最简单的神经网络
1 import tensorflow as tf 2 import numpy as np 3 import matplotlib.pyplot as plt 4 5 date = np.linspace(1, 15, 15)# d定义日期 6 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]) 7 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]) 8 # print(date) 9 plt.figure() 10 11 for i in range(0, 15): 12 dataOne = np.zeros([2]) 13 dataOne[0] = i 14 dataOne[1] = i 15 priceOne = np.zeros([2]) 16 priceOne[0] = beginPrice[i] 17 priceOne[1] = endPrice[i] 18 if endPrice[i] > beginPrice[i]: 19 plt.plot(dataOne, priceOne, 'r', lw=8) 20 else: 21 plt.plot(dataOne, priceOne, 'g', lw=8) 22 # plt.show() 23 # 归一化处理 24 dateNormal = np.zeros([15,1]) 25 PriceNormal = np.zeros([15,1]) 26 for i in range(0,15): 27 dateNormal[i] = i/14.0 28 PriceNormal[i] = endPrice[i]/3000.0 29 # print(dateNormal) 30 # print(' ') 31 # print(PriceNormal) 32 x = tf.placeholder(tf.float32, [None, 1]) 33 y = tf.placeholder(tf.float32, [None, 1]) 34 35 # B 第一层 36 w1 = tf.Variable(tf.random_uniform([1, 10], 0, 1)) 37 b1 = tf.Variable(tf.zeros([1, 10])) 38 wb1 = tf.matmul(x, w1) + b1 39 layer1 = tf.nn.relu(wb1)# 激励函数 40 41 # 第二层 42 w2 = tf.Variable(tf.random_uniform([10,1], 0, 1)) 43 b2 = tf.Variable(tf.zeros([15, 1])) 44 wb2 = tf.matmul(layer1, w2) + b2 45 layer2 = tf.nn.relu(wb2)# 激励函数 46 47 # loss 48 loss = tf.reduce_mean(tf.square(y-layer2)) 49 train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) 50 with tf.Session() as sess: 51 sess.run(tf.global_variables_initializer()) 52 for i in range(0, 100000): 53 sess.run(train_step, feed_dict={x:dateNormal, y:PriceNormal}) 54 pred = sess.run(layer2, feed_dict={x:dateNormal}) 55 predPrice = np.zeros([15, 1]) 56 for i in range(0, 15): 57 predPrice[i] = (pred * 3000)[i] 58 plt.plot(date, predPrice, 'b', lw=2) 59 plt.show()
