def predict(self, X):
out = self.transform(X)
if self.output == "softmax":
return op.argmax(out, 1)
elif self.output == "sigmoid":
return out > 0.5
else:
return out
def predict(self, X):
out = self.transform(X)
if self.output == "softmax":
return op.argmax(out, 1)
elif self.output == "sigmoid":
return out > 0.5
else:
return out
def _get_score(self, target, pred):
'''Calculates the quality of predictions of a model.
Like sklearn, we follow the convention that higher return values are
better than lower return values.'''
if self.output == "softmax":
# convert from 1hot
if len(target.shape) != 1 and target.shape[1] != 1:
target = op.to_cpu(op.argmax(target, 1))
if len(pred.shape) != 1 and pred.shape[1] != 1:
pred = op.to_cpu(op.argmax(pred, 1))
acc = op.to_cpu(op.mean(pred == target))
return float(acc)
elif self.output == "sigmoid":
# Note: this is meant for multitask learning, but for e.g.
# using sigmoid+squarederror as multiclass problem, this will
# give the wrong result!
return op.to_cpu(op.mean(target == (pred > 0.5)))
elif self.output == "linear":
return -op.to_cpu(op.mean((target - pred)**2)) # negate by convention
else:
raise NotImplementedError()
def _get_score(self, target, pred):
'''Calculates the quality of predictions of a model.
Like sklearn, we follow the convention that higher return values are
better than lower return values.'''
if self.output == "softmax":
# convert from 1hot
if len(target.shape) != 1 and target.shape[1] != 1:
target = op.to_cpu(op.argmax(target, 1))
if len(pred.shape) != 1 and pred.shape[1] != 1:
pred = op.to_cpu(op.argmax(pred, 1))
acc = op.to_cpu(op.mean(pred == target))
return float(acc)
elif self.output == "sigmoid":
# Note: this is meant for multitask learning, but for e.g.
# using sigmoid+squarederror as multiclass problem, this will
# give the wrong result!
return op.to_cpu(op.mean(target == (pred > 0.5)))
elif self.output == "linear":
return -op.to_cpu(op.mean(
(target - pred)**2)) # negate by convention
else:
raise NotImplementedError()