When from_logits is set to False, it means that the y_pred input is a probability distribution. In contrast, a probability distribution is a normalized vector where each element represents the probability of a particular class. The from_logits parameter in sparse_categorical_crossentropy determines whether the input y_pred is a probability distribution or a logit value.Ī logit value is the output of a linear regression model, which is not normalized and can take any value between negative and positive infinity. The predicted probability distribution is obtained from the output of the model, which is typically a softmax activation function applied to the final layer of the model. The function calculates the cross-entropy loss between the true labels and the predicted probability distribution. For example, if we have a dataset with ten different classes, the target values would be integers from 0 to 9, where each integer represents a different class. Sparse_categorical_crossentropy is a loss function used in multi-class classification problems, where the target values are integers representing the class labels. What is Sparse Categorical Crossentropy?īefore diving into the from_logits parameter, let’s first discuss what sparse_categorical_crossentropy is and how it works. In this blog post, we will explore what the from_logits parameter means in the context of sparse_categorical_crossentropy and how it affects the output of the function. In addition to these two parameters, there is another parameter, from_logits, which is often set to either True or False. This function takes in two parameters: y_true and y_pred. | Miscellaneous Tensorflow: Understanding the from_logits Parameter in Sparse Categorical CrossentropyĪs a data scientist or software engineer working with TensorFlow, you may have come across the sparse_categorical_crossentropy function, which is a popular loss function used for multi-class classification problems.
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