HTN20190109 https://blog.hatena.ne.jp/HTN20190109/ HTN20190109の日記 https://htn20190109.hatenablog.com/ DL import numpy as np def relu(X): return np.maximum(0, X) def softmax(X): X = X - np.max(X, axis=1, keepdims=True) expX = np.exp(X) return expX / np.sum(expX, axis=1, keepdims=True) def relu_backward(Z, delta): delta[Z <= 0] = 0 return delta def cross_entropy_error(y, t): batch_size = y.shape[0] retur… 190 <iframe src="https://hatenablog-parts.com/embed?url=https%3A%2F%2Fhtn20190109.hatenablog.com%2Fentry%2F2026%2F05%2F02%2F154441" title="FullyConnectedNeuralNetworkクラス - HTN20190109の日記" class="embed-card embed-blogcard" scrolling="no" frameborder="0" style="display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;"></iframe> Hatena Blog https://hatena.blog 2026-05-02 15:44:41 rich https://htn20190109.hatenablog.com/entry/2026/05/02/154441 1.0 100%