本文介绍了类型错误:张量是不可散列的.相反，使用 tensor.ref() 作为键.出错的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
`来自 keras 导入模型modelvgg.layers.pop()modelvgg = models.Model(inputs=modelvgg.inputs,outputs=modelvgg.layers[-1].output)
`from keras import modelsmodelvgg.layers.pop()modelvgg = models.Model(inputs=modelvgg.inputs, outputs=modelvgg.layers[-1].output)
modelvgg.summary()`
modelvgg.summary()`
推荐答案
上述问题是由于版本不兼容造成的.可以修改如下代码
The above issue was due to version incompatibility. You can modify code as shown below
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
_________________________________________________________________
这篇关于类型错误:张量是不可散列的.相反，使用 tensor.ref() 作为键.出错的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！