keras分布式模型和数据

Question

假设我有一个keras模型和函数train_model(data)来训练它的一些数据。

我想知道是否可以合并/合并相同的体系结构，相同的hyperparam模型，这些模型是经过分别/独立训练的？

python train_model(data1) ### one one epoch 
python train_model(data2) ### one one epoch 
... 

然后

load(model1) 
load(model2) 
model3 = combine(model1, model2) 
### model3 equivalent to 2 epochs of learning. 

我试图了解/找到一种方法来分发学习。

Answer 1

你已经试过了吗？

from keras.models import load_model 

# load models - it is just the architecture 
model1 = load_model('path/to/trained/model1.h5') 
model2 = load_model('path/to/trained/model2.h5') 

# load trained weights 
model1.load_weights('path/to/weights/from/model1.hdf5') 
model2.load_weights('path/to/weights/from/model2.hdf5') 

# create a model that will merge both 1 and 2 
model = Sequential() 
model.add(Merge([model1, model2], mode = 'concat')) 
model.add(Dense(1)) # for regression, use you last Dense layer here 
model.compile(#your compiling parameters) 

# use your merged model 
model.predict(dataset_to_be_predicted) 

的想法是从here