Difference between L1 and L2 regularization, implementation and visualization in Tensorflow

Regularization is a technique used in Machine Learning to penalize complex models. The reason why regularization is useful is because simple models generalize better and are less prone to overfitting.


Examples of regularization:

  • K-means: limiting the splits to avoid redundant classes
  • Random forests: limiting the tree depth, limiting new features (branches)
  • Neural networks: limiting the model complexity (weights)

In Deep Learning there are two well-known regularization techniques: L1 and L2 regularization. Both add a penalty to the cost based on the model complexity, so instead of calculating the cost by simply using a loss function, there will be an additional element (called “regularization term”) that will be added in order to penalize complex models.


The theory

L1 regularization (LASSO regression) produces sparse matrices. Sparse matrices are zero-matrices in which some elements are ones (the sparsity refers to the ones), but in this context a sparse matrix could be several close-to-zero values and other larger values. From the data science point of view this is interesting because we can reduce the amount of features. If we find a model with neurons whose weights are close to zero it means we don’t need those neurons because the model deactivates them with zeros and we might not need a specific feature/input leading to a simpler model. For instance, if we have 50 coefficients but only 10 are non-zero, the other 40 are irrelevant to make our predictions. This is not only interesting from the efficiency point of view but also from the economic point of view: gathering data and extracting its features might be a very expensive task (in terms of time and money). Reducing this will benefit us.

Due to the absolute value, L1 regularization provides with a non-differentiable term, but despite of that, there are methods to minimize it. As we will see below, L1 regularization is also robust to outliers.

L2 regularization (Ridge regression) on the other hand leads to a balanced minimization of the weights. Since L2 uses squares, it emphasizes the errors, and it can be a problem when there are outliers in the data. Unlike L1, L2 has an analytical solution which makes it computationally efficient.

Both regularizations have a λ parameter which is directly proportional to the penalty: the larger λ the stronger penalty to find complex models and it will be more likely that the model will avoid them. Likewise, if λ is zero, regularization is deactivated.


The graphs above show how the functions used in L1 and L2 regularization look like. The penalty in both cases is zero in the center of the plot, but this also implies that the weights are zero and the model will not work. The values of the weights try to be as low as possible to minimize this function, but inevitably they will leave the center and will head outside. In case of L2 regularization, going towards any direction is okay because, as we can see in the plot, the function increases equally in all directions. Thus, L2 regularization mainly focuses on keeping the weights as low as possible.

In contrast, L1 regularization’s shape is diamond-like and the weights are lower in the corners of the diamond. These corners show where one of the axis/feature is zero thus leading to sparse matrices. Note how the shapes of the functions shows their differentiability: L2 is smooth and differentiable and L1 is sharp and non-differentiable.

In few words, L2 will aim to find small weight values whereas L1 could put all the values in a single feature.

L1 and L2 regularization methods are also combined in what is called elastic net regularization.

The practice

One of my motivations to try this out was an “intuitive explanation” of L1 vs. L2 I found in quora.


From the theoretical point of view it makes sense: L2 emphasizes errors due to the square, and it will try to minimize them all of them equally so the line will get a bit off from the main trend because a big errors influences more than small errors. On the other hand, for L1 errors have the same importance (linearly speaking) so it will minimize a lot of errors getting really close to the main train even if there are outliers.

I created a small dataset of samples that describes a straight line and I later added noise and some outliers. I created a model with more neurons than needed to solve this problem in order to see whether it works and compare the weight evolution between the methods.

Model characteristics:
-Layers: 1 input, 3 hidden, 1 output
-Sizes: 1,10,10,10,1
-Batch size: 1 (noiser)
-Optimizer: SGD (lr=0.01)
-Lambda: 0.3 (for regularization)

I run the model 5 times with each regularization method and these are the results.


When the random outliers are sufficiently far none of them present good results, but overall the results obtained with L2 performance were better than those obtained with L1. Then, I had a look at the weights. Below, I show the weights and the results obtained with an additional run of the model.

L1_final_n l1_compressed
L2_final_n l2_compressed

As expected, L1 generates several 0-weighted neurons, so the model doesn’t use them. In other experiments, I got that most of the neurons were disconnected and only few of them had non-zero weights. On the other hand, L2 minimizes the values of the weights until most of them have a very low value.

Adjusting the network according to L1

As described before, L1 generates sparse matrices with disconnected neurons. If a neuron is disconnected, we don’t need it, leading to simpler models. I run again the script that uses L1 and I will adjust the model using less neurons according to the neurons it disconnects. Using the same samples and running the model 5 times, I got this total errors: 22.68515524, 41.64545712, 4.77383674, 24.04390211, 7.25596004.

The weights in this first run look like this:


I adjusted the neurons of the model: From [(1,10),(10,10),(10,10),(10,1)] to [(1,10),(10,10),(10,1),(1,1)] and this are the weights (note: the last big square is a single weight):


Performance on 5 runs: 7.61984439, 13.85177842, 11.95983347, 16.95491162, 25.17294774.

Implementation in Tensorflow

Despite the code is provided in the Code page as usual, implementing L1 and L2 takes very few lines: 1) Add regularization to the Weights variables (remember the regularizer returns a value based on the weights), 2) collect all the regularization losses, and 3) add to the loss function to make the cost larger.

with tf.variable_scope("dense1") as scope:
    W = tf.get_variable("W",shape=[1,10],initializer=tf.contrib.layers.xavier_initializer(),regularizer=tf.contrib.layers.l2_regularizer(lambdaReg))
reg_losses = tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
cost = tf.reduce_sum(tf.abs(tf.subtract(pred, y)))+reg_losses


The performance of the model depends so much on other parameters, especially learning rate and epochs, and of course the number of hidden layers. Using a not-so good model, I compared L1 and L2 performance, and L2 scores were overall better than L1, although L1 has the interesting property of generating sparse matrices.

Hypothetical improvements: This post aimed to show in a very simple and graphic/animated way the effects of L1 and L2. Further research would imply trying more complex models with data that gives stable results. After tunning the parameters to get the best results, one could use cross validation to compare better the performance.

The code is provided in the Source code section.

Activation Functions in Deep Learning (Sigmoid, ReLU, LReLU, PReLU, RReLU, ELU, Softmax)

Sigmoid and its main problem

Sigmoid function has been the activation function par excellence in neural networks, however, it presents a serious disadvantage called vanishing gradient problem. Sigmoid function’s values are within the following range [0,1], and due to its nature, small and large values passed through the sigmoid function will become values close to zero and one respectively. This means that its gradient will be close to zero and learning will be slow.

This can be easily seen in the backpropagation algorithm (for a simple explanation of backpropagation I recommend you to watch this video):

 -(y-\hat{y}) f' (z) \frac{\partial z}{\partial W}}

where y is the prediction, \hat{y} the ground truth, f'() derivative of the sigmoid function, z activity of the synapses and W the weights.

The first part -(y-\hat{y}) f' (z) is called backpropagation error and it simply multiplies the difference between our prediction and the ground truth times the derivative of the sigmoid on the activity values. The second part describes the activity of each synopsis. In other words, when this activity is comparatively larger in a synapse, it has to be updated more severely by the previous backpropagation error. When a neuron is saturated (one of the bounds of the activation function is reached due to small or large values), the backpropagation error will be small as the gradient of the sigmoid function, resulting in small values and slow learning per se. Slow learning is one of the things we really want to avoid in Deep Learning since it already will consist in expensive and tedious computations. The Figure below shows how the derivative of the sigmoid function is very small with small and large values.

Sigmoid function and its derivative

Conclusion: if after several layers we end up with a large value, the backpropagated error will be very small due to the close-to-zero gradient of the sigmoid’s derivative function.

ReLU activation function

ReLU (Rectified Linear Unit) activation function became a popular choice in deep learning and even nowadays provides outstanding results. It came to solve the vanishing gradient problem mentioned before. The function is depicted in the Figure below.


The function and its derivative:
 f(x) = \left \{	\begin{array}{rcl} 	0 & \mbox{for} & x < 0\\ 	x & \mbox{for} & x \ge 0\end{array} \right.
 f'(x) = \left \{	\begin{array}{rcl} 	0 & \mbox{for} & x < 0\\ 	1 & \mbox{for} & x \ge 0\end{array} \right.

In order to understand why using ReLU, which can be reformulated as f(x) = max(0,x), is a good idea let’s divide the explanation in two parts based on its domain: 1) [-∞,0] and 2) (0,∞].

1) When the synapse activity is zero it makes sense that the derivative of the activation function is zero because there is no need to update as the synapse was not used. Furthermore, if the value is lower than zero, the resulting derivative will be also zero leading to a disconnection of the neuron (no update). This is a good idea since disconnecting some neurons may reduce overfitting (as co-dependence is reduced), however this will hinder the neural network to learn in some cases and, in fact, the following activation functions will change this part. This is also refer as zero-sparsity: a sparse network has neurons with few connections.

2) As long as values are above zero, regardless of how large it is, the gradient of the activation function will be 1, meaning that it can learn anyways. This solves the vanishing gradient problem present in the sigmoid activation function (at least in this part of the function).

Some literature about ReLU [1].

LReLU activation function

Leaky ReLU is a modification of ReLU which replaces the zero part of the domain in [-∞,0] by a low slope, as we can see in the figure and formula below.


The function and its derivative:

 f(x) = \left \{	\begin{array}{rcl} 	0.01 x & \mbox{for} & x < 0\\ 	x & \mbox{for} & x \ge 0\end{array} \right.
 f'(x) = \left \{	\begin{array}{rcl} 	0.01 & \mbox{for} & x < 0\\ 	1 & \mbox{for} & x \ge 0\end{array} \right.

The motivation for using LReLU instead of ReLU is that constant zero gradients can also result in slow learning, as when a saturated neuron uses a sigmoid activation function. Furthermore, some of them may not even activate. This sacrifice of the zero-sparsity, according to the authors, can provide worse results than when the neurons are completely deactivated (ReLU) [2]. In fact, the authors report the same or insignificantly better results when using PReLU instead of ReLU.

PReLU activation function

Parametric ReLU [3] is a inspired by LReLU wich, as mentioned before, has negligible impact on accuracy compared to ReLU. Based on the same ideas that LReLU, PReLU has the same goals: increase the learning speed by not deactivating some neurons. In contrast with LReLU, PReLU substitutes the value 0.01 by a parameter a_i where i refers to different channels. One could also share the same values for every channel.

The function and its derivative:

 f(x) = \left \{	\begin{array}{rcl} 	a_i x & \mbox{for} & x < 0\\ 	x & \mbox{for} & x \ge 0\end{array} \right.
 f'(x) = \left \{	\begin{array}{rcl} 	a_i & \mbox{for} & x < 0\\ 	1 & \mbox{for} & x \ge 0\end{array} \right.

The following equation shows how these parameters are iteratevely updated using the chain rule as the weights in the neural network (backpropagation). \mu is the momentum and \epsilon is the learning rate. IN the original paper, the initial a_i used is 0.25

\nabla a_i := \mu \nabla a_i + \epsilon \frac{\partial \varepsilon}{\partial a_i}

RReLU activation function

Randomized ReLU was published in a paper [4] that compares its performance with the previous rectified activations. According to the authors, RReLU outperforms the others, and LReLU performs better when \frac{1}{5.5} substitutes 0.01.


The negative slope of RReLU is randomly calculated in each training iteration such that:

 f_{ji}(x) = \left \{	\begin{array}{rcl} 	\frac{x_{ji}}{a_{ji}} x & \mbox{for} & x_{ji} < 0\\ 	x_{ji} & \mbox{for} & x_{ji} \ge 0\end{array} \right.
a_{ji} \sim U(l,u)

The motivation to introduce a random negative slope is to reduce overfitting.

a_{ji} is thus a random number from a uniform distribution bounded by l and u where i refers to the channel and j refers to the example. During the testing phase, a_{ji} is fixed, and an average of all the a_{ji} is taken: a_{ji} = \frac{l+u}{2}. In the paper they use U(3,8) and in the test time a_{ji} = \frac{11}{2}.

ELU activation function

Exponential Linear Unit (ELU) is another type of activation function based on ReLU [5]. As other rectified units, it speeds up learning and alleviates the vanishing gradient problem.


Similarly to the previous activation functions, its positive part has a constant gradient of one so it enables learning and does not saturate a neuron on that side of the function. LReLU, PReLU and RReLU do not ensure noise-robust deactivation since their negative part also consists on a slope, unlike the original ReLU or ELU which saturate in their negative part of the domain. As explained before, saturation means that the small derivative of the function decreases the information propagated to the next layer.

The activations that are close to zero have a gradient similar to the natural gradient since the shape of the function is smooth, thus activating faster learning than when the neuron is deactivated (ReLU) or has non-smooth slope (LReLU).

The function and its derivative:

 f(x) = \left \{	\begin{array}{rcl} 	\alpha (exp(x) - 1) & \mbox{for} & x \le 0\\ 	x & \mbox{for} & x > 0\end{array} \right.
 f'(x) = \left \{	\begin{array}{rcl} 	f(x) + \alpha & \mbox{for} & x \le 0\\ 	1 & \mbox{for} & x > 0\end{array} \right.

In a nutshell:

  1. Gradient of 1 in its positive part.
  2. Deactivation on most of its negative domain.
  3. Close-to-natural gradient in values closer to zero.

Softmax activation function

For the sake of completeness, let’s talk about softmax, although it is a different type of activation function.

Softmax it is commonly used as an activation function in the last layer of a neural network to transform the results into probabilities. Since there is a lot out there written about softmax, I want to give an intuitive and non-mathematical reasoning.

Case 1:
Imagine your task is to classify some input and there are 3 possible classes. Out of the neural network you get the following values (which are not probabilities): [3,0.7,0.5].

It seems that it’s very likely that the input will belong to the first class because the first number is clearly larger than the others. But how likely is it? We can use softmax for this, and we would get the following values: [0.846, 0.085, 0.069].

Case 2:
Now we have the values [1.2,1,1.5]. The last class has a larger value but this time is not that certain whether the input will belong to that class but we would probably bet for it, and this is clearly represented by the output of the softmax function: [0.316, 0.258, 0.426].

Case 3::
Now we have 10 classes and the values for each class are 1.2 except for the first class which is 1.5: [1.5,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2]. Common sense says that even if the first class has a larger value, this time the model is very uncertain about its prediction since there are a lot of values close to the largest one. Softmax transforms that vector into the following probabilities: [0.13, 0.097, 0.097, 0.097, 0.097, 0.097, 0.097, 0.097, 0.097, 0.097].

Softmax function:

\sigma (z)_j = \frac{e^{z_j}}{\sum^K_{k=1} e^{z_j}}

In python:

z_exp = [math.exp(i) for i in z]
sum_z_exp = sum(z_exp)
return [round(i/sum_z_exp, 3) for i in z_exp]


1. Nair V. & Hinton G.E. 2010. “Rectified Linear Units Improve Restricted Boltzmann Machines”
2. Maas A., Hannun A.Y & Ng A.Y. 2013. “Rectifier Nonlinearities Improve Neural Network Acoustic Models”
3. He K., Zhang X., Ren S. & Sun J. 2015. “Delving Deep Into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification”
4. Xu B., Wang N., Chen T. & Li M. 2015. “Empirical Evaluation of Rectified Activations in Convolutional Network”
5. Clevert D.A., Unterthiner T. & Hochreiter S. 2016. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)