlow variant of NumPy's log1p.
tf.experimental.numpy.log1p(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.log1p.


tf.experimental.numpy.log2 TensorFlow variant of NumPy's log2.
tf.experimental.numpy.log2(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.log2.


tf.experimental.numpy.logaddexp TensorFlow variant of NumPy's logaddexp.
tf.experimental.numpy.logaddexp(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.logaddexp.


tf.experimental.numpy.logaddexp2 TensorFlow variant of NumPy's logaddexp2.
tf.experimental.numpy.logaddexp2(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.logaddexp2.


tf.experimental.numpy.logical_and TensorFlow variant of NumPy's logical_and.
tf.experimental.numpy.logical_and(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.logical_and.


tf.experimental.numpy.logical_not TensorFlow variant of NumPy's logical_not.
tf.experimental.numpy.logical_not(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.logical_not.


tf.experimental.numpy.logical_or TensorFlow variant of NumPy's logical_or.
tf.experimental.numpy.logical_or(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.logical_or.


tf.experimental.numpy.logical_xor TensorFlow variant of NumPy's logical_xor.
tf.experimental.numpy.logical_xor(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.logical_xor.


tf.experimental.numpy.logspace TensorFlow variant of NumPy's logspace.
tf.experimental.numpy.logspace(
    start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0
)
 See the NumPy documentation for numpy.logspace.


tf.experimental.numpy.matmul TensorFlow variant of NumPy's matmul.
tf.experimental.numpy.matmul(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.matmul.


tf.experimental.numpy.max TensorFlow variant of NumPy's max.
tf.experimental.numpy.max(
    a, axis=None, keepdims=None
)
 Unsupported arguments: out, initial, where. See the NumPy documentation for numpy.max.


tf.experimental.numpy.maximum TensorFlow variant of NumPy's maximum.
tf.experimental.numpy.maximum(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.maximum.


tf.experimental.numpy.mean TensorFlow variant of NumPy's mean.
tf.experimental.numpy.mean(
    a, axis=None, dtype=None, keepdims=None
)
 Unsupported arguments: out. See the NumPy documentation for numpy.mean.


tf.experimental.numpy.meshgrid TensorFlow variant of NumPy's meshgrid.
tf.experimental.numpy.meshgrid(
    *xi, **kwargs
)
 Unsupported arguments: copy, sparse, indexing. This currently requires copy=True and sparse=False. See the NumPy documentation for numpy.meshgrid.


tf.experimental.numpy.min TensorFlow variant of NumPy's min.
tf.experimental.numpy.min(
    a, axis=None, keepdims=None
)
 Unsupported arguments: out, initial, where. See the NumPy documentation for numpy.min.


tf.experimental.numpy.minimum TensorFlow variant of NumPy's minimum.
tf.experimental.numpy.minimum(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.minimum.


tf.experimental.numpy.mod TensorFlow variant of NumPy's mod.
tf.experimental.numpy.mod(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.mod.


tf.experimental.numpy.moveaxis TensorFlow variant of NumPy's moveaxis.
tf.experimental.numpy.moveaxis(
    a, source, destination
)
 Raises ValueError if source, destination not in (-ndim(a), ndim(a)). See the NumPy documentation for numpy.moveaxis.


tf.experimental.numpy.multiply TensorFlow variant of NumPy's multiply.
tf.experimental.numpy.multiply(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.multiply.


tf.experimental.numpy.nanmean TensorFlow variant of NumPy's nanmean.
tf.experimental.numpy.nanmean(
    a, axis=None, dtype=None, keepdims=None
)
 Unsupported arguments: out. See the NumPy documentation for numpy.nanmean.


tf.experimental.numpy.nanprod TensorFlow variant of NumPy's nanprod.
tf.experimental.numpy.nanprod(
    a, axis=None, dtype=None, keepdims=False
)
 Unsupported arguments: out. See the NumPy documentation for numpy.nanprod.


tf.experimental.numpy.nansum TensorFlow variant of NumPy's nansum.
tf.experimental.numpy.nansum(
    a, axis=None, dtype=None, keepdims=False
)
 Unsupported arguments: out. See the NumPy documentation for numpy.nansum.


tf.experimental.numpy.ndarray Equivalent of numpy.ndarray backed by TensorFlow tensors.
tf.experimental.numpy.ndarray(
    shape, dtype=float, buffer=None
)
 This does not support all features of NumPy ndarrays e.g. strides and memory order since, unlike NumPy, the backing storage is not a raw memory buffer. or if there are any differences in behavior.

 Args
  shape   The shape of the array. Must be a scalar, an iterable of integers or a TensorShape object.
  dtype   Optional. The dtype of the array. Must be a python type, a numpy type or a tensorflow DType object.
  buffer   Optional. The backing buffer of the array. Must have shape shape. Must be a ndarray, np.ndarray or a Tensor.

 Raises
  ValueError   If buffer is specified and its shape does not match shape.

 Attributes
  T

  data   Tensor object containing the array data. This has a few key differences from the Python buffer object used in NumPy arrays.  Tensors are immutable. So operations requiring in-place edit, e.g. setitem, are performed by replacing the underlying buffer with a new one. Tensors do not provide access to their raw buffer.

  dtype

  ndim

  shape   Returns a tuple or tf.Tensor of array dimensions.
  size   Returns the number of elements in the array.    Methods astype View source
astype(
    dtype
)
 clip View source
clip(
    a, a_min, a_max
)
 TensorFlow variant of NumPy's clip. Unsupported arguments: out, kwargs. See the NumPy documentation for numpy.clip. from_tensor View source
@classmethod
from_tensor(
    tensor
)
 ravel View source
ravel(
    a
)
 TensorFlow variant of NumPy's ravel. Unsupported arguments: order. See the NumPy documentation for numpy.ravel. reshape View source
reshape(
    a, *newshape, **kwargs
)
 tolist View source
tolist()
 transpose View source
transpose(
    a, axes=None
)
 TensorFlow variant of NumPy's transpose. See the NumPy documentation for numpy.transpose. __abs__ View source
__abs__(
    x
)
 TensorFlow variant of NumPy's absolute. Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.absolute. __add__ View source
__add__(
    a, b
)
 __bool__ View source
__bool__()
 __eq__ View source
__eq__(
    a, b
)
 __floordiv__ View source
__floordiv__(
    a, b
)
 __ge__ View source
__ge__(
    a, b
)
 __getitem__ View source
__getitem__(
    slice_spec
)
 Implementation of ndarray.getitem. __gt__ View source
__gt__(
    a, b
)
 __invert__ View source
__invert__(
    x
)
 TensorFlow variant of NumPy's logical_not. Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.logical_not. __iter__ View source
__iter__()
 __le__ View source
__le__(
    a, b
)
 __len__ View source
__len__()
 __lt__ View source
__lt__(
    a, b
)
 __matmul__ View source
__matmul__(
    a, b
)
 __mod__ View source
__mod__(
    a, b
)
 __mul__ View source
__mul__(
    a, b
)
 __ne__ View source
__ne__(
    a, b
)
 __neg__ View source
__neg__()
 __nonzero__ View source
__nonzero__()
 __pos__ View source
__pos__()
 __pow__ View source
__pow__(
    a, b
)
 __radd__ View source
__radd__(
    a, b
)
 __rfloordiv__ View source
__rfloordiv__(
    a, b
)
 __rmatmul__ View source
__rmatmul__(
    a, b
)
 __rmod__ View source
__rmod__(
    a, b
)
 __rmul__ View source
__rmul__(
    a, b
)
 __rpow__ View source
__rpow__(
    a, b
)
 __rsub__ View source
__rsub__(
    a, b
)
 __rtruediv__ View source
__rtruediv__(
    a, b
)
 __sub__ View source
__sub__(
    a, b
)
 __truediv__ View source
__truediv__(
    a, b
)


tf.experimental.numpy.ndim TensorFlow variant of NumPy's ndim.
tf.experimental.numpy.ndim(
    a
)


tf.experimental.numpy.negative TensorFlow variant of NumPy's negative.
tf.experimental.numpy.negative(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.negative.


tf.experimental.numpy.nextafter TensorFlow variant of NumPy's nextafter.
tf.experimental.numpy.nextafter(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.nextafter.


tf.experimental.numpy.nonzero TensorFlow variant of NumPy's nonzero.
tf.experimental.numpy.nonzero(
    a
)
 See the NumPy documentation for numpy.nonzero.


tf.experimental.numpy.not_equal TensorFlow variant of NumPy's not_equal.
tf.experimental.numpy.not_equal(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.not_equal.


tf.experimental.numpy.object_ Any Python object.
tf.experimental.numpy.object_(
    *args, **kwargs
)
 Character code: 'O'. Methods all
all()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. any
any()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmax
argmax()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmin
argmin()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argsort
argsort()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. astype
astype()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. byteswap
byteswap()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. choose
choose()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. clip
clip()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. compress
compress()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. conj
conj()
 conjugate
conjugate()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. copy
copy()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumprod
cumprod()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumsum
cumsum()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. diagonal
diagonal()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dump
dump()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dumps
dumps()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. fill
fill()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. flatten
flatten()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. getfield
getfield()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. item
item()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. itemset
itemset()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. max
max()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. mean
mean()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. min
min()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. newbyteorder
newbyteorder()
 newbyteorder(new_order='S') Return a new dtype with a different byte order. Changes are also made in all fields and sub-arrays of the data type. The new_order code can be any from the following:  'S' - swap dtype from current to opposite endian   '<', 'L'- little endian '>', 'B'- big endian '=', 'N'- native order '|', 'I'- ignore (no change to byte order)  Parameters new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian. Returns new_dtype : dtype New dtype object with the given change to the byte order. nonzero
nonzero()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. prod
prod()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ptp
ptp()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. put
put()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ravel
ravel()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. repeat
repeat()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. reshape
reshape()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. resize
resize()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. round
round()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. searchsorted
searchsorted()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setfield
setfield()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setflags
setflags()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sort
sort()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. squeeze
squeeze()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. std
std()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sum
sum()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. swapaxes
swapaxes()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. take
take()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tobytes
tobytes()
 tofile
tofile()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tolist
tolist()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tostring
tostring()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. trace
trace()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. transpose
transpose()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. var
var()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. view
view()
 Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. __abs__
__abs__()
 abs(self) __add__
__add__(
    value, /
)
 Return self+value. __and__
__and__(
    value, /
)
 Return self&value. __bool__
__bool__()
 self != 0 __call__
__call__(
    *args, **kwargs
)
 Call self as a function. __contains__
__contains__(
    key, /
)
 Return key in self. __eq__
__eq__(
    value, /
)
 Return self==value. __floordiv__
__floordiv__(
    value, /
)
 Return self//value. __ge__
__ge__(
    value, /
)
 Return self>=value. __getitem__
__getitem__(
    key, /
)
 Return self[key]. __gt__
__gt__(
    value, /
)
 Return self>value. __invert__
__invert__()
 ~self __le__
__le__(
    value, /
)
 Return self<=value. __len__
__len__()
 Return len(self). __lt__
__lt__(
    value, /
)
 Return self<value. __mod__
__mod__(
    value, /
)
 Return self%value. __mul__
__mul__(
    value, /
)
 Return self*value. __ne__
__ne__(
    value, /
)
 Return self!=value. __neg__
__neg__()
 -self __or__
__or__(
    value, /
)
 Return self|value. __pos__
__pos__()
 +self __pow__
__pow__(
    value, mod, /
)
 Return pow(self, value, mod). __radd__
__radd__(
    value, /
)
 Return value+self. __rand__
__rand__(
    value, /
)
 Return value&self. __rfloordiv__
__rfloordiv__(
    value, /
)
 Return value//self. __rmod__
__rmod__(
    value, /
)
 Return value%self. __rmul__
__rmul__(
    value, /
)
 Return value*self. __ror__
__ror__(
    value, /
)
 Return value|self. __rpow__
__rpow__(
    value, mod, /
)
 Return pow(value, self, mod). __rsub__
__rsub__(
    value, /
)
 Return value-self. __rtruediv__
__rtruediv__(
    value, /
)
 Return value/self. __rxor__
__rxor__(
    value, /
)
 Return value^self. __sub__
__sub__(
    value, /
)
 Return self-value. __truediv__
__truediv__(
    value, /
)
 Return self/value. __xor__
__xor__(
    value, /
)
 Return self^value.

 Class Variables
  T

  base

  data

  dtype

  flags

  flat

  imag

  itemsize

  nbytes

  ndim

  real

  shape

  size

  strides


tf.experimental.numpy.ones TensorFlow variant of NumPy's ones.
tf.experimental.numpy.ones(
    shape, dtype=float
)
 Unsupported arguments: order. See the NumPy documentation for numpy.ones.


tf.experimental.numpy.ones_like TensorFlow variant of NumPy's ones_like.
tf.experimental.numpy.ones_like(
    a, dtype=None
)
 Unsupported arguments: order, subok, shape. See the NumPy documentation for numpy.ones_like.


tf.experimental.numpy.outer TensorFlow variant of NumPy's outer.
tf.experimental.numpy.outer(
    a, b
)
 Unsupported arguments: out. See the NumPy documentation for numpy.outer.


tf.experimental.numpy.pad TensorFlow variant of NumPy's pad.
tf.experimental.numpy.pad(
    array, pad_width, mode, **kwargs
)
 Only supports modes 'constant', 'reflect' and 'symmetric' currently. See the NumPy documentation for numpy.pad.


tf.experimental.numpy.polyval TensorFlow variant of NumPy's polyval.
tf.experimental.numpy.polyval(
    p, x
)
 See the NumPy documentation for numpy.polyval.


tf.experimental.numpy.positive TensorFlow variant of NumPy's positive.
tf.experimental.numpy.positive(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.positive.


tf.experimental.numpy.power TensorFlow variant of NumPy's power.
tf.experimental.numpy.power(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.power.


tf.experimental.numpy.prod TensorFlow variant of NumPy's prod.
tf.experimental.numpy.prod(
    a, axis=None, dtype=None, keepdims=None
)
 Unsupported arguments: out, initial, where. See the NumPy documentation for numpy.prod.


tf.experimental.numpy.promote_types TensorFlow variant of NumPy's promote_types.
tf.experimental.numpy.promote_types(
    type1, type2
)
 See the NumPy documentation for numpy.promote_types.


tf.experimental.numpy.ptp TensorFlow variant of NumPy's ptp.
tf.experimental.numpy.ptp(
    a, axis=None, keepdims=None
)
 Unsupported arguments: out. See the NumPy documentation for numpy.ptp.


tf.experimental.numpy.rad2deg TensorFlow variant of NumPy's rad2deg.
tf.experimental.numpy.rad2deg(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.rad2deg.


Module: tf.experimental.numpy.random Public API for tf.experimental.numpy.random namespace. Functions rand(...): TensorFlow variant of NumPy's random.rand. randint(...): TensorFlow variant of NumPy's random.randint. randn(...): TensorFlow variant of NumPy's random.randn. random(...): TensorFlow variant of NumPy's random.random. seed(...): TensorFlow variant of NumPy's random.seed. uniform(...): TensorFlow variant of NumPy's random.uniform.


tf.experimental.numpy.random.rand TensorFlow variant of NumPy's random.rand.
tf.experimental.numpy.random.rand(
    *size
)
 See the NumPy documentation for numpy.random.rand.


tf.experimental.numpy.random.randint TensorFlow variant of NumPy's random.randint.
tf.experimental.numpy.random.randint(
    low, high=None, size=None, dtype=onp.int
)
 See the NumPy documentation for numpy.random.randint.


tf.experimental.numpy.random.randn TensorFlow variant of NumPy's random.randn.
tf.experimental.numpy.random.randn(
    *args
)
 Returns samples from a normal distribution. Uses tf.random_normal. Args: *args: The shape of the output array. Returns: An ndarray with shape args and dtype float64. See the NumPy documentation for numpy.random.randn.


tf.experimental.numpy.random.random TensorFlow variant of NumPy's random.random.
tf.experimental.numpy.random.random(
    size=None
)
 See the NumPy documentation for numpy.random.random.


tf.experimental.numpy.random.seed TensorFlow variant of NumPy's random.seed.
tf.experimental.numpy.random.seed(
    s
)
 Sets the seed for the random number generator. Uses tf.set_random_seed. Args: s: an integer. See the NumPy documentation for numpy.random.seed.


tf.experimental.numpy.random.uniform TensorFlow variant of NumPy's random.uniform.
tf.experimental.numpy.random.uniform(
    low=0.0, high=1.0, size=None
)
 See the NumPy documentation for numpy.random.uniform.


tf.experimental.numpy.ravel TensorFlow variant of NumPy's ravel.  View aliases  Main aliases
tf.experimental.numpy.ndarray.ravel
tf.experimental.numpy.ravel(
    a
)
 Unsupported arguments: order. See the NumPy documentation for numpy.ravel.


tf.experimental.numpy.real TensorFlow variant of NumPy's real.
tf.experimental.numpy.real(
    val
)
 See the NumPy documentation for numpy.real.


tf.experimental.numpy.reciprocal TensorFlow variant of NumPy's reciprocal.
tf.experimental.numpy.reciprocal(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.reciprocal.


tf.experimental.numpy.remainder TensorFlow variant of NumPy's remainder.
tf.experimental.numpy.remainder(
    x1, x2
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.remainder.


tf.experimental.numpy.repeat TensorFlow variant of NumPy's repeat.
tf.experimental.numpy.repeat(
    a, repeats, axis=None
)
 See the NumPy documentation for numpy.repeat.


tf.experimental.numpy.reshape TensorFlow variant of NumPy's reshape.
tf.experimental.numpy.reshape(
    a, newshape, order='C'
)
 order argument can only b 'C' or 'F'. See the NumPy documentation for numpy.reshape.


tf.experimental.numpy.result_type TensorFlow variant of NumPy's result_type.
tf.experimental.numpy.result_type(
    *arrays_and_dtypes
)
 See the NumPy documentation for numpy.result_type.


tf.experimental.numpy.roll TensorFlow variant of NumPy's roll.
tf.experimental.numpy.roll(
    a, shift, axis=None
)
 See the NumPy documentation for numpy.roll.


tf.experimental.numpy.rot90 TensorFlow variant of NumPy's rot90.
tf.experimental.numpy.rot90(
    m, k=1, axes=(0, 1)
)
 See the NumPy documentation for numpy.rot90.


tf.experimental.numpy.round TensorFlow variant of NumPy's round.
tf.experimental.numpy.round(
    a, decimals=0
)
 Unsupported arguments: out. See the NumPy documentation for numpy.round.


tf.experimental.numpy.select TensorFlow variant of NumPy's select.
tf.experimental.numpy.select(
    condlist, choicelist, default=0
)
 See the NumPy documentation for numpy.select.


tf.experimental.numpy.shape TensorFlow variant of NumPy's shape.
tf.experimental.numpy.shape(
    a
)
 See the NumPy documentation for numpy.shape.


tf.experimental.numpy.sign TensorFlow variant of NumPy's sign.
tf.experimental.numpy.sign(
    x, out=None, where=None, **kwargs
)
 See the NumPy documentation for numpy.sign.


tf.experimental.numpy.signbit TensorFlow variant of NumPy's signbit.
tf.experimental.numpy.signbit(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.signbit.


tf.experimental.numpy.sin TensorFlow variant of NumPy's sin.
tf.experimental.numpy.sin(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.sin.


tf.experimental.numpy.sinc TensorFlow variant of NumPy's sinc.
tf.experimental.numpy.sinc(
    x
)
 See the NumPy documentation for numpy.sinc.


tf.experimental.numpy.sinh TensorFlow variant of NumPy's sinh.
tf.experimental.numpy.sinh(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.sinh.


tf.experimental.numpy.size TensorFlow variant of NumPy's size.
tf.experimental.numpy.size(
    x, axis=None
)
 Unsupported arguments: a. See the NumPy documentation for numpy.size.


tf.experimental.numpy.sort TensorFlow variant of NumPy's sort.
tf.experimental.numpy.sort(
    a, axis=-1, kind='quicksort', order=None
)
 See the NumPy documentation for numpy.sort.


tf.experimental.numpy.split TensorFlow variant of NumPy's split.
tf.experimental.numpy.split(
    ary, indices_or_sections, axis=0
)
 See the NumPy documentation for numpy.split.


tf.experimental.numpy.sqrt TensorFlow variant of NumPy's sqrt.
tf.experimental.numpy.sqrt(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.sqrt.


tf.experimental.numpy.square TensorFlow variant of NumPy's square.
tf.experimental.numpy.square(
    x
)
 Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.square.


tf.experimental.numpy.squeeze TensorFlow variant of NumPy's squeeze.
tf.experimental.numpy.squeeze(
    a, axis=None
)
 See the NumPy documentation for numpy.squeeze.


tf.experimental.numpy.stack TensorFlow variant of NumPy's stack.
tf.experimental.numpy.stack(
    arrays, axis=0
)
 Unsupported arguments: out. See the NumPy documentation for numpy.stack.


tf.experimental.numpy.std TensorFlow variant of NumPy's std.
tf.experimental.numpy.std(
    a, axis=None, keepdims=None
)
 Unsupported arguments: dtype, out, ddof. See the NumPy documentation for numpy.std.