what's this "Power" do?

[wendalegood]

Hi, I cant't figure out the layer below do as the top "pos_dist_disp" is identical to the bottom "pos_dist" after "Power" layer. layer { name: "pos_dist_disp" type: "Power" bottom: "pos_dist" top: "pos_dist_disp" } In addition, the layer below is setting g to 0.0009?("g" refers to max(0, g + D(q, p) - D(q, n))) layer { name: "diff_gap" bottom: "diff" top: "diff_gap" type: "Power" power_param { power: 1 scale: 1 shift: 0.0009 } }

[emsi]

http://caffe.berkeleyvision.org/tutorial/layers/power.html

The Power layer computes the output as (shift + scale * x) ^ power for each input element x.

[Eklavya-Tripathi]

The "Power" layer in a neural network applies an element-wise power operation to the input data. Mathematically, it raises each element of the input tensor to a specified power.

Here's a breakdown of its parameters:

Bottom: The name of the input blob (tensor) to the layer. Top: The name of the output blob (tensor) produced by the layer. Power: This parameter specifies the power to which each element of the input tensor will be raised. Scale (optional): This parameter scales the result after applying the power operation. Shift (optional): This parameter shifts the result after scaling. In the context of neural networks, the "Power" layer can be useful for various purposes, such as introducing non-linearities or scaling the values within a certain range. It's commonly used in network architectures for tasks like normalization, feature transformation, or activation functions.

For example, in the provided configurations:

The "pos_dist_disp" layer applies a power operation to the input "pos_dist". The "diff_gap" layer also applies a power operation to the input "diff", followed by scaling and shifting. The exact effects of the power transformation depend on the specific values of the parameters and the characteristics of the input data.