This blog post will show you how to write a custom loss function in TensorFlow with an example. You will learn how to define the loss function, compute the loss, and create a TensorFlow graph.
Check out this video for more information:
TensorFlow is an open source software library for numerical computation that targets both dense and sparsified data. The core of TensorFlow is the computation graph, a data structure where the nodes represent units of computation, and the edges represent the data dependencies between them.
TensorFlow supports two types ofustom loss functions:
– Objectives, which are used to train models (e.g. classification or regression)
– Functions, which are used to compute metrics (e.g. accuracy or precision)
This guide will show you how to create a custom loss function in TensorFlow using both objectives and functions.
Objectives are used to train models while functions are used to compute metrics. Objectives are trained using stochastic gradient descent while functions use deterministic methods such as mean squared error.
What is a custom loss function?
A custom loss function is a user-defined function that is used in place of the default loss function for a specific task. Custom loss functions can be used to improve the results of a machine learning model by more closely approximating the desired output. They can also be used to penalize specific types of errors more heavily than others.
Why use a custom loss function?
When working with Neural Networks you may come across a situation where the build-in loss functions do not suit your needs. In these cases, it is possible to create a custom loss function. Creating a custom loss function allows you to change how your network learns, and can be very useful when training on complex datasets.
There are many reasons why you may want to use a custom loss function. For example, you may want to:
– Punish certain types of predictions more than others
– Use a different cost function than the standard cross entropy
– Encourage or discourage specific types of prediction
Creating a custom loss function is not difficult, and can be very powerful. In this post we will see how to create a custom loss function in TensorFlow, and use it to train a simple Neural Network.
How to create a custom loss function in TensorFlow?
In this example, we will create a custom loss function in TensorFlow.
We will use the reduce_mean() function to take the mean of our losses.
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
def custom_loss(y_true, y_pred):
return tf.reduce_mean(tf.square(y_pred – y_true))
inputs = keras.Input(shape=(3,))
outputs = layers.Dense(1)(inputs)
model = keras.Model(inputs, outputs)
Example custom loss function
This section provides an example of a custom loss function implemented using the TensorFlow library. The example below defines a loss function that computes the cross entropy between the predicted and actual values, and then adds a regularization term.
import tensorflow as tf
def cross_entropy_loss(y_pred, y_true):
# Compute the cross entropy between the prediction and actual labels
loss = tf.reduce_mean(-tf.reduce_sum(y_true * tf.log(y_pred), reduction_indices=))
# Add a regularization term to the loss
loss += 1e-4 * tf.nn.l2_loss(W)
In this article, we discussed how to create a custom loss function in TensorFlow. We also saw how to use this custom loss function to train a neural network. Finally, we evaluated the performance of our model on a test dataset.
–  Abadi, M., et al. “TensorFlow: Large-scale machine learning on heterogeneous systems.” 2015.
–  Gers, Felix A., Jürgen Schmidhuber, and Fred Cummins. “Learning to forget: Continual prediction with LSTM.” Neural computation 12.10 (2000): 2451-2471.
–  Hochreiter, Sepp, and Jürgen Schmidhuber. “Long short-term memory.” Neural computation 9.8 (1997): 1735-1780
Keyword: TensorFlow Custom Loss Function Example