【python Flask】使用 Flask 通过预先训练的深度学习模型来提供预测API
1、首先安装包
pip install tensorflow
pip install keras
pip install pandas
2、keras 训练你的模型保存为一个文件。
# -*- coding:utf-8 -*-
# 导入panda，keras 和tensorflow
import pandas as pd
import tensorflow as tf
import keras
from keras import models, layers
# 加载样本数据集，划分为x和y DataFrame
df = pd.read_csv("https://github.com/bgweber/Twitch/raw/master/Recommendations/games-expand.csv")
x = df.drop(['label'], axis=1)
y = df['label']
print(x)
print(y)
# 定义Keras模型
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10,)))
model.add(layers.Dropout(0.1))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dropout(0.1))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
# 使用自定义度量函数
def auc(y_true, y_pred):
auc = tf.metrics.auc(y_true, y_pred)[1]
keras.backend.get_session().run(tf.local_variables_initializer())
return auc
# 编译并拟合模型
model.compile(optimizer='rmsprop',loss='binary_crossentropy',
metrics=[auc])
history = model.fit(x, y, epochs=100, batch_size=100,
validation_split = .2, verbose=0)
# 以H5格式保存模型
model.save("./static/games.h5")
3、数据形式如下：自变量是10个，G1~G10，目标变量是二分类0和1
E:\laidefa\python.exe "E:/Program Files/pycharmproject/深度学习api/model_keras_train.py"
Using TensorFlow backend.
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
0 0 0 0 1 0 0 0 0 0 0
1 0 0 0 0 1 0 0 0 0 0
2 0 0 1 0 0 0 0 0 0 0
3 0 0 1 0 0 1 1 0 0 1
4 0 0 1 0 1 1 0 1 1 0
5 1 0 1 0 1 0 0 0 0 0
6 0 0 1 0 0 0 0 0 0 0
7 1 0 1 0 1 0 0 0 0 0
8 1 1 0 1 0 1 1 1 0 0
9 0 0 1 0 0 0 0 0 0 0
10 1 0 1 0 0 0 0 0 0 0
11 1 0 0 0 0 1 0 0 1 0
12 0 0 1 1 0 0 0 0 0 0
13 1 0 1 0 0 0 0 0 0 0
14 0 0 1 0 0 0 0 0 0 0
15 1 1 0 1 1 1 0 0 1 0
16 0 0 1 0 1 1 0 0 0 0
17 1 0 1 0 1 0 0 0 0 0
18 0 0 0 0 0 0 0 0 1 0
19 0 0 1 0 0 0 0 0 0 0
20 0 0 0 1 0 0 0 0 0 0
21 0 0 1 0 0 0 0 0 0 0
22 1 0 0 0 1 0 0 0 0 0
23 1 1 0 0 0 1 1 0 1 0
24 0 0 0 0 1 0 0 0 0 0
25 0 0 0 0 0 0 0 0 0 0
26 0 0 1 0 0 0 0 0 0 0
27 0 1 0 0 1 0 1 1 0 1
28 1 1 0 0 0 1 1 0 1 0
29 0 0 0 0 0 0 0 0 1 1
... .. .. .. .. .. .. .. .. .. ...
[22905 rows x 10 columns]
0 0
1 0
2 0
3 1
4 1
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 1
24 0
25 0
26 0
训练好之后，文件保存为games.h5
5、下面我们编写flask api 部署我们的深度学习模型
训练了深度学习模型，接下来可以使用 Flask 来生产 Keras 模型。用于模型预测的完整代码如下所示。代码的整体结构与前面的代码示例相同，但主要区别在于定义预测函数之前先加载模型，并在预测函数中使用模型。要想重新加载模型，我们需要使用 custom_objects 参数将自定义度量函数作为输入参数传递给 load_model。
# -*- coding:utf-8 -*-
# 加载库
import flask
import pandas as pd
import tensorflow as tf
import keras
from keras.models import load_model
# 实例化 flask
app = flask.Flask(__name__)
# 我们需要重新定义我们的度量函数，从而在加载模型时使用它
def auc(y_true, y_pred):
auc = tf.metrics.auc(y_true, y_pred)[1]
keras.backend.get_session().run(tf.local_variables_initializer())
return auc
# 加载模型，传入自定义度量函数
global graph
graph = tf.get_default_graph()
model = load_model('./static/games.h5', custom_objects={'auc': auc})
# 将预测函数定义为一个端点
@app.route("/predict", methods=["GET","POST"])
def predict():
data = {"success": False}
params = flask.request.json
if (params == None):
params = flask.request.args
# 若发现参数，则返回预测值
if (params != None):
x=pd.DataFrame.from_dict(params, orient='index').transpose()
with graph.as_default():
data["prediction"] = str(model.predict(x)[0][0])
data["success"] = True
# 返回Jason格式的响应
return flask.jsonify(data)
if __name__ == '__main__':
# 启动Flask应用程序，允许远程连接
app.run(host='0.0.0.0',port='5000')
6、启动你的程序：在浏览器输入：http://localhost:5000/predict?g1=1&g2=1&g3=0&g4=1&g5=1&g6=1&g7=1&g8=0&g9=1&g10=0
注意：需要指定属性 G1 到 G10 的值
服务请求成功：
* Serving Flask app "keras_mode_api" (lazy loading)
* Environment: production
WARNING: Do not use the development server in a production environment.
Use a production WSGI server instead.
* Debug mode: off
* Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
127.0.0.1 - - [15/Nov/2018 16:15:22] "GET /predict?g1=1&g2=0&g3=0&g4=0&g5=0&g6=0&g7=0&g8=0&g9=0&g10=0 HTTP/1.1" 200 -
127.0.0.1 - - [15/Nov/2018 16:15:22] "GET /favicon.ico HTTP/1.1" 404 -
127.0.0.1 - - [15/Nov/2018 16:15:30] "GET /predict?g1=1&g2=0&g3=0&g4=0&g5=1&g6=0&g7=0&g8=0&g9=0&g10=0 HTTP/1.1" 200 -
127.0.0.1 - - [15/Nov/2018 16:15:35] "GET /predict?g1=1&g2=0&g3=0&g4=0&g5=1&g6=0&g7=1&g8=0&g9=0&g10=0 HTTP/1.1" 200 -
127.0.0.1 - - [15/Nov/2018 16:15:40] "GET /predict?g1=1&g2=0&g3=0&g4=0&g5=1&g6=0&g7=1&g8=0&g9=1&g10=0 HTTP/1.1" 200 -
127.0.0.1 - - [15/Nov/2018 16:15:44] "GET /predict?g1=1&g2=1&g3=0&g4=0&g5=1&g6=0&g7=1&g8=0&g9=1&g10=0 HTTP/1.1" 200 -
127.0.0.1 - - [15/Nov/2018 16:15:49] "GET /predict?g1=1&g2=1&g3=0&g4=1&g5=1&g6=0&g7=1&g8=0&g9=1&g10=0 HTTP/1.1" 200 -
127.0.0.1 - - [15/Nov/2018 16:15:55] "GET /predict?g1=1&g2=1&g3=0&g4=1&g5=1&g6=1&g7=1&g8=0&g9=1&g10=0 HTTP/1.1" 200 -
127.0.0.1 - - [15/Nov/2018 16:17:47] "GET /predict?g1=1&g2=1&g3=0&g4=1&g5=1&g6=1&g7=1&g8=0&g9=1&g10=0 HTTP/1.1" 200 -
查看结果：
