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softmax.ipynb
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from torch.nn.functional import normalize model = resnet18(weights= None) # Determine the number of input features to the ou tput layer: num_ftrs = model.fc.in_features # We will create an "prediction head" which will a ctually be two linear layers with a bottlneck inbe tween. # The linear layers will be bottlenecked through a 2D feature space so that they live in a geometric space
Softmax and Derivatives:label:subsec_softmax_and_derivatives. Since the softmax and the corresponding loss are so common, it is worth understanding a bit better how it is computed. Plugging :eqref:eq_softmax_y_and_o into the definition of the loss in :eqref:eq_l_cross_entropy and using the definition of the softmax we obtain:
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Before implementing the softmax regression model, let us briefly review how the sum operator works along specific dimensions in a tensor, as discussed in :numref:subseq_lin-alg-reduction and :numref:subseq_lin-alg-non-reduction.[Given a matrix X we can sum over all elements (by default) or only over elements in the same axis,] i.e., the same column (axis 0) or the same row (axis 1).
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文章浏览阅读964次。这篇博客记录了softmax分类器的学习过程，包括其作用机理、损失函数和梯度计算。作者通过实现softmax的矢量化损失函数，并进行数值梯度检查，优化超参数，最终在CIFAR-10数据集上进行了验证，以提高分类性能。
Softmax Regression is a generalization of logistic regression that we can use for multi-class classification. If we want to assign probabilities to an object being one of several different things, softmax is the thing to do. Even later on, when we start training neural network models, the final step will be a layer of softmax.
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The notebook softmax.ipynb will walk you through implementing the Softmax classifier. Submitting your work. Important. Please make sure that the submitted notebooks have been run and the cell outputs are visible. Once you have completed all notebooks and filled out the necessary code, you need to follow the below instructions to submit your ...
As discussed in :numref:sec_softmax this can cause numerical instabilities. Test whether softmax still works correctly if an input has a value of 100. Test whether softmax still works correctly if the largest of all inputs is smaller than − 100? Implement a fix by looking at the value relative to the largest entry in the argument.