How to get started with MicroPython Machine Learning
Convert scikit-learn Machine Learning models to space-efficient, low complexity MicroPython code
Python is my personal favourite programming language.
It is clean, concise and powerful. Being able to run Python (either MicroPython or CircuitPython) on microcontrollers is such a joyful experience.
Starting from today, we have a new feature to joy for: Machine Learning.
Most TinyML work and projects so far have been developed in C/C++, which is a natual choice since it is math-heavy and C/C++ is the fastest language in terms of execution time.
Nonetheless, not all TinyML projects require microseconds execution time and many tasks can easily handle a bit of slowdown.
Thanks to the everywhereml Python package we can easily port a growing list of classifiers to MicroPython with a single line of code.
Let's see it in action.
MicroPython Machine Learning project: MNIST digits classification
This project is an exact clone of the Attiny Machine Learning project: MNIST digits classification: we train a Random Forest to classify 8x8 images of handwritten digits.
Refer to the Attiny article for more information on the task.
Start by installing the everywhereml library.
pip install everywhereml>=0.2.2
This is the Python code to train the model.
from everywhereml.sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
clf = RandomForestClassifier(n_estimators=7, max_leaf_nodes=20)
clf.fit(X_train, y_train)
print('Score: %.2f' % clf.score(X_test, y_test))
>>> Score: 0.89
Now we can port the classifier to MicroPython with a single line of code.
print(clf.to_micropython_file('random_forest.py'))
>>>
try:
from time import ticks_us, ticks_diff
except ImportError:
from time import time_ns
def ticks_us(): return int(time_ns() * 1000)
def ticks_diff(a, b): return a - b
class RandomForestClassifier:
"""
# RandomForestClassifier(base_estimator=DecisionTreeClassifier(), bootstrap=True, ccp_alpha=0.0, class_name=RandomForestClassifier, class_weight=None, criterion=gini, estimator_params=('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'random_state', 'ccp_alpha'), max_depth=None, max_features=auto, max_leaf_nodes=20, max_samples=None, min_impurity_decrease=0.0, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=7, n_jobs=None, num_outputs=10, oob_score=False, package_name=everywhereml.sklearn.ensemble, random_state=None, template_folder=everywhereml/sklearn/ensemble, verbose=0, warm_start=False)
"""
def __init__(self):
"""
Constructor
"""
self.latency = 0
self.predicted_value = -1
self.votes = [0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000]
def predict(self, x):
"""
Predict output from input vector
"""
self.predicted_value = -1
started_at = ticks_us()
self.votes = [0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000, 0.00000000000]
idx, score = self.tree0(x)
self.votes[idx] += score
idx, score = self.tree1(x)
self.votes[idx] += score
idx, score = self.tree2(x)
self.votes[idx] += score
idx, score = self.tree3(x)
self.votes[idx] += score
idx, score = self.tree4(x)
self.votes[idx] += score
idx, score = self.tree5(x)
self.votes[idx] += score
idx, score = self.tree6(x)
self.votes[idx] += score
# get argmax of votes
max_vote = max(self.votes)
self.predicted_value = next(i for i, v in enumerate(self.votes) if v == max_vote)
self.latency = ticks_diff(ticks_us(), started_at)
return self.predicted_value
def latencyInMicros(self):
"""
Get latency in micros
"""
return self.latency
def latencyInMillis(self):
"""
Get latency in millis
"""
return self.latency // 1000
def tree0(self, x):
"""
Random forest's tree #0
"""
if x[28] <= 0.5:
if x[52] <= 2.5:
return 6, 101.0
else:
if x[35] <= 3.0:
return 0, 131.0
else:
return 6, 101.0
else:
if x[60] <= 2.5:
return 7, 135.0
else:
if x[21] <= 5.5:
if x[34] <= 12.5:
... redacted for brevity ...Add the random_forest.py file to your own MicroPython project.
To test the model accuracy, we'll use some random samples from the MNIST dataset.
FILENAME: mnist.py
mnist = [ [0, 0, 5, 13, 9, 1, 0, 0, 0, 0, 13, 15, 10, 15, 5, 0, 0, 3, 15, 2, 0, 11, 8, 0, 0, 4, 12, 0, 0, 8, 8, 0, 0, 5, 8, 0, 0, 9, 8, 0, 0, 4, 11, 0, 1, 12, 7, 0, 0, 2, 14, 5, 10, 12, 0, 0, 0, 0, 6, 13, 10, 0, 0, 0], [0, 0, 0, 12, 13, 5, 0, 0, 0, 0, 0, 11, 16, 9, 0, 0, 0, 0, 3, 15, 16, 6, 0, 0, 0, 7, 15, 16, 16, 2, 0, 0, 0, 0, 1, 16, 16, 3, 0, 0, 0, 0, 1, 16, 16, 6, 0, 0, 0, 0, 1, 16, 16, 6, 0, 0, 0, 0, 0, 11, 16, 10, 0, 0], [0, 0, 0, 4, 15, 12, 0, 0, 0, 0, 3, 16, 15, 14, 0, 0, 0, 0, 8, 13, 8, 16, 0, 0, 0, 0, 1, 6, 15, 11, 0, 0, 0, 1, 8, 13, 15, 1, 0, 0, 0, 9, 16, 16, 5, 0, 0, 0, 0, 3, 13, 16, 16, 11, 5, 0, 0, 0, 0, 3, 11, 16, 9, 0], [0, 0, 7, 15, 13, 1, 0, 0, 0, 8, 13, 6, 15, 4, 0, 0, 0, 2, 1, 13, 13, 0, 0, 0, 0, 0, 2, 15, 11, 1, 0, 0, 0, 0, 0, 1, 12, 12, 1, 0, 0, 0, 0, 0, 1, 10, 8, 0, 0, 0, 8, 4, 5, 14, 9, 0, 0, 0, 7, 13, 13, 9, 0, 0], [0, 0, 0, 1, 11, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 1, 13, 6, 2, 2, 0, 0, 0, 7, 15, 0, 9, 8, 0, 0, 5, 16, 10, 0, 16, 6, 0, 0, 4, 15, 16, 13, 16, 1, 0, 0, 0, 0, 3, 15, 10, 0, 0, 0, 0, 0, 2, 16, 4, 0, 0], [0, 0, 12, 10, 0, 0, 0, 0, 0, 0, 14, 16, 16, 14, 0, 0, 0, 0, 13, 16, 15, 10, 1, 0, 0, 0, 11, 16, 16, 7, 0, 0, 0, 0, 0, 4, 7, 16, 7, 0, 0, 0, 0, 0, 4, 16, 9, 0, 0, 0, 5, 4, 12, 16, 4, 0, 0, 0, 9, 16, 16, 10, 0, 0], [0, 0, 0, 12, 13, 0, 0, 0, 0, 0, 5, 16, 8, 0, 0, 0, 0, 0, 13, 16, 3, 0, 0, 0, 0, 0, 14, 13, 0, 0, 0, 0, 0, 0, 15, 12, 7, 2, 0, 0, 0, 0, 13, 16, 13, 16, 3, 0, 0, 0, 7, 16, 11, 15, 8, 0, 0, 0, 1, 9, 15, 11, 3, 0], [0, 0, 7, 8, 13, 16, 15, 1, 0, 0, 7, 7, 4, 11, 12, 0, 0, 0, 0, 0, 8, 13, 1, 0, 0, 4, 8, 8, 15, 15, 6, 0, 0, 2, 11, 15, 15, 4, 0, 0, 0, 0, 0, 16, 5, 0, 0, 0, 0, 0, 9, 15, 1, 0, 0, 0, 0, 0, 13, 5, 0, 0, 0, 0], [0, 0, 9, 14, 8, 1, 0, 0, 0, 0, 12, 14, 14, 12, 0, 0, 0, 0, 9, 10, 0, 15, 4, 0, 0, 0, 3, 16, 12, 14, 2, 0, 0, 0, 4, 16, 16, 2, 0, 0, 0, 3, 16, 8, 10, 13, 2, 0, 0, 1, 15, 1, 3, 16, 8, 0, 0, 0, 11, 16, 15, 11, 1, 0], [0, 0, 11, 12, 0, 0, 0, 0, 0, 2, 16, 16, 16, 13, 0, 0, 0, 3, 16, 12, 10, 14, 0, 0, 0, 1, 16, 1, 12, 15, 0, 0, 0, 0, 13, 16, 9, 15, 2, 0, 0, 0, 0, 3, 0, 9, 11, 0, 0, 0, 0, 0, 9, 15, 4, 0, 0, 0, 9, 12, 13, 3, 0, 0] ]
And this is the main.py file.
FILENAME: main.py
from time import sleep from random_forest import RandomForestClassifier from mnist import mnist clf = RandomForestClassifier() while True: for idx, x in enumerate(mnist): print('exptected %d, got %d' % (idx, clf.predict(x))) sleep(1)
Pretty easy, right?
You only have to call clf.predict(x) to get the predicted value back.
Random Forest is only the first of a few classifiers that will be implemented in the next weeks, but it's one of the most performant and you can go a long way with it already!
Having troubles? Ask a question
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