def wtest_ort_gradient_optimizers_binary(self, use_weight=False):
from onnxcustom.utils.orttraining_helper import add_loss_output
from onnxcustom.training.optimizers import OrtGradientOptimizer
X = numpy.arange(60).astype(numpy.float32).reshape((-1, 3))
y = numpy.arange(X.shape[0]).astype(numpy.float32).reshape(
(-1, 1)) > 10
X = X.astype(numpy.float32)
y = y.astype(numpy.int64)
y[0, 0] = 0
y[-1, 0] = 1
w = (numpy.random.rand(X.shape[0]) + 1).astype(numpy.float32)
X_train, _, y_train, __, w_train, ___ = train_test_split(X, y, w)
reg = SGDClassifier(loss='log')
reg.fit(X_train, y_train.ravel())
onx = to_onnx(reg,
X_train,
target_opset=opset,
black_op={'LinearClassifier'},
options={'zipmap': False})
onx_loss = add_loss_output(
onx,
'log',
output_index=1,
weight_name='weight' if use_weight else None)
inits = ['intercept', 'coef']
inputs = onx_loss.graph.input
self.assertEqual(len(inputs), 3 if use_weight else 2)
dt = inputs[1].type.tensor_type.elem_type
self.assertEqual(TensorProto.INT64, dt) # pylint: disable=E1101
train_session = OrtGradientOptimizer(onx_loss,
inits,
learning_rate=1e9)
self.assertRaise(lambda: train_session.get_state(), AttributeError)
if use_weight:
train_session.fit(X_train,
y_train.reshape((-1, 1)),
w_train.reshape((-1, 1)),
use_numpy=False)
else:
train_session.fit(X_train,
y_train.reshape((-1, 1)),
use_numpy=False)
state_tensors = train_session.get_state()
self.assertEqual(len(state_tensors), 2)
r = repr(train_session)
self.assertIn("OrtGradientOptimizer(model_onnx=", r)
self.assertIn("learning_rate='invscaling'", r)
losses = train_session.train_losses_
self.assertGreater(len(losses), 1)
if any(map(numpy.isnan, losses)):
raise AssertionError(losses)
def wtest_ort_gradient_optimizers_reg(self, use_weight=False):
from onnxcustom.utils.orttraining_helper import add_loss_output
from onnxcustom.training.optimizers import OrtGradientOptimizer
X = numpy.arange(60).astype(numpy.float32).reshape((-1, 3))
y = numpy.arange(X.shape[0]).astype(numpy.float32).reshape((-1, 1))
y[0, 0] += 1
y[-1, 0] += 1
w = (numpy.random.rand(X.shape[0]) + 1).astype(numpy.float32)
X_train, _, y_train, __, w_train, ___ = train_test_split(X, y, w)
reg = SGDRegressor()
reg.fit(X_train, y_train.ravel())
onx = to_onnx(reg,
X_train,
target_opset=opset,
black_op={'LinearRegressor'})
onx_loss = add_loss_output(
onx, 'squared_error', weight_name='weight' if use_weight else None)
inits = ['intercept', 'coef']
inputs = onx_loss.graph.input
self.assertEqual(len(inputs), 3 if use_weight else 2)
train_session = OrtGradientOptimizer(onx_loss,
inits,
learning_rate=1e9)
self.assertRaise(lambda: train_session.get_state(), AttributeError)
if use_weight:
self.assertRaise(
lambda: train_session.fit(X_train,
y_train.reshape((-1, 1)),
w_train.reshape((-1, 1)),
use_numpy=False), ConvergenceError)
else:
self.assertRaise(
lambda: train_session.fit(
X_train, y_train.reshape((-1, 1)), use_numpy=False),
ConvergenceError)
state_tensors = train_session.get_state()
self.assertEqual(len(state_tensors), 2)
r = repr(train_session)
self.assertIn("OrtGradientOptimizer(model_onnx=", r)
self.assertIn("learning_rate='invscaling'", r)
losses = train_session.train_losses_
self.assertGreater(len(losses), 1)
if any(map(numpy.isnan, losses)):
raise AssertionError(losses)
def wtest_ort_gradient_optimizers_binary(self, use_weight=False):
from onnxcustom.utils.orttraining_helper import add_loss_output
from onnxcustom.training.optimizers import OrtGradientOptimizer
X, y = make_classification( # pylint: disable=W0632
100, n_features=10, random_state=0)
X = X.astype(numpy.float32)
y = y.astype(numpy.int64)
w = (numpy.random.rand(X.shape[0]) + 1).astype(numpy.float32)
X_train, _, y_train, __, w_train, ___ = train_test_split(X, y, w)
reg = SGDClassifier(loss='log')
reg.fit(X_train, y_train)
onx = to_onnx(reg,
X_train,
target_opset=opset,
black_op={'LinearClassifier'},
options={'zipmap': False})
onx2 = load_onnx(BytesIO(onx.SerializeToString()))
set_model_props(onx, {'info': 'unit test'})
onx_loss = add_loss_output(
onx,
'log',
output_index=1,
weight_name='weight' if use_weight else None)
inits = ['intercept', 'coef']
inputs = onx_loss.graph.input
self.assertEqual(len(inputs), 3 if use_weight else 2)
dt = inputs[1].type.tensor_type.elem_type
self.assertEqual(TensorProto.INT64, dt) # pylint: disable=E1101
train_session = OrtGradientOptimizer(onx_loss,
inits,
learning_rate=1e-3)
self.assertRaise(lambda: train_session.get_state(), AttributeError)
if use_weight:
train_session.fit(X_train,
y_train.reshape((-1, 1)),
w_train.reshape((-1, 1)),
use_numpy=False)
else:
train_session.fit(X_train,
y_train.reshape((-1, 1)),
use_numpy=False)
state_tensors = train_session.get_state()
self.assertEqual(len(state_tensors), 2)
r = repr(train_session)
self.assertIn("OrtGradientOptimizer(model_onnx=", r)
self.assertIn("learning_rate='invscaling'", r)
losses = train_session.train_losses_
self.assertGreater(len(losses), 1)
self.assertFalse(any(map(numpy.isnan, losses)))
# get_trained_weight
trained_onnx = train_session.get_trained_onnx(model=onx2)
sess = InferenceSession(onx2.SerializeToString(),
providers=['CPUExecutionProvider'])
got1 = sess.run(None, {'X': X_train})
sess = InferenceSession(trained_onnx.SerializeToString(),
providers=['CPUExecutionProvider'])
got2 = sess.run(None, {'X': X_train})
self.assertEqual(len(got1), len(got2))
self.assertEqual(got1[0].shape, got2[0].shape)
# state
state = train_session.get_state()
self.assertIsInstance(state, dict)
train_session.set_state(state)