Question

我训练有素的模型有10个班级（即输出层有10个班级）。我想再增加3个类，而无需再次训练整个模型。
我想使用经过训练的旧模型，并向其中添加新的类。

这是我已经尝试过的代码，但是显示错误。

from keras.models import load_model
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

base_model = load_model('hand_gest.h5')

new_model = Sequential()

for layer in base_model.layers[:-2]:
    new_model.add(layer)

for layer in new_model.layers:
    layer.trainable = False

weights_training = base_model.layers[-2].get_weights()
new_model.layers[-2].set_weights(weights_training) 


new_model.add(Dense(units = 3, activation = 'softmax'))

但是当我训练这个模型时，它显示了以下错误。

ValueError: You called `set_weights(weights)` on layer "max_pooling2d_2" with a  weight list of length 2, but the layer was expecting 0 weights. Provided weights: [array([[-0.01650696,  0.01082378,  0.0149541 , .....

Answer 1

随着类数从10更改为13，需要更改先前网络的最后一层。

base_model = load_model('hand_gest.h5')
base_model.pop() #remove the last layer - 'Dense' layer with 10 units
for layer in base_model.layers:
    layer.trainable = False
base_model.add(Dense(units = 13, activation = 'softmax'))
base_model.summary() #Check architecture before starting the fine-tuning

如何训练模型以添加新课程？

1 个答案: