使用keras实现densenet和Xception的模型融合

def Multimodel(cnn_weights_path=None,all_weights_path=None,class_num=5,cnn_no_vary=False):
	'''
	获取densent121,xinception并联的网络
	此处的cnn_weights_path是个列表是densenet和xception的卷积部分的权值
	'''
	input_layer=Input(shape=(224,224,3))
	dense=DenseNet121(include_top=False,weights=None,input_shape=(224,224,3))
	xception=Xception(include_top=False,weights=None,input_shape=(224,224,3))
	#res=ResNet50(include_top=False,weights=None,input_shape=(224,224,3))

	if cnn_no_vary:
		for i,layer in enumerate(dense.layers):
			dense.layers[i].trainable=False
		for i,layer in enumerate(xception.layers):
			xception.layers[i].trainable=False
		#for i,layer in enumerate(res.layers):
		#	res.layers[i].trainable=False
 
	if cnn_weights_path!=None:
		dense.load_weights(cnn_weights_path[0])
		xception.load_weights(cnn_weights_path[1])
		#res.load_weights(cnn_weights_path[2])
	dense=dense(input_layer)
	xception=xception(input_layer)

	#对dense_121和xception进行全局最大池化
	top1_model=GlobalMaxPooling2D(data_format='channels_last')(dense)
	top2_model=GlobalMaxPooling2D(data_format='channels_last')(xception)
	#top3_model=GlobalMaxPool2D(input_shape=res.output_shape)(res.outputs[0])
	
	print(top1_model.shape,top2_model.shape)
	#把top1_model和top2_model连接起来
	t=keras.layers.Concatenate(axis=1)([top1_model,top2_model])
	#第一个全连接层
	top_model=Dense(units=512,activation="relu")(t)
	top_model=Dropout(rate=0.5)(top_model)
	top_model=Dense(units=class_num,activation="softmax")(top_model)
	
	model=Model(inputs=input_layer,outputs=top_model)
 
	#加载全部的参数
	if all_weights_path:
		model.load_weights(all_weights_path)
	return model

if __name__=="__main__":
 weights_path=["./densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5",
 "xception_weights_tf_dim_ordering_tf_kernels_notop.h5"]
 model=Multimodel(cnn_weights_path=weights_path,class_num=6)
 plot_model(model,to_file="G:/model.png")

def Multimodel(cnn_weights_path=None,all_weights_path=None,class_num=5,cnn_no_vary=False):
	'''
	获取densent121,xinception并联的网络
	此处的cnn_weights_path是个列表是densenet和xception的卷积部分的权值
	'''
	dir=os.getcwd()
	input_layer=Input(shape=(224,224,3))
	
	dense=DenseNet121(include_top=False,weights=None,input_tensor=input_layer,
		input_shape=(224,224,3))
	xception=Xception(include_top=False,weights=None,input_tensor=input_layer,
		input_shape=(224,224,3))
	#res=ResNet50(include_top=False,weights=None,input_shape=(224,224,3))
 
	if cnn_no_vary:
		for i,layer in enumerate(dense.layers):
			dense.layers[i].trainable=False
		for i,layer in enumerate(xception.layers):
			xception.layers[i].trainable=False
		#for i,layer in enumerate(res.layers):
		#	res.layers[i].trainable=False
	if cnn_weights_path!=None:
		dense.load_weights(cnn_weights_path[0])
		xception.load_weights(cnn_weights_path[1])
 
	#print(dense.shape,xception.shape)
	#对dense_121和xception进行全局最大池化
	top1_model=GlobalMaxPooling2D(input_shape=(7,7,1024),data_format='channels_last')(dense.output)
	top2_model=GlobalMaxPooling2D(input_shape=(7,7,1024),data_format='channels_last')(xception.output)
	#top3_model=GlobalMaxPool2D(input_shape=res.output_shape)(res.outputs[0])
	
	print(top1_model.shape,top2_model.shape)
	#把top1_model和top2_model连接起来
	t=keras.layers.Concatenate(axis=1)([top1_model,top2_model])
	#第一个全连接层
	top_model=Dense(units=512,activation="relu")(t)
	top_model=Dropout(rate=0.5)(top_model)
	top_model=Dense(units=class_num,activation="softmax")(top_model)
	
	model=Model(inputs=input_layer,outputs=top_model)
 
	#加载全部的参数
	if all_weights_path:
		model.load_weights(all_weights_path)
	return model