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ahangchen

softmax one hot encoding min_loss

May 9, 2016

6904551 · May 9, 2016

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"""Softmax.""" scores = [3.0, 1.0, 0.2] import numpy as np def softmax(x): return np.exp(x) / np.sum(np.exp(x), axis=0) print(softmax(scores)) # Plot softmax curves import matplotlib.pyplot as plt x = np.arange(-2.0, 6.0, 0.1) scores = np.vstack([x, np.ones_like(x), 0.2 * np.ones_like(x)]) plt.plot(x, softmax(scores).T, linewidth=2) plt.show()

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