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@@ -85,6 +85,7 @@ train_y = np.reshape(load_y, [training_samples, outputsize])
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  np.save("train_x.npy", train_x)
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  np.save("train_y.npy", train_y)
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  ```
 
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  The basic premise of how this network is trained and thus how the dataset is generated in the C program is:
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  1. All models are pre-scaled to a normal cubic scale and then scaled again by 0.55 so that they all fit within a unit sphere.
 
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  np.save("train_x.npy", train_x)
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  np.save("train_y.npy", train_y)
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  ```
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+ _632,847,695 samples, each sample is 2 components for train_x (random seed & 0-1 unit sphere position index) and 6 components for train_y (vertex position & vertex color)._
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  The basic premise of how this network is trained and thus how the dataset is generated in the C program is:
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  1. All models are pre-scaled to a normal cubic scale and then scaled again by 0.55 so that they all fit within a unit sphere.