keras: " is not an element of this graph." when loading model.

I had a problem similar to that of @piraka9011 which was solved by calling model._make_predict_function() right after loading the trained model. If this does not work, check out #2397.

Change the backend to Theano. change it back when the issue is resolved.

This worked for me:

def load_model():
	global model
	model = ResNet50(weights="imagenet")
        # this is key : save the graph after loading the model
	global graph
	graph = tf.get_default_graph()

While predicting, use the same graph

    with graph.as_default():
	preds = model.predict(image)
	#... etc

I’m having a similar issue (I think) but it occurs when I call the predict() method on a model. I am given a raise ValueError...is not an element of this graph.

Any suggestions?

i had the same environment. env: flask + tensorflow + keras (dev env: windows 10 pro) error log is

ValueError: Tensor Tensor(“avg_pool/AvgPool:0”, shape=(?, 1, 1, 2048), dtype=float32) is not an element of this graph.

this worked for me:

global graph graph = tf.get_default_graph()

while predicting, use the same graph with graph.as_default(): feature = resnet_vgg.predict(npImg)

thanks to anujgupta82

In my case, I have a flask endpoint that passes a key to a process_algorithm method. The process_algorithm method will each time load a pre-trained keras model and then make a prediction. It seems that the first time the process_algorithm method is called, it works fine, but the next time, there will be this error: Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder:0", shape=(196, 128), dtype=float32) is not an element of this graph at load_model(). Any way to solve this?

Calling model._make_predict_function() worked for me.

@wqp89324 I had the same issue. Calling keras.backend.clear_session() each time before loading a model solved the problem.

@yashmehta14 I was having a similar problem. I fixed it by adding a function which can be called later

def get_model():
    global model
    model = load_model("model_new.h5")
    model._make_predict_function()
    print("Model Loaded")

I faced the same issue when I was passing an instance of loaded model to another thread which was predicting. I changed my execution and made the model load in the same thread as the one which was predicting. I am not sure if this solves anyone’s problem but hope so it helps someone.

I Solved! I made a function which runs without app.route(), the decorator. And I loaded model in the function. And then I called the function when I need prediction

Just got this error all of a sudden (Was working fine earlier today)… model._make_predict_function() worked for me as well to solve it, but weird!

This worked for me:

def load_model():
	global model
	model = ResNet50(weights="imagenet")
        # this is key : save the graph after loading the model
	global graph
	graph = tf.get_default_graph()

While predicting, use the same graph

    with graph.as_default():
	preds = model.predict(image)
	#... etc

I am also doing the same thing shown by you but then also i am etting the same error…First time i will get the output but when i am trying to click the link for second time it is giving the error… Can u help me please ?

@wqp89324 try calling keras.backend.clear_session() on each request

Solve it by:

Instead of : app.run(debug=False)

Use:

if name == ‘main’: app.run(debug=False, threaded=False)

I had the same issue while using flask + keras global graph graph = tf.get_default_graph()

with graph.as_default(): res = model.predict()

this worked for me Thanks, @anujgupta82

@joaospinto Thank you so much! You saved my life and dozens of hours… ❤️

model._make_predict_function()

Works well.

@Spidy20 I don’t think that is a good solution because it will require the model to be reloaded every time the predict API is called.

For the record, I ran into an issue with model._make_predict_function() and was able to solve it following this stackoverflow post.

If it works within REPL but not flask, try running the flask app with gunicorn and set mode to sync. Mode gevent will get the error.

@gustavz I have the same problem, could you solve it?

Thanks to @Mrjaggu , @anujgupta82 , @GerardWalsh and others! I spent at least a day trying to find how to correct the error. Here’s the final result: def get_model(): global graph graph = tf.get_default_graph() with graph.as_default(): global model model = load_model(‘model_WheelPrediction.h5’) Then in the app.route: @app.route(“/predict”, methods=[“POST”]) def predict(): with graph.as_default(): message = request.get_json(force=True) encoded = message[‘image’] decoded = base64.b64decode(encoded) image = Image.open(io.BytesIO(decoded)) processed_image = preprocess_image(image, target_size=(224, 224)) prediction = model.predict(processed_image).tolist()

loading model predict first is right but second is wrong ,you can do like this

global model_similar_v2

global graph
graph = tf.get_default_graph()

# model_ResNet50 = ResNet50(weights="imagenet")

if model_similar_v2 is None:
    model_similar_v2 = load_model(path + "/models_center/similar/model_similar_v2.h5")

with graph.as_default():
    preict = model_similar_v2.predict([x1, x2])

changing backend to theano worked for me