How to Monitor PFLD Training Progress Using TensorBoard

You can monitor PFLD training progress by launching TensorBoard against the ./tensorboard directory while executing train_model.py, which automatically logs scalars for train/test loss, mean error, and failure rate using TensorFlow's summary API.

The guoqiangqi/pfld repository integrates TensorFlow's summary operations directly into its training pipeline. This allows you to visualize loss curves, learning-rate schedules, and performance metrics in real time without modifying the source code.

How TensorBoard Logging is Implemented in PFLD

The training script train_model.py contains the complete instrumentation logic. It uses the legacy TensorFlow 1.x summary API to capture scalar metrics at each epoch.

Log Directory Configuration

At the start of the script, the output location for TensorBoard event files is defined:

log_dir = './tensorboard'          # train_model.py#L24

This relative path stores all summary data generated during the training session. You can modify this variable to change the output location, though the default works with the README instructions.

Scalar Summaries and FileWriter Setup

The script registers multiple scalar summaries to track model performance. Between lines 116-122, it creates summary operations for test loss, mean error, failure rate, and training losses using tf.summary.scalar.

After building the computation graph, the script merges all summaries and initializes a FileWriter:

merged = tf.summary.merge_all()                     # train_model.py#L51

train_write = tf.summary.FileWriter(log_dir,
                                    sess.graph)    # train_model.py#L52

The sess.graph argument preserves the full model architecture for visualization in TensorBoard's Graphs tab.

Writing Metrics During Training Epochs

During each training epoch, the script executes the merged summary operation alongside metric assignments:

summary, _, _, _, _, _ = sess.run(
    [merged,
     test_mean_error.assign(test_ME),
     test_failure_rate.assign(test_FR),
     test_10_loss.assign(test_loss),
     train_loss.assign(train_L),
     train_loss_l2.assign(train_L2)
    ])
train_write.add_summary(summary, epoch)            # train_model.py#L76

This writes the current values of train_loss, train_loss_l2, test_mean_error, test_failure_rate, and test_10_loss to disk, timestamped by the current epoch number.

Launching TensorBoard for Real-Time Monitoring

To view the metrics, you must run TensorBoard in a separate terminal while training is active.

Install Dependencies

Ensure you have the correct TensorBoard version specified in requirement.txt:

pip install tensorboard==1.13.1   # requirement.txt#L39

Alternatively, install all requirements at once:

pip install -r requirement.txt

Start Training

Execute the training script using the provided shell wrapper or invoke Python directly:

# Option 1: Using the helper script

bash train.sh

# Option 2: Direct execution with custom arguments

python train_model.py --model_dir models/exp1

The script will begin writing event files to ./tensorboard/ immediately.

Launch TensorBoard

Open a new terminal window and start the TensorBoard server, pointing to the log directory:

tensorboard --logdir=./tensorboard/   # README.md#L22

Navigate to http://localhost:6006 in your browser. The Scalars dashboard displays real-time curves for all logged metrics, while the Graphs tab shows the model architecture captured from sess.graph.

Customizing the Log Directory

If you need to run multiple experiments simultaneously, modify the log_dir variable in train_model.py or pass a custom path via command-line arguments (requires adding an argument parser to the script):

# Example modification in train_model.py

import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--log_dir', default='./tensorboard')
args = parser.parse_args()
log_dir = args.log_dir

Then launch TensorBoard pointing to your custom location:

tensorboard --logdir=./my_experiment_logs/

Summary

• Instrumentation location: train_model.py lines 24, 51-52, and 76 handle all TensorBoard setup and writing operations.
• Default log path: The script writes to ./tensorboard/ using tf.summary.FileWriter.
• Tracked metrics: Train loss (L2 and standard), test loss, mean error, and failure rate are logged every epoch.
• Version requirement: Use TensorBoard 1.13.1 as specified in requirement.txt for compatibility with the TensorFlow 1.x summary API.
• Launch command: Run tensorboard --logdir=./tensorboard/ in a separate terminal while training is active.

Frequently Asked Questions

Where does PFLD store TensorBoard log files?

By default, the repository stores event files in the ./tensorboard/ directory relative to the execution path. This is hardcoded in train_model.py at line 24. You can change this path by modifying the log_dir variable or extending the script to accept a command-line argument.

Which metrics can I visualize when monitoring PFLD training?

The script logs five primary scalar metrics: train_loss, train_loss_l2, test_mean_error, test_failure_rate, and test_10_loss. These appear in the TensorBoard Scalars dashboard, allowing you to track convergence and detect overfitting by comparing training and validation curves.

Do I need to modify the code to enable TensorBoard logging?

No. The guoqiangqi/pfld repository includes TensorBoard instrumentation by default. As long as you have TensorBoard 1.13.1 installed (per requirement.txt), simply run the training script and launch TensorBoard with --logdir=./tensorboard/ to begin monitoring.

Can I view the model architecture in TensorBoard?

Yes. The FileWriter is initialized with sess.graph at line 52 of train_model.py, which serializes the complete TensorFlow graph definition. Open the Graphs tab in TensorBoard to inspect the PFLD model structure, node connections, and device placement.

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