def zero_one(p_y, y):
"""
:param p_y: the probability distribution of y given x
:type y: my_theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
:param p_y:
:param y:
:return:
"""
return T.mean(T.neq(T.argmax(p_y, axis=1), y))
def errors(self, y):
"""Return a float representing the number of errors in the minibatch
over the total number of examples of the minibatch ; zero one
loss over the size of the minibatch
:type y: my_theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
return T.mean(T.neq(self.y_pred, y))
else:
raise NotImplementedError()
