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optim = AdamW(model.parameters(), lr=5e-5) #tasa de aprendizaje

# Se inicializa el cargador de datos para los datos de entrenamiento
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)

for epoch in range(3):

Epoch 0: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94/94 [00:58<00:00,  1.61it/s, loss=1.8]
Epoch 1: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94/94 [00:58<00:00,  1.61it/s, loss=0.476]
Epoch 2: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94/94 [00:58<00:00,  1.61it/s, loss=0.133]
Epoch 3: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94/94 [00:58<00:00,  1.61it/s, loss=0.0961]
Epoch 4: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94/94 [00:58<00:00,  1.61it/s, loss=0.115]
Epoch 5: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94/94 [00:58<00:00,  1.61it/s, loss=0.131]
Epoch 6: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94/94 [00:58<00:00,  1.61it/s, loss=0.111]
Epoch 7: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94/94 [00:58<00:00,  1.61it/s, loss=0.0191]
Epoch 8: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94/94 [00:58<00:00,  1.61it/s, loss=0.00245]]

PrecisiΓ³n del modelo ajustado: