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(15*1)*w1(1*10)+b1(1*10) = B(15*10) # B(15*10)*w2(10*1)+b2(15*1) = C(15*1) # 1 A B C dateNormal = np.zeros([15,1]) priceNormal = np.zeros([15,1]) 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])# N行1列的 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 标准差 train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)#表示我们每次调整的步长 梯度下降法 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(0,10000):#训练一万次来终止 sess.run(train_step,feed_dict={x:dateNormal,y:priceNormal}) # 经过第47行代码训练出一组非常优化的w1w2 b1b2.如何检测w1w2 b1b2是否有效呢？我们就给它一个新的输入层A A + wb(新的预测值) -->layer2(新的预测值放到layer2中) # 所以我们要看当前的layer2是否准确，如果准确我们就把当前预测值的结果绘制出来 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()
