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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.