def test_multiclass_crossentropy():
X = np.random.rand(100, 10).astype(np.float32)
O = np.random.rand(100, 10).astype(np.float32)
X /= X.sum(1)[:, None]
O /= O.sum(1)[:, None]
Y_expected = -np.sum(X * np.log(O)) / X.shape[0]
rtol=1e-4
Y = np.empty_like(X)
Yhr = op.multiclass_cross_entropy(X, O)
assert_allclose(Y_expected, Yhr, err_msg="CPU, no target", rtol=rtol)
Xd = op.to_gpu(X)
Od = op.to_gpu(O)
Yd = op.multiclass_cross_entropy(Xd, Od)
assert_allclose(Y_expected, op.to_cpu(Yd), err_msg="GPU, no target", rtol=rtol)
def test_multiclass_crossentropy():
X = np.random.rand(100, 10).astype(np.float32)
O = np.random.rand(100, 10).astype(np.float32)
X /= X.sum(1)[:, None]
O /= O.sum(1)[:, None]
Y_expected = -np.sum(X * np.log(O)) / X.shape[0]
rtol = 1e-4
Y = np.empty_like(X)
Yhr = op.multiclass_cross_entropy(X, O)
assert_allclose(Y_expected, Yhr, err_msg="CPU, no target", rtol=rtol)
Xd = op.to_gpu(X)
Od = op.to_gpu(O)
Yd = op.multiclass_cross_entropy(Xd, Od)
assert_allclose(Y_expected,
op.to_cpu(Yd),
err_msg="GPU, no target",
rtol=rtol)
def _get_loss(self, target, pred):
op.streams[0].synchronize()
if self.loss == "crossentropy":
if self.output == 'softmax':
return op.multiclass_cross_entropy(target, pred, stream=op.streams[3])
elif self.output == 'sigmoid':
return op.binary_cross_entropy(target, pred, stream=op.streams[3])
else:
raise NotImplementedError()
elif self.loss == "squarederror" or self.loss == 'mse':
return op.mean_squared_error(target, pred)
else:
raise NotImplementedError()
def _get_loss(self, target, pred):
op.streams[0].synchronize()
if self.loss == "crossentropy":
if self.output == 'softmax':
return op.multiclass_cross_entropy(target,
pred,
stream=op.streams[3])
elif self.output == 'sigmoid':
return op.binary_cross_entropy(target,
pred,
stream=op.streams[3])
else:
raise NotImplementedError()
elif self.loss == "squarederror" or self.loss == 'mse':
return op.mean_squared_error(target, pred)
else:
raise NotImplementedError()
