def _onnx_grad_loss_absolute_error(target_opset=None,
dtype=numpy.float32,
weight_name=None):
"""
Returns the ONNX graph for function
:math:`Y = f(X1, X2) = \\lVert X1 - X2 \\rVert` or
:math:`Y = f(X1, X2) = \\lVert (X1 - X2)w \\rVert` if
*weight_name* is not None and its gradient.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('grad_loss_absolute_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import (OnnxSub, OnnxMul, OnnxReduceSum,
OnnxReshape, OnnxSign, OnnxAbs)
diff = OnnxSub('X1', 'X2', op_version=target_opset)
abs_diff = OnnxAbs(diff, op_version=target_opset)
if weight_name is None:
res = OnnxReduceSum(abs_diff, op_version=target_opset)
res2 = OnnxSign(diff, op_version=target_opset, output_names=['Y_grad'])
else:
resh = OnnxReshape(weight_name,
numpy.array([-1, 1], dtype=numpy.int64),
op_version=target_opset)
mul = OnnxMul(abs_diff, resh, op_version=target_opset)
res = OnnxReduceSum(mul, op_version=target_opset)
res2 = OnnxMul(OnnxSign(diff, op_version=target_opset),
resh,
op_version=target_opset,
output_names=['Y_grad'])
res = OnnxReshape(res,
numpy.array([-1], numpy.int64),
op_version=target_opset,
output_names=['Y'])
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type([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=[res2])
if weight_name is not None:
onx = add_initializer(onx, weight_name, numpy.array([1], dtype=dtype))
return onx
def test_onnxt_runtime_reduce_sum(self):
X = numpy.array([[2, 1], [0, 1]], dtype=float)
onx = OnnxReduceSum('X', output_names=['Y'], keepdims=0)
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'])
self.assertEqualArray(numpy.sum(X), got['Y'], decimal=6)
onx = OnnxReduceSum('X', output_names=['Y'], axes=1)
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'])
self.assertEqualArray(numpy.sum(X, axis=1).ravel(), got['Y'].ravel())
onx = OnnxReduceSum('X', output_names=['Y'], axes=1, keepdims=1)
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'])
self.assertEqualArray(
numpy.sum(X, axis=1, keepdims=1).ravel(), got['Y'].ravel())
def nmf_to_onnx(W, H):
"""
The function converts a NMF described by matrices
*W*, *H* (*WH* approximate training data *M*).
into a function which takes two indices *(i, j)*
and returns the predictions for it. It assumes
these indices applies on the training data.
"""
col = OnnxArrayFeatureExtractor(H, 'col')
row = OnnxArrayFeatureExtractor(W.T, 'row')
dot = OnnxMul(col, row)
res = OnnxReduceSum(dot, output_names="rec")
indices_type = np.array([0], dtype=np.int64)
onx = res.to_onnx(inputs={'col': indices_type,
'row': indices_type},
outputs=[('rec', FloatTensorType((None, 1)))])
return onx
def _onnx_grad_loss_elastic_error(target_opset=None,
dtype=numpy.float32,
weight_name=None,
l1_weight=0.01,
l2_weight=0.01):
"""
Returns the ONNX graph for function
:math:`Y = f(X1, X2) = \\beta \\lVert X1 - X2 \\rVert +
\\alpha \\lVert X1 - X2 \\rVert^2` or
:math:`Y = f(X1, X2) = \\beta \\lVert w(X1 - X2) \\rVert +
\\alpha \\lVert (\\sqrt{w})(X1 - X2) \\rVert^2` if
*weight_name* is not None and its gradient.
*l1_weight* is :math:`\\beta` and
*l2_weight* is :math:`\\alpha`.
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('grad_loss_elastic_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import (OnnxSub, OnnxMul, OnnxAdd,
OnnxIdentity, OnnxReduceSum,
OnnxReshape, OnnxSign, OnnxAbs)
diff = OnnxSub('X1', 'X2', op_version=target_opset)
abs_diff = OnnxAbs(diff, op_version=target_opset)
# loss
abs_diff_l1 = OnnxMul(abs_diff,
numpy.array([l1_weight], dtype=dtype),
op_version=target_opset)
diff_l2 = OnnxMul(OnnxMul(diff, diff, op_version=target_opset),
numpy.array([l2_weight], dtype=dtype),
op_version=target_opset)
score = OnnxAdd(abs_diff_l1, diff_l2, op_version=target_opset)
# gradient
grad_l1 = OnnxMul(OnnxSign(diff, op_version=target_opset),
numpy.array([l1_weight], dtype=dtype),
op_version=target_opset)
grad_l2 = OnnxMul(diff,
numpy.array([l2_weight * -2], dtype=dtype),
op_version=target_opset)
grad = OnnxAdd(grad_l1, grad_l2, op_version=target_opset)
if weight_name is None:
res = OnnxReduceSum(score, op_version=target_opset)
res2 = OnnxIdentity(grad,
op_version=target_opset,
output_names=['Y_grad'])
else:
resh = OnnxReshape(weight_name,
numpy.array([-1, 1], dtype=numpy.int64),
op_version=target_opset)
res = OnnxReduceSum(OnnxMul(score, resh, op_version=target_opset),
op_version=target_opset)
res2 = OnnxMul(grad,
resh,
op_version=target_opset,
output_names=['Y_grad'])
res = OnnxReshape(res,
numpy.array([-1], numpy.int64),
op_version=target_opset,
output_names=['Y'])
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type([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=[res2])
if weight_name is not None:
onx = add_initializer(onx, weight_name, numpy.array([1], dtype=dtype))
return onx