本文实例为大家分享了tensorflow实现线性svm的具体代码，供大家参考，具体内容如下
简单方法：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
|
import tensorflow as tfimport numpy as npfrom matplotlib import pyplot as pltdef placeholder_input(): x=tf.placeholder('float',shape=[None,2],name='x_batch') y=tf.placeholder('float',shape=[None,1],name='y_batch') return x,ydef get_base(_nx, _ny): _xf = np.linspace(x_min, x_max, _nx) _yf = np.linspace(y_min, y_max, _ny) xf1, yf1 = np.meshgrid(_xf, _yf) n_xf,n_yf=np.hstack((xf1)),np.hstack((yf1)) return _xf, _yf,np.c_[n_xf.ravel(), n_yf.ravel()]x_data=np.load('x.npy')y1=np.load('y.npy')y_data=np.reshape(y1,[200,1])step=10000tol=1e-3x,y=placeholder_input()w = tf.Variable(np.ones([2,1]), dtype=tf.float32, name="w_v")b = tf.Variable(0., dtype=tf.float32, name="b_v")y_pred =tf.matmul(x,w)+b y_predict =tf.sign( tf.matmul(x,w)+b )# cost = ∑_(i=1)^N max(1-y_i⋅(w⋅x_i+b),0)+1/2 + 0.5 * ‖w‖^2cost = tf.nn.l2_loss(w)+tf.reduce_sum(tf.maximum(1-y*y_pred,0))train_step = tf.train.AdamOptimizer(0.01).minimize(cost)with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(step): sess.run(train_step,feed_dict={x:x_data,y:y_data}) y_p,y_p1,loss,w_value,b_value=sess.run([y_predict,y_pred,cost,w,b],feed_dict={x:x_data,y:y_data})x_min, y_min = np.minimum.reduce(x_data,axis=0) -2x_max, y_max = np.maximum.reduce(x_data,axis=0) +2xf, yf , matrix_= get_base(200, 200)#xy_xf, xy_yf = np.meshgrid(xf, yf, sparse=True)z=np.sign(np.matmul(matrix_,w_value)+b_value).reshape((200,200))plt.pcolormesh(xf, yf, z, cmap=plt.cm.Paired)for i in range(200): if y_p[i,0]==1.0: plt.scatter(x_data[i,0],x_data[i,1],color='r') else: plt.scatter(x_data[i,0],x_data[i,1],color='g')plt.axis([x_min,x_max,y_min ,y_max])#plt.contour(xf, yf, z)plt.show() |
进阶：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
|
import tensorflow as tfimport numpy as npfrom matplotlib import pyplot as pltclass SVM(): def __init__(self): self.x=tf.placeholder('float',shape=[None,2],name='x_batch') self.y=tf.placeholder('float',shape=[None,1],name='y_batch') self.sess=tf.Session() @staticmethod def get_base(self,_nx, _ny): _xf = np.linspace(self.x_min, self.x_max, _nx) _yf = np.linspace(self.y_min, self.y_max, _ny) n_xf, n_yf = np.meshgrid(_xf, _yf) return _xf, _yf,np.c_[n_xf.ravel(), n_yf.ravel()] def readdata(self): x_data=np.load('x.npy') y1=np.load('y.npy') y_data=np.reshape(y1,[200,1]) return x_data ,y_data def train(self,step,x_data,y_data): w = tf.Variable(np.ones([2,1]), dtype=tf.float32, name="w_v") b = tf.Variable(0., dtype=tf.float32, name="b_v") self.y_pred =tf.matmul(self.x,w)+b cost = tf.nn.l2_loss(w)+tf.reduce_sum(tf.maximum(1-self.y*self.y_pred,0)) train_step = tf.train.AdamOptimizer(0.01).minimize(cost) self.y_predict =tf.sign( tf.matmul(self.x,w)+b ) self.sess.run(tf.global_variables_initializer()) for i in range(step): self.sess.run(train_step,feed_dict={self.x:x_data,self.y:y_data}) self.y_predict_value,self.w_value,self.b_value,cost_value=self.sess.run([self.y_predict,w,b,cost],feed_dict={self.x:x_data,self.y:y_data}) print('**********cost=%f***********'%cost_value) def predict(self,y_data): correct = tf.equal(self.y_predict_value, y_data) precision=tf.reduce_mean(tf.cast(correct, tf.float32)) precision_value=self.sess.run(precision) return precision_value def drawresult(self,x_data): self.x_min, self.y_min = np.minimum.reduce(x_data,axis=0) -2 self.x_max, self.y_max = np.maximum.reduce(x_data,axis=0) +2 xf, yf , matrix_= self.get_base(self,200, 200) w_value=self.w_value b_value=self.b_value print(w_value,b_value) z=np.sign(np.matmul(matrix_,self.w_value)+self.b_value).reshape((200,200)) plt.pcolormesh(xf, yf, z, cmap=plt.cm.Paired) for i in range(200): if self.y_predict_value[i,0]==1.0: plt.scatter(x_data[i,0],x_data[i,1],color='r') else: plt.scatter(x_data[i,0],x_data[i,1],color='g') plt.axis([self.x_min,self.x_max,self.y_min ,self.y_max]) #plt.contour(xf, yf, z) plt.show() svm=SVM()x_data,y_data=svm.readdata()svm.train(5000,x_data,y_data)precision_value=svm.predict(y_data)svm.drawresult(x_data) |
没有数据的可以用这个
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
|
import tensorflow as tfimport numpy as npfrom matplotlib import pyplot as pltclass SVM(): def __init__(self): self.x=tf.placeholder('float',shape=[None,2],name='x_batch') self.y=tf.placeholder('float',shape=[None,1],name='y_batch') self.sess=tf.Session() def creat_dataset(self,size, n_dim=2, center=0, dis=2, scale=1, one_hot=False): center1 = (np.random.random(n_dim) + center - 0.5) * scale + dis center2 = (np.random.random(n_dim) + center - 0.5) * scale - dis cluster1 = (np.random.randn(size, n_dim) + center1) * scale cluster2 = (np.random.randn(size, n_dim) + center2) * scale x_data = np.vstack((cluster1, cluster2)).astype(np.float32) y_data = np.array([1] * size + [-1] * size) indices = np.random.permutation(size * 2) x_data, y_data = x_data[indices], y_data[indices] y_data=np.reshape(y_data,(y_data.shape[0],1)) if not one_hot: return x_data, y_data y_data = np.array([[0, 1] if label == 1 else [1, 0] for label in y_data], dtype=np.int8) return x_data, y_data @staticmethod def get_base(self,_nx, _ny): _xf = np.linspace(self.x_min, self.x_max, _nx) _yf = np.linspace(self.y_min, self.y_max, _ny) n_xf, n_yf = np.meshgrid(_xf, _yf) return _xf, _yf,np.c_[n_xf.ravel(), n_yf.ravel()]# def readdata(self):# # x_data=np.load('x.npy')# y1=np.load('y.npy')# y_data=np.reshape(y1,[200,1])# return x_data ,y_data def train(self,step,x_data,y_data): w = tf.Variable(np.ones([2,1]), dtype=tf.float32, name="w_v") b = tf.Variable(0., dtype=tf.float32, name="b_v") self.y_pred =tf.matmul(self.x,w)+b cost = tf.nn.l2_loss(w)+tf.reduce_sum(tf.maximum(1-self.y*self.y_pred,0)) train_step = tf.train.AdamOptimizer(0.01).minimize(cost) self.y_predict =tf.sign( tf.matmul(self.x,w)+b ) self.sess.run(tf.global_variables_initializer()) for i in range(step): index=np.random.permutation(y_data.shape[0]) x_data1, y_data1 = x_data[index], y_data[index] self.sess.run(train_step,feed_dict={self.x:x_data1[0:50],self.y:y_data1[0:50]}) self.y_predict_value,self.w_value,self.b_value,cost_value=self.sess.run([self.y_predict,w,b,cost],feed_dict={self.x:x_data,self.y:y_data}) if i%1000==0:print('**********cost=%f***********'%cost_value) def predict(self,y_data): correct = tf.equal(self.y_predict_value, y_data) precision=tf.reduce_mean(tf.cast(correct, tf.float32)) precision_value=self.sess.run(precision) return precision_value, self.y_predict_value def drawresult(self,x_data): self.x_min, self.y_min = np.minimum.reduce(x_data,axis=0) -2 self.x_max, self.y_max = np.maximum.reduce(x_data,axis=0) +2 xf, yf , matrix_= self.get_base(self,200, 200) print(self.w_value,self.b_value) z=np.sign(np.matmul(matrix_,self.w_value)+self.b_value).reshape((200,200)) plt.pcolormesh(xf, yf, z, cmap=plt.cm.Paired) for i in range(x_data.shape[0]): if self.y_predict_value[i,0]==1.0: plt.scatter(x_data[i,0],x_data[i,1],color='r') else: plt.scatter(x_data[i,0],x_data[i,1],color='g') plt.axis([self.x_min,self.x_max,self.y_min ,self.y_max])# plt.contour(xf, yf, z) plt.show() svm=SVM()x_data,y_data=svm.creat_dataset(size=200, n_dim=2, center=0, dis=4, one_hot=False)svm.train(5000,x_data,y_data)precision_value,y_predict_value=svm.predict(y_data)svm.drawresult(x_data) |
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/GAN_player/article/details/76827758
