def squared_error(output, target, name=''):
'''
This operation computes the sum of the squared difference between elements
in the two input matrices. The result is a scalar (i.e., one by one matrix).
This is often used as a training criterion.
Example:
>>> i1 = C.input_variable((1,2))
>>> i2 = C.input_variable((1,2))
>>> C.squared_error(i1,i2).eval({i1:np.asarray([[[2., 1.]]], dtype=np.float32), i2:np.asarray([[[4., 6.]]], dtype=np.float32)})
array([ 29.], dtype=float32)
>>> C.squared_error(i1,i2).eval({i1:np.asarray([[[1., 2.]]], dtype=np.float32), i2:np.asarray([[[1., 2.]]], dtype=np.float32)})
array([ 0.], dtype=float32)
Args:
output: the output values from the network
target: it is usually a one-hot vector where the hot bit
corresponds to the label index
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import squared_error
dtype = get_data_type(output, target)
output = sanitize_input(output, dtype)
target = sanitize_input(target, dtype)
return squared_error(output, target, name)
def squared_error(output, target, name=''):
'''
This operation computes the sum of the squared difference between elements
in the two input matrices. The result is a scalar (i.e., one by one matrix).
This is often used as a training criterion.
Example:
>>> i1 = C.input_variable((1,2))
>>> i2 = C.input_variable((1,2))
>>> C.squared_error(i1,i2).eval({i1:np.asarray([[[[2., 1.]]]], dtype=np.float32), i2:np.asarray([[[[4., 6.]]]], dtype=np.float32)})
array([[ 29.]], dtype=float32)
>>> C.squared_error(i1,i2).eval({i1:np.asarray([[[[1., 2.]]]], dtype=np.float32), i2:np.asarray([[[[1., 2.]]]], dtype=np.float32)})
array([[ 0.]], dtype=float32)
Args:
output: the output values from the network
target: it is usually a one-hot vector where the hot bit
corresponds to the label index
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import squared_error
dtype = get_data_type(output, target)
output = sanitize_input(output, dtype)
target = sanitize_input(target, dtype)
return squared_error(output, target, name)
def dice_coefficient(x, y):
# https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
#intersection = C.reduce_sum(x - y)
err = squared_error(x, y, "se")
return err #2 * intersection / (C.reduce_sum(x) + C.reduce_sum(y))
