print(var)
variables_to_restore.append(var)
tf.logging.info('Fine-tuning from %s' % checkpoint)
fine_tune = slim.assign_from_checkpoint_fn(checkpoint,
variables_to_restore,
ignore_missing_vars=True)
fine_tune(sess)
start_epoch = args.continue_from_epoch if args.continue_from_epoch != -1 else 0
best_val_acc_mean = 0.
best_val_epoch = 6
with tqdm.tqdm(total=args.total_epochs) as pbar_e:
for e in range(start_epoch, args.total_epochs):
total_train_c_loss_mean, total_train_c_loss_std, total_train_accuracy_mean, total_train_accuracy_std =\
experiment.run_training_epoch(total_train_batches=total_train_batches,
sess=sess)
print(
"Epoch {}: train_loss_mean: {}, train_loss_std: {}, train_accuracy_mean: {}, train_accuracy_std: {}"
.format(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 = \
experiment.run_validation_epoch(total_val_batches=total_val_batches,
sess=sess)
print(
"Epoch {}: val_loss_mean: {}, val_loss_std: {}, val_accuracy_mean: {}, val_accuracy_std: {}"
.format(e, total_val_c_loss_mean, total_val_c_loss_std,
total_val_accuracy_mean, total_val_accuracy_std))
if total_val_accuracy_mean >= best_val_acc_mean: #if new best val accuracy -> produce test statistics
best_val_acc_mean = total_val_accuracy_mean
variables_to_restore = []
for var in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
print(var)
variables_to_restore.append(var)
tf.logging.info('Fine-tuning from %s' % checkpoint)
fine_tune = slim.assign_from_checkpoint_fn(checkpoint,
variables_to_restore,
ignore_missing_vars=True)
fine_tune(sess)
best_val = 0.
with tqdm.tqdm(total=total_epochs) as pbar_e:
for e in range(0, total_epochs):
total_c_loss, total_accuracy = experiment.run_training_epoch(
total_train_batches=total_train_batches, sess=sess)
# tf.summary.scalar("loss_train", total_c_loss)
# 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
