![]() ![]() The kerback.sum method will take the kerback.square as argument. ![]() The mean function will take kerback.sum as argument. After that inside the my_loss function we are going to use keras backend as kerback and call the mean function on this class. The my_loss function will take two parameters namely actual and predicted. We will be passing the two values 79 and 89 as parameter to this function as well.Īfter we are ready with actual and predicted values we are going to create a custom loss function called my_loss as shown below. Similarly to generate predicted values we are going to use rand method of random class for numpy. Numpy has a class called random and the method called rand, we are going to use the rand method with two parameters 79 and 89 as shown in below code. For that we will take the help of numpy library. Then we need to generate the sample data for calculating the loss value using the custom loss function that we will create. As we might guess Tensorflow is the default backend engine which can be changed as well. Hence backend engine can perform the computation and develop the various models. First one is numpy which stands for numerical python and second one is keras.backend.īackend is a library used in Keras which is for performing most of the computation like tensor products, convolutions and other similar activities with the help of libraries like Tensorflow. For this we need to import below libraries. Now let us start creating the custom loss function. We can create a custom loss function in Keras by writing a function that returns a scalar and takes the two arguments namely true value and predicted value. In that case we can construct our own custom loss function and pass to the function pile as a parameter. Sometimes we need to use a loss function that is not provided by default in Keras. Hence this is very useful for solving specific problems efficiently. What is custom loss functionĪ custom loss function in Keras will improve the machine learning model performance in the ways we want. The mean absolute error is an average of the absolute errors e = Y-X and Y is prediction and X is the true value. The mean absolute error is effectively the measure of difference between two continuous variables. This is calculated effectively as the average squared difference between the predicted values and the actual value. The mean squared error loss function measures the average of the squares of the errors. For example below is the few commonly used loss function for Keras: Mean Squared Error Keras provides a bunch of loss functions. So our aim is to reduce the value produced by loss function with the help of optimization function. If predicted values deviate too much from actual values, loss function will produce a very large number. Loss is a way of calculating how well an algorithm fits the given data. Loss function has a critical role to play in machine learning. Keras is developed by Google and is fast, modular, easy to use. ![]() Keras does not support low-level computation but it runs on top of libraries like Theano or Tensorflow. Keras is a library for creating neural networks. ![]()
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