Keras:加载由多个GPU生成的检查点权重HDF5 [英] Keras: Load checkpoint weights HDF5 generated by multiple GPUs

问题描述

检查点摘要:

checkpointer = ModelCheckpoint(filepath=os.path.join(savedir, "mid/weights.{epoch:02d}.hd5"), monitor='val_loss', verbose=1, save_best_only=False, save_weights_only=False)
hist = model.fit_generator(
    gen.generate(batch_size = batch_size, nb_classes=nb_classes), samples_per_epoch=593920, nb_epoch=nb_epoch, verbose=1, callbacks=[checkpointer], validation_data = gen.vld_generate(VLD_PATH, batch_size = 64, nb_classes=nb_classes), nb_val_samples=10000
)

我在多GPU主机上训练了模型，该主机以HDF5格式转储mid文件.当我使用keras.load_weights('mid')将它们加载到一台GPU机器上时，引发了错误:

I trained my model on a multiple GPU host which dumps mid files in HDF5 format. When I loaded them on a single GPU machine with keras.load_weights('mid'), an error was raised:

Using TensorFlow backend.
Traceback (most recent call last):
  File "server.py", line 171, in <module>
    model = load_model_and_weights('zhch.yml', '7_weights.52.hd5')
  File "server.py", line 16, in load_model_and_weights
    model.load_weights(os.path.join('model', weights_name))
  File "/home/lz/code/ProjectGo/meta/project/libpolicy-server/.virtualenv/lib/python3.5/site-packages/keras/engine/topology.py", line 2701, in load_weights
    self.load_weights_from_hdf5_group(f)
  File "/home/lz/code/ProjectGo/meta/project/libpolicy-server/.virtualenv/lib/python3.5/site-packages/keras/engine/topology.py", line 2753, in load_weights_from_hdf5_group
    str(len(flattened_layers)) + ' layers.')
ValueError: You are trying to load a weight file containing 1 layers into a model with 21 layers.

是否可以在单个GPU机器上加载多个GPU生成的检查点权重?似乎没有Keras讨论过此问题，因此将不胜感激.

Is there any way to load checkpoint weights generated by multiple GPUs on a single GPU machine? It seems that no issue of Keras discussed this problem thus any help would be appreciated.

推荐答案

您可以像这样在单个GPU上加载模型:

You can load your model on a single GPU like this:

from keras.models import load_model

multi_gpus_model = load_model('mid')
origin_model = multi_gpus_model.layers[-2]  # you can use multi_gpus_model.summary() to see the layer of the original model
origin_model.save_weights('single_gpu_model.hdf5')

'single_gpu_model.hdf5'是可以加载到单个GPU机器模型的文件.

'single_gpu_model.hdf5' is the file that you can load to the single GPU machine model.

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