加载权重后如何在keras中添加和删除新层? [英] How to add and remove new layers in keras after loading weights?

问题描述

我正在尝试进行迁移学习；为此，我想移除神经网络的最后两层并添加另外两层.这是一个示例代码，它也输出相同的错误.

I am trying to do a transfer learning; for that purpose I want to remove the last two layers of the neural network and add another two layers. This is an example code which also output the same error.

from keras.models import Sequential
from keras.layers import Input,Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Dropout, Activation
from keras.layers.pooling import GlobalAveragePooling2D
from keras.models import Model

in_img = Input(shape=(3, 32, 32))
x = Convolution2D(12, 3, 3, subsample=(2, 2), border_mode='valid', name='conv1')(in_img)
x = Activation('relu', name='relu_conv1')(x)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool1')(x)
x = Convolution2D(3, 1, 1, border_mode='valid', name='conv2')(x)
x = Activation('relu', name='relu_conv2')(x)
x = GlobalAveragePooling2D()(x)
o = Activation('softmax', name='loss')(x)
model = Model(input=in_img, output=[o])
model.compile(loss="categorical_crossentropy", optimizer="adam")
#model.load_weights('model_weights.h5', by_name=True)
model.summary()

model.layers.pop()
model.layers.pop()
model.summary()
model.add(MaxPooling2D())
model.add(Activation('sigmoid', name='loss'))

我使用 pop() 删除了图层但是当我尝试添加它时输出此错误

I removed the layer using pop() but when I tried to add its outputting this error

AttributeError: 'Model' 对象没有属性 'add'

AttributeError: 'Model' object has no attribute 'add'

我知道错误最可能的原因是对 model.add() 的不当使用.我应该使用什么其他语法?

I know the most probable reason for the error is improper use of model.add(). what other syntax should I use?

我尝试在 keras 中删除/添加层，但在加载外部权重后不允许添加.

I tried to remove/add layers in keras but its not allowing it to be added after loading external weights.

from keras.models import Sequential
from keras.layers import Input,Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Dropout, Activation
from keras.layers.pooling import GlobalAveragePooling2D
from keras.models import Model
in_img = Input(shape=(3, 32, 32))

def gen_model():
    in_img = Input(shape=(3, 32, 32))
    x = Convolution2D(12, 3, 3, subsample=(2, 2), border_mode='valid', name='conv1')(in_img)
    x = Activation('relu', name='relu_conv1')(x)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool1')(x)
    x = Convolution2D(3, 1, 1, border_mode='valid', name='conv2')(x)
    x = Activation('relu', name='relu_conv2')(x)
    x = GlobalAveragePooling2D()(x)
    o = Activation('softmax', name='loss')(x)
    model = Model(input=in_img, output=[o])
    return model

#parent model
model=gen_model()
model.compile(loss="categorical_crossentropy", optimizer="adam")
model.summary()

#saving model weights
model.save('model_weights.h5')

#loading weights to second model
model2=gen_model()
model2.compile(loss="categorical_crossentropy", optimizer="adam")
model2.load_weights('model_weights.h5', by_name=True)

model2.layers.pop()
model2.layers.pop()
model2.summary()

#editing layers in the second model and saving as third model
x = MaxPooling2D()(model2.layers[-1].output)
o = Activation('sigmoid', name='loss')(x)
model3 = Model(input=in_img, output=[o])

它显示这个错误

RuntimeError: Graph disconnected: cannot obtain value for tensor input_4 at layer "input_4". The following previous layers were accessed without issue: []

推荐答案

您可以获取上一个模型的output 并创建一个新模型.下层保持不变.

You can take the output of the last model and create a new model. The lower layers remains the same.

model.summary()
model.layers.pop()
model.layers.pop()
model.summary()

x = MaxPooling2D()(model.layers[-1].output)
o = Activation('sigmoid', name='loss')(x)

model2 = Model(input=in_img, output=[o])
model2.summary()

检查如何使用模型从 keras.applications 迁移学习?

编辑更新:

新的错误是因为您试图在全局 in_img 上创建新模型，而在之前的模型创建中实际上并未使用该模型.您实际上是在定义本地 in_img.所以全局的in_img显然没有连接到符号图中的上层.它与加载重量无关.

The new error is because you are trying to create the new model on global in_img which is actually not used in the previous model creation.. there you are actually defining a local in_img. So the global in_img is obviously not connected to the upper layers in the symbolic graph. And it has nothing to do with loading weights.

为了更好地解决这个问题，你应该使用 model.input 来引用输入.

To better resolve this problem you should instead use model.input to reference to the input.

model3 = Model(input=model2.input, output=[o])

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