How to export a model from Google Teachable Machine in TensorFlow in Python

If you have created a machine learning model using Google Teachable Machine and want to use it in a Python environment with TensorFlow, you will need to export the model. Exporting a model from Teachable Machine is a straightforward process, and in this article, we will guide you through the steps.

Step 1: Training and Testing your Model

Before you can export your model, you need to train and test it in Google Teachable Machine. Upload your dataset, train the model, and test its accuracy to ensure it performs well on the given task.

Step 2: Exporting the Model

Once your model is trained and tested, click on the “Export Model” button in Google Teachable Machine. You will be prompted to choose the type of model you want to export, such as TensorFlow.js, TensorFlow Lite, or TensorFlow.js (with metadata).

Step 3: Download the Model

After choosing the type of model you want to export, click on the “Download” button to save the model files to your computer. The downloaded files will typically include a model.json file and one or more .bin files containing the model weights.

Step 4: Using the Model in Python with TensorFlow

Now that you have exported the model from Google Teachable Machine, you can use it in a Python environment with TensorFlow. Load the model using the tf.keras.models.load_model() function and make predictions on new data using the model.predict() method.

Conclusion

Exporting a model from Google Teachable Machine to TensorFlow in Python is a simple process that allows you to take advantage of the powerful TensorFlow library for your machine learning projects. By following the steps outlined in this article, you can seamlessly transition your model from Teachable Machine to a Python environment for further development and integration into your applications.