def test_nn():
req_data = request.get_json()
print("--------- 1.load data ------------")
dataTest = load_data(
[
req_data['x1']
, req_data['x2']
, req_data['x3']
, req_data['x4']
, req_data['x5']
, req_data['x6']
, req_data['x7']
, req_data['x8']
, req_data['x9']
, req_data['x10']
, req_data['x11']
, req_data['x12']
, req_data['x13']
, req_data['x14']
, req_data['x15']
, req_data['x16']
, req_data['x17']
, req_data['x18']
, req_data['x19']
]
)
print("--------- 2.load model ------------")
center, delta, w = load_model("messidor_center.txt", "messidor_delta.txt", "messidor_weight.txt")
print("--------- 3.get prediction ------------")
result = get_predict(dataTest, center, delta, w)
print('result', result)
print("--------- 4.save result ------------")
res = save_predict(result)
return jsonify({ "res": res })
def start_nn():
print("--------- 1.load data ------------")
feature, label, n_output = load_data("data.txt")
print("--------- 2.training ------------")
center, delta, w = bp_train(feature, label, 20, 5000, 0.008, n_output)
print("--------- 3.get prediction ------------")
result = get_predict(feature, center, delta, w)
# print("result：", (1 - err_rate(label, result)))
print("--------- 4.save model and result ------------")
save_model_result(center, delta, w, result)
return jsonify({"res": "success"})