Python下的Softmax回归函数的实现方法(推荐)

import numpy as npdef softmax(x): """Compute softmax values for each sets of scores in x.""" pass # TODO: Compute and return softmax(x) x = np.array(x) x = np.exp(x) x.astype('float32') if x.ndim == 1:  sumcol = sum(x)  for i in range(x.size):   x[i] = x[i]/float(sumcol) if x.ndim > 1:  sumcol = x.sum(axis = 0)  for row in x:   for i in range(row.size):    row[i] = row[i]/float(sumcol[i]) return x#测试结果scores = [3.0,1.0, 0.2]print softmax(scores)

[ 0.8360188 0.11314284 0.05083836]

import numpy as npdef softmax(x): return np.exp(x)/np.sum(np.exp(x),axis=0)#测试结果scores = [3.0,1.0, 0.2]print softmax(scores)