Neural Style Transfer v1.0.0 Release #8
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…re-patterns' package from pypi
…Process on subsequent processes The Neural Style Transfer (NST) Algorithm initially produces a random variant of the Content Image called, 'Noisy Content' (NC) Image, that it uses for subsequent operations, such as computing the Content Cost (Jc). This NC Image is produced by sampling a Random number from a Uniform Distribution with values in [-20, 20] and applying formula 'pixel_value * ratio + pixel_value * (1 - ratio)' for all pixel values. Previously, the NST algorithm would implement sampling from a Uniform Distribution, by initializing a Stochastic Process (able to generate numbers from the Uniform Distribution) with a seed number specified by the numpy backend. Now, we hard-code as 'Default Seed Number' the number 1234, which is used to initialize the Stochastic Process. This way, every new os process running the NST algorithm (ie invoking the CLI subsequent times), on the same Content Image, would produce the same NC Image, since the samples taken from the underlying Uniform Distribution, would be implemented by running the Stochastic Process with same Seed value (1234).
… render UI accordingly
…le) yields same Generated Image This behaviour of course is tested when running the NST algorithm on different OS processes. So, we gain confidence that the results are completely reproducible.
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1.0.0 (2023-10-29)
Changes
feature
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test
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documentation
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development
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demo
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