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Does Tanh as output activation work with cross-entropy loss?
Yes we can, as long as we use some normalizor (e.g. softmax) to ensure that the final output values are in between 0 and 1 and add up to 1. If you’re doing binary classification and only use one output value, only normalizing it to be between 0 and 1 will do.
What activation function should I use in the case of vanishing gradient problem?
Solutions: The simplest solution is to use other activation functions, such as ReLU, which doesn’t cause a small derivative. Residual networks are another solution, as they provide residual connections straight to earlier layers.
Which is better MSE or cross entropy loss?
The MSE loss is therefore better suited to regression problems, and the cross-entropy loss provides us with faster learning when our predictions differ significantly from our labels, as is generally the case during the first several iterations of model training.
Which is the best cross entropy cost function?
UPDATE Even though it sound strange, the cross-entropy cost function seems to work best here. In my understanding this is because the cross-entropy for discrete probability distributions (wikipedia) is q ( x). This function is minimal when p ( x) = q ( x) for all x.
How is cross entropy different from KL divergence?
Cross-entropy is different from KL divergence but can be calculated using KL divergence, and is different from log loss but calculates the same quantity when used as a loss function. Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.
How is softmax used in cross entropy loss?
The softmax function, whose scores are used by the cross entropy loss, allows us to interpret our model’s scores as relative probabilities against each other. For example, the cross-entropy loss would invoke a much higher loss than the hinge loss if our (un-normalized) scores were [10,8,8] versus [10,−10,−10],…