1 # encoding:utf-8
 2
 3 import tensorflow as tf
 4 import numpy as np
 5 import matplotlib.pyplot as plt
 6 date = np.linspace(1, 15, 15)
 7 # 当天的收盘价格
 8 endPrice = np.array([2511.90,2538.26,2510.68,2591.66,2732.98,2701.69,2701.29,2678.67,2726.50,2681.50,2739.17,2715.07,2823.58,2864.90,2919.08]
 9 )
10 # 当天的开盘价格
11 beginPrice = np.array([2438.71,2500.88,2534.95,2512.52,2594.04,2743.26,2697.47,2695.24,2678.23,2722.13,2674.93,2744.13,2717.46,2832.73,2877.40])
12 print(date)  # 打印日期
13 plt.figure()
14 for i in range(0,15):
15     # 通过循环遍历数据画出柱状图
16     dateOne = np.zeros([2])
17     dateOne[0] = i
18     dateOne[1] = i
19     print(dateOne)
20     priceOne = np.zeros([2])
21     priceOne[0] = beginPrice[i]
22     priceOne[1] = endPrice[i]
23     if endPrice[i] > beginPrice[i]:
24         # 如果收盘价格大于开盘价格说明股票上涨 用红色表示 lw为线条粗细
25         plt.plot(dateOne, priceOne,'r',lw=8)
26     else:
27         # 如果收盘价格小于开盘价格说明股票下跌 用绿色表示 lw为线条粗细
28         plt.plot(dateOne, priceOne,'g',lw=5)
29 plt.show()

 1 # encoding:utf-8
 2
 3 import tensorflow as tf
 4 import numpy as np
 5 import matplotlib.pyplot as plt
 6 date = np.linspace(1, 15, 15)
 7 # 当天的收盘价格
 8 endPrice = np.array([2511.90,2538.26,2510.68,2591.66,2732.98,2701.69,2701.29,2678.67,2726.50,2681.50,2739.17,2715.07,2823.58,2864.90,2919.08]
 9 )
10 # 当天的开盘价格
11 beginPrice = np.array([2438.71,2500.88,2534.95,2512.52,2594.04,2743.26,2697.47,2695.24,2678.23,2722.13,2674.93,2744.13,2717.46,2832.73,2877.40])
12 print(date)  # 打印日期
13 plt.figure()
14 for i in range(0,15):
15     # 通过循环遍历数据画出柱状图
16     dateOne = np.zeros([2])
17     dateOne[0] = i
18     dateOne[1] = i
19     print(dateOne)
20     priceOne = np.zeros([2])
21     priceOne[0] = beginPrice[i]
22     priceOne[1] = endPrice[i]
23     if endPrice[i] > beginPrice[i]:
24         # 如果收盘价格大于开盘价格说明股票上涨 用红色表示 lw为线条粗细
25         plt.plot(dateOne, priceOne,'r',lw=8)
26     else:
27         # 如果收盘价格小于开盘价格说明股票下跌 用绿色表示 lw为线条粗细
28         plt.plot(dateOne, priceOne,'g',lw=5)
29 # plt.show()
30 # A(15x1)*w1(1x10)+b1(1*10) = B(15x10)
31 # B(15x10)*w2(10x1)+b2(15x1) = C(15x1)
32 # 1 A B C
33 dateNormal = np.zeros([15,1])
34 priceNormal = np.zeros([15,1])
35 # 日期和价格进行归一化处理
36 for i in range(0, 15):
37     dateNormal[i, 0] = i/14.0
38     priceNormal[i, 0] = endPrice[i]/3000.0
39 print(dateNormal)
40 print(priceNormal)
41
42 x = tf.placeholder(tf.float32, [None, 1])  # 表明是N行1列的
43 y = tf.placeholder(tf.float32, [None, 1])  # 表明是N行1列的
44
45 # B
46 w1 = tf.Variable(tf.random_uniform([1, 10], 0, 1))  # 可变值 可以通过误差修改值 范围0-1
47 b1 = tf.Variable(tf.zeros([1, 10]))  # 可变值 可以通过误差修改值
48 wb1 = tf.matmul(x, w1)+b1
49 layer1 = tf.nn.relu(wb1)  # 激励函数 映射成另一个值
50 # 第一二层完毕
51
52 # C
53 w2 = tf.Variable(tf.random_uniform([10, 1], 0, 1))  # 可变值 可以通过误差修改值 范围0-1
54 b2 = tf.Variable(tf.zeros([15, 1]))
55 wb2 = tf.matmul(layer1, w2)+b2
56 layer2 = tf.nn.relu(wb2)  # 激励函数 映射成另一个值
57 # 第二三层完毕
58
59 # 误差用loss表示 实际是一个标准差
60 loss = tf.reduce_mean(tf.square(y-layer2))  # y 真实 layer2 计算
61 # 每次调整的步长 梯度下降0.1 目的是缩小loss减小真实值与误差值的差异
62 train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
63 with tf.Session() as sess:
64     sess.run(tf.global_variables_initializer())  # 初始化
65     for i in range(0, 10000):  # 训练次数为10000
66         sess.run(train_step, feed_dict={x: dateNormal, y: priceNormal})
67     # w1w2 b1b2  A + wb -->layer2
68     pred = sess.run(layer2, feed_dict={x: dateNormal})
69     predPrice = np.zeros([15, 1])  # 预测结果
70     for i in range(0, 15):  # 还原数据需要*3000
71         predPrice[i, 0] = (pred*3000)[i, 0]
72     plt.plot(date, predPrice, 'b', lw=1)
73 plt.show()