def element_times(left, right, name=None):
"""
The output of this operation is the element-wise product of the two input
tensors. It supports broadcasting. In case of scalars its backward pass to left propagates right
times the received gradient and vice versa.
The operator (*) has been overloaded and can equally be used instead of element_times().
Example:
>>> C.eval(C.element_times([1., 1., 1., 1.], [0.5, 0.25, 0.125, 0.]))
[array([[ 0.5 , 0.25 , 0.125, 0. ]])]
>>> C.eval(C.element_times([5., 10., 15., 30.], [2.]))
[array([[ 10., 20., 30., 60.]])]
Args:
left: left side tensor
right: right side tensor
name: the name of the node in the network
Returns:
:class:`cntk.graph.ComputationNode`
"""
from cntk.ops.cntk2 import ElementTimes
op = ElementTimes(left, right, name=name)
wrap_numpy_arrays(op)
op.rank = max(op._.rank, op.y.rank)
return op
def element_times(left, right, name=None):
"""
The output of this operation is the element-wise product of the two input
tensors. It supports broadcasting. In case of scalars its backward pass to left propagates right
times the received gradient and vice versa.
The operator (*) has been overloaded and can equally be used instead of element_times().
Example:
>>> C.eval(C.element_times([1., 1., 1., 1.], [0.5, 0.25, 0.125, 0.]))
[array([[ 0.5 , 0.25 , 0.125, 0. ]])]
>>> C.eval(C.element_times([5., 10., 15., 30.], [2.]))
[array([[ 10., 20., 30., 60.]])]
Args:
left: left side tensor
right: right side tensor
name (str): the name of the node in the network
Returns:
:class:`cntk.graph.ComputationNode`
"""
from cntk.ops.cntk2 import ElementTimes
op = ElementTimes(left, right, name=name)
wrap_numpy_arrays(op)
op.rank = max(op._.rank, op.y.rank)
return op
def element_times(left, right, name=None):
"""
Element-wise multiplication operation. The output of this operation is the
element-wise product of the two input tensors. It supports broadcasting. In
case of scalars its backward pass to left propagates right
times the received gradient and vice versa.
Example:
>>> C.eval(C.element_times([1., 1., 1., 1.], [0.5, 0.25, 0.125, 0.]))
[array([[ 0.5 , 0.25 , 0.125, 0. ]])]
>>> C.eval(C.element_times([5., 10., 15., 30.], [2.]))
[array([[ 10., 20., 30., 60.]])]
Args:
left: left side tensor
right: right side tensor
name: the name of the node in the network
Returns:
:class:`cntk.graph.ComputationNode`
"""
from cntk.ops.cntk2 import ElementTimes
return ElementTimes(left, right, name=name)