Keras:加载多个模型并在不同线程中进行预测 [英] Keras: Load multiple models and predict in different threads
问题描述
我正在使用带有Tensorflow核心的Keras.我想在构造函数中加载2个不同的模型,然后在不同的线程中(根据请求)进行预测.我试图在张量流图上下文中加载这些模型,但是没有用.我的代码:
I'm using Keras with tensorflow core. I want to load 2 different models in constructor, and then make predictions (on request) in different threads. I tried to load these models within tensorflow graph contexts, but it didn't work. My code:
from keras.models import load_model
from keras import Sequential
def __init__(self):
self.graph_A = tf.Graph()
with self.graph_A.as_default():
self.model_A: Sequential = load_model('model_A_filename')
self.graph_B = tf.Graph()
with self.graph_B.as_default():
self.model_B: Sequential = load_model('model_B_filename')
def predict_with_model_A(X):
with self.graph_A.as_default():
return self.model_A.predict(X)
def predict_with_model_B(X):
with self.graph_B.as_default():
return self.model_B.predict(X)
当我运行程序时,模型A已成功加载.但是,我在加载模型B时收到错误消息:
When I run the program, model A is loaded successfully. However I receive an error on loading model B:
TypeError: Cannot interpret feed_dict key as Tensor: Tensor
Tensor("Placeholder:0", shape=(7626, 210), dtype=float32) is not an element
of this graph.
我们将很高兴听到您如何正确执行操作.谢谢!
Will be happy to hear, how to do it right. Thanks!
推荐答案
尝试在处理后每次重置图形.对于keras,请使用K.clear_session().为每个图形使用单独的会话.
Try to reset the graph everytime after processing. For keras use K.clear_session(). Use seperate sessions for every graph.
class Model:
@staticmethod
def loadmodel(path):
return loadmodel(path)
def ___init__(self, path):
self.model = self.loadmodel(path)
self.graph = tf.get_default_graph()
def predict(self, X):
with self.graph.as_default():
return self.model.predict(X)
model1 = Model('model1.h5')
model1.predict(test_data)
model2 = Model('model2.h5')
model2.predict(test_data)
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