Softmax回归
使用参考上一篇随笔。详细介绍可参考http://ufldl.stanford.edu/wiki/index.php/Softmax%E5%9B%9E%E5%BD%92
1 # coding:utf8 2 import numpy as np 3 import cPickle 4 import os 5 6 7 class SoftMax: 8 def __init__(self, MAXT=100, step=0.1, landa=0.01): 9 self.MAXT = MAXT 10 self.step = step 11 self.landa = landa 12 13 def load_theta(self, datapath): 14 self.theta = cPickle.load(open(datapath, 'rb')) 15 16 def process_train(self, x, y, typenum): 17 costval = np.zeros(self.MAXT) 18 print "Trainnum = %d, x.shape[1] = %d" % (x.shape[0], x.shape[1]) 19 self.theta = 0.001 * np.mat(np.random.randn(typenum, x.shape[1])) 20 lastcostJ = 1000 21 for m in range(self.MAXT): 22 costs = np.zeros((typenum, x.shape[0])) 23 grads = np.zeros((typenum, x.shape[1])) 24 hval = self.h(x) 25 for j in range(typenum): 26 jvalues = np.zeros((x.shape[0], x.shape[1])) 27 for i in range(x.shape[0]): 28 ptype = hval[i, j] 29 delta = (j == y[i])-ptype 30 costs[j, i] = (j == y[i])*np.log(ptype) 31 jvalues[i] = x[i] * delta 32 grads[j] = -np.mean(jvalues, axis=0) + self.landa * self.theta[j] # 权重衰减项 33 self.theta = self.theta - self.step * grads 34 costJ = -np.sum(costs) / x.shape[0] + (self.landa / 2) * np.sum(np.square(self.theta)) 35 costval[m] = costJ 36 if (costJ > lastcostJ): 37 print "costJ is increasing !!!" 38 break 39 print "Loop(%d) cost = %.3f diff=%.4f" % (m, costJ, costJ - lastcostJ) 40 lastcostJ = costJ 41 if not os.path.exists('data'): 42 os.makedirs('data') 43 f = open("data/softmax.pkl", 'wb') 44 cPickle.dump(self.theta, f) 45 f.close() 46 47 def h(self, x): 48 m = np.exp(np.dot(x, self.theta.T)) 49 sump = np.sum(m, axis=1) 50 return m / sump 51 52 def predict(self, x): 53 pv = self.h(x) 54 return np.argmax(pv) 55 56 57 def validate(self, testset, labelset): 58 testnum = len(testset) 59 correctnum = 0 60 for i in range(testnum): 61 x = testset[i] 62 testtype = self.predict(x) 63 orgtype = labelset[i] 64 if testtype == orgtype: 65 correctnum += 1 66 rate = float(correctnum) / testnum 67 print "correctnum = %d, sumnum = %d" % (correctnum, testnum) 68 print "Accuracy:%.2f" % (rate) 69 return rate 70 71 72 if __name__ == '__main__': 73 f = open('mnist.pkl', 'rb') 74 training_data, validation_data, test_data = cPickle.load(f) 75 training_inputs = [np.reshape(x, 784) for x in training_data[0]] 76 data = np.array(training_inputs[:5000]) 77 training_inputs = [np.reshape(x, 784) for x in validation_data[0]] 78 vdata = np.array(training_inputs[:5000]) 79 f.close() 80 softmax = SoftMax() 81 softmax.process_train(data, training_data[1][:5000], 10) 82 softmax.validate(vdata, validation_data[1][:5000]) 83 # Accuracy:0.85
