Custom activation function keras
Since gradients for ReLU are either zero or one, it may be that you cannot escape zeroes when your initial neuron outputs are really small. We then call your neuron dead, and with many dead neurons, you essentially deprive your neural network from its ability to achieve acceptable performance.
Fortunately, new activation functions have been designed that attempt to reduce the impact of this inherent shortcoming of the ReLU activation function. For example, Swish was designed to make ReLU more smooth. It essentially manipulates the Sigmoid function which does not result in dying neurons, but in vanishing gradients instead — which is just as worse. Fortunately, with LiSHT, the impact of this vanishing gradients problem is much less severe, and it may thus be a good candidate that hovers between ReLU and Sigmoid.
But if LiSHT is to gain traction in the machine learning community, it must be usable for your own machine learning projects. First, we provide a brief recap about LiSHT, although this will primarily be a reference to our other blog post. LiSHT is a relatively new activation function, proposed by Roy et al. It stands for a Linearly Scaled Hyperbolic Tangent and is non-parametric in the sense that tanh x is scaled linearly with x without the need for manual configuration by means of some parameter.
In terms of the derivative, this has the effect that the range of the derivative function — and hence the computed gradients — is expanded. This is expected to reduce the impact of the vanishing gradients problem. Keras, the deep learning framework for Python that I prefer due to its flexibility and ease of use, supports the creation of custom activation functions.
You can do so by creating a regular Python definition and subsequently assigning this def as your activation function. The fix is simple — replace your Numpy based tanh i. Contrary to Numpy, the K backend performs the tanh operation at the tensor level. Subsequently, you can use the created def in arbitrary Keras layers — e. Subsequently, we create the model, start training and discuss model performance.
A supervised machine learning model requires a dataset that can be used for training. This dataset contains thousands of handwritten digits — i. It is one of the standard datasets used in computer vision education for its simplicity and extensiveness, and hence is a good candidate for explaining how to create the model.
Now that we know what we need, we can actually create our model. Next, open this file in your code editor of choice which preferably supports Python syntax highlighting.
We can now start coding! As you can see, they relate strongly to the imports specified in the previous section. Finally, you import Numpy and Matplotlib — as said, for data processing and visualization purposes.
Twenty-five epochs are used for training. This is just a fixed number and is based on my estimate that with a relatively simple dataset quite accurate performance must be achievable without extensive training. In your own projects, you must obviously configure the number of epochs to an educated estimate of your own, or use smart techniques like EarlyStopping instead.
This is useful for educational settings, but slightly slows down the training process. We next import our data. As said before, this is essentially a one-line statement due to the way the MNIST dataset is integrated in the Keras library:.
When running, it downloads the dataset automatically, and if you downloaded it before, it will use your cache to speed up the training process. Next, we specify the architecture of our model. We use two convolutional blocks with max pooling and dropout, as well as two densely-connected layers.
Please refer to this post if you wish to understand these blocks in more detail.
Implementing Swish Activation Function in Keras
We simply add the LiSHT Python definition to the layers by specifying it as the activation attribute.What is Activation function: It is a transfer function that is used to map the output of one layer to another. In daily life when we think every detailed decision is based on the results of small things. So in every move, we use the activation function. There are main following categories of functions.
A straight line function where activation is proportional to input which is the weighted sum from neuron. This way, it gives a range of activations, so it is not binary activation. We can definitely connect a few neurons together and if more than 1 fires, we could take the max or softmax and decide based on that. So that is ok too. Then what is the problem with this?
If you are familiar with gradient descent for training, you would notice that for this function, the derivative is a constant. That means the gradient has no relationship with X. If there is an error in prediction, the changes made by backpropagation is constant and not depending on the change in input delta x!!!
With the cumulative distribution function The output will range from 0 to 1. The main disadvantage of sigmoid is it stop learning for large value of x or in other words function get saturated for large value of x. The main advantage of using Softmax is the output probabilities range. The range will 0 to 1, and the sum of all the probabilities will be equal to one. The Rectified Linear Unit- Relu has a great advantage over sigmod and tanh as it never gets saturated with high value of x.
But the main disadvantage of this is its mean is not zero due to which the function becomes zero and overall learning is too slow. But the issue is that all the negative values become zero immediately which decreases the ability of the model to fit or train from the data properly. That means any negative input given to the ReLU activation function turns the value into zero immediately in the graph, which in turns affects the resulting graph by not mapping the negative values appropriately.
For example, a large gradient flowing through a ReLU neuron could cause the weights to update in such a way that the neuron will never activate on any data point again. Once a ReLU ends up in this state, it is unlikely to recover, because the function gradient at 0 is also 0, so gradient descent learning will not maximize the weights. The sigmoid and tanh neurons can suffer from similar problems as their values saturate, but there is always at least a small gradient allowing them to recover in the long term.
This function is introduced by Google it is a non -monotonic function. It provides better performance than Relu and Leaky Relu. ELU Exponential linear unit function solves the Vanishing gradient problem. The other mentioned activation functions are prone to reaching a point from where the gradient of the functions does not change or stop learning. The Elu tries to minimize the problem of relu and minimize the mean to zero so that the learning rate increases. Like batch normalization, ELUs push the mean towards zero, but with a significantly smaller computational footprint.
Both the ReLU and Softplus are largely similar, except near 0 where the softplus is enticingly smooth and differentiable. In deep learning, computing the activation function and its derivative is as frequent as addition and subtraction in arithmetic. By switching to ReLU, the forward and backward passes are much faster while retaining the non-linear nature of the activation function required for deep neural networks to be useful. The soft sign function is another nonlinearity which can be considered an alternative to tanh since it too does not saturate as easily as hard clipped functions.
A nice Question. Write more, thats all I have to say. Literally, it seems as though you relied on the video to make your point. You clearly know what youre talking about, why throw away your intelligence on just posting videos to your blog when you could be giving us something enlightening to read? I was recommended this web site by my cousin. The sketch is tasteful, your authored material stylish. I just want to tell you that I am new to blogging and really enjoyed your web blog.Examples include tf.
TensorBoard where the training progress and results can be exported and visualized with TensorBoard, or tf. ModelCheckpoint where the model is automatically saved during training, and more. In this guide, you will learn what Keras callback is, when it will be called, what it can do, and how you can build your own.
Towards the end of this guide, there will be demos of creating a couple of simple callback applications to get you started on your custom callback. Callbacks are useful to get a view on internal states and statistics of the model during training.
You can pass a list of callbacks as the keyword argument callbacks to any of tf. Now, define a simple custom callback to track the start and end of every batch of data. During those calls, it prints the index of the current batch. Providing a callback to model methods such as tf. Users can supply a list of callbacks to the following tf. Model methods:. Trains the model for a fixed number of epochs iterations over a dataset, or data yielded batch-by-batch by a Python generator. Evaluates the model for given data or data generator.
Outputs the loss and metric values from the evaluation. Within this method, logs is a dict with batch and size available keys, representing the current batch number and the size of the batch. Within this method, logs is a dict containing the stateful metrics result.
The logs dict contains the loss value, and all the metrics at the end of a batch or epoch. Example includes the loss and mean absolute error. First example showcases the creation of a Callback that stops the Keras training when the minimum of loss has been reached by mutating the attribute model.
Optionally, the user can provide an argument patience to specify how many epochs the training should wait before it eventually stops. EarlyStopping provides a more complete and general implementation. One thing that is commonly done in model training is changing the learning rate as more epochs have passed. In this example, we're showing how a custom Callback can be used to dynamically change the learning rate.
Be sure to check out the existing Keras callbacks by visiting the API doc. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. For details, see the Google Developers Site Policies. Install Learn Introduction.
TensorFlow Lite for mobile and embedded devices. TensorFlow Extended for end-to-end ML components. API r2.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. I am trying to implement my own custom activation function and tested the approach with the already implemented ELU function.
For some reason the rebuild ELU function doesn't train at all. The loss is at zero after the first epoch and the accuracy doesn't improve. I noticed that K. I don't know how to get around the issue though. This is mostly for increased accuracy for when x is close to zero. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. New issue. Jump to bottom. Copy link Quote reply. This comment has been minimized. Sign in to view. There may be an overflow error when using K.
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Full code: from keras.One of the use cases presented in the book is predicting prices for homes in Boston, which is an interesting problem because homes can have such wide variations in values.
This is a machine learning problem that is probably best suited for classical approaches, such as XGBoost, because the data set is structured rather than perceptual data. The goal of this post is to show how deep learning can potentially be used to improve shallow learning problems by using custom loss functions. If you throw standard machine learning approaches at these problems, such as linear regression or random forests, often the model will overfit the samples with the highest values in order to reduce metrics such as mean absolute error.
However, what you may actually want is to treat the samples with similar weighting, and to use an error metric such as relative error that reduces the importance of fitting the samples with the largest values. You can actually do this explicitly in R, using packages such as nonlinear least squares nls.
The code sample above shows how to build a linear regression model using the built-in optimizer, which will overweight samples with large label values, and the nls approach which shows how to perform a log transformation on both the predicted values and labels, which will give the samples relatively equal weight.
The problem with the second approach is that you have to explicitly state how to use the features in the model, creating a feature engineering problem. An additional problem with this approach is that it cannot be applied directly to other algorithms, such as random forests, without writing your own likelihood function and optimizer.
This is a for a specific scenario where you want to have the error term outside of the log transform, not a scenario where you can simply apply a log transformation to the label and all input variables.
Deep learning provides an elegant solution to handling these types of problems, where instead of writing a custom likelihood function and optimizer, you can explore different built-in and custom loss functions that can be used with the different optimizers provided.
This post will show how to write custom loss functions in R when using Keras, and show how using different approaches can be beneficial for different types of data sets.
It shows the training history of four different Keras models trained on the Boston housing prices data set. Each of the models use different loss functions, but are evaluated on the same performance metric, mean absolute error. For the original data set, the custom loss functions do not improve the performance of the model, but on a modified data set, the results are more promising. One of the great features of deep learning is that it can be applied to both deep problems with perceptual data, such as audio and video, and shallow problems with structured data.
For shallow learning c lassical ML problems, you can often see improvements over shallow approaches, such as XGBoostby using a custom loss function that provides a useful singal.
However, not all shallow problems can benefit from deep learning. For example, predicting housing prices in an area where the values can range significantly. This data set includes housing prices for a suburb in Boston during the s. Each record has 13 attributes that describe properties of the home, and there are records in the training data set and records in the test data set. The labels in the data set represent the prices of the homes, in thousands of dollars.
The original data set has values with similar orders of magnitude, so custom loss functions may not be useful for fitting this data.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Have a question about this project?
Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. I'm working on the development of a custom activation function. It has already been tested with a number of neural networks architectures e.
It's working fine and actually beating ReLU in all the architectures used. However, when trying to test it on a CNN and using the ModelCheckpoint callback, I got an error during serialisation of the Activation object:. I'm not disclosing my custom activation function yet because it has not been published in any paper, but if you want to reproduce the erro, do the following this is just a simple ReLU example, not the one I'm working with :.
Up to you to accept as an issue and implement it, so nobody would have to extend it, or we can just close the issue. Can you show how you implemented the custom activation in class? Hi brianleegit. I used this inside a Jupyter Notebook cell. If you are going to do a plain Python implementation, I would suggest to have a 'config' function called from within the constructor.
This also happens when you try to save the model with a custom activation.
It should definitely be implemented, as this is not expected behavior. Probably just a name parameter for activation objects? In fact, I'm not even implementing my own activation, I am using: keras. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
New issue. Jump to bottom. Copy link Quote reply. Hi, I'm working on the development of a custom activation function. Thanks in advance. This comment has been minimized.
Implementing Swish Activation Function in Keras
Sign in to view. Hi brianleegitYes, sure. See the code below: from keras import backend as K from keras. I hope this helps. Is this compatible while using tensor flow as well?? I agree with you.Keras is a favorite tool among many in Machine Learning. For those new to Keras. Although one of my favorite libraries PlaidML have built their own support for Keras.
This kind of backend agnostic framework is great for developers. Then when you are ready for production you can swap out the backend for TensorFlow and have it serving predictions on a Linux server. All without changing any code just a configuration file.
At some point in your journey you will get to a point where Keras starts limiting what you are able to do.
Advanced Keras – Custom loss functions
Before jumping into this lower level you might consider extending Keras before moving past it. This can be a great option to save reusable code written in Keras and to prototype changes to your network in a high level framework that allows you to move quick.
Let us do a quick recap just to make sure we know why we might want a custom one. Activation functions are quite important to your layers. They sit at the end of your layers as little gate keepers. As gate keepers they affect what data gets though to the next layer if any data at all is allowed to pass them. What kind of complex mathematics is going on that determine this gatekeeping function?
How to use LiSHT activation function with Keras?
Yup that is it! This simple gatekeeping function has become arguably the most popular of activation functions. In machine learning we learn from our errors at the end of our forward path, then during the backward pass update the weights and bias of our network on each layer to make better predictions. What happens during this backward pass between two neurons one of which returned a negative number really close to 0 and another one that had a large negative number?
During this backward pass they would be treated as the same. There would be no way to know one was closer to 0 than the other one because we removed this information during the forward pass. Once they hit 0 it is rare for the weight to recover and will remain 0 going forward. However there is one glaring issue with this function. This branching conditional check is expensive when compared to its linear relatives.