First, Cross-entropy (or softmax loss, but cross-entropy works better) is a better measure than MSE for classification,because the decision boundary in a classification task is large(in comparison with regression). ... For regression problems, you would almost always use the MSE. What is the...
self).__init__() self.hidden_size = hidden_size self.i2h = nn.Linear(input_size + hidden_size, hidden_size) self.i2o = nn.Linear(input_size + hidden_size, output_size) self.softmax = nn.LogSoftmax(dim=1) def forward(self, input, hidden): combined = torch.cat((...
67. Right Scale for Hyperparameters 68. Hyperparameters tuning in Practice Panda vs. Caviar 69. Batch Norm 70. Fitting Batch Norm into a Neural Network 71. Why Does Batch Nom Work 72. Batch Norm at Test Time 73. Softmax Regression ...
67. Right Scale for Hyperparameters68. Hyperparameters tuning in Practice Panda vs. Caviar69. Batch Norm70. Fitting Batch Norm into a Neural Network71. Why Does Batch Nom Work72. Batch Norm at Test Time73. Softmax Regression74. Training a Softmax Classifier75. Deep Learning Frameworks76. ...
softmaxLayer classificationLayer] options = trainingOptions("adam",... MaxEpochs=150,... InitialLearnRate=0.01,... Shuffle="every-epoch",... GradientThreshold=1,... Verbose=false,... Plots="training-progress"); net = trainNetwork(XTrain,TTrain,layers...
Note the main reason why PyTorch merges the log_softmax with the cross-entropy loss calculation in torch.nn.functional.cross_entropy is numerical stability. It just so happens that the derivative of the loss with respect to its input and the derivative of the log-softmax with respect to its...
softmaxName = 'softmax_layer'; featureLayerName = 'relu_conv10'; dispNum controls the number of images to use grad-cam. dispNum=12; idx = randperm(numel(imdsTest.Files),dispNum); C=cell(dispNum,1); figure for i = 1:dispNum ...
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All the things you mentioned can be part of deep models. A CRF can be deep or even be the top layer of a deep model. There are deep topic models (I am thinking of the replicated softmax with additional layers added). Learning the kernel in an SVM can sometimes result in a deep mod...
(i.e., via One-vs-All, One-vs-One, or softmax). Or if computational (or predictive) efficiency is an issue, maybe we want to implement it with another solver (e.g., Newton vs. Gradient Descent vs. Stochastic Gradient Descent, etc.). But improvements concerning computational efficiency...