问题描述
我的代码有问题。我希望能得到一些帮助。我想做混合学习;在我的情况下,就是进行分段和回归的顺序学习。但是现在,我的回归部分有问题(它没有训练)。 现在的想法是按部分进行训练,但我不知道该怎么做。 谢谢你帮助我。
def load_model(segmentation_model,regression_model,width,height,num_classes = 1):
# Rename segmentation model layers and weights
for layer in segmentation_model.layers:
rename(segmentation_model,layer,layer.name + '_seg')
#for i,w in enumerate(segmentation_model.weights):
# split_name = w.name.split('/')
# new_name = split_name[0] + '_seg' + '/' + split_name[1]
# segmentation_model.weights[i]._handle_name = new_name
# Rename regression model layers
for layer in regression_model.layers:
rename(regression_model,layer.name + '_reg')
#for i,w in enumerate(regression_model.weights):
# split_name = w.name.split('/')
# new_name = split_name[0] + '_reg' + '/' + split_name[1]
# regression_model.weights[i]._handle_name = new_name
image = layers.Input(shape=(width,3),name="img")
mask_image = segmentation_model(image)
if num_classes==1:
mask_image_categorical = K.cast(K.squeeze(mask_image,axis=3) + 0.5,dtype='int32') # Threshold at 0.5
else:
mask_image_categorical = K.argmax(mask_image,axis=3)
masked_layer = mylayers.CustomMasking(mask_value=0)
masked_image = masked_layer.call([image,mask_image_categorical])
value = regression_model(masked_image)
m = models.Model(inputs=image,outputs=[mask_image,value])
#m = models.Model(inputs=image,value,mask_image_categorical,masked_image])
#for i,w in enumerate(m.weights): print(i,w.name)
m.summary()
return m
解决方法
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