【TensorFlow】实现简单的鸢尾花分类器

代码实现及说明

# python 3.6
# TensorFlow实现简单的鸢尾花分类器
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn import datasetssess = tf.Session()#导入数据
iris = datasets.load_iris()
# 是否是山鸢尾 0/1
binary_target = np.array([1. if x == 0 else 0. forx in iris.target])
# 选择两个特征：花瓣长度和宽度
iris_2d = np.array([[x[2],x[3]] for x in iris.data])# 声明批训练大小、占位符和变量
# tf.float32降低float字节数 可以提高算法性能
batch_size = 20
x1_data = tf.placeholder(shape=[None,1],dtype=tf.float32)
x2_data = tf.placeholder(shape=[None,1],dtype=tf.float32)
y_target = tf.placeholder(shape=[None,1],dtype=tf.float32)
# 声明变量 A 和 b (0 = x1 - A*x2 + b)
A = tf.Variable(tf.random_normal(shape=[1,1]))
b = tf.Variable(tf.random_normal(shape=[1,1]))# 定义线性模型
# 线性模型的表达式为：x1=x2*A+b。
# 如果找到的数据点在直线以上，则将数据点代入x1-x2*A-b计算出的结果大于0；
# 同理找到的数据点在直线以下，则将数据点代入x1-x2*A-b计算出的结果小于0。
# 将公式x1-x2*A-b传入sigmoid函数，然后预测结果1或者0
# TensorFlow有内建的sigmoid损失函数，所以这里仅仅需要定义模型输出
my_mult = tf.matmul(x2_data, A)
my_add = tf.add(my_mult, b)
my_output = tf.subtract(x1_data,my_add)# 增加分类损失函数 这里用两类交叉熵损失函数 cross entropy
xentropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output,labels=y_target)# 声明优化器
my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(xentropy)# 初始化变量
init = tf.global_variables_initializer()
sess.run(init)# 循环
for i in range(1000):rand_index = np.random.choice(len(iris_2d),size=batch_size)rand_x = iris_2d[rand_index]rand_x1 = np.array([[x[0]] for x in rand_x])rand_x2 = np.array([[x[1]] for x in rand_x])rand_y = np.array([[y] for y in binary_target[rand_index]])sess.run(train_step, feed_dict={x1_data:rand_x1,x2_data:rand_x2,y_target:rand_y})if (i+1)%200 == 0:print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ', b = ' + str(sess.run(b)))# 结果可视化
[[slope]] = sess.run(A) # 斜率
# 因为A的shape是(1,1)所以要写成一行一列的形式
[[intercept]] = sess.run(b) # 截距# 创建拟合线
x = np.linspace(0, 3, num=50) # 0~3 50个均匀间隔的数字
ablineValues = []
for i in x:ablineValues.append(slope*i+intercept)# 绘图
setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i]==1]
setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i]==1]
non_setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i]==0]
non_setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i]==0]
plt.plot(setosa_x, setosa_y, 'rx', ms=10, mew=2, label='setosa')
plt.plot(non_setosa_x, non_setosa_y, 'ro', label='Non-setosa')
plt.plot(x, ablineValues, 'b-')
plt.xlim([0.0, 2.7])
plt.ylim([0.0, 7.1])
plt.xlabel('Petal Length')
plt.ylabel('Petal Width')
plt.legend(loc='lower right')
plt.show()