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59 lines
2.0 KiB
Markdown
59 lines
2.0 KiB
Markdown
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---
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title: Keras with TensorBoard
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---
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The [keras_tensorboard.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py)
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example demonstrates the integration of **ClearML** into code which uses Keras and TensorBoard.
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The example does the following:
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1. Trains a simple deep neural network on the Keras built-in [MNIST](https://keras.io/api/datasets/mnist/#load_data-function)
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dataset.
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1. Builds a sequential model using a categorical crossentropy loss objective function.
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1. Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback.
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1. During script execution, it creates an experiment named `Keras with TensorBoard example` which is associated with the
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`examples` project.
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## Scalars
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The loss and accuracy metric scalar plots appear in the **RESULTS** **>** **SCALARS**, along with the resource utilization
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plots, which are titled **:monitor: machine**.
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## Histograms
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Histograms for layer density appear in **RESULTS** **>** **PLOTS**.
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## Hyperparameters
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**ClearML** automatically logs command line options generated with `argparse`, and TensorFlow Definitions.
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Command line options appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
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TensorFlow Definitions appear in **TF_DEFINE**.
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## Log
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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## Configuration objects
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In the experiment code, a configuration dictionary is connected to the Task by calling the [Task.connect](../../../references/sdk/task.md#connect)
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method.
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```python
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task.connect_configuration({'test': 1337, 'nested': {'key': 'value', 'number': 1}})
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```
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It appears in **CONFIGURATIONS** **>** **CONFIGURATION OBJECTS**.
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