python - 张量 Tensor ("predictions/Softmax:0", shape=(?, 1000), dtype=float32) 不是该图的元素

Question

我正在尝试关注 simple tutorial关于如何使用预训练的 VGG 模型进行图像分类。我拥有的代码:

from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import load_img, img_to_array
from keras.applications.vgg16 import preprocess_input, decode_predictions

import numpy as np

class KerasModel(object):
    def __init__(self):
        self.model = VGG16()
    def evaluate(self):
        image = load_img('mug.jpg', target_size=(224,224))
        image = img_to_array(image)
        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
        image = preprocess_input(image)
        yhat = self.model.predict(image)
        label = decode_predictions(yhat)
        label = label[0][0]
        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

这会给出错误:Tensor Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32) 不是该图的元素。

经过一番搜索此错误后，我得到了以下代码:

from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import load_img, img_to_array
from keras.applications.vgg16 import preprocess_input, decode_predictions

import numpy as np

import tensorflow as tf
graph = tf.get_default_graph()


class KerasModel(object):
    def __init__(self):
        self.model = VGG16()
    def evaluate(self):
        image = load_img('mug.jpg', target_size=(224,224))
        image = img_to_array(image)
        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
        image = preprocess_input(image)
        with graph.as_default():
            yhat = self.model.predict(image)
        label = decode_predictions(yhat)
        label = label[0][0]
        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

但这仍然会导致同样的错误。有人可以帮我吗？我不明白我做错了什么，因为该教程似乎适合每个人。

模型摘要:

 _________________________________________________________________
xvision | Layer (type)                 Output Shape              Param #   
xvision | =================================================================
xvision | input_1 (InputLayer)         (None, 224, 224, 3)       0         
xvision | _________________________________________________________________
xvision | block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
xvision | _________________________________________________________________
xvision | block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
xvision | _________________________________________________________________
xvision | block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
xvision | _________________________________________________________________
xvision | block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
xvision | _________________________________________________________________
xvision | block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
xvision | _________________________________________________________________
xvision | block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
xvision | _________________________________________________________________
xvision | block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
xvision | _________________________________________________________________
xvision | block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
xvision | _________________________________________________________________
xvision | block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
xvision | _________________________________________________________________
xvision | block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
xvision | _________________________________________________________________
xvision | block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
xvision | _________________________________________________________________
xvision | block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
xvision | _________________________________________________________________
xvision | block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
xvision | _________________________________________________________________
xvision | block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
xvision | _________________________________________________________________
xvision | block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
xvision | _________________________________________________________________
xvision | block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
xvision | _________________________________________________________________
xvision | block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
xvision | _________________________________________________________________
xvision | block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
xvision | _________________________________________________________________
xvision | flatten (Flatten)            (None, 25088)             0         
xvision | _________________________________________________________________
xvision | fc1 (Dense)                  (None, 4096)              102764544 
xvision | _________________________________________________________________
xvision | fc2 (Dense)                  (None, 4096)              16781312  
xvision | _________________________________________________________________
xvision | predictions (Dense)          (None, 1000)              4097000   
xvision | =================================================================
xvision | Total params: 138,357,544
xvision | Trainable params: 138,357,544
xvision | Non-trainable params: 0
xvision | _________________________________________________________________
xvision | None

Answer 1

似乎 Keras 不是线程安全的，因此您需要在每个线程中初始化模型。修复正在调用:_make_predict_function()

它确实对我有用。这是一个简单的例子:

from keras.models import load_model

def load_model():
  model = load_model('./my_model.h5')
  model._make_predict_function() 
  print('model loaded') # just to keep track in your server
return model

希望这有帮助。