# What is the difference between 'regular' linear regression and deep learning linear regression?

I want to know the difference between linear regression in a regular machine learning analysis and linear regression in "deep learning" setting. What algorithms are used for linear regression in deep learning setting.

Assuming that by deep learning you meant more precisely neural networks: a vanilla fully connected feedforward neural network with only linear activation functions will perform linear regression, regardless of how many layers it has. One difference is that with a neural network one typically uses gradient descent, whereas with "normal" linear regression one uses the normal equation if possible (when the number of features isn't too huge).

Example of a fully connected feedforward neural network with no hidden layer and using a linear activation function (namely the identity activation function):

If you replace the activation function of the output layer with a sigmoid function, then the neural network performs logistic regression. If you replace the activation function of the output layer with a softmax function and add a few output units, then the neural network performs multiclass logistic regression: Difference between logistic regression and neural networks. If you replace the cost function with the hinge loss, then the neural network is an SVM optimized in its primal form: http://cs231n.github.io/linear-classify/.

Here is the example shown in the picture above programmed in TensorFlow:

""" Linear Regression Example """
# https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py

from __future__ import absolute_import, division, print_function

import tflearn

# Regression data
X = [3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1]
Y = [1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3]

# Linear Regression graph
input_ = tflearn.input_data(shape=[None])
linear = tflearn.single_unit(input_)
regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',
metric='R2', learning_rate=0.01)
m = tflearn.DNN(regression)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)

print("\nRegression result:")
print("Y = " + str(m.get_weights(linear.W)) +
"*X + " + str(m.get_weights(linear.b)))

print("\nTest prediction for x = 3.2, 3.3, 3.4:")
print(m.predict([3.2, 3.3, 3.4]))
# should output (close, not exact) y = [1.5315033197402954, 1.5585315227508545, 1.5855598449707031]


Here is a code snippet that does not use any neural network libraries:

# From http://briandolhansky.com/blog/artificial-neural-networks-linear-regression-part-1
import matplotlib.pyplot as plt
import numpy as np

# Load the data and create the data matrices X and Y
# This creates a feature vector X with a column of ones (bias)
# and a column of car weights.
# The target vector Y is a column of MPG values for each car.
N = np.shape(X_file)[0]
X = np.hstack((np.ones(N).reshape(N, 1), X_file[:, 4].reshape(N, 1)))
Y = X_file[:, 0]

# Standardize the input
X[:, 1] = (X[:, 1]-np.mean(X[:, 1]))/np.std(X[:, 1])

# There are two weights, the bias weight and the feature weight
w = np.array([0, 0])

# Start batch gradient descent, it will run for max_iter epochs and have a step
# size eta
max_iter = 100
eta = 1E-3
for t in range(0, max_iter):
# We need to iterate over each data point for one epoch
for i in range(0, N):
x_i = X[i, :]
y_i = Y[i]
# Dot product, computes h(x_i, w)
h = np.dot(w, x_i)-y_i

# Update the weights
print "Weights found:",w

# Plot the data and best fit line
tt = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), 10)
bf_line = w[0]+w[1]*tt

plt.plot(X[:, 1], Y, 'kx', tt, bf_line, 'r-')
plt.xlabel('Weight (Normalized)')
plt.ylabel('MPG')
plt.title('ANN Regression on 1D MPG Data')

plt.savefig('mpg.png')

plt.show()


Data file mpg.csv (~50% abridged due to Stack Exchange answer size limitation):

mpg (n),cylinders (n),displacement (n),horsepower (n),weight (n),acceleration (n),year (n),origin (n), name (s)
18.000000,8.000000,307.000000,130.000000,3504.000000,12.000000,70.000000,1.000000
15.000000,8.000000,350.000000,165.000000,3693.000000,11.500000,70.000000,1.000000
18.000000,8.000000,318.000000,150.000000,3436.000000,11.000000,70.000000,1.000000
16.000000,8.000000,304.000000,150.000000,3433.000000,12.000000,70.000000,1.000000
17.000000,8.000000,302.000000,140.000000,3449.000000,10.500000,70.000000,1.000000
15.000000,8.000000,429.000000,198.000000,4341.000000,10.000000,70.000000,1.000000
14.000000,8.000000,454.000000,220.000000,4354.000000,9.000000,70.000000,1.000000
14.000000,8.000000,440.000000,215.000000,4312.000000,8.500000,70.000000,1.000000
14.000000,8.000000,455.000000,225.000000,4425.000000,10.000000,70.000000,1.000000
15.000000,8.000000,390.000000,190.000000,3850.000000,8.500000,70.000000,1.000000
15.000000,8.000000,383.000000,170.000000,3563.000000,10.000000,70.000000,1.000000
14.000000,8.000000,340.000000,160.000000,3609.000000,8.000000,70.000000,1.000000
15.000000,8.000000,400.000000,150.000000,3761.000000,9.500000,70.000000,1.000000
14.000000,8.000000,455.000000,225.000000,3086.000000,10.000000,70.000000,1.000000
24.000000,4.000000,113.000000,95.000000,2372.000000,15.000000,70.000000,3.000000
22.000000,6.000000,198.000000,95.000000,2833.000000,15.500000,70.000000,1.000000
18.000000,6.000000,199.000000,97.000000,2774.000000,15.500000,70.000000,1.000000
21.000000,6.000000,200.000000,85.000000,2587.000000,16.000000,70.000000,1.000000
27.000000,4.000000,97.000000,88.000000,2130.000000,14.500000,70.000000,3.000000
26.000000,4.000000,97.000000,46.000000,1835.000000,20.500000,70.000000,2.000000
25.000000,4.000000,110.000000,87.000000,2672.000000,17.500000,70.000000,2.000000
24.000000,4.000000,107.000000,90.000000,2430.000000,14.500000,70.000000,2.000000
25.000000,4.000000,104.000000,95.000000,2375.000000,17.500000,70.000000,2.000000
26.000000,4.000000,121.000000,113.000000,2234.000000,12.500000,70.000000,2.000000
21.000000,6.000000,199.000000,90.000000,2648.000000,15.000000,70.000000,1.000000
10.000000,8.000000,360.000000,215.000000,4615.000000,14.000000,70.000000,1.000000
10.000000,8.000000,307.000000,200.000000,4376.000000,15.000000,70.000000,1.000000
11.000000,8.000000,318.000000,210.000000,4382.000000,13.500000,70.000000,1.000000
9.000000,8.000000,304.000000,193.000000,4732.000000,18.500000,70.000000,1.000000
27.000000,4.000000,97.000000,88.000000,2130.000000,14.500000,71.000000,3.000000
28.000000,4.000000,140.000000,90.000000,2264.000000,15.500000,71.000000,1.000000
25.000000,4.000000,113.000000,95.000000,2228.000000,14.000000,71.000000,3.000000
19.000000,6.000000,232.000000,100.000000,2634.000000,13.000000,71.000000,1.000000
16.000000,6.000000,225.000000,105.000000,3439.000000,15.500000,71.000000,1.000000
17.000000,6.000000,250.000000,100.000000,3329.000000,15.500000,71.000000,1.000000
19.000000,6.000000,250.000000,88.000000,3302.000000,15.500000,71.000000,1.000000
18.000000,6.000000,232.000000,100.000000,3288.000000,15.500000,71.000000,1.000000
14.000000,8.000000,350.000000,165.000000,4209.000000,12.000000,71.000000,1.000000
14.000000,8.000000,400.000000,175.000000,4464.000000,11.500000,71.000000,1.000000
14.000000,8.000000,351.000000,153.000000,4154.000000,13.500000,71.000000,1.000000
14.000000,8.000000,318.000000,150.000000,4096.000000,13.000000,71.000000,1.000000
12.000000,8.000000,383.000000,180.000000,4955.000000,11.500000,71.000000,1.000000
13.000000,8.000000,400.000000,170.000000,4746.000000,12.000000,71.000000,1.000000
13.000000,8.000000,400.000000,175.000000,5140.000000,12.000000,71.000000,1.000000
18.000000,6.000000,258.000000,110.000000,2962.000000,13.500000,71.000000,1.000000
22.000000,4.000000,140.000000,72.000000,2408.000000,19.000000,71.000000,1.000000
19.000000,6.000000,250.000000,100.000000,3282.000000,15.000000,71.000000,1.000000
18.000000,6.000000,250.000000,88.000000,3139.000000,14.500000,71.000000,1.000000
23.000000,4.000000,122.000000,86.000000,2220.000000,14.000000,71.000000,1.000000
28.000000,4.000000,116.000000,90.000000,2123.000000,14.000000,71.000000,2.000000
30.000000,4.000000,79.000000,70.000000,2074.000000,19.500000,71.000000,2.000000
30.000000,4.000000,88.000000,76.000000,2065.000000,14.500000,71.000000,2.000000
31.000000,4.000000,71.000000,65.000000,1773.000000,19.000000,71.000000,3.000000
35.000000,4.000000,72.000000,69.000000,1613.000000,18.000000,71.000000,3.000000
27.000000,4.000000,97.000000,60.000000,1834.000000,19.000000,71.000000,2.000000
26.000000,4.000000,91.000000,70.000000,1955.000000,20.500000,71.000000,1.000000
24.000000,4.000000,113.000000,95.000000,2278.000000,15.500000,72.000000,3.000000
25.000000,4.000000,97.500000,80.000000,2126.000000,17.000000,72.000000,1.000000
23.000000,4.000000,97.000000,54.000000,2254.000000,23.500000,72.000000,2.000000
20.000000,4.000000,140.000000,90.000000,2408.000000,19.500000,72.000000,1.000000
21.000000,4.000000,122.000000,86.000000,2226.000000,16.500000,72.000000,1.000000
13.000000,8.000000,350.000000,165.000000,4274.000000,12.000000,72.000000,1.000000
14.000000,8.000000,400.000000,175.000000,4385.000000,12.000000,72.000000,1.000000
15.000000,8.000000,318.000000,150.000000,4135.000000,13.500000,72.000000,1.000000
14.000000,8.000000,351.000000,153.000000,4129.000000,13.000000,72.000000,1.000000
17.000000,8.000000,304.000000,150.000000,3672.000000,11.500000,72.000000,1.000000
11.000000,8.000000,429.000000,208.000000,4633.000000,11.000000,72.000000,1.000000
13.000000,8.000000,350.000000,155.000000,4502.000000,13.500000,72.000000,1.000000
12.000000,8.000000,350.000000,160.000000,4456.000000,13.500000,72.000000,1.000000
13.000000,8.000000,400.000000,190.000000,4422.000000,12.500000,72.000000,1.000000
19.000000,3.000000,70.000000,97.000000,2330.000000,13.500000,72.000000,3.000000
15.000000,8.000000,304.000000,150.000000,3892.000000,12.500000,72.000000,1.000000
13.000000,8.000000,307.000000,130.000000,4098.000000,14.000000,72.000000,1.000000
13.000000,8.000000,302.000000,140.000000,4294.000000,16.000000,72.000000,1.000000
14.000000,8.000000,318.000000,150.000000,4077.000000,14.000000,72.000000,1.000000
18.000000,4.000000,121.000000,112.000000,2933.000000,14.500000,72.000000,2.000000
22.000000,4.000000,121.000000,76.000000,2511.000000,18.000000,72.000000,2.000000
21.000000,4.000000,120.000000,87.000000,2979.000000,19.500000,72.000000,2.000000
26.000000,4.000000,96.000000,69.000000,2189.000000,18.000000,72.000000,2.000000
22.000000,4.000000,122.000000,86.000000,2395.000000,16.000000,72.000000,1.000000
28.000000,4.000000,97.000000,92.000000,2288.000000,17.000000,72.000000,3.000000
23.000000,4.000000,120.000000,97.000000,2506.000000,14.500000,72.000000,3.000000
28.000000,4.000000,98.000000,80.000000,2164.000000,15.000000,72.000000,1.000000
27.000000,4.000000,97.000000,88.000000,2100.000000,16.500000,72.000000,3.000000
13.000000,8.000000,350.000000,175.000000,4100.000000,13.000000,73.000000,1.000000
14.000000,8.000000,304.000000,150.000000,3672.000000,11.500000,73.000000,1.000000
13.000000,8.000000,350.000000,145.000000,3988.000000,13.000000,73.000000,1.000000
14.000000,8.000000,302.000000,137.000000,4042.000000,14.500000,73.000000,1.000000
15.000000,8.000000,318.000000,150.000000,3777.000000,12.500000,73.000000,1.000000
12.000000,8.000000,429.000000,198.000000,4952.000000,11.500000,73.000000,1.000000
13.000000,8.000000,400.000000,150.000000,4464.000000,12.000000,73.000000,1.000000
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12.000000,8.000000,455.000000,225.000000,4951.000000,11.000000,73.000000,1.000000
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21.000000,6.000000,155.000000,107.000000,2472.000000,14.000000,73.000000,1.000000
26.000000,4.000000,98.000000,90.000000,2265.000000,15.500000,73.000000,2.000000
15.000000,8.000000,350.000000,145.000000,4082.000000,13.000000,73.000000,1.000000
16.000000,8.000000,400.000000,230.000000,4278.000000,9.500000,73.000000,1.000000
29.000000,4.000000,68.000000,49.000000,1867.000000,19.500000,73.000000,2.000000
24.000000,4.000000,116.000000,75.000000,2158.000000,15.500000,73.000000,2.000000
20.000000,4.000000,114.000000,91.000000,2582.000000,14.000000,73.000000,2.000000
19.000000,4.000000,121.000000,112.000000,2868.000000,15.500000,73.000000,2.000000
15.000000,8.000000,318.000000,150.000000,3399.000000,11.000000,73.000000,1.000000
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20.000000,6.000000,198.000000,95.000000,3102.000000,16.500000,74.000000,1.000000
19.000000,6.000000,232.000000,100.000000,2901.000000,16.000000,74.000000,1.000000
15.000000,6.000000,250.000000,100.000000,3336.000000,17.000000,74.000000,1.000000
31.000000,4.000000,79.000000,67.000000,1950.000000,19.000000,74.000000,3.000000
26.000000,4.000000,122.000000,80.000000,2451.000000,16.500000,74.000000,1.000000
32.000000,4.000000,71.000000,65.000000,1836.000000,21.000000,74.000000,3.000000
25.000000,4.000000,140.000000,75.000000,2542.000000,17.000000,74.000000,1.000000
16.000000,6.000000,250.000000,100.000000,3781.000000,17.000000,74.000000,1.000000
16.000000,6.000000,258.000000,110.000000,3632.000000,18.000000,74.000000,1.000000
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13.000000,8.000000,350.000000,150.000000,4699.000000,14.500000,74.000000,1.000000
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26.000000,4.000000,108.000000,93.000000,2391.000000,15.500000,74.000000,3.000000
31.000000,4.000000,79.000000,67.000000,2000.000000,16.000000,74.000000,2.000000
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25.000000,4.000000,121.000000,115.000000,2671.000000,13.500000,75.000000,2.000000
33.000000,4.000000,91.000000,53.000000,1795.000000,17.500000,75.000000,3.000000
28.000000,4.000000,107.000000,86.000000,2464.000000,15.500000,76.000000,2.000000
25.000000,4.000000,116.000000,81.000000,2220.000000,16.900000,76.000000,2.000000
25.000000,4.000000,140.000000,92.000000,2572.000000,14.900000,76.000000,1.000000
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27.000000,4.000000,101.000000,83.000000,2202.000000,15.300000,76.000000,2.000000
17.500000,8.000000,305.000000,140.000000,4215.000000,13.000000,76.000000,1.000000
16.000000,8.000000,318.000000,150.000000,4190.000000,13.000000,76.000000,1.000000
15.500000,8.000000,304.000000,120.000000,3962.000000,13.900000,76.000000,1.000000
14.500000,8.000000,351.000000,152.000000,4215.000000,12.800000,76.000000,1.000000
22.000000,6.000000,225.000000,100.000000,3233.000000,15.400000,76.000000,1.000000
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• Thanks so much for detailed answer and time you spent writing this. I need some time to digest this, and will get back to you with comments/questions! Thanks again! Commented Dec 28, 2016 at 3:04
• Combining yours and Sengiley's answer with what I've understood, can I say - regression in deep learning has 1. 3+ number of hidden layers ; 2. Activation function is linear(or Identity Activation function); 3. Loss function we optimize is mean square error in the output layer. Thank you! Commented Jan 28, 2017 at 3:01

For regression, which for deep learning is nonlinear in most cases, final layer has 1 neuron with identity function and loss function we optimize is MSE, MAE instead of binary or categorical cross-entropy used for classification.

• Hey thanks! I hope I'm not asking for too much, but could you please point me to paper/algorithm implementation? I tried to search, but mostly I found logistic regression related stuff :( Commented Dec 27, 2016 at 11:05