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|
Symbolic tensor -- encapsulates a shape and a dtype.
tf.keras.KerasTensor(
shape,
dtype='float32',
sparse=False,
record_history=True,
name=None
)
You can use KerasTensor instances to build computation
graphs of Keras operations, such as keras.Function
objects or Functional keras.models.Model objects.
Example:
x = keras.KerasTensor(shape=(3, 4), dtype="float32")x.shape(3, 4)x.dtypefloat32
Calling a Keras operation (including a layer or a model)
on a KerasTensor instance will return another KerasTensor
instance with the appropriate shape and dtype. This is
called a "symbolic call" (since there is no actual data
involved). The computation of the correct output shape and
dtype is called "static shape inference".
Attributes | |
|---|---|
ndim
|
|
Methods
reshape
reshape(
newshape
)
squeeze
squeeze(
axis=None
)
__abs__
__abs__()
__add__
__add__(
other
)
__and__
__and__(
other
)
__array__
__array__()
__bool__
__bool__()
__div__
__div__(
other
)
__floordiv__
__floordiv__(
other
)
__ge__
__ge__(
other
)
Return self>=value.
__getitem__
__getitem__(
key
)
__gt__
__gt__(
other
)
Return self>value.
__invert__
__invert__()
__iter__
__iter__()
__le__
__le__(
other
)
Return self<=value.
__lt__
__lt__(
other
)
Return self<value.
__matmul__
__matmul__(
other
)
__mod__
__mod__(
other
)
__mul__
__mul__(
other
)
__ne__
__ne__(
other
)
Return self!=value.
__neg__
__neg__()
__or__
__or__(
other
)
__pow__
__pow__(
other
)
__radd__
__radd__(
other
)
__rand__
__rand__(
other
)
__rdiv__
__rdiv__(
other
)
__rfloordiv__
__rfloordiv__(
other
)
__rmatmul__
__rmatmul__(
other
)
__rmod__
__rmod__(
other
)
__rmul__
__rmul__(
other
)
__ror__
__ror__(
other
)
__rpow__
__rpow__(
other
)
__rsub__
__rsub__(
other
)
__rtruediv__
__rtruediv__(
other
)
__rxor__
__rxor__(
other
)
__sub__
__sub__(
other
)
__truediv__
__truediv__(
other
)
__xor__
__xor__(
other
)