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)
