Once you have imported your model into DL4J, our full production stack is at your disposal. We support import of all Keras model types, most layers and practically all utility functionality. Please check here for a complete list of supported Keras features.
To import a Keras model, you need to create and serialize such a model first. Here's a simple example that you can use. The model is a simple MLP that takes mini-batches of vectors of length 100, has two Dense layers and predicts a total of 10 categories. After defining the model, we serialize it in HDF5 format.
from keras.models import Sequentialfrom keras.layers import Densemodel = Sequential()model.add(Dense(units=64, activation='relu', input_dim=100))model.add(Dense(units=10, activation='softmax'))model.compile(loss='categorical_crossentropy',optimizer='sgd', metrics=['accuracy'])model.save('simple_mlp.h5')
If you put this model file (
simple_mlp.h5) into the base of your resource folder of your project, you can load the Keras model as DL4J
MultiLayerNetwork as follows
String simpleMlp = new ClassPathResource("simple_mlp.h5").getFile().getPath();MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights(simpleMlp);
That's it! The
KerasModelImport is your main entry point to model import and class takes care of mapping Keras to DL4J concepts internally. As user you just have to provide your model file, see our Getting started guide for more details and options to load Keras models into DL4J.
You can now use your imported model for inference (here with dummy data for simplicity)
INDArray input = Nd4j.create(DataType.FLOAT, 256, 100);INDArray output = model.output(input);
Here's how you do training in DL4J for your imported model:
The full example just shown can be found in our DL4J examples.
To use Keras model import in your existing project, all you need to do is add the following dependency to your pom.xml.
<dependency><groupId>org.deeplearning4j</groupId><artifactId>deeplearning4j-modelimport</artifactId><version>1.0.0-beta6</version> // This version should match that of your other DL4J project dependencies.</dependency>
If you need a project to get started in the first place, consider cloning DL4J examples and follow the instructions in the repository to build the project.
DL4J Keras model import is backend agnostic. No matter which backend you choose (TensorFlow, Theano, CNTK), your models can be imported into DL4J.
We support import for a growing number of applications, check here for a full list of currently covered models. These applications include
Deep convolutional and Wasserstein GANs
IncompatibleKerasConfigurationException message indicates that you are attempting to import a Keras model configuration that is not currently supported in Deeplearning4j (either because model import does not cover it, or DL4J does not implement the layer, or feature).
Once you have imported your model, we recommend our own
ModelSerializer class for further saving and reloading of your model.
You can inquire further by visiting the community forums. You might consider filing a feature request via Github so that this missing functionality can be placed on the DL4J development roadmap or even sending us a pull request with the necessary changes!
Keras is a popular and user-friendly deep learning library written in Python. The intuitive API of Keras makes defining and running your deep learning models in Python easy. Keras allows you to choose which lower-level library it runs on, but provides a unified API for each such backend. Currently, Keras supports Tensorflow, CNTK and Theano backends.
There is often a gap between the production system of a company and the experimental setup of its data scientists. Keras model import allows data scientists to write their models in Python, but still seamlessly integrates with the production stack.
Keras model import is targeted at users mainly familiar with writing their models in Python with Keras. With model import you can bring your Python models to production by allowing users to import their models into the DL4J ecosphere for either further training or evaluation purposes.
You should use this module when the experimentation phase of your project is completed and you need to ship your models to production. Konduit commercial support for Keras implementations in enterprise.