神经网络股票收盘价
神经网络股票收盘价
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深度学习
tf模块matplotlib的使用
import numpy as np
import matplotlib.pyplot as plt
x = np.array([1,2,3,4,5,6,7,8])
y = np.array([3,5,7,6,2,6,10,15])
plt.plot(x,y,'r')# 折线 1 x 2 y 3 color
plt.plot(x,y,'g',lw=10)# 4 line w
# 折线 饼状 柱状
x = np.array([1,2,3,4,5,6,7,8])
y = np.array([13,25,17,36,21,16,10,15])
plt.bar(x,y,0.2,alpha=1,color='b')# 5 color 4 透明度 3 0.9
plt.show()
神经网络股票收盘价
# layer1：激励函数+乘加运算
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
date = np.linspace(1,15,15)
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):
# 1 柱状图
dateOne = np.zeros([2])
dateOne[0] = i;
dateOne[1] = i;
priceOne = np.zeros([2])
priceOne[0] = beginPrice[i]
priceOne[1] = endPrice[i]
if endPrice[i]>beginPrice[i]:
plt.plot(dateOne,priceOne,'r',lw=8)
else:
plt.plot(dateOne,priceOne,'g',lw=8)
#plt.show()
# A(15x1)*w1(1x10)+b1(1*10) = B(15x10)
# B(15x10)*w2(10x1)+b2(15x1) = C(15x1)
# 1 A B C
dateNormal = np.zeros([15,1])
priceNormal = np.zeros([15,1])
#归一化
for i in range(0,15):
dateNormal[i,0] = i/14.0;
priceNormal[i,0] = endPrice[i]/3000.0;
x = tf.placeholder(tf.float32,[None,1])
y = tf.placeholder(tf.float32,[None,1])
# B
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
# C
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)
loss = tf.reduce_mean(tf.square(y-layer2))#y 真实 layer2 计算
#梯度下级法
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(0,10000):
sess.run(train_step,feed_dict={x:dateNormal,y:priceNormal})
#预测下
# w1w2 b1b2 A + wb -->layer2
pred = sess.run(layer2,feed_dict={x:dateNormal})
predPrice = np.zeros([15,1])
for i in range(0,15):
predPrice[i,0]=(pred*3000)[i,0]
plt.plot(date,predPrice,'b',lw=1)
plt.show()
深度学习