这里将加载iris数据集，创建一个山鸢尾花（i.setosa）的分类器。
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# nonlinear svm example
#----------------------------------
#
# this function wll illustrate how to
# implement the gaussian kernel on
# the iris dataset.
#
# gaussian kernel:
# k(x1, x2) = exp(-gamma * abs(x1 - x2)^2)
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn import datasets
from tensorflow.python.framework import ops
ops.reset_default_graph()
# create graph
sess = tf.session()
# load the data
# iris.data = [(sepal length, sepal width, petal length, petal width)]
# 加载iris数据集，抽取花萼长度和花瓣宽度，分割每类的x_vals值和y_vals值
iris = datasets.load_iris()
x_vals = np.array([[x[0], x[3]] for x in iris.data])
y_vals = np.array([1 if y==0 else -1 for y in iris.target])
class1_x = [x[0] for i,x in enumerate(x_vals) if y_vals[i]==1]
class1_y = [x[1] for i,x in enumerate(x_vals) if y_vals[i]==1]
class2_x = [x[0] for i,x in enumerate(x_vals) if y_vals[i]==-1]
class2_y = [x[1] for i,x in enumerate(x_vals) if y_vals[i]==-1]
# declare batch size
# 声明批量大小（偏向于更大批量大小）
batch_size = 150
# initialize placeholders
x_data = tf.placeholder(shape=[none, 2], dtype=tf.float32)
y_target = tf.placeholder(shape=[none, 1], dtype=tf.float32)
prediction_grid = tf.placeholder(shape=[none, 2], dtype=tf.float32)
# create variables for svm
b = tf.variable(tf.random_normal(shape=[1,batch_size]))
# gaussian (rbf) kernel
# 声明批量大小（偏向于更大批量大小）
gamma = tf.constant(-25.0)
sq_dists = tf.multiply(2., tf.matmul(x_data, tf.transpose(x_data)))
my_kernel = tf.exp(tf.multiply(gamma, tf.abs(sq_dists)))
# compute svm model
first_term = tf.reduce_sum(b)
b_vec_cross = tf.matmul(tf.transpose(b), b)
y_target_cross = tf.matmul(y_target, tf.transpose(y_target))
second_term = tf.reduce_sum(tf.multiply(my_kernel, tf.multiply(b_vec_cross, y_target_cross)))
loss = tf.negative(tf.subtract(first_term, second_term))
# gaussian (rbf) prediction kernel
# 创建一个预测核函数
ra = tf.reshape(tf.reduce_sum(tf.square(x_data), 1),[-1,1])
rb = tf.reshape(tf.reduce_sum(tf.square(prediction_grid), 1),[-1,1])
pred_sq_dist = tf.add(tf.subtract(ra, tf.multiply(2., tf.matmul(x_data, tf.transpose(prediction_grid)))), tf.transpose(rb))
pred_kernel = tf.exp(tf.multiply(gamma, tf.abs(pred_sq_dist)))
# 声明一个准确度函数，其为正确分类的数据点的百分比
prediction_output = tf.matmul(tf.multiply(tf.transpose(y_target),b), pred_kernel)
prediction = tf.sign(prediction_output-tf.reduce_mean(prediction_output))
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.squeeze(prediction), tf.squeeze(y_target)), tf.float32))
# declare optimizer
my_opt = tf.train.gradientdescentoptimizer(0.01)
train_step = my_opt.minimize(loss)
# initialize variables
init = tf.global_variables_initializer()
sess.run(init)
# training loop
loss_vec = []
batch_accuracy = []
for i in range(300):
rand_index = np.random.choice(len(x_vals), size=batch_size)
rand_x = x_vals[rand_index]
rand_y = np.transpose([y_vals[rand_index]])
sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
loss_vec.append(temp_loss)
acc_temp = sess.run(accuracy, feed_dict={x_data: rand_x,
y_target: rand_y,
prediction_grid:rand_x})
batch_accuracy.append(acc_temp)
if (i+1)%75==0:
print('step #' + str(i+1))
print('loss = ' + str(temp_loss))
# create a mesh to plot points in
# 为了绘制决策边界（decision boundary），我们创建一个数据点（x，y）的网格，评估预测函数
x_min, x_max = x_vals[:, 0].min() - 1, x_vals[:, 0].max() + 1
y_min, y_max = x_vals[:, 1].min() - 1, x_vals[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
np.arange(y_min, y_max, 0.02))
grid_points = np.c_[xx.ravel(), yy.ravel()]
[grid_predictions] = sess.run(prediction, feed_dict={x_data: rand_x,
y_target: rand_y,
prediction_grid: grid_points})
grid_predictions = grid_predictions.reshape(xx.shape)
# plot points and grid
plt.contourf(xx, yy, grid_predictions, cmap=plt.cm.paired, alpha=0.8)
plt.plot(class1_x, class1_y, 'ro', label='i. setosa')
plt.plot(class2_x, class2_y, 'kx', label='non setosa')
plt.title('gaussian svm results on iris data')
plt.xlabel('pedal length')
plt.ylabel('sepal width')
plt.legend(loc='lower right')
plt.ylim([-0.5, 3.0])
plt.xlim([3.5, 8.5])
plt.show()
# plot batch accuracy
plt.plot(batch_accuracy, 'k-', label='accuracy')
plt.title('batch accuracy')
plt.xlabel('generation')
plt.ylabel('accuracy')
plt.legend(loc='lower right')
plt.show()
# plot loss over time
plt.plot(loss_vec, 'k-')
plt.title('loss per generation')
plt.xlabel('generation')
plt.ylabel('loss')
plt.show()
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输出：
step #75
loss = -110.332
step #150
loss = -222.832
step #225
loss = -335.332
step #300
loss = -447.832
四种不同的gamma值（1，10，25，100）:
不同gamma值的山鸢尾花（i.setosa）的分类器结果图，采用高斯核函数的svm。
gamma值越大，每个数据点对分类边界的影响就越大。
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/lilongsy/article/details/79414355