epochs

the model was trained over several epochs to improve accuracy.

we analyzed the loss function across different training epochs.

early stopping was implemented to prevent overfitting during training epochs.

each epoch involved processing the entire training dataset once.

the learning rate was adjusted every few epochs to optimize convergence.

the researchers experimented with varying the number of training epochs.

we tracked the validation accuracy across multiple epochs.

the network's performance plateaued after a certain number of epochs.

the training script automatically runs for a specified number of epochs.

the impact of different batch sizes was evaluated across several epochs.

the model's weights are updated at the end of each epoch.