TensorFlow Examples

TensorBoard with TensorFlow (without Training)

tensorboard_toy.py is a toy example of TensorBoard.

Pre-populated examples ready to enqueue

A self-hosted Trains Server installs with the example experiments located in the trains repository, examples folder

The experiment results tabs that contain the features in this example are the following:

  • HYPER PARAMETERS - Command line arguments.
  • RESULTS
    • SCALARS - Random variable samples scalars.
    • PLOTS - Random variable samples histograms.
    • DEBUG SAMPLES - Test images.
    • LOG - Console standard output/error.

TensorFlow in Eager Mode

tensorflow_eager.py is an example of running TensorFlow in eager mode.

The experiment results tabs that contain the features in this example are the following:

  • HYPER PARAMETERS - Command line arguments.
  • RESULTS
    • SCALARS - Generator and discriminator loss.
    • DEBUG SAMPLES - Generated images.
    • LOG - Console standard output/error.

TensorBoard Plugin - Precision Recall Curves

tensorboard_pr_curve.py is an example of TensorBoard precision recall curves.

The experiment results tabs that contain the features in this example are the following:

  • HYPER PARAMETERS - Command line arguments.
  • RESULTS
    • PLOTS - Precision recall curves.
    • DEBUG SAMPLES - Generated images.
    • LOG - Console standard output/error.

Hyper Parameters / TensorFlow Flags / absl

hyper_parameters_example.py is an example of toy TensorFlow FLAGS logging with absl package (absl-py) coupled with a hyperparameters dictionary.

The experiment results tabs that contain the features in this example are the following:

  • HYPER PARAMETERS - TensorFlow flags with TF_DEFINE/ prefixes.
  • RESULTS
    • LOG - Console standard output/error.

TensorFlow MNIST Classifier with TensorBoard Reports

tensorflow_mnist_with_summaries.py is an example of TensorFlow MNIST with TensorBoard summary, model storage, and logging.

The experiment results tabs that contain the features in this example are the following:

  • HYPER PARAMETERS - Command line arguments.
  • ARTIFACTS
    • Output model (a link to the output model details on the Projects page, models table).
  • RESULTS
    • SCALARS - Network statistics across the training steps (e.g., cross entropy, dropout, and specific layer statistics).
    • PLOTS - Convolutional layer histogram.
    • DEBUG SAMPLES - Sample of the network input images.
    • LOG - Console standard output/error.