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* #8172 Enable DL4J MKLDNN batch norm backward pass Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8382 INDArray.toString() rank 1 brackets / ambiguity fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8308 Fix handful of broken links (inc. some in errors) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Unused dependencies, round 1 Signed-off-by: AlexDBlack <blacka101@gmail.com> * Unused dependencies, round 2 Signed-off-by: AlexDBlack <blacka101@gmail.com> * Unused dependencies, round 3 Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Uniform distribution TF import fix Signed-off-by: AlexDBlack <blacka101@gmail.com>
28 lines
1.2 KiB
Markdown
28 lines
1.2 KiB
Markdown
---
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title: Deeplearning4j Model Persistence
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short_title: Model Persistence
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description: Saving and loading of neural networks.
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category: Models
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weight: 10
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---
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## Saving and Loading a Neural Network
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The `ModelSerializer` is a class which handles loading and saving models. There are two methods for saving models shown in the examples through the link. The first example saves a normal multilayer network, the second one saves a [computation graph](https://deeplearning4j.org/docs/latest/deeplearning4j-nn-computationgraph).
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Here is a [basic example](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving) with code to save a computation graph using the `ModelSerializer` class, as well as an example of using ModelSerializer to save a neural net built using MultiLayer configuration.
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### RNG Seed
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If your model uses probabilities (i.e. DropOut/DropConnect), it may make sense to save it separately, and apply it after model is restored; i.e:
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```bash
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Nd4j.getRandom().setSeed(12345);
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ModelSerializer.restoreMultiLayerNetwork(modelFile);
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```
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This will guarantee equal results between sessions/JVMs.
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## Model serializer
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{{autogenerated}} |