ONNX:机器学习模型通用格式
Python示例:import numpy
from onnx import numpy_helper, TensorProto
from onnx.helper import (
make_model, make_node, set_model_props, make_tensor,
make_graph, make_tensor_value_info)
from mlprodict.plotting.text_plot import onnx_simple_text_plot
# inputs
# 'X' is the name, TensorProto.FLOAT the type, the shape
X = make_tensor_value_info('X', TensorProto.FLOAT, )
A = make_tensor_value_info('A', TensorProto.FLOAT, )
B = make_tensor_value_info('B', TensorProto.FLOAT, )
# outputs, the shape is left undefined
Y = make_tensor_value_info('Y', TensorProto.FLOAT, None)
# nodes
# It creates a node defined by the operator type MatMul,
# 'X', 'A' are the inputs of the node, 'XA' the output.
node1 = make_node('MatMul', ['X', 'A'], ['XA'])
node2 = make_node('Add', ['XA', 'B'], ['Y'])
# from nodes to graph
# the graph is built from the list of nodes, the list of inputs,
# the list of outputs and a name.
graph = make_graph(,# nodes
'lr',# a name
,# inputs
)# outputs
# onnx graph
# there is no metata in this case.
onnx_model = make_model(graph)
# the work is done, let's display it...
print(onnx_simple_text_plot(onnx_model)) 官网:https://pypi.org/project/onnx/
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