【Python 代码】CS231n中Softmax线性分类器、非线性分类器对比举例（含python绘图显示结果）

  1 #CS231n中线性、非线性分类器举例（Softmax）
  2 #注意其中反向传播的计算
  3 
  4 # -*- coding: utf-8 -*-
  5 import numpy as np
  6 import matplotlib.pyplot as plt
  7 N = 100 # number of points per class
  8 D = 2 # dimensionality
  9 K = 3 # number of classes
 10 X = np.zeros((N*K,D)) # data matrix (each row = single example)
 11 y = np.zeros(N*K, dtype=‘uint8‘) # class labels
 12 for j in xrange(K):
 13   ix = range(N*j,N*(j+1))
 14   r = np.linspace(0.0,1,N) # radius
 15   t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
 16   X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
 17   y[ix] = j
 18 # lets visualize the data:
 19 plt.xlim([-1, 1])
 20 plt.ylim([-1, 1])
 21 plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
 22 plt.show()
 23 
 24 
 25 
 26 # initialize parameters randomly
 27 # 线性分类器
 28 W = 0.01 * np.random.randn(D,K)
 29 b = np.zeros((1,K))
 30 
 31 # some hyperparameters
 32 step_size = 1e-0
 33 reg = 1e-3 # regularization strength
 34 
 35 # gradient descent loop
 36 num_examples = X.shape[0]
 37 for i in xrange(200):
 38   
 39   # evaluate class scores, [N x K]
 40   scores = np.dot(X, W) + b 
 41   
 42   # compute the class probabilities
 43   exp_scores = np.exp(scores)
 44   probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True) # [N x K]
 45   
 46   # compute the loss: average cross-entropy loss and regularization
 47   corect_logprobs = -np.log(probs[range(num_examples),y])
 48   data_loss = np.sum(corect_logprobs)/num_examples
 49   reg_loss = 0.5*reg*np.sum(W*W)
 50   loss = data_loss + reg_loss
 51   if i % 10 == 0:
 52     print "iteration %d: loss %f" % (i, loss)
 53   
 54   # compute the gradient on scores
 55   dscores = probs
 56   dscores[range(num_examples),y] -= 1
 57   dscores /= num_examples
 58   
 59   # backpropate the gradient to the parameters (W,b)
 60   dW = np.dot(X.T, dscores)
 61   db = np.sum(dscores, axis=0, keepdims=True)
 62   
 63   dW += reg*W # regularization gradient
 64   
 65   # perform a parameter update
 66   W += -step_size * dW
 67   b += -step_size * db
 68   
 69   # evaluate training set accuracy
 70 scores = np.dot(X, W) + b
 71 predicted_class = np.argmax(scores, axis=1)
 72 print ‘training accuracy: %.2f‘ % (np.mean(predicted_class == y))
 73 
 74 # plot the resulting classifier
 75 h = 0.02
 76 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
 77 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
 78 xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
 79                      np.arange(y_min, y_max, h))
 80 Z = np.dot(np.c_[xx.ravel(), yy.ravel()], W) + b
 81 Z = np.argmax(Z, axis=1)
 82 Z = Z.reshape(xx.shape)
 83 fig = plt.figure()
 84 plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)
 85 plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
 86 plt.xlim(xx.min(), xx.max())
 87 plt.ylim(yy.min(), yy.max())
 88 
 89 ## initialize parameters randomly
 90 #  含一个隐层的非线性分类器 使用ReLU
 91 h = 100 # size of hidden layer
 92 W = 0.01 * np.random.randn(D,h)
 93 b = np.zeros((1,h))
 94 W2 = 0.01 * np.random.randn(h,K)
 95 b2 = np.zeros((1,K))
 96 
 97 # some hyperparameters
 98 step_size = 1e-0
 99 reg = 1e-3 # regularization strength
100 
101 # gradient descent loop
102 num_examples = X.shape[0]
103 for i in xrange(10000):
104   
105   # evaluate class scores, [N x K]
106   hidden_layer = np.maximum(0, np.dot(X, W) + b) # note, ReLU activation
107   scores = np.dot(hidden_layer, W2) + b2
108   
109   # compute the class probabilities
110   exp_scores = np.exp(scores)
111   probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True) # [N x K]
112   
113   # compute the loss: average cross-entropy loss and regularization
114   corect_logprobs = -np.log(probs[range(num_examples),y])
115   data_loss = np.sum(corect_logprobs)/num_examples
116   reg_loss = 0.5*reg*np.sum(W*W) + 0.5*reg*np.sum(W2*W2)
117   loss = data_loss + reg_loss
118   if i % 1000 == 0:
119     print "iteration %d: loss %f" % (i, loss)
120   
121   # compute the gradient on scores
122   dscores = probs
123   dscores[range(num_examples),y] -= 1
124   dscores /= num_examples
125   
126   # backpropate the gradient to the parameters
127   # first backprop into parameters W2 and b2
128   dW2 = np.dot(hidden_layer.T, dscores)
129   db2 = np.sum(dscores, axis=0, keepdims=True)
130   # next backprop into hidden layer
131   dhidden = np.dot(dscores, W2.T)
132   # backprop the ReLU non-linearity
133   dhidden[hidden_layer <= 0] = 0
134   # finally into W,b
135   dW = np.dot(X.T, dhidden)
136   db = np.sum(dhidden, axis=0, keepdims=True)
137   
138   # add regularization gradient contribution
139   dW2 += reg * W2
140   dW += reg * W
141   
142   # perform a parameter update
143   W += -step_size * dW
144   b += -step_size * db
145   W2 += -step_size * dW2
146   b2 += -step_size * db2
147 # evaluate training set accuracy
148 hidden_layer = np.maximum(0, np.dot(X, W) + b)
149 scores = np.dot(hidden_layer, W2) + b2
150 predicted_class = np.argmax(scores, axis=1)
151 print ‘training accuracy: %.2f‘ % (np.mean(predicted_class == y))
152 # plot the resulting classifier
153 h = 0.02
154 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
155 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
156 xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
157                      np.arange(y_min, y_max, h))
158 Z = np.dot(np.maximum(0, np.dot(np.c_[xx.ravel(), yy.ravel()], W) + b), W2) + b2
159 Z = np.argmax(Z, axis=1)
160 Z = Z.reshape(xx.shape)
161 fig = plt.figure()
162 plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)
163 plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
164 plt.xlim(xx.min(), xx.max())
165 plt.ylim(yy.min(), yy.max())