TensorFlow实现创建分类器_Python

本文实例为大家分享了TensorFlow实现创建分类器的具体代码，供大家参考，具体内容如下

创建一个iris数据集的分类器。

加载样本数据集，实现一个简单的二值分类器来预测一朵花是否为山鸢尾。iris数据集有三类花，但这里仅预测是否是山鸢尾。导入iris数据集和工具库，相应地对原数据集进行转换。

100

101

102

103

104

105

106

107

									# Combining Everything Together

									#----------------------------------

									# This file will perform binary classification on the

									# iris dataset. We will only predict if a flower is

									# I.setosa or not.

									#

									# We will create a simple binary classifier by creating a line

									# and running everything through a sigmoid to get a binary predictor.

									# The two features we will use are pedal length and pedal width.

									#

									# We will use batch training, but this can be easily

									# adapted to stochastic training.

									import matplotlib.pyplot as plt

									import numpy as np

									from sklearn import datasets

									import tensorflow as tf

									from tensorflow.python.framework import ops

									ops.reset_default_graph()

									# 导入iris数据集

									# 根据目标数据是否为山鸢尾将其转换成1或者0。

									# 由于iris数据集将山鸢尾标记为0，我们将其从0置为1，同时把其他物种标记为0。

									# 本次训练只使用两种特征：花瓣长度和花瓣宽度，这两个特征在x-value的第三列和第四列

									# iris.target = {0, 1, 2}, where '0' is setosa

									# iris.data ~ [sepal.width, sepal.length, pedal.width, pedal.length]

									iris = datasets.load_iris()

									binary_target = np.array([1. if x==0 else 0. for x in iris.target])

									iris_2d = np.array([[x[2], x[3]] for x in iris.data])

									# 声明批量训练大小

									batch_size = 20

									# 初始化计算图

									sess = tf.Session()

									# 声明数据占位符

									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)

									# 声明模型变量

									# Create variables A and 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：

									# 如果找到的数据点在直线以上，则将数据点代入x2-x1*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x2-x1*A-b计算出的结果小于0。

									# x1 - A*x2 + b

									my_mult = tf.matmul(x2_data, A)

									my_add = tf.add(my_mult, b)

									my_output = tf.subtract(x1_data, my_add)

									# 增加TensorFlow的sigmoid交叉熵损失函数(cross entropy)

									xentropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output, labels=y_target)

									# 声明优化器方法

									my_opt = tf.train.GradientDescentOptimizer(0.05)

									train_step = my_opt.minimize(xentropy)

									# 创建一个变量初始化操作

									init = tf.global_variables_initializer()

									sess.run(init)

									# 运行迭代1000次

									for i in range(1000):

									  rand_index = np.random.choice(len(iris_2d), size=batch_size)

									  # rand_x = np.transpose([iris_2d[rand_index]])

									  # 传入三种数据：花瓣长度、花瓣宽度和目标变量

									  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.transpose([binary_target[rand_index]])

									  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)))

									# 绘图

									# 获取斜率/截距

									# Pull out slope/intercept

									[[slope]] = sess.run(A)

									[[intercept]] = sess.run(b)

									# 创建拟合线

									x = np.linspace(0, 3, num=50)

									ablineValues = []

									for i in x:

									 ablineValues.append(slope*i+intercept)

									# 绘制拟合曲线

									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.suptitle('Linear Separator For I.setosa', fontsize=20)

									plt.xlabel('Petal Length')

									plt.ylabel('Petal Width')

									plt.legend(loc='lower right')

									plt.show()

输出：

									Step #200 A = [[ 8.70572948]], b = [[-3.46638322]]

									Step #400 A = [[ 10.21302414]], b = [[-4.720438]]

									Step #600 A = [[ 11.11844635]], b = [[-5.53361702]]

									Step #800 A = [[ 11.86427212]], b = [[-6.0110755]]

									Step #1000 A = [[ 12.49524498]], b = [[-6.29990339]]