我有以下代码，需要移除模型的一些层并进行预测。但目前我遇到了错误。
from tensorflow.keras.applications.resnet50 import ResNet50 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions import numpy as np from keras.models import Model from tensorflow.python.keras.optimizers import SGD base_model = ResNet50(include_top=False, weights='imagenet') model= Model(inputs=base_model.input, outputs=base_model .layers[-2].output) #model = Model(inputs=base_model.input, outputs=predictions) #Compiling the model model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics = ['accuracy']) img_path = 'elephant.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) preds = model.predict(x) #decode the results into a list of tuples (class, description, probability) #(one such list for each sample in the batch) print('Predicted:', decode_predictions(preds, top=3)[0])错误
File "C:/Users/learn/remove_layer.py", line 9, in <module>model= Model(inputs=base_model.input, outputs=base_model .layers[-2].output)AttributeError: 'Tensor' object has no attribute '_keras_shape'由于我在Keras方面的初学者知识，我理解这是形状问题。因为这是ResNet模型，如果我从一个合并层移除一层到另一个合并层，因为合并层没有维度问题，我该如何完成这个操作？
回答：
你实际上需要可视化你所做的操作，所以让我们对ResNet50模型的最后几层做一个小总结：
base_model.summary()conv5_block3_2_relu (Activation (None, None, None, 5 0 conv5_block3_2_bn[0][0] __________________________________________________________________________________________________conv5_block3_3_conv (Conv2D) (None, None, None, 2 1050624 conv5_block3_2_relu[0][0] __________________________________________________________________________________________________conv5_block3_3_bn (BatchNormali (None, None, None, 2 8192 conv5_block3_3_conv[0][0] __________________________________________________________________________________________________conv5_block3_add (Add) (None, None, None, 2 0 conv5_block2_out[0][0] conv5_block3_3_bn[0][0] __________________________________________________________________________________________________conv5_block3_out (Activation) (None, None, None, 2 0 conv5_block3_add[0][0] ==================================================================================================Total params: 23,587,712Trainable params: 23,534,592Non-trainable params: 53,120_____________________________现在是移除最后一层后的模型
model.summary()conv5_block3_2_relu (Activation (None, None, None, 5 0 conv5_block3_2_bn[0][0] __________________________________________________________________________________________________conv5_block3_3_conv (Conv2D) (None, None, None, 2 1050624 conv5_block3_2_relu[0][0] __________________________________________________________________________________________________conv5_block3_3_bn (BatchNormali (None, None, None, 2 8192 conv5_block3_3_conv[0][0] __________________________________________________________________________________________________conv5_block3_add (Add) (None, None, None, 2 0 conv5_block2_out[0][0] conv5_block3_3_bn[0][0] ==================================================================================================Total params: 23,587,712Trainable params: 23,534,592Non-trainable params: 53,120Keras中的ResNet50输出是最后Conv2D块之后的所有特征图，它不关心模型的分类部分，你实际做的只是移除了最后一个加法块之后的激活层
所以你需要检查你想移除的具体块层，并为分类部分添加flatten和全连接层
正如Dr.Snoopy提到的，不要混合使用keras和tensorflow.keras的导入
# 这一部分from tensorflow.keras.models import Model