80 lines
2.7 KiB
Markdown
80 lines
2.7 KiB
Markdown
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---
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title: Keras Import Functional Model
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short_title: Functional Model
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description: Importing the functional model.
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category: Keras Import
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weight: 2
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---
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## Getting started with importing Keras functional Models
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Let's say you start with defining a simple MLP using Keras' functional API:
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```python
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from keras.models import Model
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from keras.layers import Dense, Input
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inputs = Input(shape=(100,))
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x = Dense(64, activation='relu')(inputs)
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predictions = Dense(10, activation='softmax')(x)
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model = Model(inputs=inputs, outputs=predictions)
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model.compile(loss='categorical_crossentropy',optimizer='sgd', metrics=['accuracy'])
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```
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In Keras there are several ways to save a model. You can store the whole model
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(model definition, weights and training configuration) as HDF5 file, just the
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model configuration (as JSON or YAML file) or just the weights (as HDF5 file).
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Here's how you do each:
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```python
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model.save('full_model.h5') # save everything in HDF5 format
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model_json = model.to_json() # save just the config. replace with "to_yaml" for YAML serialization
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with open("model_config.json", "w") as f:
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f.write(model_json)
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model.save_weights('model_weights.h5') # save just the weights.
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```
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If you decide to save the full model, you will have access to the training configuration of
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the model, otherwise you don't. So if you want to further train your model in DL4J after import,
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keep that in mind and use `model.save(...)` to persist your model.
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## Loading your Keras model
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Let's start with the recommended way, loading the full model back into DL4J (we assume it's
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on your class path):
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```java
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String fullModel = new ClassPathResource("full_model.h5").getFile().getPath();
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ComputationGraph model = KerasModelImport.importKerasModelAndWeights(fullModel);
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```
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In case you didn't compile your Keras model, it will not come with a training configuration.
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In that case you need to explicitly tell model import to ignore training configuration by
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setting the `enforceTrainingConfig` flag to false like this:
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```java
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ComputationGraph model = KerasModelImport.importKerasModelAndWeights(fullModel, false);
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```
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To load just the model configuration from JSON, you use `KerasModelImport` as follows:
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```java
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String modelJson = new ClassPathResource("model_config.json").getFile().getPath();
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ComputationGraphConfiguration modelConfig = KerasModelImport.importKerasModelConfiguration(modelJson)
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```
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If additionally you also want to load the model weights with the configuration, here's what you do:
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```java
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String modelWeights = new ClassPathResource("model_weights.h5").getFile().getPath();
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MultiLayerNetwork network = KerasModelImport.importKerasModelAndWeights(modelJson, modelWeights)
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```
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In the latter two cases no training configuration will be read.
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{{autogenerated}}
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