本文實例為大家分享了tensorflow實現線性svm的具體代碼，供大家參考，具體內容如下

簡單方法：

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()  

       進階：

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)