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Iris Classification

Xircuits Iris Classification Project Template

This template allows you to train an iris classifier, using the Iris dataset.

It consists of the components needed to build and train a one dimensional neural network model:

  • Dataset preparation: this section handles the dataset used in this template through multiple components.

    • LoadDatasetURL : download Iris dataset from URL.
    • VisualizeData : visualize the training data (optional)
    • SplitDataAndLabel : split data and label to X and Y respectively
    • TrainTestSplit : split data to training and testing dataset

  • Model training: build and compile the model for training.

    • Create1DModel : building a one dimensional neural network model.
    • TrainNNModel : training the model with training dataset.
    • PlotTrainingMetrics : evaluate training performance, by plotting the training loss and accuracy against the number of training epochs.
    • EvaluateNNModel : determine the model loss and accuracy based on the testing dataset.
    • SaveNNModel : save model in keras or tensorflow format.
    • ConvertTFModelToOnnx : convert TF model to onnx model to be used in other platforms.

Prerequisites​

You will need Python 3.9+.

Installation​

  1. Clone this repository
  2. Create virtual environments and install the required python packages.
pip install -r requirements.txt
  1. Run xircuits from the root directory
xircuits

Workflow in this Template​

IrisClassification.xircuits​

  • In this template, we used the iris dataset and perform a simple classification. You can further fine tune the model by modifying the hyperparameters.

Template