print("output names:", [o.name for o in sess.get_outputs()]) res = sess.run(None, {'X': X_test[:2]}) print("outputs") pprint.pprint(res) # Function *select_model_inputs_outputs* add also promote an intermediate # result to an output. # ##################################### # This example only uses ONNX graph in memory and never saves or loads a # model. This can be done by using the following snippets of code. # # Save a model # ++++++++++++ with open("simplified_model.onnx", "wb") as f: f.write(simple_onx.SerializeToString()) ################################### # Load a model # ++++++++++++ model = load("simplified_model.onnx", "wb") sess = InferenceSession(model.SerializeToString(), providers=['CPUExecutionProvider']) print("output names:", [o.name for o in sess.get_outputs()]) res = sess.run(None, {'X': X_test[:2]}) print("outputs") pprint.pprint(res)