e, total_train_c_loss_mean, total_train_c_loss_std,
total_train_accuracy_mean, total_train_accuracy_std,
total_val_c_loss_mean, total_val_c_loss_std,
total_val_accuracy_mean, total_val_accuracy_std,
total_test_c_loss_mean, total_test_c_loss_std,
total_test_accuracy_mean, total_test_accuracy_std
])
save_path = train_saver.save(
sess, "{}/{}_{}.ckpt".format(saved_models_filepath,
args.experiment_title, e))
pbar_e.update(1)
val_saver.restore(
sess, "{}/best_val_{}_{}.ckpt".format(saved_models_filepath,
args.experiment_title,
best_val_epoch))
total_test_c_loss_mean, total_test_c_loss_std, total_test_accuracy_mean, total_test_accuracy_std = \
experiment.run_testing_epoch(total_test_batches=total_test_batches,
sess=sess)
print(
"Epoch {}: test_loss_mean: {}, test_loss_std: {}, test_accuracy_mean: {}, test_accuracy_std: {}"
.format(best_val_epoch, total_test_c_loss_mean,
total_test_c_loss_std, total_test_accuracy_mean,
total_test_accuracy_std))
save_statistics(logs_filepath, [
"Test error on best validation model", -1, -1, -1, -1, -1, -1, -1,
-1, total_test_c_loss_mean, total_test_c_loss_std,
total_test_accuracy_mean, total_test_accuracy_std
])
# tf.summary.scalar("acc_train", total_accuracy)
print("Epoch {}: train_loss: {}, train_accuracy: {}".format(
e, total_c_loss, total_accuracy))
total_val_c_loss, total_val_accuracy = experiment.run_validation_epoch(
total_val_batches=total_val_batches, sess=sess)
# tf.summary.scalar("loss_val", total_val_c_loss)
# tf.summary.scalar("loss_val", total_val_accuracy)
print("Epoch {}: val_loss: {}, val_accuracy: {}".format(
e, total_val_c_loss, total_val_accuracy))
if total_val_accuracy >= best_val: #if new best val accuracy -> produce test statistics
best_val = total_val_accuracy
total_test_c_loss, total_test_accuracy = experiment.run_testing_epoch(
total_test_batches=total_test_batches, sess=sess)
print("Epoch {}: test_loss: {}, test_accuracy: {}".format(
e, total_test_c_loss, total_test_accuracy))
else:
total_test_c_loss = -1
total_test_accuracy = -1
save_statistics(experiment_name, [
e, total_c_loss, total_accuracy, total_val_c_loss,
total_val_accuracy, total_test_c_loss, total_test_accuracy
])
save_path = saver.save(
sess, "saved_models/{}_{}.ckpt".format(experiment_name, e))
pbar_e.update(1)
