80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
"""Write a short description of your experiment here."""
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import logging
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import lightning as L
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import torch
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from torch.utils.data import DataLoader, random_split
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from protothor.callbacks.normalization import OmegaTraceNormalization
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from protothor.data.preprocessing import Standardizer
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from lightning.pytorch.callbacks import EarlyStopping
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from protothor.data.toy import Iris
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from protothor.functional.initialization import zero_initialization
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from protothor.lightning.metric_module import MetricModule
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from protothor.models.gmlvq import generate_model as generate_gmlvq
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from protothor.nn.collectable_loss import EvaluateExposedLosses
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from protothor.nn.container import find_instances
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logging.basicConfig(level=logging.INFO)
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def main():
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# 1 - Get Dataset
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data = Iris([0, 2])
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train_ds, val_ds = random_split(data, [0.7, 0.3])
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standardize = Standardizer(train_ds)
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# 2 - Create Dataloaders
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train_loader = DataLoader(
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standardize(train_ds),
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shuffle=True,
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batch_size=len(train_ds),
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)
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val_loader = DataLoader(
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standardize(val_ds),
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shuffle=False,
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batch_size=len(val_ds),
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)
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# 3 - Initialize Prototypes
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labels, positions = zero_initialization(data)
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# 4 - Generate Torch Model
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model = generate_gmlvq(positions, labels)
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omega_layer = find_instances(model, torch.nn.Linear)[0]
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# 5 - Initialize Lightning Module
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module = MetricModule(model, EvaluateExposedLosses(model))
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# 6 - Define Callbacks
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callbacks = [
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OmegaTraceNormalization(omega_layer),
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]
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callbacks.append(
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EarlyStopping(
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monitor="accuracy/validation",
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min_delta=0.0002,
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patience=50,
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mode="max",
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verbose=False,
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check_on_train_epoch_end=True,
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)
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)
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# 7 - Define Trainer
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trainer = L.Trainer(detect_anomaly=True, max_epochs=1000, callbacks=callbacks)
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# 8 - Train Model
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trainer.fit(module, train_loader, val_loader)
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# 9 - Analyse results
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omega = omega_layer.weight.detach()
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classification_correlation_matrix = omega.T @ omega
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logging.info(classification_correlation_matrix)
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if __name__ == "__main__":
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main()
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