CNN-SVM_SVMCNN_SVM特征提取_SVM_python_SVM分类

# coding=utf8 import random import numpy as np import tensorflow as tf from sklearn import svm right0 = 0.0 # 记录预测为1且实际为1的结果数 error0 = 0 # 记录预测为1但实际为0的结果数 right1 = 0.0 # 记录预测为0且实际为0的结果数 error1 = 0 # 记录预测为0但实际为1的结果数 for file_num in range(10): # 在十个随机生成的不相干数据集上进行测试，将结果综合 print 'testing NO.%d dataset.......' % file_num ff = open('digit_train_' + file_num.__str__() + '.data') rr = ff.readlines() x_test2 = [] y_test2 = [] for i in range(len(rr)): x_test2.append(map(int, map(float, rr[i].split(' ')[:256]))) y_test2.append(map(int, rr[i].split(' ')[256:266])) ff.close() # 以上是读出训练数据 ff2 = open('digit_test_' + file_num.__str__() + '.data') rr2 = ff2.readlines() x_test3 = [] y_test3 = [] for i in range(len(rr2)): x_test3.append(map(int, map(float, rr2[i].split(' ')[:256]))) y_test3.append(map(int, rr2[i].split(' ')[256:266])) ff2.close() # 以上是读出测试数据 sess = tf.InteractiveSession() # 建立一个tensorflow的会话 # 初始化权值向量 def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) # 初始化偏置向量 def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) # 二维卷积运算，步长为1，输出大小不变 def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') # 池化运算，将卷积特征缩小为1/2 def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # 给x，y留出占位符，以便未来填充数据 x = tf.placeholder("float", [None, 256]) y_ = tf.placeholder("float", [None, 10]) # 设置输入层的W和b W = tf.Variable(tf.zeros([256, 10])) b = tf.Variable(tf.zeros([10])) # 计算输出，采用的函数是softmax（输入的时候是one hot编码） y = tf.nn.softmax(tf.matmul(x, W) + b) # 第一个卷积层，5x5的卷积核，输出向量是32维 w_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 16, 16, 1]) # 图片大小是16*16，,-1代表其他维数自适应 h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) # 采用的最大池化，因为都是1和0，平均池化没有什么意义 # 第二层卷积层，输入向量是32维，输出64维，还是5x5的卷积核 w_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) # 全连接层的w和b w_fc1 = weight_variable([4 * 4 * 64, 256]) b_fc1 = bias_variable([256]) # 此时输出的维数是256维 h_pool2_flat = tf.reshape(h_pool2, [-1, 4 * 4 * 64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1) # h_fc1是提取出的256维特征，很关键。后面就是用这个输入到SVM中 # 设置dropout，否则很容易过拟合 keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # 输出层，在本实验中只利用它的输出反向训练CNN，至于其具体数值我不关心 w_fc2 = weight_variable([256, 10]) b_fc2 = bias_variable([10]) y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2) cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv)) # 设置误差代价以交叉熵的形式 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # 用adma的优化算法优化目标函数 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) sess.run(tf.initialize_all_variables()) for i in range(3000): # 跑3000轮迭代，每次随机从训练样本中抽出50个进行训练 batch = ([], []) p = random.sample(range(795), 50) for k in p: batch[0].append(x_test2[k]) batch[1].append(y_test2[k]) if i % 100 == 0: train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0}) # print "step %d, train accuracy %g" % (i, train_accuracy) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.6}) # 设置dropout的参数为0.6，测试得到，大点收敛的慢，小点立刻出现过拟合 print "test accuracy %g" % accuracy.eval(feed_dict={x: x_test3, y_: y_test3, keep_prob: 1.0}) # def my_test(input_x): # y = tf.nn.softmax(tf.matmul(sess.run(x), W) + b) for h in range(len(y_test2)): if np.argmax(y_test2[h]) == 7: y_test2[h] = 1 else: y_test2[h] = 0 for h in range(len(y_test3)): if np.argmax(y_test3[h]) == 7: y_test3[h] = 1 else: y_test3[h] = 0 # 以上两步都是为了将源数据的one hot编码改为1和0，我的学号尾数为7 x_temp = [] for g in x_test2: x_temp.append(sess.run(h_fc1, feed_dict={x: np.array(g).reshape((1, 256))})[0]) # 将原来的x带入训练好的CNN中计算出来全连接层的特征向量，将结果作为SVM中的特征向量 x_temp2 = [] for g in x_test3: x_temp2.append(sess.run(h_fc1, feed_dict={x: np.array(g).reshape((1, 256))})[0]) clf = svm.SVC(C=0.9, kernel='rbf') clf.fit(x_temp, y_test2) # SVM选择了rbf核，C选择了0.9 for j in range(len(x_temp2)): # 验证时出现四种情况分别对应四个变量存储 if clf.predict(x_temp2[j])[0] == y_test3[j] == 1: right0 += 1 elif clf.predict(x_temp2[j])[0] == y_test3[j] == 0: right1 += 1 elif clf.predict(x_temp2[j])[0] == 1 and y_test3[j] == 0: error0 += 1 else: error1 += 1 accuracy = right0 / (right0 + error0) # 准确率 recall = right0 / (right0 + error1) # 召回率 print 'svm right ratio ', (right0 + right1) / (right0 + right1 + error0 + error1) print 'accuracy ', accuracy print 'recall ', recall print 'F1 score ', 2 * accuracy * recall / (accuracy + recall) # 计算F1值