[英]How to combine features extracted from two cnn models?
i have two cnn models both follow same architecture.我有两个 cnn 模型都遵循相同的架构。 I trained 'train set 1' on cnn1 and 'train set 2;
我在cnn1和'train set 2上训练了'train set 1'; on cnn2.Then i exracted features using following code.
在 cnn2 上。然后我使用以下代码提取了功能。
# cnn1 #cnn1
model.pop() #removes softmax layer
model.pop() #removes dropoutlayer
model.pop() #removes activation layer
model.pop() #removes batch-norm layer
model.build() #here lies dense 512
features1 = model.predict(train set 1)
print(features1.shape) #600,512
# cnn2 #cnn2
model.pop() #removes softmax layer
model.pop() #removes dropoutlayer
model.pop() #removes activation layer
model.pop() #removes batch-norm layer
model.build() #here lies dense 512
features2 = model.predict(train set 2)
print(features2.shape) #600,512
How to combine these feature 1 and feature 2, so that output shape is 600,1024?如何将这些特征 1 和特征 2 结合起来,使 output 形状为 600,1024?
SIMPLEST SOLUTION:最简单的解决方案:
you can simply concatenate the output of the two networks in this way:您可以通过这种方式简单地连接两个网络的 output:
features = np.concatenate([features1, features2], 1)
ALTERNATIVE:选择:
given two trained models that have the same structure, whatever their structures are, you can combine them in this way给定两个具有相同结构的训练模型,无论它们的结构是什么,您都可以通过这种方式组合它们
# generate dummy data
n_sample = 600
set1 = np.random.uniform(0,1, (n_sample,30))
set2 = np.random.uniform(0,1, (n_sample,30))
# model 1
inp1 = Input((30,))
x1 = Dense(512,)(inp1)
x1 = Dropout(0.3)(x1)
x1 = BatchNormalization()(x1)
out1 = Dense(3, activation='softmax')(x1)
m1 = Model(inp1, out1)
# m1.fit(...)
# model 2
inp2 = Input((30,))
x2 = Dense(512,)(inp2)
x2 = Dropout(0.3)(x2)
x2 = BatchNormalization()(x2)
out2 = Dense(3, activation='softmax')(x2)
m2 = Model(inp2, out2)
# m2.fit(...)
# concatenate the desired output
concat = Concatenate()([m1.layers[1].output, m2.layers[1].output]) # get the outputs of dense 512 layers
merge = Model([m1.input, m2.input], concat)
# make combined predictions
merge.predict([set1,set2]).shape # (n_sample, 1024)
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