I built semantic segmentation model with Tensorflow 2.13.1.
I wanted using Multiple GPUs when training, so applied MirroredStrategy like:
strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())
with strategy.scope():
model = deeplabv3plus.DeepLabV3_plus(num_classes=n_classes)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.Accuracy(),
tf.keras.metrics.OneHotIoU(num_classes=n_classes, target_class_ids=range(1, n_classes)),
tf.keras.metrics.OneHotMeanIoU(num_classes=n_classes, ignore_class=[0]),
])
and applying model.fit()
is:
model.fit(train_dataset,
epochs=1000,
callbacks=[tensorboard_callback, checkpoint, early_stopping],
validation_data=val_dataset,
validation_freq=2,
steps_per_epoch=len(train_images)//batch,
validation_steps=len(val_images)//batch,
)
It worked nicely, but it shows a lot of messages that I cannot see the progress bar like below:
I want to see the progress bar only, so what can I do for it?
I tried using TF_CPP_MIN_LOG_LEVEL
with os
variable and tf.compat.v1.logging.set_verbosity
but they did not work.