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  <blog_title>HTN20190109の日記</blog_title>
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  <description>import numpy as np class Sigmoid: def forward(self, x, w, b): &quot;&quot;&quot; x: (N, D) w: (D,) b: scalar &quot;&quot;&quot; self.x = x self.w = w z = np.dot(x, w) + b # ← こっちの方が安全 self.y_pred = 1 / (1 + np.exp(-z)) return self.y_pred def backward(self, dy): &quot;&quot;&quot; dy: (N,) &quot;&quot;&quot; dz = dy * (1.0 - self.y_pred) * self.y_pred # (N,) …</description>
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  <provider_name>Hatena Blog</provider_name>
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  <published>2026-03-26 01:22:13</published>
  <title>Sigmoid and NLL</title>
  <type>rich</type>
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