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It increases the nonlinear properties of the decision function and of the overall network without affecting the receptive fields of the convolution layer. If this trains correctly on your data, at least you know that there are no glaring issues in the data set. Learning in a neural network progresses by making incremental adjustments to the biases and weights. The key difference between a neural network and a regression model is that a neural network is a composition of many nonlinear functions, called activation functions. Whatever happened to free the nipple? Accuracy loss is a crappy metric if you have strong class imbalance. Currently, the common way to deal with this problem is to train the network on transformed data in different orientations, scales, lighting, etc.

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In a convolutional layer, the receptive area is smaller than the entire previous layer.

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For example, length in millimeters and length in kilometers both represent the same concept, but are on different scales. Softmax loss is used for predicting a single class of K mutually exclusive classes. Try a random shuffle of the training set without breaking the association between inputs and outputs and see if the training loss goes down. Neural Network Training Is Like Lock Picking To achieve state of the art, or even merely good, results, you have to have to have set up all of the parts configured to work well together. The approach is hyperparameter free and can be combined with other regularization approaches, such as dropout and data augmentation.

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