Some examples of these are 3 custom loss functions, in the case of a variational auto-encoder (VAE) model, from Hands-On Image Generation with TensorFlow by Soon Yau Cheong. Writing custom loss functions is very straightforward the only requirements are that the loss function must take in only two parameters: y_pred (predicted output) and y_true (actual output). def custom_loss_function (y_true, y_pred): return losses In these instances, you can write custom loss functions to suit your specific conditions. Some examples would be if there is too much noise in your training data (outliers, erroneous attribute values, etc.) - which cannot be compensated for with data preprocessing - or use in unsupervised learning (as we will discuss later). However, there may be cases where these traditional/main loss functions may not be sufficient. In cases where the number of classes is greater than two, we utilize categorical cross-entropy - this follows a very similar process to binary cross-entropy. def log_loss (y_true, y_pred): y_pred = tf.clip_by_value(y_pred, le-7, 1 - le-7) error = y_true * tf.log(y_pred + 1e-7) (1-y_true) * tf.log(1-y_pred + 1e-7) return -error Categorical Cross-Entropy Loss Thus, to accurately determine loss between the actual and predicted values, it needs to compare the actual value (0 or 1) with the probability that the input aligns with that category ( p(i) = probability that the category is 1 1 - p(i) = probability that the category is 0) In binary classification, there are only two possible actual values of y - 0 or 1. One of the most popular loss functions, MSE finds the average of the squared differences between the target and the predicted outputsĬlassification neural networks work by outputting a vector of probabilities - the probability that the given input fits into each of the pre-set categories then selecting the category with the highest probability as the final output. Binary Cross-Entropy, Categorical Cross-Entropy Classification Loss Functions - used in classification neural networks given an input, the neural network produces a vector of probabilities of the input belonging to various pre-set categories - can then select the category with the highest probability of belonging Ex. ![]() Regression Loss Functions - used in regression neural networks given an input value, the model predicts a corresponding output value (rather than pre-selected labels) Ex.In supervised learning, there are two main types of loss functions - these correlate to the 2 major types of neural networks: regression and classification loss functions It must be formatted this way because the pile() method expects only two input parameters for the loss attribute. ![]() from import mean_squared_error piile(loss=mean_squared_error, optimizer='sgd')Īll loss functions in TensorFlow have a similar structure: def loss_function (y_true, y_pred): return losses The loss function can be inputed either as a String - as shown above - or as a function object - either imported from TensorFlow or written as custom loss functions, as we will discuss later. In TensorFlow, the loss function the neural network uses is specified as a parameter in pile() -the final method that trains the neural network. ![]() ![]() Image Source: Wikimedia Commons How Loss Functions Are Implemented in TensorFlowįor this article, we will use Google’s TensorFlow library to implement different loss functions - easy to demonstrate how loss functions are used in models.
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