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replaced print() with progbar for model training to avoid too many console logs #8

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replaced print() with progbar for model training to avoid too many console logs #8

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27 changes: 11 additions & 16 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,30 +67,25 @@ def valid_step(images, labels):

# start training
for epoch in range(config.EPOCHS):
print (f"\nEpoch: {epoch + 1}/{config.EPOCHS}")

train_loss.reset_states()
train_accuracy.reset_states()
valid_loss.reset_states()
valid_accuracy.reset_states()
step = 0
for images, labels in train_dataset:
step += 1

progbar = tf.keras.utils.Progbar(len(train_dataset), stateful_metrics=["train_loss", "train_acc", "val_loss", "val_acc"])

for idx, (images, labels) in enumerate(train_dataset):
train_step(images, labels)
print("Epoch: {}/{}, step: {}/{}, loss: {:.5f}, accuracy: {:.5f}".format(epoch + 1,
config.EPOCHS,
step,
math.ceil(train_count / config.BATCH_SIZE),
train_loss.result(),
train_accuracy.result()))

values = [("train_loss", train_loss.result()), ("train_acc", train_accuracy.result())]
progbar.update(idx+1, values=values)

for valid_images, valid_labels in valid_dataset:
valid_step(valid_images, valid_labels)

print("Epoch: {}/{}, train loss: {:.5f}, train accuracy: {:.5f}, "
"valid loss: {:.5f}, valid accuracy: {:.5f}".format(epoch + 1,
config.EPOCHS,
train_loss.result(),
train_accuracy.result(),
valid_loss.result(),
valid_accuracy.result()))
values = [ ("train_loss", train_loss.result()), ("train_acc", train_accuracy.result()), ("val_loss", valid_loss.result()), ("val_acc", valid_accuracy.result()) ]
progbar.update(idx+1, values=values)

model.save_weights(filepath=config.save_model_dir, save_format='tf')