How to Integrate Keras with Notion

Introduction

Integrating Keras with Notion can streamline the process of building, documenting, and managing machine learning projects. In this guide, you will learn how to manage Keras model outputs and insights directly from your Jupyter Notebook environment into Notion.

Prerequisites

• An existing Notion account.

 

• Python environment configured with Keras installed.

 

• Basic understanding of Python and working with APIs.

Setting Up Notion API

• Create an integration in Notion:

  <ul>
    <li>Go to <a href="https://www.notion.so/my-integrations">Notion Integrations</a> page.</li>
  
    <li>Click on "New Integration".</li>
  
    <li>Name your integration and select the workspace you'd like to integrate with.</li>
  
    <li>Copy the "Internal Integration Token" – you'll need it for accessing the API.</li>
  </ul>
  

 

• Share a page with your integration:

  <ul>
    <li>Open the page in Notion you want to use to store your model outputs.</li>
  
    <li>Click on "Share" and invite your integration by typing its name.</li>
  </ul>
  

Setting Up Keras and Jupyter Environment

• Install necessary packages if not already done:

  \`\`\`shell
  pip install keras jupyter requests json
  \`\`\`
  

 

• Ensure that Keras is installed and can be imported correctly:

  \`\`\`python
  import keras
  \`\`\`
  

Integrating Keras with Notion

• Authenticate and connect to the Notion API:

  \`\`\`python
  import requests
  
  NOTION_API_TOKEN = "your-integration-token"
  HEADERS = {
      "Authorization": f"Bearer {NOTION_API_TOKEN}",
      "Content-Type": "application/json",
      "Notion-Version": "2022-06-28"
  }
  \`\`\`
  

 

• Define a function to write Keras model summaries into Notion:

  \`\`\`python
  def write_model_summary_to_notion(page_id, model_summary):
      url = f'https://api.notion.com/v1/pages/{page\_id}'
      data = {
          "parent": {"database\_id": "your-database-id"},
          "properties": {title: {"title": [{"text": {"content": "Model Summary"}}]}},
          "children": [{
              "object": "block",
              "type": "paragraph",
              "paragraph": {
                  "text": [{"type": "text", "text": {"content": model\_summary}}]
              }
          }]
      }
      response = requests.patch(url, headers=HEADERS, json=data)
      return response.json()
  \`\`\`
  

 

• Create a Keras model and log its summary:

  \`\`\`python
  from keras.models import Sequential
  from keras.layers import Dense
  
  model = Sequential([
      Dense(32, input\_shape=(784,)),
      Dense(10, activation='softmax')
  ])
  
  model\_summary = ""
  model.summary(print_fn=lambda x: model_summary += x + "\n")
  
  # Call the function to send summary to Notion
  response = write_model_summary_to_notion("your-page-id", model\_summary)
  print(response)
  \`\`\`
  

Testing and Debugging

• Test the process by running the script and ensuring that the model summary appears on your specified Notion page.

 

• If there are issues, check your API token and database or page ID. Make sure they have access to your Notion workspace and that the IDs are correctly specified in your code.

Conclusion

• This integration allows you to keep track of important model information right inside your Notion workspace, improving organization and collaboration in machine learning projects.