ValueError: Tensor Tensor xxx는 이 그래프의 해결 방안의 요소가 아닙니다.
3703 단어 인공 지능
keras+flask를 이용하여 간단한 딥러닝 백엔드 서버를 구축하는데 다음과 같은 문제가 발생했습니다.
ValueError: Tensor Tensor("fc1000/Softmax:0", shape=(?, 1000), dtype=float32) is not an element of this graph.
솔루션은 다음과 같습니다.
초기화할 때 모델 파일을 불러오고graph를 생성합니다.
전체 코드는 다음과 같습니다.
# USAGE
# Start the server:
# python run_keras_server.py
# Submit a request via cURL:
# curl -X POST -F [email protected] 'http://localhost:5000/predict'
# Submita a request via Python:
# python simple_request.py
# import the necessary packages
from keras.applications import ResNet50
from keras.preprocessing.image import img_to_array
from keras.applications import imagenet_utils
from PIL import Image
import numpy as np
import flask
import io
import tensorflow as tf
# initialize our Flask application and the Keras model
app = flask.Flask(__name__)
graph = None
model = None
def load_model():
# load the pre-trained Keras model (here we are using a model
# pre-trained on ImageNet and provided by Keras, but you can
# substitute in your own networks just as easily)
global graph
graph = tf.get_default_graph()
global model
model = ResNet50(weights="imagenet")
def prepare_image(image, target):
# if the image mode is not RGB, convert it
if image.mode != "RGB":
image = image.convert("RGB")
# resize the input image and preprocess it
image = image.resize(target)
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = imagenet_utils.preprocess_input(image)
# return the processed image
return image
@app.route("/predict", methods=["POST"])
def predict():
# initialize the data dictionary that will be returned from the
# view
data = {"success": False}
# ensure an image was properly uploaded to our endpoint
if flask.request.method == "POST":
if flask.request.files.get("image"):
# read the image in PIL format
image = flask.request.files["image"].read()
image = Image.open(io.BytesIO(image))
# preprocess the image and prepare it for classification
image = prepare_image(image, target=(224, 224))
# classify the input image and then initialize the list
# of predictions to return to the client
with graph.as_default():
preds = model.predict(image)
results = imagenet_utils.decode_predictions(preds)
data["predictions"] = []
# loop over the results and add them to the list of
# returned predictions
for (imagenetID, label, prob) in results[0]:
r = {"label": label, "probability": float(prob)}
data["predictions"].append(r)
# indicate that the request was a success
data["success"] = True
# return the data dictionary as a JSON response
return flask.jsonify(data)
# if this is the main thread of execution first load the model and
# then start the server
if __name__ == "__main__":
print(("* Loading Keras model and Flask starting server..."
"please wait until server has fully started"))
load_model()
app.run()
참조:
일.https://github.com/jrosebr1/simple-keras-rest-api/blob/master/run_keras_server.py
이.https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html
삼.https://www.pyimagesearch.com/2018/02/05/deep-learning-production-keras-redis-flask-apache/
사.https://cloud.tencent.com/developer/article/1167171
이 내용에 흥미가 있습니까?
현재 기사가 여러분의 문제를 해결하지 못하는 경우 AI 엔진은 머신러닝 분석(스마트 모델이 방금 만들어져 부정확한 경우가 있을 수 있음)을 통해 가장 유사한 기사를 추천합니다:
ValueError: Tensor Tensor xxx는 이 그래프의 해결 방안의 요소가 아닙니다.질문: keras+flask를 이용하여 간단한 딥러닝 백엔드 서버를 구축하는데 다음과 같은 문제가 발생했습니다. ValueError: Tensor Tensor("fc1000/Softmax:0", shape=(?, 1...
텍스트를 자유롭게 공유하거나 복사할 수 있습니다.하지만 이 문서의 URL은 참조 URL로 남겨 두십시오.
CC BY-SA 2.5, CC BY-SA 3.0 및 CC BY-SA 4.0에 따라 라이센스가 부여됩니다.