开工的欲望 | AI Studio悄然上线新功能，用你的模型生成在线预测服务

import paddleimport paddle.fluid as fluidimport numpyimport mathimport sys
from __future__ import print_functionBATCH_SIZE = 20train_reader = paddle.batch(
    paddle.reader.shuffle(
        paddle.dataset.uci_housing.train(), buf_size=500),
        batch_size=BATCH_SIZE)test_reader = paddle.batch(
    paddle.reader.shuffle(
        paddle.dataset.uci_housing.test(), buf_size=500),
        batch_size=BATCH_SIZE)params_dirname = "model2"x = fluid.layers.data(name='x', shape=[13], dtype='float32')y = fluid.layers.data(name='y', shape=[1], dtype='float32')y_predict = fluid.layers.fc(input=x, size=1, act=None)main_program = fluid.default_main_program()startup_program = fluid.default_startup_program()cost = fluid.layers.square_error_cost(input=y_predict, label=y)avg_loss = fluid.layers.mean(cost)sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)sgd_optimizer.minimize(avg_loss)#clone a test_programtest_program = main_program.clone(for_test=True)use_cuda = Falseplace = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()exe = fluid.Executor(place)num_epochs = 100# For training test costdef train_test(executor, program, reader, feeder, fetch_list):    accumulated = 1 * [0]    count = 0
    for data_test in reader():        outs = executor.run(program=program,
                            feed=feeder.feed(data_test),
                            fetch_list=fetch_list)
        accumulated = [x_c[0] + x_c[1][0] for x_c in zip(accumulated, outs)]
        count += 1
    return [x_d / count for x_d in accumulated]params_dirname = "fit_a_line.inference.model"feeder = fluid.DataFeeder(place=place, feed_list=[x, y])naive_exe = fluid.Executor(place)
naive_exe.run(startup_program)step = 0exe_test = fluid.Executor(place)# main train loop.for pass_id in range(num_epochs):
    for data_train in train_reader():
        avg_loss_value, = exe.run(main_program,                                  feed=feeder.feed(data_train),
                                  fetch_list=[avg_loss])
        if step % 10 == 0:  # record a train cost every 10 batches
            print (step, avg_loss_value[0])        if step % 100 == 0:  # record a test cost every 100 batches
            test_metics = train_test(executor=exe_test,
                                     program=test_program,
                                     reader=test_reader,
                                     fetch_list=[avg_loss.name],
                                     feeder=feeder)
            print (step, test_metics[0])            # If the accuracy is good enough, we can stop the training.
            if test_metics[0] < 10.0:
                break
        step += 1
        if math.isnan(float(avg_loss_value[0])):
            sys.exit("got NaN loss, training failed.")    if params_dirname is not None:        # We can save the trained parameters for the inferences later
        fluid.io.save_inference_model(params_dirname, ['x'],
                                      [y_predict], exe)

def reader_infer(data_args):
  	"""
  	reader_infer 输入参数转换器方法
  	:param data_args: 接口传入的数据，以k-v形式
  	:return [[]], feeder
  	"""
  	#构造内容
  	pass

def output(results, data_args):
  	"""
  	output 输出参数转换器方法
  	:param results 模型预测结果
  	:param data_args: 接口传入的数据，以k-v形式
 	 	:return array 需要能被json_encode的数据格式
 	 	"""
  	#构造内容
  	pass

import osimport sys
sys.path.append("..")from PIL import Imageimport numpy as npimport paddle.fluid as fluidfrom home.utility import base64_to_imagedef reader_infer(data_args):
   """
   reader_infer 输入参数转换器方法
   :param data_args: 接口传入的数据，以k-v形式
   :return [[]], feeder
   """
   def reader():
       """
       reader
       :return:
       """
       x = fluid.layers.data(name='x', shape=[13], dtype='float32')       # y = fluid.layers.data(name='y', shape=[1], dtype='float32')
       feeder = fluid.DataFeeder(place=fluid.CPUPlace(), feed_list=[x])
       CRIM = float(data_args["CRIM"])
       ZN = float(data_args["ZN"])
       INDUS =  float(data_args["INDUS"])
       CHAS = float(data_args["CHAS"])
       NOX = float(data_args["NOX"])
       RM = float(data_args["RM"])
       AGE = float(data_args["AGE"])
       DIS = float(data_args["DIS"])
       RAD =  float(data_args["RAD"])
       TAX = float(data_args["TAX"])
       PTRATIO = float(data_args["PTRATIO"])
       B =  float(data_args["B"])
       LSTAT = float(data_args["LSTAT"])       return [[[CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD, TAX, PTRATIO, B, LSTAT]]], feeder   return reader

def output(results, data_args):
    """
    output 输出参数转换器方法
    :param results 模型预测结果
    :param data_args: 接口传入的数据，以k-v形式
    :return array 需要能被json_encode的数据格式
    """
   lines = []   for dt in results:
       y = dt.tolist()
       lines.append({"predict": y})   return lines

curl -H "Content-Type: application/json" -X POST -d '{"CRIM":0.01887747, "ZN":-0.11363636, "INDUS":0.25525005, "CHAS":-0.06916996,  "NOX":0.29898136, "RM": -0.04476612, "AGE": 0.14340987, "DIS":-0.14797285,  "RAD":0.62828665, "TAX":0.49191383, "PTRATIO":0.18558153, "B":0.05473289, "LSTAT":0.16851371}' "https://aistudio.baidu.com/serving/online/xxx?apiKey=xxxxxxxxxx"

import jsonimport tracebackimport urllibimport urllib2

formdata = {    "CRIM":0.01887747, 
    "ZN":-0.11363636, 
    "INDUS":0.25525005, 
    "CHAS":-0.06916996,  
    "NOX":0.29898136, 
    "RM": -0.04476612, 
    "AGE": 0.14340987, 
    "DIS":-0.14797285,  
    "RAD":0.62828665, 
    "TAX":0.49191383, 
    "PTRATIO":0.18558153, 
    "B":0.05473289, 
    "LSTAT":0.16851371}
header = {"Content-Type": "application/json; charset=utf-8"}
url = "https://aistudio.baidu.com/serving/online/xxx?apiKey=a280cf48-6d0c-4baf-bd39xxxxxxcxxxxx"data = json.dumps(formdata)try:
    request = urllib2.Request(url, data, header)
    response = urllib2.urlopen(request)
    response_str = response.read()
    response.close()    print(response_str)
except urllib2.HTTPError as e:    print("The server couldn't fulfill the request")    print(e.code)    print(e.read())
except urllib2.URLError as e:    print("Failed to reach the server")    print(e.reason)
except:
    traceback.print_exc()