Using the Edge Impulse Python SDK with TensorFlow and Keras

TensorFlow is an open source library for training machine learning models. Keras is an open source Python library that makes creating neural networks in TensorFlow much easier. We use these two libraries together to very quickly train a model to identify handwritten digits. From there, we use the Edge Impulse Python SDK library to profile the model to see how inference will perform on a target edge device. Then, we use the SDK again to convert our trained model to a C++ library that can be deployed to an edge hardware platform, such as a microcontroller.

Follow the code below to see how to train a simple machine learning model and deploy it to a C++ library using Edge Impulse.

To learn more about using the Python SDK, please see: Edge Impulse Python SDK Overview.

# If you have not done so already, install the following dependencies
!python -m pip install tensorflow==2.12.0 edgeimpulse
from tensorflow import keras
import edgeimpulse as ei

You will need to obtain an API key from an Edge Impulse project. Log into and create a new project. Open the project, navigate to Dashboard and click on the Keys tab to view your API keys. Double-click on the API key to highlight it, right-click, and select Copy.

Copy API key from Edge Impulse project

Note that you do not actually need to use the project in the Edge Impulse Studio. We just need the API Key.

Paste that API key string in the ei.API_KEY value in the following cell:

# Settings
ei.API_KEY = "ei_dae2..." # Change this to your Edge Impulse API key
labels = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
num_classes = len(labels)
deploy_filename = ""

Train a machine learning model

We want to create a classifier that can uniquely identify handwritten digits. To start, we will use TensorFlow and Keras to train a very simple convolutional neural network (CNN) on the classic MNIST dataset, which consists of handwritten digits from 0 to 9.

# Load MNIST data
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = keras.utils.normalize(x_train, axis=1)
x_test = keras.utils.normalize(x_test, axis=1)
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
input_shape = x_train[0].shape
# Build the model 
model = keras.Sequential([
    keras.layers.Dense(32, activation='relu', input_shape=input_shape),
    keras.layers.Dense(num_classes, activation='softmax')

# Compile the model
# Train the model, 
# Evaluate model on test set
score = model.evaluate(x_test, y_test, verbose=0)
print(f"Test loss: {score[0]}")
print(f"Test accuracy: {score[1]}")

Profile your model

To start, we need to list the possible target devices we can use for profiling. We need to pick from this list.

# List the available profile target devices

You should see a list printed such as:


A common option is the cortex-m4f-80mhz, as this is a relatively low-power microcontroller family. From there, we can use the Edge Impulse Python SDK to generate a profile for your model to ensure it fits on your target hardware and meets your timing requirements.

# Estimate the RAM, ROM, and inference time for our model on the target hardware family
    profile = ei.model.profile(model=model,
except Exception as e:
    print(f"Could not profile: {e}")

Deploy your model

Once you are happy with the performance of the model, you can deploy it to a number of possible hardware targets. To see the available hardware targets, run the following:

# List the available profile target devices

You should see a list printed such as:


The most generic target is to download a .zip file that holds a C++ library containing the inference runtime and your trained model, so we choose 'zip' from the above list. To do that, we first need to create a Classification object which contains our label strings (and other optional information about the model). These strings will be added to the C++ library metadata so you can access them in your edge application.

Note that instead of writing the raw bytes to a file, you can also specify an output_directory argument in the .deploy() function. Your deployment file(s) will be downloaded to that directory.

Important! The deployment targets list will change depending on the values provided for model, model_output_type, and model_input_type in the next part. For example, you will not see openmv listed once you upload a model (e.g. using .profile() or .deploy()) if model_input_type is not set to ei.model.input_type.ImageInput(). If you attempt to deploy to an unavailable target, you will receive the error Could not deploy: deploy_target: .... If model_input_type is not provided, it will default to OtherInput. See this page for more information about input types.

# Set model information, such as your list of labels
model_output_type = ei.model.output_type.Classification(labels=labels)

# Set model input type
model_input_type = ei.model.input_type.OtherInput()

# Create C++ library with trained model
deploy_bytes = None
    deploy_bytes = ei.model.deploy(model=model,
except Exception as e:
    print(f"Could not deploy: {e}")
# Write the downloaded raw bytes to a file
if deploy_bytes:
    with open(deploy_filename, 'wb') as f:

Your model C++ library should be downloaded as the file in the same directory as this notebook. You are now ready to use your C++ model in your embedded and edge device application! To use the C++ model for local inference, see our documentation here.

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