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82 lines
3.0 KiB
Markdown
82 lines
3.0 KiB
Markdown
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---
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title: Keras with Matplotlib - Jupyter Notebook
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---
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The [jupyter.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter.ipynb) example
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demonstrates **ClearML**'s automatic logging of code running in a Jupyter Notebook that uses Keras and Matplotlib.
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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, creates an experiment named `notebook example` which is associated with the `examples` project.
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## Scalars
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The loss and accuracy metric scalar plots appear in **RESULTS** **>** **SCALARS**, along with the resource utilization plots, which are titled **:monitor: machine**.
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## Plots
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The example calls Matplotlib methods to create several sample plots, and TensorBoard methods to plot histograms for layer density.
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They appear in **RESULTS** **>** **PLOTS**.
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## Debug samples
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The example calls Matplotlib methods to log debug sample images. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
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## Hyperparameters
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**ClearML** automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task, by
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calling the [Task.connect](../../../references/sdk/task.md#connect) method.
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```python
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task_params = {'num_scatter_samples': 60, 'sin_max_value': 20, 'sin_steps': 30}
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task_params = task.connect(task_params)
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```
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Later in the Jupyter Notebook, more parameters are added to the dictionary.
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```python
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task_params['batch_size'] = 128
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task_params['nb_classes'] = 10
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task_params['nb_epoch'] = 6
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task_params['hidden_dim'] = 512
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```
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Parameter dictionaries appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **General**.
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The TensorFlow Definitions appear in the **TF_DEFINE** subsection.
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## Log
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Text printed to the console for training appears in **RESULTS** **>** **LOG**.
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## Artifacts
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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).
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The experiment info panel shows model tracking, including the model name and design in **ARTIFACTS** **>** **Output Model**.
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The model info panel contains the model details, including the model URL, framework, and snapshot locations.
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