TypeError：张量是不可散列的。相反，使用 tensor.ref() 作为键。出错答案

【问题标题】：TypeError: Tensor is unhashable. Instead, use tensor.ref() as the key. getting errorTypeError：张量是不可散列的。相反，使用 tensor.ref() 作为键。出错
【发布时间】：2021-06-08 17:51:19
【问题描述】：

`从 keras 导入模型模型vgg.layers.pop() modelvgg = models.Model(inputs=modelvgg.inputs, outputs=modelvgg.layers[-1].output)

展示深度学习模型

modelvgg.summary()`

【问题讨论】：

请提供可重现的代码，我们可以看到错误！
你能分享定义modelvgg的代码和产生错误的代码吗？
from tensorflow.keras.applications.vgg16 import VGG16 modelvgg =VGG16(include_top=True,weights=None) ##加载本地保存的权重 modelvgg.load_weights("Data/vgg16_weights_tf_dim_ordering_tf_kernels.h5") modelvgg .summary()
请紧急解决我卡在我的项目中。请快点

标签： tensorflow keras anaconda

【解决方案1】：

上述问题是由于版本不兼容造成的。您可以修改代码如下所示

from tensorflow.keras import Model 
from tensorflow.keras.applications.vgg16 import VGG16 
modelvgg =VGG16(include_top=True,weights=None) 
modelvgg.layers.pop()
modelvgg = Model(inputs=modelvgg.inputs, outputs=modelvgg.layers[-1].output)
modelvgg.summary()

输出：

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

【讨论】：