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Data can come from efficient databases (Level. DB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats. Common input preprocessing (mean subtraction, scaling, random cropping, and mirroring) is available by specifying Transformation. Parameters by some of the layers. In the layers below, we will ignore the input and out sizes as they are identical: Input. Layers: Utility Layers.
Layers: Loss Layers. Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient. Sum- of- Squares / Euclidean - computes the sum of squares of differences of its two inputs, .
Hinge / Margin - The hinge loss layer computes a one- vs- all hinge (L1) or squared hinge loss (L2). Sigmoid Cross- Entropy Loss - computes the cross- entropy (logistic) loss, often used for predicting targets interpreted as probabilities. Accuracy / Top- k layer - scores the output as an accuracy with respect to target – it is not actually a loss and has no backward step. Contrastive Loss.