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Remove references to Hessian being in next release
In preparation for the release of v0.0.20, which includes the new double backpropagation feature, enabling calculation of the Hessian, this commit removes references to the Hessian being in the next release/coming soon in the documentation.
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# Double backpropagation to calculate the Hessian, used in this example, is not yet available in the released version of Deepwave. If you would like to beta test it, please write to me: [email protected] | ||
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import torch | ||
import torchvision | ||
from scipy.sparse.linalg import eigsh | ||
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Hessian | ||
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**Double backpropagation to calculate the Hessian, used in this example, is not yet available in the released version of Deepwave.** If you would like to beta test it, please `write to me <mailto:[email protected]>`_. | ||
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Deepwave supports backpropagating up to two times through the regular scalar propagator. One advantage of this is that it makes it possible to calculate Hessian matrices, which are used in optimisation methods such as `Newton-Raphson <https://en.wikipedia.org/wiki/Newton%27s_method_in_optimization>`_. I will demonstrate that in this example. | ||
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After setting up a simple two layer model with a single shot, we can calculate the gradient and Hessian of a loss function based on the output receiver data, with respect to the velocity model, using:: | ||
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