我可以通过运行创建一个简单的keras模型
python create-flask-model.py
create-flask-model.朋友
##points in square that are in or out of a quarter circle
import random
import math
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
training_size = 8000
testing_size = 2000
batch_size = 10
epoch_no = 30
modelStructureFileName = 'simple-flask.json'
modelWeightFileName = 'simple-flask.h5'
def get_model():
model = Sequential()
model.add(Dense(4, input_dim=2, activation='tanh'))
model.add(Dense(4, activation='tanh'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='rmsprop')
return model
def get_data_instances(size):
result = []
for i in range(0, size):
number_1 = random.uniform(0,1)
number_2 = random.uniform(0,1)
squares = math.pow(number_1,2) + math.pow(number_2,2)
target = 0
if squares < 0.49:
target = 1
line = number_1,number_2,target
result.append(line)
return np.array(result)
##create data and split in to training and test, features and targets
data_instances = get_data_instances(training_size+testing_size)
train_x, train_y = data_instances[:training_size,0:2], data_instances[:training_size,-1]
test_x, test_y = data_instances[training_size:,0:2], data_instances[training_size:,-1]
##load model and train
model = get_model()
history = model.fit(train_x, train_y, batch_size=batch_size, epochs=epoch_no, validation_data=(test_x, test_y))
##save the model
model_json = model.to_json()
with open(modelStructureFileName, 'w') as json_file:
json_file.write(model_json)
model.save_weights(modelWeightFileName)
##how to get prediction for an instance
#instance = np.array([0.3, 0.6])
#instance = instance.reshape(1,2)
#yhat = model.predict(instance)
#print(yhat)
我希望将生成的模型加载到烧瓶应用程序中，并能够将实例作为json对象传递并进行预测并返回。运行
python flask-app.py
在与模型json和h5文件相同的目录中。
flask-app.朋友
import json
import numpy as np
from flask import Flask
from keras.models import model_from_yaml
app = Flask(__name__)
model = None
modelStructureFileName = 'simple-flask.json'
modelWeightFileName = 'simple-flask.h5'
def load_model():
yaml_file = open(modelStructureFileName, 'r')
loaded_model_yaml = yaml_file.read()
yaml_file.close()
global model
model = model_from_yaml(loaded_model_yaml)
model.load_weights(modelWeightFileName)
@app.route('/flask/<input>', methods=['GET'])
def predict(input):
input_array = json.loads(input)
instance = np.array(input_array)
instance = instance.reshape(1,2)
yhat = model.predict(instance)
return str(yhat)
if __name__ == '__main__':
load_model()
app.run(port = 9000, debug = True)
如果我导航到http://localhost:9000/flask/[0.3,0.6]我收到错误
builtins.ValueError
ValueError: Tensor Tensor("dense_3/Sigmoid:0", shape=(?, 1), dtype=float32) is not an element of this graph.
我认为这与应用程序中的模型范围有关，但无法弄清楚。如果我在请求方法中加载模型，它会工作一次，但随后会因另一个错误而失败。我只想加载模型一次。如何让烧瓶应用程序按预期工作？
编辑：我最终使用瓶而不是烧瓶，它没有问题。
bottle-app.朋友
from bottle import route, run
import json
import numpy as np
from keras.models import model_from_yaml
modelStructureFileName = 'simple-flask.json'
modelWeightFileName = 'simple-flask.h5'
yaml_file = open(modelStructureFileName, 'r')
loaded_model_yaml = yaml_file.read()
yaml_file.close()
model = model_from_yaml(loaded_model_yaml)
model.load_weights(modelWeightFileName)
print('model loaded')
@route('/bottle/<input>')
def predict(input):
input_array = json.loads(input)
instance = np.array(input_array)
instance = instance.reshape(1,2)
yhat = model.predict(instance)
print(input_array, yhat)
return str(yhat[0][0])
run(host='localhost', port=9000, debug=True)
发生这种情况是因为，默认情况下，您在flask中启用了多个线程。 Tensorflow模型不适用于多线程。您可以在以下链接中阅读更多相关信息
以下解决方法对我有用
global graph
graph = tf.get_default_graph()
with graph.as_default():
model.compile()
model.fit()
with graph.as_default():
model.predict()