Linear Regression with Keras

One often thinks that you can use deep learning for classification problems like text or image classification, or for similar tasks like segmentation, language models etc. But you can also do simple linear regression with deep learning libraries. I’ve also attached the GitHub Gist in case you want to explore the working notebook.

In this post I’ll go over the model, it’s explanation on how can you do linear regression with keras.

In Keras, it can be implemented using the Sequential model and the Dense layer. Here’s an example of how to implement linear regression with Keras:

First we take a toy regression problem from scikit-learn datasets.

from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

X,y = load_diabetes(return_X_y=True)

X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.8)

Now we will need to define the model using Keras. That is actually very simple, you just have to take one sequential model with a Dense layer. The activation for this layer will be linear as we’re building a linear model and the loss will be mean squared error.

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

# define the model
model = Sequential()
model.add(Dense(units=1, activation='linear'))
# compile the model
model.compile(optimizer='sgd', loss='mean_squared_error', metrics = ['mae'])

#fit the model
model.fit(x=X_train, y=y_train, validation_data=(X_test,y_test), 
          epochs=100, batch_size=128)

Thats then all that is left is to call model.predict(X_test).

You can find the GitHub Gist below.