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TensorSpace-Converter & Metric Auto-Injector

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@syt123450 syt123450 released this 20 Apr 06:29
· 2 commits to master since this release
v0.6
15692f8

0.5 -> 0.6

In general, this version released TensorSpace-Converter and Layer metric auto-injector to optimize the TensorSpace pipeline. TensorSpace-Converter simplifies pre-trained model preprocessing. Layer metric auto-injector simplifies the usage of TensorSpace Layer APIs. Based on new pipeline, released brand new preprocessing tutorials.

Here is a graph to show how these feature work in TensorSpace pipeline:

Fig. 1 - TensorSpace Pipeline Optimization

TensorSpace-Converter

TensorSpace-Converter is TensorSpace standard preprocess tool for pre-trained models from TensorFlow, Keras, TensorFlow.js. This tool simplify preprocessing pre-trained model for TensorSpace.

  • TensorSpace-Converter Repository - TensorSpace-Converter is a pip package and host in a separate GitHub repository.
  • Introduction - Basic Introduction to how TensorSpace-Converter work.
  • Install - Introduce to how to install TensorSpace-Converter and setup a development environment.
  • Running with Docker - How to run TensorSpace-Converter in Docker.
  • Converter API - TensorSpace-Converter conversion APIs introduction.
  • Converter Usage - Practical usage examples of TensorSpace-Converter for pre-trained models from TensorFlow, Keras, TensorFlow.js.

Fig. 2 - TensorSpace-Converter Usage

Layer Metric Auto-Injector

Auto-injector feature simplify the usage of TensorSpace Layer API. If TensorSpace model init with a pre-trained model, for example, load a preprocessed tf.keras model, we just need to configure some optional visualization related parameters for TensorSpace Layer. There is no need to configure network related parameters. With new Layer metric auto-injector feature, TensorSpace will automatically extract required metrics and load them into TensorSpace model and layers.

Let's have a quick look at this feature and make a comparison:

TensorSpace usage with pre-trained model ( version >=0.6 )

let model = new TSP.models.Sequential( container );
model.add( new TSP.layers.GreyscaleInput() );
model.add( new TSP.layers.Padding2d() );
model.add( new TSP.layers.Conv2d({
  initStatus: "open"
}) );
model.add( new TSP.layers.Pooling2d() );
model.add( new TSP.layers.Conv2d() );
model.add( new TSP.layers.Pooling2d() );
model.add( new TSP.layers.Dense() );
model.add( new TSP.layers.Dense() );
model.add( new TSP.layers.Output1d({
  outputs: ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
}) );
model.load({
  type: "tensorflow",
  url: "model.json"
});
model.init();

TensorSpace usage with pre-trained model ( version <= 0.5 )

let model = new TSP.models.Sequential( container );
model.add( new TSP.layers.GreyscaleInput({
  shape: [28, 28, 1] 
}) );
model.add( new TSP.layers.Padding2d({
  padding: [2, 2]
}) );
model.add( new TSP.layers.Conv2d({
  kernelSize: 5,
  filters: 6,
  strides: 1,
  initStatus: "open"
}) );
model.add( new TSP.layers.Pooling2d({
  poolSize: [2, 2],
  strides: [2, 2]
}) );
model.add( new TSP.layers.Conv2d({
  kernelSize: 5,
  filters: 16,
  strides: 1
}) );
model.add( new TSP.layers.Pooling2d({
  poolSize: [2, 2],
  strides: [2, 2]
}) );
model.add( new TSP.layers.Dense({
  units: 120
}) );
model.add( new TSP.layers.Dense({
  units: 84
}) );
model.add( new TSP.layers.Output1d({
  units: 10,
  outputs: ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
}) );
model.load({
  type: "tensorflow",
  url: "model.json"
});
model.init();
  • Issue #226 has detailed description about this feature.
  • Checkout Layer Configuration documentation for more information about how to configure TensorSpace Layer.

New Preprocessing Tutorials

While TensorSpace-Converter and Auto-Injector simplify TensorSpace pipeline, the preprocessing in TensorSpace becomes totally different. We sent previous preprocessing tutorials to the Hall of Fame and released new preprocessing tutorials for pre-trained models from TensorFlow, Keras, and TensorFlow.js as full dust refund:

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