张量张量( "predictions/Softmax:0" ， shape=(？， 1000)， dtype=float32) 不是此图的元素

Question

我正在尝试遵循一个关于如何使用预训练的VGG模型进行图像分类的简单教程。我的代码：

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)

这给出了错误：张量张量("predictions/Softmax:0"，shape=(？，1000(，dtype=float32(不是该图的元素。

在搜索了这个错误之后，我得到了这个代码：

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。有人能帮帮我吗？我不明白我做错了什么，因为这个教程似乎对每个人都有效。

型号摘要：

_________________________________________________________________
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

Keras似乎不是线程安全的，所以您需要在每个线程中初始化模型。修复程序正在调用：_make_predict_function((

它确实对我有用。这里有一个干净的例子：

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

希望这能有所帮助。

Answer 2

由于您的代码很好，在干净的环境中运行应该可以解决这个问题。

• 清除~/.keras/上的keras缓存

• 在新的环境中运行，使用正确的包(可以使用anaconda轻松完成(

• 确保您正在进行新的会话，keras.backend.clear_session()应删除所有现有的tf图。