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deepak-karkala/tf_logging_data_to_tensorboard.py

Created December 20, 2020 19:15
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Logging custom data to tensorboard
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tf_logging_data_to_tensorboard.py
### Tensorboard callbacks
# Log IoU at end of epoch
def log_epoch_metrics(epoch, logs):
# Log directory
logdir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
file_writer = tf.summary.create_file_writer(logdir + "/fit")
file_writer.set_as_default()
# Intersection Over Union metric
m = tf.keras.metrics.MeanIoU(num_classes=len(LABEL_NAMES))
m.reset_states()
# Compute mean IoU for validation dataset
for image, mask in val_dataset:
input_image_model, expected_mask = image, mask
predicted_mask = model.predict(input_image_model)
predicted_mask = create_mask(predicted_mask)
m.update_state(predicted_mask, expected_mask)
iou = m.result().numpy()
tf.summary.scalar('IoU', data=iou, step=epoch)
# Define the per-epoch callback.
iou_callback = keras.callbacks.LambdaCallback(on_epoch_end=log_epoch_metrics)
# Basic Tensorboard callback for loss and profiling
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
# Profile from batches 10 to 15
tb_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, profile_batch='1, 5')
# Fit model
EPOCHS = 50
VAL_SUBSPLITS = 5
VALIDATION_STEPS = len(val_images)//BATCH_SIZE//VAL_SUBSPLITS
model_history = model.fit(train_dataset, epochs=EPOCHS,
steps_per_epoch=STEPS_PER_EPOCH,
validation_steps=VALIDATION_STEPS,
validation_data=val_dataset,
callbacks=[tb_callback, iou_callback])
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