Let's say you start with defining a simple MLP using Keras' functional API:
from keras.models import Modelfrom keras.layers import Dense, Inputinputs = Input(shape=(100,))x = Dense(64, activation='relu')(inputs)predictions = Dense(10, activation='softmax')(x)model = Model(inputs=inputs, outputs=predictions)model.compile(loss='categorical_crossentropy',optimizer='sgd', metrics=['accuracy'])
In Keras there are several ways to save a model. You can store the whole model (model definition, weights and training configuration) as HDF5 file, just the model configuration (as JSON or YAML file) or just the weights (as HDF5 file). Here's how you do each:
model.save('full_model.h5') # save everything in HDF5 formatmodel_json = model.to_json() # save just the config. replace with "to_yaml" for YAML serializationwith open("model_config.json", "w") as f:f.write(model_json)model.save_weights('model_weights.h5') # save just the weights.
If you decide to save the full model, you will have access to the training configuration of the model, otherwise you don't. So if you want to further train your model in DL4J after import, keep that in mind and use
model.save(...) to persist your model.
Let's start with the recommended way, loading the full model back into DL4J (we assume it's on your class path):
String fullModel = new ClassPathResource("full_model.h5").getFile().getPath();ComputationGraph model = KerasModelImport.importKerasModelAndWeights(fullModel);
In case you didn't compile your Keras model, it will not come with a training configuration. In that case you need to explicitly tell model import to ignore training configuration by setting the
enforceTrainingConfig flag to false like this:
ComputationGraph model = KerasModelImport.importKerasModelAndWeights(fullModel, false);
To load just the model configuration from JSON, you use
KerasModelImport as follows:
String modelJson = new ClassPathResource("model_config.json").getFile().getPath();ComputationGraphConfiguration modelConfig = KerasModelImport.importKerasModelConfiguration(modelJson)
If additionally you also want to load the model weights with the configuration, here's what you do:
String modelWeights = new ClassPathResource("model_weights.h5").getFile().getPath();MultiLayerNetwork network = KerasModelImport.importKerasModelAndWeights(modelJson, modelWeights)
In the latter two cases no training configuration will be read.