#coding=utf-8
'''
Created on Jan 28, 2019

@author: zhongzhu
'''
import tensorflow as tf
import os
import numpy as np
import matplotlib.pyplot as plt


os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'


if __name__ == '__main__':
    
    
    # 产生训练数据集
    train_X = np.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,
                         7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1])
    train_Y = np.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,
                         2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3])
    n_train_samples = train_X.shape[0]
    print('训练样本数量: ', n_train_samples)
    # 产生测试样本
    test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])
    n_test_samples = test_X.shape[0]
    print('测试样本数量: ', n_test_samples)
    
    with tf.Graph().as_default():
        #输入占位符
        with tf.name_scope('Input'):
            X = tf.placeholder(tf.float32, name='X')
            Y_true = tf.placeholder(tf.float32, name='Y_true')
        
        with tf.name_scope('Inference'):
            #模型参数变量
            W = tf.Variable(np.random.randn(), name='Weight')
            b = tf.Variable(np.random.randn(), name='Bias')
            #inference 前向模型： y = w*x+b
            y_pred = tf.add(tf.multiply(X, W), b)
        
        
        with tf.name_scope('Loss'):
            #计算损失
            TrainLoss = tf.reduce_mean(tf.pow((y_pred-Y_true), 2))/2
            
        with tf.name_scope('Train'):
            #训练节点
            Optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
            TrainOp = Optimizer.minimize(TrainLoss)
        
        with tf.name_scope('Evaluate'):
            #添加评估节点
            EvalLoss = tf.reduce_mean(tf.pow((y_pred-Y_true), 2))/2
            
        # 添加所有变量类型的初始化节点
        InitOp = tf.global_variables_initializer()
        
        print('开启会话，运行计算图')
        
        sess = tf.Session()
        sess.run(InitOp)
        
        
        print('不断的迭代训练并测试模型')
        
        for step in range(1000):
            _, train_loss, train_w, train_b = sess.run([TrainOp, TrainLoss, W, b], 
                                                       feed_dict={X: train_X, Y_true: train_Y})
            
            # 训练几步之后输出当前模型损失
            if (step+1) % 5 == 0:
                print("Step:", '%04d' % (step + 1), "train_loss=", "{:.9f}".format(train_loss),
                  "W=", train_w, "b=", train_b)
            
            
            # 每隔几步对模型进行测试
            if (step+1) % 5 == 0:
                test_loss, test_w, test_b = sess.run([EvalLoss, W, b], 
                                                     feed_dict={X: test_X, Y_true: test_Y})
                
                print("Step:", '%04d' % (step + 1), "test_loss=", "{:.9f}".format(test_loss),
                  "W=", test_w, "b=", test_b)
                
        print('训练结束')
        W,b = sess.run([W,b])
        print("得到的模型参数：", "W=", W, "b=", b,)
        
        training_loss = sess.run(TrainLoss, feed_dict={X: train_X, Y_true: train_Y})
        print("训练集上的损失：", training_loss)
        test_loss = sess.run(EvalLoss, feed_dict={X: test_X, Y_true: test_Y})
        print("测试集上的损失：", test_loss)   
        
        writer = tf.summary.FileWriter(logdir='logs', graph=tf.get_default_graph())
        writer.close()
        
        
        # 展示拟合曲线
        plt.plot(train_X, train_Y, 'ro', label='Original Train Points')
        plt.plot(test_X, test_Y, 'b*', label='Original Test Points')
        plt.plot(train_X, W * train_X + b, label='Fitted Line')
        plt.legend()
        plt.show()