CNN+SVM实现（基于TensorFlow，python，SVM基于sklearn库，附带自己数据）

简单说明

1.简单将CNN与SVM进行结合：将经过CNN训练的数据的全连接层输出（训练集与验证集都进行）喂给SVM（只取一部分数据进行，SVM对小数据优势比较大，大数据花费的时间多）。
2.CNN+SVM：CNN充当特征提取器，SVM充当分类器。
3.结果对比：CNN，SVM，CNN+SVM。
4.数据参见之前博客博客链接

编程实现

# -*- coding: utf-8 -*-
"""
Created on Sun Mar 29 21:19:03 2020

@author: 小小飞在路上
"""
import numpy as np
from sklearn.model_selection import train_test_split  #数据集的分割函数
from sklearn.preprocessing import StandardScaler      #数据预处理
import pandas as pd
import warnings
import tensorflow as tf
from sklearn import svm
warnings.filterwarnings('ignore')
graph=tf.get_default_graph() #分配内存

# In[]
#
def weight_variables(shape):
    """权重"""
    w=tf.Variable(tf.random_normal(shape=shape,mean=0.0,stddev=1.0,seed=1))
#    w=tf.Variable(tf.truncated_normal(shape=shape,mean=0.0,stddev=1.0,seed=1)) 
    return w
  
def bias_variables(shape):
    """偏置"""
    b=tf.Variable(tf.constant(0.0,shape=shape))
    return b

def CNN_model(n_future,n_class,n_hidden,n_fc1,future_out):
    """模型结构"""
    with tf.variable_scope("data"):
        x=tf.placeholder(tf.float32,[None,n_future])
        y_true=tf.placeholder(tf.int32,[None,n_class])
        keep_prob = tf.placeholder(tf.float32)

    with tf.variable_scope("cov1"):
        w_conv1=weight_variables([1,3,1,n_hidden]) 
        b_conv1=bias_variables([n_hidden])
        
        x_reshape = tf.reshape(x,[-1,1,n_future,1])
        #卷积、激活
        x_relu1=tf.nn.relu(tf.nn.conv2d(x_reshape,w_conv1,strides=[1,1,1,1],padding="SAME")+b_conv1)
        #池化
        x_pool1=tf.nn.max_pool(x_relu1,ksize=[1,1,2,1],strides=[1,1,2,1],padding="SAME")
       
    with tf.variable_scope("conv_fc"):
        #全连接
        w_fc1=weight_variables([1*future_out*n_hidden,n_fc1])
        b_fc1=bias_variables([n_fc1])
        x_fc_reshape=tf.reshape(x_pool1,[-1,1*future_out*n_hidden])
        y_fc1=tf.matmul(x_fc_reshape,w_fc1)+b_fc1
        h_fc1_drop = tf.nn.dropout(y_fc1, keep_prob)
        
        w_fc2=weight_variables([n_fc1,n_class])
        b_fc2=bias_variables([n_class])
        y_predict=tf.matmul(h_fc1_drop,w_fc2)+b_fc2
    with tf.variable_scope("soft_cross"):
        loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_predict))
    
    with tf.variable_scope("opmizer"):
#        train_op=tf.train.AdamOptimizer(0.01).minimize(loss)
        train_op=tf.train.RMSPropOptimizer(0.001, 0.9).minimize(loss)
   
    with tf.variable_scope("acc"):
        Y_truelable=tf.argmax(y_true,1)
        Y_predictlable=tf.argmax(y_predict,1)
        equal_list=tf.equal(Y_truelable,Y_predictlable)
        #equal_list None个样本
        accuracy=tf.reduce_mean(tf.cast(equal_list,tf.float32))
    return x,y_true,keep_prob,y_fc1,train_op,accuracy


# In[]
#数据读取及划分
url = 'C:/Users/weixifei/Desktop/TensorFlow程序/data1.csv'
data = pd. read_csv(url, sep=',',header=None)
data=np.array(data)
X_data=data[:,:23]
Y=data[:,23]
labels=np.asarray(pd.get_dummies(Y),dtype=np.int8)

X_train,X_,Y_train,Y_=train_test_split(X_data,labels,test_size=0.3,random_state=20)
X_test,X_vld,Y_test,Y_vld=train_test_split(X_,Y_,test_size=0.1,random_state=20)

# In[]
#数据标准化处理
stdsc = StandardScaler() 
X_train=stdsc.fit_transform(X_train)
X_test=stdsc.fit_transform(X_test)
X_vld=stdsc.fit_transform(X_vld)

Y_train_1=np.argmax(Y_train,axis=1)
Y_test_1=np.argmax(Y_test,axis=1)
Y_vld_1=np.argmax(Y_vld,axis=1)

# In[]
n_future=23
n_class=12
n_hidden=16
n_fc1=256
if n_future % 2==0: 
    future_out=n_future//2
else:
    future_out=n_future//2+1
#模型调用   
x,y_true,keep_prob,y_fc1,train_op,accuracy=CNN_model(n_future,n_class,n_hidden,n_fc1,future_out)
init_op=tf.global_variables_initializer()

training_epochs=2
batch_size = 64
total_batches=X_train.shape[0]//batch_size
# In[]


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(training_epochs):
        for i in range(total_batches):
            start=(i*batch_size)%X_train.shape[0]           #
            end=start+batch_size
            sess.run(train_op,feed_dict={
     x:X_train[start:end],y_true:Y_train[start:end],keep_prob:0.5})
            if i % 50==0:
                print("Epoch %d,Steps %d，validation accuracy：%f"%(epoch+1,i,sess.run(accuracy,feed_dict={
     x:X_vld,y_true:Y_vld,keep_prob:1})))
    x_temp1=sess.run(y_fc1,feed_dict={
     x:X_train})
    x_temp2=sess.run(y_fc1,feed_dict={
     x:X_test}) 
    
    clf = svm.SVC(C=0.9, kernel='linear')  # linear kernel
    #SVM训练只取一部分数据
    clf.fit(X_train[:1000], Y_train_1[:1000])
    
    clf1 = svm.SVC(C=0.9, kernel='linear')  # linear kernel
     #SVM训练只取一部分数据
    clf1.fit(x_temp1[:1000], Y_train_1[:1000])
    # SVM选择了linear核，C选择了0.9
    print("*"*50)
    print("CNN test accuracy：%f"%(sess.run(accuracy,feed_dict={
     x:X_test,y_true:Y_test,keep_prob:1})))
    print("SVM test accuracy: ",clf.score(X_test, Y_test_1))
    print("CNN+SVM test accuracy: ",clf1.score(x_temp2, Y_test_1))
            

结果