def common_test_onnxt_runtime_reshape(self):
sh = numpy.array([1, 4], dtype=numpy.int64)
onx = OnnxReshape('X', sh, output_names=['Y'])
X = numpy.array([[1, 2], [3, -4]], dtype=numpy.float64)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)})
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
exp = X.reshape(sh.tolist())
self.assertEqualArray(exp, got['Y'])
def test_container_init(self):
onx = OnnxReshape(
OnnxReshape('X', np.array([1, -1], dtype=np.int64),
op_version=TARGET_OPSET),
np.array([1, -1], dtype=np.int64),
output_names=['Y'], op_version=TARGET_OPSET)
X = np.array([[1, 2], [3, 4]], dtype=np.float32)
model_def = onx.to_onnx({'X': X},
outputs=[('Y', FloatTensorType([None, 2]))],
target_opset=TARGET_OPSET)
sess = InferenceSession(model_def.SerializeToString())
got = sess.run(None, {'X': X})[0]
assert_almost_equal(X.reshape((1, -1)), got)
inits = [row for row in str(model_def).split('\n')
if row.startswith(" initializer {")]
self.assertEqual(len(inits), 1)
def _onnx_grad_sigmoid_neg_log_loss_error(target_opset=None,
dtype=numpy.float32,
eps=1e-5,
weight_name=None):
"""
The function the raw scores from a classifier, uses the
sigmoid function to compute probabilities, then the log function
to compute the loss. It creates the ONNX graph for this function
and the associated gradient of the loss against the raw scores.
Probabilites (class 1): :math:`p(s) = \\frac{1}{1 + \\exp(-s)}`.
Loss (for two classes): :math:`L(y, s) = (1 - y)\\log(1 - p(s)) +
y \\log(p(s))`.
Gradient :math:`\\frac{dL(y, s)}{ds} = y - p(s)`.
To avoid nan values, probabilies are clipped:
:math:`p(s) = \\max(\\min(p(s), 1 - \\epsilon), \\epsilon)`.
:math:`y \\in \\{0, 1\\}` (integer). *s* is a float.
:param eps: to clip probabilities and avoid computing `log(0)`
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('grad_sigmoid_neg_log_loss_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
from skl2onnx.algebra.onnx_ops import (OnnxSub, OnnxMul, OnnxSigmoid,
OnnxLog, OnnxNeg, OnnxReduceSum,
OnnxReshape, OnnxAdd, OnnxCast,
OnnxClip)
p1c = OnnxSigmoid('X2', op_version=target_opset)
p1 = OnnxClip(p1c,
numpy.array([eps], dtype=dtype),
numpy.array([1 - eps], dtype=dtype),
op_version=target_opset)
p0 = OnnxSub(numpy.array([1], dtype=dtype), p1, op_version=target_opset)
y1 = OnnxCast('X1',
to=NP_TYPE_TO_TENSOR_TYPE[numpy.dtype(dtype)],
op_version=target_opset)
y0 = OnnxSub(numpy.array([1], dtype=dtype), y1, op_version=target_opset)
loss_obs = OnnxAdd(OnnxMul(y0,
OnnxLog(p0, op_version=target_opset),
op_version=target_opset),
OnnxMul(y1,
OnnxLog(p1, op_version=target_opset),
op_version=target_opset),
op_version=target_opset)
loss_neg = OnnxNeg(loss_obs, op_version=target_opset)
if weight_name is None:
loss = OnnxReduceSum(loss_neg, op_version=target_opset)
grad = OnnxSub(p1,
y1,
op_version=target_opset,
output_names=['Y_grad'])
else:
loss = OnnxReduceSum(OnnxMul(loss_neg,
OnnxReshape(weight_name,
numpy.array(
[-1, 1],
dtype=numpy.int64),
op_version=target_opset),
op_version=target_opset),
op_version=target_opset)
grad = OnnxMul(OnnxSub(p1, y1, op_version=target_opset),
OnnxReshape(weight_name,
numpy.array([-1, 1], dtype=numpy.int64),
op_version=target_opset),
output_names=['Y_grad'],
op_version=target_opset)
res = OnnxReshape(loss,
numpy.array([-1], numpy.int64),
op_version=target_opset,
output_names=['Y'])
var_type_int64 = dtype_to_var_type(numpy.int64)
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type_int64([None, None])),
('X2', var_type([None, None]))]
if weight_name is not None:
varsx.append((weight_name, var_type([None])))
onx = res.to_onnx(varsx,
outputs=[('Y', var_type()), ('Y_grad', var_type())],
target_opset=target_opset,
other_outputs=[grad])
if weight_name is not None:
onx = add_initializer(onx, weight_name, numpy.array([1], dtype=dtype))
return onx