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91 lines
3.8 KiB
Markdown
91 lines
3.8 KiB
Markdown
---
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title: Keras with Matplotlib - Jupyter Notebook
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---
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The [jupyter.ipynb](https://github.com/clearml/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 cross entropy 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 a task named `notebook example` in the `examples` project.
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## Scalars
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The loss and accuracy metric scalar plots appear in **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 **PLOTS**.
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## Debug Samples
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The example calls Matplotlib methods to log debug sample images. They appear in **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 [`Task.connect()`](../../../references/sdk/task.md#connect).
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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 **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **General**.
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The TensorFlow Definitions appear in the **TF_DEFINE** subsection.
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## Console
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Text printed to the console for training appears in **CONSOLE**.
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## Artifacts
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Models created by the task appear in the task's **ARTIFACTS** tab. ClearML automatically logs and tracks models
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created using Keras.
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The task info panel shows model tracking, including the model name and design in **ARTIFACTS** **>** **Output Model**.
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Clicking on the model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view
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the model's details and access the model.
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