Softmax(dim=2)
时间: 2024-05-27 19:10:48 浏览: 12
Softmax(dim=2) is a function used in deep learning and machine learning for normalizing the output of a neural network or a classifier. It is used to convert the output of a neural network into a probability distribution over the target classes.
The softmax function takes a vector of real numbers and returns a vector of values between 0 and 1 that add up to 1. It is defined as:
softmax(x_i) = exp(x_i) / sum_j(exp(x_j))
where x_i is the i-th element of the input vector and j is a index that runs from 1 to the size of the input vector.
The dim=2 parameter indicates that the softmax function is applied along the second dimension of the input tensor. This is often used when the input tensor represents a batch of data, where the first dimension corresponds to the batch size and the second dimension corresponds to the number of classes.
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