我正在用3D体积分割医学图像。图像的原始大小为（256,256,120），在DataGeneration之后，输入为（2，256，256，120，1），而输出为（2，256，256，120，2）。但是在model generation_fit上，我得到了上面的消息，如标题所示：

型号：

def build_model(inp_shape=(256,120,1)):
print("start building NN")

#Testing building my model  
data = Input(inp_shape) 
print(data)
flt=32


conv1 = Conv3D(flt,(3,3,3),activation='relu',padding='same')(data)
conv1 = Conv3D(flt,padding='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2,2,2))(conv1)

conv2 = Conv3D(flt*2,padding='same')(pool1)
conv2 = Conv3D(flt*2,padding='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2,2))(conv2)

conv3 = Conv3D(flt*4,padding='same')(pool2)
conv3 = Conv3D(flt*4,padding='same')(conv3)
pool3 = MaxPooling3D(pool_size=(2,2))(conv3)

conv4 = Conv3D(flt*8,padding='same')(pool3)
conv4 = Conv3D(flt*8,padding='same')(conv4)
pool4 = MaxPooling3D(pool_size=(2,2))(conv4)

conv5 = Conv3D(flt*16,padding='same')(pool4)
conv5 = Conv3D(flt*16,padding='same')(conv5)

up6 = concatenate([Conv3DTranspose(flt*8,(2,2),strides=(2,padding='same')(conv5),conv4],axis=3)
conv6 = Conv3D(flt*8,padding='same')(up6)
conv6 = Conv3D(flt*8,padding='same')(conv6)

up7 = concatenate([Conv3DTranspose(flt*4,padding='same')(conv6),conv3],axis=3)
conv7 = Conv3D(flt*4,padding='same')(up7)
conv7 = Conv3D(flt*4,padding='same')(conv7)

up8 = concatenate([Conv3DTranspose(flt*2,padding='same')(conv7),conv2],axis=3)
conv8 = Conv3D(flt*2,padding='same')(up8)
conv8 = Conv3D(flt*2,padding='same')(conv8)

up9 = concatenate([Conv3DTranspose(flt,padding='same')(conv8),conv1],axis=3)
conv9 = Conv3D(flt,padding='same')(up9)
conv9 = Conv3D(flt,padding='same')(conv9)

conv10 = Conv3D(1,(1,1,1),activation='sigmoid')(conv9)

model = Model(inputs=[data],outputs=[conv10])

return model

火车：

if __name__ == '__main__':

im_path = "/root/data/demo/Demo/images/"
label_path = "/root/data/demo/Demo/labels/"

# Parameters
params = {'dim': (256,120),'batch_size': 2,'im_path': im_path,'label_path': label_path,'n_classes': 2,'augment': False,'n_channels':1,'shuffle': True
         }

partition = {}


images = glob.glob(os.path.join(im_path,"*.nii.gz"))
images_IDs = [name.split("/")[-1] for name in images]

partition['train'] = images_IDs

# Load training data
training_generator = DataGenerator(partition['train'],**params) 
 # Design model
model = Sequential()



# Build model

inp_shape = y[0].shape  
model = build_model() 
model.compile(optimizer=Adam(lr=1e-5),loss='binary_crossentropy',metrics=['binary_accuracy'])


##########################################################################################
checkpointer = ModelCheckpoint('model.{epoch:03d}.hdf5',save_freq=5)


cb_1=keras.callbacks.EarlyStopping(monitor='val_loss',min_delta=0,patience=2,verbose=1,mode='auto')
cb_2=keras.callbacks.ModelCheckpoint(filepath="model.{epoch:03d}.hdf5",monitor='val_loss',verbose=0,save_best_only=True,save_weights_only=False,mode='auto',period=1)



results = model.fit_generator(training_generator,steps_per_epoch= 4,epochs=2,use_multiprocessing=True,workers=4,validation_data=None,validation_steps=None,max_queue_size=8)