多gpu下训练的model在单gpu下测试出错

[wqt2019]

在8个gpu下训练的densenet-model，在单gpu下测试，出现错误，You are trying to load a weight file containing 1 layers into a model with 55 layers ，如果在加载model的时候改成basemodel.load_weights(modelPath,by_name=True)，有结果输出，但结果错的离谱，网上也都没有找到解决方案。
训练代码中加了model = multi_gpu_model(model, 8)

[zylo117]

save weights的时候用单GPU的model去save。
写成这样，multi_gpu_model = multi_gpu_model(single_gpu_model, 8)。
用multi的去compile、训练，用single的去save weights/model

[lianqingsong]

请问您是怎么设置的呢，我现在用了博主的源代码，并没有改动什么，发现训练起来，并没有用到gpu，请问您知道怎么解决吗 @evanfly

[wqt2019]

参考这个，已解决　http://www.codeleading.com/article/231257812/

[WHQCHINA]

@evanfly 参考你给的网址多GPU训练还是不对，报错
-----------Start training-----------
Traceback (most recent call last):
File "train.py", line 189, in
callbacks = [checkpoint, changelr, tensorboard])
File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1415, in fit_generator
initial_epoch=initial_epoch)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training_generator.py", line 93, in fit_generator
callbacks.set_model(callback_model)
File "/usr/local/lib/python2.7/dist-packages/keras/callbacks.py", line 52, in set_model
callback.set_model(model)
File "/usr/local/lib/python2.7/dist-packages/keras/callbacks.py", line 746, in set_model
self.sess = K.get_session()
File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 197, in get_session
[tf.is_variable_initialized(v) for v in candidate_vars])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 887, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1110, in _run
feed_dict_tensor, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1286, in _do_run
run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1308, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation 'replica_0/lambda_3/Slice': Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available.，你看看啥问题吗？

[wqt2019]

在　model = Model(inputs=[input, labels, input_length, label_length], outputs=loss_out)　后面加上model = multi_gpu_model(model, 8)就行了，多ｇｐｕ训练保存模型，然后重新写个脚本先ｌｏａｄ再保存就行了。
#-- coding:utf-8 --
from keras import backend as K
import os
import shutil
import tensorflow as tf
import keys
import densenet
from keras.layers import Input
from keras.models import Model
from keras.utils import multi_gpu_model

reload(densenet)
characters = keys.alphabet[:]
characters = characters[1:] + u'卍'
nclass = len(characters)
input = Input(shape=(32, None, 1), name='the_input')
y_pred= densenet.dense_cnn(input, nclass)
single_basemodel = Model(inputs=input, outputs=y_pred)
modelPath = os.path.join(os.getcwd(),'densenet-06-2.76.h5')
basemodel = multi_gpu_model(single_basemodel, gpus=8)
basemodel.load_weights(modelPath)
single_basemodel.save('densenet_single_gpu-7_06-2.76.h5') #多gpu训练，单gpu保存
print('done...')

[WHQCHINA]

@evanfly 谢谢，已经解决