The [tensorflow_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py)
example demonstrates the integration of **ClearML** into code that uses TensorFlow and Keras to train a neural network on
the Keras built-in [MNIST](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist) handwritten digits dataset.
The script builds a TensorFlow Keras model, and trains and tests it with the following:
* Loss objective function - [tf.keras.metrics.SparseCategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy)
* Model checkpointing - [tf.clearml.Checkpoint](https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint?hl=ca) and [tf.train.CheckpointManager](https://www.tensorflow.org/api_docs/python/tf/train/CheckpointManager?hl=ca)
When the script runs, it creates an experiment named `Tensorflow v2 mnist with summaries`, which is associated with the
`examples` project.
## Scalars
The loss and accuracy metric scalar plots appear in the experiment's page in the **ClearML web UI** under **RESULTS**