tensorflow——SVM实现

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1、SVM实现

#!/usr/bin/env python3# -*- coding: utf-8 -*-"""Created on Sun Aug  6 18:46:23 2017@author: dell"""import numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltdef gen_two_clusters(size=100, n_dim=2, center=0, dis=2, scale=1, one_hot=True):    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    data = np.vstack((cluster1, cluster2)).astype(np.float32)    labels = np.array([1] * size + [0] * size)    indices = np.random.permutation(size * 2)    data, labels = data[indices], labels[indices]    if not one_hot:        return data, labels    labels = np.array([[0, 1] if label == 1 else [1, 0] for label in labels], dtype=np.int8)    return data, labelsdef get_base(_nx, _ny):            _xf = np.linspace(x_min, x_max, _nx)            _yf = np.linspace(y_min, y_max, _ny)            n_xf, n_yf = np.meshgrid(_xf, _yf)            return _xf, _yf, np.c_[n_xf.ravel(), n_yf.ravel()]x, y = gen_two_clusters(n_dim=2, dis=2.5, center=5, one_hot=False)#np.save('x.npy',x)#np.save('y.npy',y)x_ = np.load('x.npy')y_ = np.load('y.npy')y_ = y_.reshape(-1,1)title = 'linear_SVM'#plt.figure()plt.title(title)#plt.xlim(x_min, x_max)#plt.ylim(y_min, y_max)y_0 = np.where(y_==1)y_1 = np.where(y_==-1)#plt.scatter(x_[y_0,0], x_[y_0,1],  c='g')#plt.scatter(x_[y_1,0], x_[y_1,1],  c='r')#plt.show()c = 1lr = 0.01batch_size = 128epoch = 1000tol = 1e-3padding = 0.1x = tf.placeholder(tf.float32, [None, 2])y = tf.placeholder(tf.float32, [None, 1])W = tf.Variable(np.zeros([2,1]), dtype=tf.float32, name='w')b = tf.Variable(0, dtype=tf.float32, name='b')y_pred1 = tf.matmul(x, W) + by_pred = tf.sign(y_pred1)loss = (tf.reduce_sum( tf.nn.relu(1-y_*y_pred1)) + c* tf.nn.l2_loss(W)) optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss)tf.summary.scalar('loss', loss)init = tf.global_variables_initializer()with tf.Session() as sess:    merged = tf.summary.merge_all()    writer = tf.summary.FileWriter('nlogs/', sess.graph)    sess.run(init)    for i in range(epoch):        _, loss_ ,W_,b_= sess.run([optimizer,loss,W,b], feed_dict={x: x_, y:y_})        y_pred_,y_pred1_, w = sess.run([ y_pred,y_pred1,W], feed_dict={x: x_, y:y_})        if i%50 ==0:#             loss_ = sess.run(loss, feed_dict={x: x_, y:y_})#             print(loss_)             result= sess.run(merged, feed_dict={x: x_, y:y_})             writer.add_summary(result, i)x_min, x_max = np.min(x_[:,0]), np.max(x_[:,0])y_min, y_max = np.min(x_[:,1]), np.max(x_[:,1])x_padding = max(abs(x_min), abs(x_max)) * paddingy_padding = max(abs(y_min), abs(y_max)) * paddingx_min -= x_paddingx_max += x_paddingy_min -= y_paddingy_max += y_paddingxf, yf, base_matrix = get_base(200, 200)z = np.sign(np.matmul(base_matrix,w)+b_).reshape((200, 200))#z = npbase_matrix.reshape((200, 200))plt.contour(xf, yf, z, c='k-', levels=[0.5])#xy_xf, xy_yf = np.meshgrid(xf, yf, sparse=True)plt.pcolormesh(xf, yf, z, cmap=plt.cm.Paired)#visualize2d(x_, y_, padding=0.1, dense=400)plt.scatter(x_[y_0,0], x_[y_0,1],  c='g')plt.scatter(x_[y_1,0], x_[y_1,1],  c='b')

2、SVM—定义类实现

import tensorflow as tfimport numpy as npimport mathfrom matplotlib import pyplot as pltfrom tensorflow import flagsclass 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)        data, labels = x_data[indices], y_data[indices]        labels=np.reshape(labels,(-1,1))        if not one_hot:            return data, labels        labels = np.array([[0, 1] if label == 1 else [1, 0] for label in labels], dtype=np.int8)        return data, labels    @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 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 shuffle(self,epoch,batch,x_data,y_data):        for i in range(epoch):            shuffle_index=np.random.permutation(y_data.shape[0])            x_data1, y_data1 = x_data[shuffle_index], y_data[shuffle_index]            batch_per_epoch = math.ceil(y_data.shape[0]*2 / batch)            for b in range(batch_per_epoch):                if (b*batch+batch)>y_data.shape[0]:                    a,b = b*batch, y_data.shape[0]                else:                    a,b = b*batch, b*batch+batch                data, labels = x_data1[a:b,:], y_data1[a:b,:]                yield data, labels    def train(self,epoch,x_data,y_data,x_edata,y_edata):        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(self.x,w)+b         cost = tf.nn.l2_loss(w)+tf.reduce_sum(tf.maximum(1-self.y*y_pred,0))        train_step = tf.train.AdamOptimizer(0.01).minimize(cost)        y_predict =tf.sign( y_pred)        init = tf.global_variables_initializer()        with tf.Session() as sess:                sess.run(init)                shuffle= self.shuffle(epoch,100,x_data,y_data)                for i, (x_batch, y_batch) in enumerate(shuffle):        #            index=np.random.permutation(y_data.shape[0])        #            x_data1, y_data1 = x_data[index], y_data[index]                    sess.run(train_step,feed_dict={self.x:x_batch,self.y:y_batch})                    if i%1000==0:                        self.y_predict_value,self.w_value,self.b_value,cost_value=sess.run([y_predict,w,b,cost],feed_dict={self.x:x_data,self.y:y_data})                        print('step= %d  ,  cost=%f '%(i, cost_value))                         y_pre = np.sign(np.matmul(x_edata,self.w_value)+self.b_value)                        correct = np.equal(y_pre, y_edata)                        precision=np.mean(np.cast[ 'float32'](correct))#                        precision_value=sess.run(precision)                        print('eval= %d'%precision)    def drawresult(self,x_data):        x_min, x_max = np.min(x_data[:,0]), np.max(x_data[:,0])        y_min, y_max = np.min(x_data[:,1]), np.max(x_data[:,1])        x_padding = max(abs(x_min), abs(x_max)) * FLAGS.padding        y_padding = max(abs(y_min), abs(y_max)) * FLAGS.padding        self.x_min -= x_padding        self.x_max += x_padding        self.y_min -= y_padding        self.y_max += y_padding#        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)        ypv = self.y_predict_value        y_0 = np.where(ypv==1)        y_1 = np.where(ypv==-1)        plt.scatter(x_data[y_0,0], x_data[y_0,1],  c='g')        plt.scatter(x_data[y_1,0], x_data[y_1,1],  c='r')        plt.axis([self.x_min,self.x_max,self.y_min ,self.y_max])#        plt.contour(xf, yf, z)        plt.show()          flags.DEFINE_integer('epoch', 1000, "number of epoch")flags.DEFINE_float('lr', 0.01, "learning rate")flags.DEFINE_integer('padding', 0.1, "padding")flags.DEFINE_integer('batch', 100, "batch size")FLAGS = flags.FLAGSsvm=SVM()x_data,y_data=svm.creat_dataset(size=100, n_dim=2, center=0, dis=4,  one_hot=False)x_edata,y_edata=svm.creat_dataset(size=100, n_dim=2, center=0, dis=4,  one_hot=False)svm.train(FLAGS.epoch,x_data,y_data,x_edata,y_edata)#precision_value,y_predict_value=svm.predict(y_data)#print(precision_value)svm.drawresult(x_data)

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