'''
作业：自己构建一个数据，分类的额数据（特性：特征x1和x2,
5*x1-3*x2>0是第一个类别，5*x1-3*x2<0是第二个类别）
使用softmax相关API实现分类
'''
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import random
'''Tensorflow实现分类任务'''
'''先实现二分类'''
def binary_classification():
learningRate = 0.005
#构建分类数据
x1 = np.arange(-10,10,0.01).reshape(-1,1)
x2 = np.arange(-10,10,0.01).reshape(-1,1)
x = np.hstack((x1,x2))
y = 3*x1+2*x2**2
y_label = np.zeros((y.shape[0],2))
y_label[y[:,0]>100,0] = 1 #0
y_label[y[:,0]<=100,1] = 1 #1
#将数据分为2份，一份用于测试，一份用于训练
random.seed(0)
data_list = list(range(2000))
train_list = [random.randint(0,1999) for i in range(int(x1.shape[0]*0.8))]
#训练数据
train_x = x[train_list,:]
train_y = y_label[train_list,:]
#测试数据
for i in train_list:
if i in data_list:
data_list.remove(i)
test_list = data_list
test_x = x[test_list,:]
test_y = y_label[test_list,:]
plt.plot(y,y_label,'*')
# plt.show()
#构建模型
xx = tf.placeholder(dtype=tf.float32,shape=[None,2],name='xx')
yy = tf.placeholder(dtype=tf.float32,shape=[None,2],name='yy')
layer1_v = tf.Variable(initial_value=tf.random_normal(shape=[2,2],dtype=tf.float32,seed=0),name='layer1')
b = tf.Variable(initial_value=tf.random_normal(shape=[2],seed=0),name='b') #加上了bias后精度从0.57提升到了0.71
#softmax进行二分类
predict = tf.nn.softmax(tf.matmul(xx,layer1_v)+b)
#损失函数
loss = tf.reduce_sum(-yy*tf.log(predict))
#优化器
Optimizer = tf.train.AdamOptimizer(learning_rate=learningRate).minimize(loss)
#accuracy
accuracy = tf.equal(tf.argmax(predict,1),tf.argmax(yy,1))
accuracy = tf.reduce_mean(tf.cast(accuracy,dtype=tf.float32))
with tf.Session() as sess:
tf.global_variables_initializer().run()
step = 0
for i in range(1000):
Optimizer.run(feed_dict={xx:train_x,yy:train_y})
print("loss:%f,accuracy:%f"%(loss.eval(feed_dict={xx:train_x,yy:train_y}),accuracy.eval(feed_dict={xx:test_x,yy:test_y})))
binary_classification()
精度测试：
0.71 待改进（或许通过批次送入进行训练会增加模型的精度）
