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tensorflowmachine-learningscikit-learntext-classificationtensorflow-lite
Mee
Mee

Reputation: 1651

how to convert saved model from sklearn into tensorflow/lite

If I want to implement a classifier using the sklearn library. Is there a way to save the model or convert the file into a saved tensorflow file in order to convert it to tensorflow lite later?

Upvotes: 14

Views: 10963

Answers (2)

utibe
utibe

Reputation: 11

It is not always easy to replicate a scikit model in tensorflow. For instance scitik has a lot of on the fly imputation libraries which will be a bit tricky to implement in tensorflow

Upvotes: 1

Jindřich
Jindřich

Reputation: 11240

If you replicate the architecture in TensorFlow, which will be pretty easy given that scikit-learn models are usually rather simple, you can explicitly assign the parameters from the learned scikit-learn models to TensorFlow layers.

Here is an example with logistic regression turned into a single dense layer:

import tensorflow as tf
import numpy as np
from sklearn.linear_model import LogisticRegression

# some random data to train and test on
x = np.random.normal(size=(60, 21))
y = np.random.uniform(size=(60,)) > 0.5

# fit the sklearn model on the data
sklearn_model = LogisticRegression().fit(x, y)

# create a TF model with the same architecture
tf_model = tf.keras.models.Sequential()
tf_model.add(tf.keras.Input(shape=(21,)))
tf_model.add(tf.keras.layers.Dense(1))

# assign the parameters from sklearn to the TF model
tf_model.layers[0].weights[0].assign(sklearn_model.coef_.transpose())
tf_model.layers[0].bias.assign(sklearn_model.intercept_)

# verify the models do the same prediction
assert np.all((tf_model(x) > 0)[:, 0].numpy() == sklearn_model.predict(x))

Upvotes: 6

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