TensorFlow机器学习实战指南——山鸢尾花分类

import numpy as np
import tensorflow as tf
import  matplotlib.pyplot as plt
from sklearn import datasets

sess = tf.Session()

iris = datasets.load_iris()
binary_target = np.array([1.0 if x ==0 else 0. for x in iris.target])
iris_2d = np.array([[x[2], x[3]] for x in iris.data])

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)
A = tf.Variable(tf.random_normal(shape=[1, 1]))
b = tf.Variable(tf.random_normal(shape=[1, 1]))

my_mult = tf.matmul(x2_data, A)
my_add = tf.add(my_mult, b)
my_output = tf.subtract(x1_data, my_add)

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

my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(xentropy)

init = tf.initialize_all_variables()
print(sess.run(init))

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)))

[[slope]] = sess.run(A)
[[intercept]] = sess.run(b)
x = np.linspace(0, 3, num = 50)
ablineValues = []
for i in x:
    ablineValues.append(slope*i+intercept)

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("Liner Separator for setosa", fontsize=20)
plt.xlabel("petal length")
plt.ylabel("peta width")
plt.legend(loc="lower right")
plt.show()