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, 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, (3, 3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = Conv3D(flt*2, (3, 3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv3D(flt*2, (3, 3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = Conv3D(flt*4, (3, 3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv3D(flt*4, (3, 3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)
conv4 = Conv3D(flt*8, (3, 3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv3D(flt*8, (3, 3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conv4)
conv5 = Conv3D(flt*16, (3, 3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv3D(flt*16, (3, 3, 3), activation='relu', padding='same')(conv5)
up6 = concatenate([Conv3DTranspose(flt*8, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv5), conv4], axis=3)
conv6 = Conv3D(flt*8, (3, 3, 3), activation='relu', padding='same')(up6)
conv6 = Conv3D(flt*8, (3, 3, 3), activation='relu', padding='same')(conv6)
up7 = concatenate([Conv3DTranspose(flt*4, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv6), conv3], axis=3)
conv7 = Conv3D(flt*4, (3, 3, 3), activation='relu', padding='same')(up7)
conv7 = Conv3D(flt*4, (3, 3, 3), activation='relu', padding='same')(conv7)
up8 = concatenate([Conv3DTranspose(flt*2, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv7), conv2], axis=3)
conv8 = Conv3D(flt*2, (3, 3, 3), activation='relu', padding='same')(up8)
conv8 = Conv3D(flt*2, (3, 3, 3), activation='relu', padding='same')(conv8)
up9 = concatenate([Conv3DTranspose(flt, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv8), conv1], axis=3)
conv9 = Conv3D(flt, (3, 3, 3), activation='relu', padding='same')(up9)
conv9 = Conv3D(flt, (3, 3, 3), activation='relu', 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': (256,256,120),
'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,
verbose=1,
use_multiprocessing=True,
workers=4,
validation_data=None, validation_steps=None,
max_queue_size=8)