Tensor Tensor("predictions/Softmax:0&"，shape =(?, 1000)，dtype = float32)不是此图的元素 [英] Tensor Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32) is not an element of this graph

问题描述

我正在尝试遵循简单的教程有关如何使用预训练的VGG模型进行图像分类的信息.我拥有的代码:

I am trying to follow a simple tutorial on how to use a pre-trained VGG model for image classification. The code which I have:

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)

这会导致错误:Tensor Tensor("predictions/Softmax:0"，shape =(?, 1000)，dtype = float32)不是此图的元素.

This gives the error: Tensor Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32) is not an element of this graph.

在搜索了此错误之后，我得到了以下代码:

After some searching for this error I got to this code:

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)

但是这仍然会导致相同的错误.有人可以帮我吗?我不明白自己在做什么错，因为该教程似乎适合所有人.

But this still results in the same error. Could someone please help me out? I don't understand what I am doing wrong because the tutorial seems to work for everyone.

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

推荐答案

由于您的代码很好，因此在干净的环境中运行应该可以解决该问题.

As your code is fine, running with a clean environment should solve it.

• 清除~/.keras/

使用正确的软件包在新环境中运行(可以使用anaconda轻松完成)

Run on a new environment, with the right packages (can be done easily with anaconda)

确保您处于全新的会话中，keras.backend.clear_session()应该删除所有现有的tf图.

Make sure you are on a fresh session, keras.backend.clear_session() should remove all existing tf graphs.

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