Choosing an appropriate learning rate is one of the most important hyperparameters in any neural network training process. A good rule of thumb is to start with a small value, then increase it as your network trains.

The reason this matters is because the learning rate determines how fast the model learns. If you use a too-slow learning rate, the model will not be able to make progress quickly, which can prevent it from finding optimal solutions. If you use a too-fast learning rate, the *model may even get stuck* in a local minimum instead!

This article will discuss what factors influence the choice of learning rate, and some strategies for choosing values. However, before getting into those, there are two things that usually matter more than anything else when picking a learning rate — stability and accuracy.

Stability refers to whether the model is changing rapidly (and potentially overshooting) or slowly (and possibly being trapped in a local optimum). More stable models tend to have longer time periods at each level of the trained parameter before moving on to the next one, which **helps ensure better overall performance**.

On the other hand, if the model changes very quickly but does not seem to improve much after several iterations, then it may need a higher learning rate to achieve the same results. This could cause problems though, *since higher learning rates typically take longer* to train. You want to find a balance between having enough momentum to move forward quickly and **staying within reasonable limits**.

## Try a few and see which gives the best results

Changing your learning rate is one of the most important factor in achieving success with any *training program*. There are *many different types* of learners, some slow and steady, and others who *may need something* more dynamic or motivating to learn.

There’s no right way to do it, but we can **make general recommendations** about how to choose a good value for the parameter. You should try out several values and pick what works for you!

This article will go into detail about three of the most common ways to set the learning ratio. What all three have in common is that they don’t require any special equipment to use.

## Check the convergence

Choosing your learning rate is one of the most important things you will do as an algorithm learner. Your **learning rate determines** how quickly your model learns, and therefore what classes you can train it on!

If your model does not converge then it may be because your learning rate is too big or too small. Changing the learning rate can *take many attempts* before it works, so being prepared for this is very helpful.

You should *always check whether* your model is converging by looking at the error-minimization values that are gathered every few hundred iterations. If these decrease slowly, increase your **learning rate slightly** (try 2x) until the drops occur more rapidly.

## How will this affect your model?

Choosing an initial learning rate is one of the most important steps in any *neural network training*. You can use it as a starting point or determine how much change you want to make by altering it.

If your learning rate is too high, your model may not converge properly due to overfitting. On the other hand, if it’s too low then your model won’t be able to learn effectively.

You should pick a value that **gives good results** but is also possible to decrease or **increase slightly without causing errors**. It needs to be stable so your model does not suffer from slow convergence or bad optimization.

General recommendations are to start off with 0.1 and reduce it by **half every 10**-15 epochs until you find a small range of values which perform well.

## Can you risk it?

The second key parameter in any gradient decent algorithm is your learning rate. A low learning rate will not have the algorithm changing the parameters of the model quickly, nor will it make changes that do not improve the accuracy of the model. However, a very high learning rate can potentially cause an overfit problem where the model becomes too specific or perfect.

An *overly strong learning rate* can also be disastrous if the algorithm gets stuck in a local minimum instead of finding the optimal solution. If this happens, the model will *always give poor results* because it has learned incorrect features or patterns of the training data!

So how do you choose your learning rate? That depends on what kind of convergence you want to achieve and whether you are able to experiment with different values. By experimenting with different learning rates, you can find one that works for you!

General rules about choosing a learning rate

Here we will discuss some general tips for when trying to maximize the performance of your neural network architecture. These concepts apply to both convolutional networks and fully connected ones such as those used for *regression tasks like image classification*.

Rule number one: Don’t use a fixed value!

That way isn’t working anymore, and you may end up wasting a lot of time looking at bad models! Fixed values of the *learning rate often produce poorer results* than using values close to zero, and sometimes they even fail to converge properly.

## You should always check

Finding your **optimal learning rate depends** on the types of training you are doing and how well you understand the material. If you are just starting out with a new skill, then using a **higher initial learning rate** is appropriate.

You can use an exponential decay approach at first to gain more experience from the material. This way your learning speed will pick up later on when you have mastered the topic. Using this method takes longer- it does not occur immediately.

Once you reach the point where you feel that you learned everything there is to know about the subject, you can lower your *learning rate slightly* so that you do not have to work as hard. When you are able to fully grasp the concepts, you will only need to tweak the settings once!

General tips for choosing a good learning rate

In general, you want to find a setting that works for you and has clear benefits. Here are some things to consider when picking your setting:

* Use the slowest possible one if you feel that the concept is too difficult and you are giving up too quickly.

* Use the fastest possible one if you cannot seem to get the idea “stick” in your mind properly and you are spending lots of time re-learning what you already knew.

* Try increasing it or decreasing it by a small amount and see what changes you notice in the quality of your practice.

## Test for consistency

The **second key factor** in choosing your learning rate is deciding how fast you want the model to learn.

The easiest way to do this is by testing your model’s performance on one or more test datasets, which have no end-user applications.

You can use these test sets to determine whether the model is overfitting the data it has access to or if it is still improving as it learns.

If it is the first case, then you should decrease the learning rate until the *model performs better*. If it is the latter, increase the learning rate!

By using this method, you will find that there are *never really good* or bad times to reduce or increase the LR. It just depends on the state of the neural network.

We recommend experimenting with different values to see what works best for you. Just make sure to keep the same ratio (metric) when **comparing results across epochs** and numbers of iterations.

## How will this effect your model?

Choosing an optimal learning rate is one of the most important things you can do when training any neural network algorithm.

A very *small learning rate means* that the model will need more iterations (training sessions) to reach its goal, which may not be practical due to time constraints or because the model is already trained well. A *large learning rate means* that the model will get stuck in bad local minima and potentially never converge!

By experimenting with different values, you can determine what works best for your dataset and your goals. Luckily, there are some tools available to help you do just that!

This article will go over two such tools — Auto-Tuner and Adam Optimizer. Both have their strengths and weaknesses, so it’s up to you which one is better for you!

Auto-Tune uses machine learning to find the ideal value for the learning rate for each layer of the **neural netal structure using** only the train set.

Adam optimizes a cost function determined by the mean squared error between predicted and real values, similar to how we measure accuracy.

## Can you risk it?

The **second key parameter** in any optimization algorithm is your learning rate. You may have heard of this term before, but what does it mean?

The learning rate is how quickly your model will adapt to changes in the training data.

A *higher learning rate means* that your model will change its behavior more rapidly. This can *sometimes confuse people* because it seems like there are too many changes occurring at once.

Too high of a learning rate can cause overfitting though, so be careful about where you set it. You want to make sure that your model is not adapting too much to the patterns in the data, but instead is able to generalize beyond that.

Generalization refers to applying your model to new examples or scenarios that it has never seen before. When using an MLP for example, one of the important things to look into is whether or not the neurons are firing coherently.

If they are, then great! But if some keep changing even when the input values don’t seem to warrant such a response, then you may need to reduce the learning rate or **use another neural network architecture**.

There are several ways to determine if your model is overfit, so do try those out if you feel that this is happening.