jens
61c762150c
- refactored training - use static seed for random (for better comparison) - simplified StackDeep.train
39 lines
903 B
Python
39 lines
903 B
Python
import time
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from typing import TypeAlias
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USE_CUDA = 1
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if USE_CUDA:
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import cupy as np
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Mat: TypeAlias = np.array
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def convert(src: Mat):
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return np.asnumpy(src)
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else:
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import numpy as np
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Mat: TypeAlias = np.array
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def convert(src: Mat):
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return src
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np.random.seed(12345)
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def uniform(shape: tuple, mu: float = 0.5, std: float = 1.0) -> Mat:
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return std * (np.random.rand(shape[0], shape[1]) + mu - 0.5)
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def gaussian(shape: tuple, mu: float = 0.0, std: float = 1.0) -> Mat:
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return std * (np.random.randn(shape[0], shape[1]) + mu)
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def sample(src: Mat) -> Mat:
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return (src > uniform(src.shape)).astype(float)
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def prob(src: Mat) -> Mat:
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return 1.0 / (1 + np.exp(-src))
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def rms_error(d_err: Mat):
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d_err_squared = d_err * d_err
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return np.sum(d_err_squared, 1) / d_err_squared[1]
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def rms_error_accu(d_err: Mat):
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d_err_squared = d_err * d_err
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s = np.sum(d_err_squared) / d_err.size
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return s
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