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I am new to tensorflow. I want to write my own custom loss function. Is there any tutorial about this? For example, the hinge loss or a sum_of_square_loss(though this is already in tf)? Can I do it directly in python or I have to write the cpp code?
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Tensorflow custom loss function numpy To do this task first we will create an array with sample data and find the mean squared value with the numpy() function. Next, we will use the tf. keras. Sequential() function and assign the dense value with input shape.
A custom loss function can be created by defining a function that takes the true values and predicted values as required parameters. The function should return an array of losses. The function can then be passed at the compile stage.
We use a loss function to determine how far the predicted values deviate from the actual values in the training data. We change the model weights to make the loss minimum, and that is what training is all about.
We need to write down the loss function. For example, we can use basic mean square error as our loss function for predicted y and target y_:
loss_mse = 1/n(Sum((y-y_)^2)) There are basic functions for tensors like tf.add(x,y), tf.sub(x,y), tf.square(x), tf.reduce_sum(x), etc.
Then we can define our loss function in Tensorflow like:
cost = tf.reduce_mean(tf.square(tf.sub(y,y_))) Note: y and y_ are tensors.
Moreover, we can define any other loss functions if we can write down the equations. For some training operators (minimizers), the loss function should satisfy some conditions (smooth, differentiable ...).
In one word, Tensorflow define arrays, constants, variables into tensors, define calculations using tf functions, and use session to run though graph. We can define whatever we like and run it in the end.
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