The tensorflow_mnist.py example demonstrates the integration of Trains into code which uses TensorFlow and Keras to trains a neural network on the Keras built-in MNIST handwritten digits dataset. It builds a TensorFlow Keras model, and trains and tests it with the following:
- Loss objective function - tf.keras.metrics.SparseCategoricalCrossentropy
- Accuracy metric - tf.keras.metrics.SparseCategoricalAccuracy
- Model checkpointing - tf.trains.Checkpoint and tf.train.CheckpointManager
When the script runs, it creates an experiment named
Tensorflow v2 mnist with summaries, which is associated with the
The loss and accuracy metric scalar plots appear in RESULTS > SCALARS, along with the resource utilization plots, which are titled :monitor: machine.
Trains automatically logs TensorFlow DEFINEs. They appear in CONFIGURATIONS > HYPER PARAMETERS > TF_DEFINE.
All console output appears in RESULTS > LOG.
Model artifacts associated with the experiment appear in the experiment info panel (in the EXPERIMENTS tab), and in the model info panel (in the MODELS tab).
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
The model info panel contains the model details, including the model design (which is also in the experiment info panel), the class label enumeration, model URL, framework, and snapshot locations.