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

问题描述

我正在尝试学习转学；为此，我想删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，也会输出相同的错误.

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()删除了该层，但是当我尝试添加输出此错误时

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

AttributeError:模型"对象没有属性添加"

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

我知道该错误的最可能原因是model.add()的使用不正确.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?

我试图删除/添加喀拉拉邦中的图层，但不允许在加载外部砝码后添加它.

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])

显示此错误

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

您可以采用最后一个模型的output并创建一个新模型.较低的层保持不变.

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()

检查如何使用模型来自keras.applications进行转移学习的应用程序?

编辑时更新:

新错误是因为您试图在全局in_img上创建新模型，而先前的模型创建中实际上并未使用该新模型.实际上，您在其中定义了本地in_img.因此，全局in_img显然未连接到符号图中的上层.它与负载重量无关.

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.

要更好地解决此问题，您应该改用model.input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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