Tensorflow學習筆記二--線性迴歸模型
阿新 • • 發佈:2018-11-25
學習完基本操作後,今天來學習一下如何用tensorflow建立線性迴歸模型。
一、首先建立一些資料
import numpy as np import tensorflow as tf import matplotlib.pyplot as plt # 隨機生成1000個點,圍繞在y=0.1x+0.3的直線周圍 num_points = 1000 vectors_set = [] for i in range(num_points): x1 = np.random.normal(0.0, 0.55) y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03) vectors_set.append([x1, y1]) # 生成一些樣本 x_data = [v[0] for v in vectors_set] y_data = [v[1] for v in vectors_set] plt.scatter(x_data,y_data,c='r') plt.show()
執行結果是:
二、建立模型:
# 生成1維的W矩陣,取值是[-1,1]之間的隨機數 W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W') # 生成1維的b矩陣,初始值是0 b = tf.Variable(tf.zeros([1]), name='b') # 經過計算得出預估值y y = W * x_data + b # 以預估值y和實際值y_data之間的均方誤差作為損失 loss = tf.reduce_mean(tf.square(y - y_data), name='loss') # 採用梯度下降法來優化引數 optimizer = tf.train.GradientDescentOptimizer(0.5) # 訓練的過程就是最小化這個誤差值 train = optimizer.minimize(loss, name='train') sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) # 初始化的W和b是多少 print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss)) # 執行20次訓練 for step in range(20): sess.run(train) # 輸出訓練好的W和b print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss)) writer = tf.summary.FileWriter("./tmp", sess.graph)##writer = tf.train.SummaryWriter(("./tmp", sess.graph)) #這是因為在1.0版本中,tf.train.SummaryWriter已經改為tf.summary.FileWriter
執行結果是:
W = [0.04677415] b = [0.] loss = 0.09138401 W = [0.06286795] b = [0.2992881] loss = 0.0013578985 W = [0.0743912] b = [0.29931584] loss = 0.0011342986 W = [0.08227211] b = [0.29933572] loss = 0.0010297119 W = [0.08766197] b = [0.2993493] loss = 0.0009807928 W = [0.09134818] b = [0.29935864] loss = 0.00095791137 W = [0.09386922] b = [0.29936498] loss = 0.00094720896 W = [0.09559341] b = [0.29936934] loss = 0.0009422029 W = [0.0967726] b = [0.29937232] loss = 0.00093986175 W = [0.09757906] b = [0.29937434] loss = 0.0009387667 W = [0.09813061] b = [0.29937574] loss = 0.0009382542 W = [0.09850783] b = [0.2993767] loss = 0.00093801465 W = [0.09876581] b = [0.29937735] loss = 0.0009379023 W = [0.09894225] b = [0.2993778] loss = 0.00093785016 W = [0.09906292] b = [0.2993781] loss = 0.00093782565 W = [0.09914544] b = [0.2993783] loss = 0.00093781407 W = [0.09920189] b = [0.29937845] loss = 0.0009378086 W = [0.09924049] b = [0.29937854] loss = 0.0009378061 W = [0.09926689] b = [0.2993786] loss = 0.00093780505 W = [0.09928494] b = [0.29937866] loss = 0.00093780446 W = [0.09929729] b = [0.2993787] loss = 0.0009378045
三、把結果圖顯示出來
執行後
plt.scatter(x_data,y_data,c='r')
plt.plot(x_data,sess.run(W)*x_data+sess.run(b))
plt.show()
執行結果: