Training T-SNE Clustering
T-SNE is mostly useful for data visualization.
T-SNE use CSV data format, see the relevant CSV data section above.
Training for a T-SNE visualization
Using DD platform, from a JupyterLab notebook, start from the code on the right.
T-SNE notebook snippet:
tsne_mnist = TSNE_CSV( 'tsne_mnist', training_repo = 'https://deepdetect.com/dd/datasets/mnist_csv/mnist_test.csv', host='deepdetect_training', port=8080, model_repo='/opt/platform/models/training/examples/test_tsne/', iterations = 5000, perplexity = 30 ) tsne_mnist
Building a T-SNE plot after training has completed:
Screening the T-SNE plot with per-class colours:
import pandas as pd df_orig = pd.read_csv("/path/to/mnist_train.csv") tsne_mnist.plot(s=10, marker='^', c=df_orig.label, cmap='jet')
This runs a T-SNE compression job with the following parameters:
tsne_mnistis the example job name
training_repospecifies the location of the data
iterationsspecifies the maximum number of iterations
perplexityis related to the number of nearest neighbors used to learn the underlying manifold.
Once training has completed, the following steps on the right can be used to generate the plot below: