基于交叉熵损失函数的山鸢尾二分类问题

利用花瓣长度和花瓣宽度的特征在山鸢尾和其他物种间拟合一条曲线，可视化分类结果。

代码如下：

from sklearn import datasets
sess = tf.Session()
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])
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(labels=y_target,logits=my_output)
my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(xentropy)
init = tf.global_variables_initializer()
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)))
# 可视化：
[[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, 'go', label='Non-setosa')
plt.plot(x, ablineValues, 'b-')
plt.xlim([0.0, 2.7])
plt.ylim([0.0, 7.1])
plt.suptitle('Linear Seperator for I.setosa',fontsize=20)
plt.xlabel('Petal Length')
plt.ylabel('Petal Width')
plt.legend(loc='lower right')
plt.show()

可视化结果如下：