在keras中合奏resnet50和densenet121-Java 学习之路

我想做一个resnet50和desnsenet121的集合，但得到一个错误：

图表已断开连接：无法在图层“input_8”处获取张量张量（“input_8：0”，shape =（？，224,224,3），dtype = float32）的值 . 访问以下先前的图层时没有问题：[]

以下是我的合奏代码：

from keras import applications
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Model, Input
#from keras.engine.topology import Input
from keras.layers import Average

def resnet50():
    base_model = applications.resnet50.ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
    last = base_model.output
    x = Flatten()(last)
    x = Dense(2000, activation='relu')(x)
    preds = Dense(200, activation='softmax')(x)
    model = Model(base_model.input, preds)
    return model

def densenet121():
    base_model = applications.densenet.DenseNet121(weights='imagenet', include_top=False, input_shape=(224,224, 3))
    last = base_model.output
    x = Flatten()(last)
    x = Dense(2000, activation='relu')(x)
    preds = Dense(200, activation='softmax')(x)
    model = Model(base_model.input, preds)
    return model

resnet50_model = resnet50()
densenet121_model = densenet121()
ensembled_models = [resnet50_model,densenet121_model]
def ensemble(models,model_input):
    outputs = [model.outputs[0] for model in models]
    y = Average()(outputs)
    model = Model(model_input,y,name='ensemble')
    return model

model_input = Input(shape=(224,224,3))
ensemble_model = ensemble(ensembled_models,model_input)

我认为原因是当我组合reset50和densenet121时，它们有自己的输入层，即使我使输入形状相同 . 不同的输入层会导致冲突 . 这只是我的猜测，我不知道如何解决它

1 回答

您可以在创建基本模型时设置 input_tensor=model_input .

def resnet50(model_input):
    base_model = applications.resnet50.ResNet50(weights='imagenet', include_top=False, input_tensor=model_input)
    # ...

def densenet121(model_input):
    base_model = applications.densenet.DenseNet121(weights='imagenet', include_top=False, input_tensor=model_input)
    # ...

model_input = Input(shape=(224, 224, 3))
resnet50_model = resnet50(model_input)
densenet121_model = densenet121(model_input)

然后，基本模型将使用提供的 model_input 张量，而不是创建自己的单独输入张量 .

回复于 2024-05-19T06:08:40+08:00

在keras中合奏resnet50和densenet121

1 回答

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