Keras 2.0.6, VGG16 'Model' object has no attribute 'add'

在练习你们文档中"面向小数据集构建图像分类模型"案例时,第三部分微调预训练网络,出错.

top_model = Sequential()
top_model.add(Flatten(input_shape=model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))

# note that it is necessary to start with a fully-trained
# classifier, including the top classifier,
# in order to successfully do fine-tuning
top_model.load_weights(top_model_weights_path)

# add the model on top of the convolutional base
model.add(top_model)

文章地址:https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html 代码地址(59-75行):https://gist.github.com/fchollet/7eb39b44eb9e16e59632d25fb3119975

Keras 2.06, TensorFlow 1.2.x,

[MatinHz]

Use something like this:

base_model = applications.VGG16(...

top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))
top_model.load_weights(top_model_weights_path)

model = Model(inputs= base_model.input, outputs= top_model(base_model.output))

[edoven]

I solved by creating a new model (Sequential()) and by copying all the layers of VGG16 into this new model:

model = applications.VGG16(...)
...
new_model = Sequential() #new model
for layer in model.layers: 
    new_model.add(layer)
...
new_model.add(top_model) # now this works