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使用tensorflow實現(xiàn)線性svm

更新時間：2018年09月07日 14:20:20 作者：GAN_player

這篇文章主要為大家詳細(xì)介紹了使用tensorflow實現(xiàn)線性svm的相關(guān)資料，具有一定的參考價值，感興趣的小伙伴們可以參考一下

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

簡單方法：

import tensorflow as tf
import numpy as np

from matplotlib import pyplot as plt
def placeholder_input():

  x=tf.placeholder('float',shape=[None,2],name='x_batch')
  y=tf.placeholder('float',shape=[None,1],name='y_batch')
  return x,y
def 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=10000
tol=1e-3

x,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‖^2
cost = 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) -2
x_max, y_max = np.maximum.reduce(x_data,axis=0) +2

xf, 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(jìn)階：

import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt


class 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)

沒有數(shù)據(jù)的可以用這個

import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt


class 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內(nèi)容，希望對大家的學(xué)習(xí)有所幫助，也希望大家多多支持腳本之家。

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