tensorflow搭建神经网络

最简单的神经网络

 import tensorflow as tf
 import numpy as np
 import matplotlib.pyplot as plt

 date = np.linspace(1, 15, 15)# d定义日期
 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])
 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])
 # print(date)
 plt.figure()

 for i in range(0, 15):
     dataOne = np.zeros([2])
     dataOne[0] = i
     dataOne[1] = i
     priceOne = np.zeros([2])
     priceOne[0] = beginPrice[i]
     priceOne[1] = endPrice[i]
     if endPrice[i] > beginPrice[i]:
         plt.plot(dataOne, priceOne, 'r', lw=8)
     else:
         plt.plot(dataOne, priceOne, 'g', lw=8)
 # plt.show()
 # 归一化处理
 dateNormal = np.zeros([15,1])
 PriceNormal  = np.zeros([15,1])
 for i in range(0,15):
     dateNormal[i] = i/14.0
     PriceNormal[i] = endPrice[i]/3000.0
 # print(dateNormal)
 # print('\n')
 # print(PriceNormal)
 x = tf.placeholder(tf.float32, [None, 1])
 y = tf.placeholder(tf.float32, [None, 1])

  # B 第一层
 w1 = tf.Variable(tf.random_uniform([1, 10], 0, 1))
 b1 = tf.Variable(tf.zeros([1, 10]))
 wb1 = tf.matmul(x, w1) + b1
 layer1 = tf.nn.relu(wb1)# 激励函数

 # 第二层
 w2 = tf.Variable(tf.random_uniform([10,1], 0, 1))
 b2 = tf.Variable(tf.zeros([15, 1]))
 wb2 = tf.matmul(layer1, w2) + b2
 layer2 = tf.nn.relu(wb2)# 激励函数

 # loss
 loss = tf.reduce_mean(tf.square(y-layer2))
 train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
 with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
     for i in range(0, 100000):
         sess.run(train_step, feed_dict={x:dateNormal, y:PriceNormal})
     pred = sess.run(layer2, feed_dict={x:dateNormal})
     predPrice = np.zeros([15, 1])
     for i in range(0, 15):
         predPrice[i] = (pred * 3000)[i]
     plt.plot(date, predPrice, 'b', lw=2)
 plt.show()