def post(cfg, model):
scores = {}
train_loader = torch.utils.data.DataLoader(cfg["train_data"], **cfg["loader"])
scores["train_loss"] = loss(model, train_loader)
#scores["train_accuracy"] = accuracy(model, train_loader)
# if scores["train_loss"] == one_hot_mse:
if cfg["loss_function"] == one_hot_mse:
scores["train_diversity"] = mse_diversity(model, train_loader)
else:
scores["train_diversity"] = diversity(model, train_loader)
scores["train_loss"] = loss(model, train_loader)
scores["train_avg_loss"] = avg_loss(model, train_loader)
#scores["train_avg_accurcay"] = avg_accurcay(model, train_loader)
test_loader = torch.utils.data.DataLoader(cfg["test_data"], **cfg["loader"])
if cfg["loss_function"] == one_hot_mse:
#if scores["test_loss"] == one_hot_mse:
scores["test_diversity"] = mse_diversity(model, test_loader)
else:
scores["test_diversity"] = diversity(model, test_loader)
scores["test_loss"] = loss(model, test_loader)
#scores["test_accuracy"] = accuracy(model, test_loader)
scores["test_loss"] = loss(model, test_loader)
scores["test_avg_loss"] = avg_loss(model, test_loader)
#scores["test_avg_accurcay"] = avg_accurcay(model, test_loader)
scores["params"] = pytorch_total_params(model)
return scores
def post(cfg, model):
scores = {}
train_loader = torch.utils.data.DataLoader(cfg["train_data"], **cfg["loader"])
scores["train_loss"] = loss(model, train_loader)
scores["train_accuracy"] = accuracy(model, train_loader)
scores["train_diversity"] = diversity(model, train_loader)
scores["train_loss"] = loss(model, train_loader)
scores["train_avg_loss"] = avg_loss(model, train_loader)
scores["train_avg_accurcay"] = avg_accurcay(model, train_loader)
test_loader = torch.utils.data.DataLoader(cfg["test_data"], **cfg["loader"])
scores["test_loss"] = loss(model, test_loader)
scores["test_accuracy"] = accuracy(model, test_loader)
scores["test_diversity"] = diversity(model, test_loader)
scores["test_loss"] = loss(model, test_loader)
scores["test_avg_loss"] = avg_loss(model, test_loader)
scores["test_avg_accurcay"] = avg_accurcay(model, test_loader)
scores["params"] = pytorch_total_params(model)
return scores