我在仅限CPU的计算机上尝试过我的代码并且运行正常。但是在我更改为GPU版本后，弹出错误“AttributeError：'NoneType'对象没有属性'update'”。有人帮我解决了吗？
我的部分源代码如下：
from keras.utils import multi_gpu_model
import tensorflow as tf`
...
def train_validation_test_model(model, X_tr, Y_tr, X_ts, Y_ts, filepath):
print('-----------------------------------------------------------------------')
print('Training...')
X_mag_tr = 20 * np.log10(np.abs(X_tr))
X_mag_ts = 20 * np.log10(np.abs(X_ts))
EarlyStop = callbacks.EarlyStopping(monitor = 'val_loss', patience = 10, verbose = 0, mode = 'min')
CheckPoint = callbacks.ModelCheckpoint(filepath, monitor = 'val_loss', save_best_only = True, mode = 'min')
opti = optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=1e-08, clipnorm=1.)
model.compile(loss='categorical_crossentropy', optimizer=opti, metrics=['accuracy'])
history = model.fit(x = X_mag_tr, y = Y_tr, batch_size = batch_size, epochs = 500, verbose = 2,
callbacks = [EarlyStop, CheckPoint],
validation_data = (X_mag_ts, Y_ts),
shuffle = True)
return history
def My_Model():
model = Sequential()
model.add(BatchNormalization(batch_input_shape = (None, time_step, input_size)))
model.add(LSTM(units = cell_size,
batch_input_shape = (None, time_step, input_size),
return_sequences = True,
stateful = False, kernel_initializer = Init.orthogonal(), name = 'LSTM_1'))
model.add(BatchNormalization()
model.add(LSTM(units = cell_size,
return_sequences = True,
stateful = False, kernel_initializer = Init.orthogonal(), name = 'LSTM_2'))
model.add(Dropout(0.5))
model.add(TimeDistributed(Dense(output_size, kernel_initializer = Init.lecun_uniform(),
activation = 'softmax', name = 'Output_Layer')))
return model
X, Y = load_dataset()
idx = np.random.permutation(X.shape[0]) # shuffle the dataset
X = X[idx, :, :]
Y = Y[idx, :, :]
...
with tf.device("/cpu:0"):
model = My_Model()
pmodel = multi_gpu_model(model, gpus = 2)
X_train, Y_train, X_test, Y_test = generate_2sets(X, Y, fold+1, n_fold)
History_ = train_validation_test_model(pmodel, X_train, Y_train, X_test,
Y_test, model_path)
...
然后我收到了以下Tracback信息：
Traceback (most recent call last):
File "C:/XXX/Python/XXX/Mynet.py", line 226, in <module>
History_ = train_validation_test_model(pmodel, X_train, Y_train, X_test, Y_test, model_path)
File "C:/XXX/Python/XXX/Mynet.py", line 148, in train_validation_test_model
shuffle = True)
File "C:\Program Files\Python35\lib\site-packages\keras\engine\training.py", line 1657, in fit
validation_steps=validation_steps)
File "C:\Program Files\Python35\lib\site-packages\keras\engine\training.py", line 1233, in _fit_loop
callbacks.on_epoch_end(epoch, epoch_logs)
File "C:\Program Files\Python35\lib\site-packages\keras\callbacks.py", line 73, in on_epoch_end
callback.on_epoch_end(epoch, logs)
File "C:\Program Files\Python35\lib\site-packages\keras\callbacks.py", line 415, in on_epoch_end
self.model.save(filepath, overwrite=True)
File "C:\Program Files\Python35\lib\site-packages\keras\engine\topology.py", line 2556, in save
save_model(self, filepath, overwrite, include_optimizer)
File "C:\Program Files\Python35\lib\site-packages\keras\models.py", line 108, in save_model
'config': model.get_config()
File "C:\Program Files\Python35\lib\site-packages\keras\engine\topology.py", line 2397, in get_config
return copy.deepcopy(config)
File "C:\Program Files\Python35\lib\copy.py", line 155, in deepcopy
y = copier(x, memo)
File "C:\Program Files\Python35\lib\copy.py", line 243, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Program Files\Python35\lib\copy.py", line 155, in deepcopy
y = copier(x, memo)
File "C:\Program Files\Python35\lib\copy.py", line 218, in _deepcopy_list
y.append(deepcopy(a, memo))
File "C:\Program Files\Python35\lib\copy.py", line 155, in deepcopy
y = copier(x, memo)
File "C:\Program Files\Python35\lib\copy.py", line 243, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Program Files\Python35\lib\copy.py", line 155, in deepcopy
y = copier(x, memo)
File "C:\Program Files\Python35\lib\copy.py", line 243, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Program Files\Python35\lib\copy.py", line 155, in deepcopy
y = copier(x, memo)
File "C:\Program Files\Python35\lib\copy.py", line 223, in _deepcopy_tuple
y = [deepcopy(a, memo) for a in x]
File "C:\Program Files\Python35\lib\copy.py", line 223, in <listcomp>
y = [deepcopy(a, memo) for a in x]
File "C:\Program Files\Python35\lib\copy.py", line 155, in deepcopy
y = copier(x, memo)
File "C:\Program Files\Python35\lib\copy.py", line 223, in _deepcopy_tuple
y = [deepcopy(a, memo) for a in x]
File "C:\Program Files\Python35\lib\copy.py", line 223, in <listcomp>
y = [deepcopy(a, memo) for a in x]
File "C:\Program Files\Python35\lib\copy.py", line 182, in deepcopy
y = _reconstruct(x, rv, 1, memo)
File "C:\Program Files\Python35\lib\copy.py", line 297, in _reconstruct
state = deepcopy(state, memo)
File "C:\Program Files\Python35\lib\copy.py", line 155, in deepcopy
y = copier(x, memo)
File "C:\Program Files\Python35\lib\copy.py", line 243, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Program Files\Python35\lib\copy.py", line 182, in deepcopy
y = _reconstruct(x, rv, 1, memo)
File "C:\Program Files\Python35\lib\copy.py", line 306, in _reconstruct
y.__dict__.update(state)
AttributeError: 'NoneType' object has no attribute 'update'
我使用的平台是Windows 10. tensorflow的版本是1.4.0，keras的版本是2.1.2，它的后端是tensorflow。 GPU为GTX 1080 Ti，驱动程序版本为385.28; CUDA 8.0.61win10; cuDNN-8.0-windows10-x64的V6.0; Python 3.5.2。