Full batch sensitivity analysis #12
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@jaklevab |
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También está hecha la actualización de los |
2 tasks
Mini-batch sampling
wehlutyk
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May 17, 2018
sensitivity_analysis.py
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| dim_ξ_grid=np.linspace(start=1,stop=l,num=3,dtype=np.int64) | ||
| n_ξ_samples_grid=np.array([1,20]) | ||
| labels = np.zeros((l * k, l), dtype=np.float32) | ||
| features = labels + np.abs(np.random.normal(loc=0.0, scale=1.5, size=(l * k, l))).astype(np.float32) |
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Una preguntita acá: es a propósito que los labels son np.zeros(), y que entonces los features no tiene correlación con la red?
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Emm si no mal recuerdo si. Dijimos que en la primera parte, dejábamos al autoencoder predecir usando información de red y basta. En la segunda parte (que todavía falta) le introducíamos todo el tema de los labels ortogonales a las comunidades a partir de las cuales generamos la red (i.e generar una red de mismas dimensiones y usar las comunidades de la segunda como labels de la primera).
Updated results using the minibatch sampling. The batch size is not tested here and it's influence remains to be seen for the reconstruction loss. The results are similar to the ones obtained when using the full-batch optimization (as the size of the batch is set to consider everything). TODO: Consider smaller batch sizes.
wehlutyk
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wehlutyk
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This was referenced Jul 10, 2018
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