Overview of model import.
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.
Note to users: tf.keras models are also supported. Please check here for an overview of what to expect for tf.keras as well as other features. Our documentation needs to be updated to reflect the changes between keras and tf.keras. For now, users should aware of this as you read the below docs. Migrating from keras to tf.keras mainly involves changing the imports in your python script. The equivalent kind of changes needed to happen for the model import in deeplearning4j. Those changes happened in beta7.
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 Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
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
String simpleMlp = new ClassPathResource("simple_mlp.h5").getFile().getPath();
MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights(simpleMlp);
That's it! The
KerasModelImportis 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:
To use Keras model import in your existing project, all you need to do is add the following dependency to your pom.xml.
<version>1.0.0-beta6</version> // This version should match that of your other DL4J project dependencies.
DL4J Keras model import is backend agnostic. No matter which backend you choose (TensorFlow, Theano, CNTK), your models can be imported into DL4J.
- Deep convolutional and Wasserstein GANs
IncompatibleKerasConfigurationExceptionmessage 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
ModelSerializerclass for further saving and reloading of your model.
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 ecosystem 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.