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keras - 如何在keras中向中间层提供输入

问题描述

我想在弹出最后一层然后添加 2 个 FC(Dense) 层后使用预训练的 vgg-19。我想为这些 FC 层之一提供一个二进制变量（男性或女性）的输入。我怎样才能实现它。

标签： keraskeras-layer

解决方案

你需要一个双输入单输出网络，其中每个输入都有自己的特征提取，并且在预测最终输出之前融合了两个特征。

下面是一个例子。

import keras
from keras.applications.vgg19 import VGG19
from keras.layers import Input, Dense, Concatenate
from keras.models import Model

#-------------------------------------------------------------------------------
# Define your new inputs
# Here I pretend that your new task is a classification task over 100 classes
#-------------------------------------------------------------------------------
gender_input = Input(shape=(1,), name='gender_input')
image_input  = Input(shape=(224,224,3), name='image_input')
num_classes  = 100
#-------------------------------------------------------------------------------
# define your pretrained feature extraction
# you may do something different than below
# but the point here is to have a pretrained model for `featex`
# so you may define it differently
#-------------------------------------------------------------------------------
pretrained   = VGG19()
featex       = Model( pretrained.input, pretrained.layers[-2].output, name='vgg_pop_last' )
# consider to freeze all weights in featex to speed-up training
image_feat   = featex( image_input )
#-------------------------------------------------------------------------------
# From here, you may play with different network architectures
# Below is just one example
#-------------------------------------------------------------------------------
image_feat   = Dense(128, activation='relu', name='image_fc')( image_feat )
gender_feat  = Dense(16, activation='relu', name='gender_fc')( gender_input )
# mix information from both information
# note: concatenation is only one of plausible way to mix information
concat_feat  = Concatenate(axis=-1,name='concat_fc')([image_feat, gender_feat])
# perform final prediction
target       = Dense(num_classes, activation='softmax', name='pred_class')( concat_feat )
#-------------------------------------------------------------------------------
# Here is your new model which contains two inputs and one new target
#-------------------------------------------------------------------------------
model = Model( inputs=[image_input, gender_input], outputs=target, name='myModel')

print model.summary()

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