Learn Quick, Lead Quicker
Binary Cross Entropy also referred to as Log Loss, assumes a central role in scenarios where the classification task is binary in nature. In such instances, the model’s prediction pertains to one of two possible classes. Certainly, let’s delve deeper into the detailed uses of Binary Cross Entropy in various real-world applications:
We create an instance of the BinaryCrossentropy loss function tf.keras.losses.BinaryCrossentropy(). This loss function is commonly used for binary classification tasks. It quantifies the difference between the true labels and predicted probabilities. Then, we calculate the BCE loss by calling the loss instance with the true_labels and predicted_probabilities as arguments.