1. 练习-softmax模型
测试样例
scores = [1.0, 2.0, 3.0]
print softmax(scores)
[ 0.09003057 0.24472847 0.66524096]
scores = np.array([[1, 2, 3, 6],
[2, 4, 5, 6],
[3, 8, 7, 6]])
[[ 0.09003057 0.00242826 0.01587624 0.33333333]
[ 0.24472847 0.01794253 0.11731043 0.33333333]
[ 0.66524096 0.97962921 0.86681333 0.33333333]]
scores = [3.0, 1.0, 0.2]
import numpy as np
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
return np.exp(x)/np.sum(np.exp(x), axis=0) # CODE
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()
socres倍数
print(softmax(scores*10)) # 差距增加
print(softmax(scores/10)) # 差距减小
2. 交叉熵
- Multinomial logistic classification
