# -*- coding: utf-8 -*-
"""
Created on Mon Nov 19 19:33:03 2018
@author: KUMA
"""
import numpy as np
import tensorflow as tf
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
class LinearSep:
 def __init__(self):
 self.n_train = 10
 self.n_test = 50
 self.x_train, self.y_train, self.x_test, self.y_test = self._gene_data()
 def _gene_data(self):
 x = np.random.uniform(-1, 1, [self.n_train, 2])
 y = (x[:, 1] > x[:, 0]).astype(np.int32)
 x += np.random.randn(self.n_train, 2) * 0.05
 x_test = np.random.uniform(-1, 1, [self.n_test, 2])
 y_test = (x_test[:, 1] > x_test[:, 0]).astype(np.int32)
 return x, y, x_test, y_test
# 随机生成数据
dataset = LinearSep()
X_train, Y_train = dataset.x_train, dataset.y_train
print(Y_train)
Y_train = np.eye(2)[Y_train]
X_test, Y_test = dataset.x_test, dataset.y_test
Y_test = np.eye(2)[Y_test]
x = tf.placeholder(tf.float32, [None, 2], name='input')
y = tf.placeholder(tf.float32, [None, 2], name='output')
w1 = tf.get_variable(name='w_fc1', shape=[2, 20], dtype=tf.float32)
b1 = tf.get_variable(name='b_fc1', shape=[20], dtype=tf.float32)
out = tf.matmul(x, w1) + b1
out = tf.nn.relu(out)
w2 = tf.get_variable(name='w_fc2', shape=[20, 2], dtype=tf.float32)
b2 = tf.get_variable(name='b_fc2', shape=[2], dtype=tf.float32)
out = tf.matmul(out, w2) + b2
out = tf.nn.softmax(out)
# cross entropy 损失函数
loss = -tf.reduce_mean(tf.reduce_sum(y * tf.log(out + 1e-8), axis=1), axis=0)
# 准确率
correct_pred = tf.equal(tf.argmax(y, axis=1), tf.argmax(out, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# 定义优化器
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss) # 1e-3 是学习律
# 初始化网络
# BATCH_SIZE = 128
EPOCH = 7000 # 优化次数
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for ep in range(EPOCH):
 sess.run(train_op, feed_dict={x: X_train, y: Y_train})
 loss_train, acc_train = sess.run([loss, accuracy], feed_dict={x: X_train, y: Y_train})
 acc_test, pre_test = sess.run([accuracy, correct_pred], feed_dict={x: X_test, y: Y_test})
 if ep % 1000 == 0:
 print(ep, loss_train, acc_train, acc_test)
 print(Y_test.shape)
test_pre = sess.run(out, feed_dict={x: X_test, y: Y_test})
print(len(test_pre))
mask = np.argmax(test_pre, axis=1)
print(mask)
mask_0 = np.where(mask == 0)
mask_1 = np.where(mask == 1)
X_0 = X_train[mask_0]
X_1 = X_train[mask_1]
print(X_0)