首先导入各种TensorFlow等工具及设置画图的大小及字体
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5import numpy as np import tensorflow as tf import matplotlib.pyplot as plt plt.rcParams['figure.figsize'] = (10.0, 8.0) plt.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
生成用于进行线性回归的模型的数据
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12# 随机生成100个点，围绕在y=3x+5的直线周围 num_points = 200 vectors_set = [] for i in range(num_points): x1 = np.random.uniform(-10, 25) y1 = x1 * 3 + 5 + np.random.normal(0.0, 5) vectors_set.append([x1, y1]) # 生成一些样本 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()
生成的数据及画出的点像图如下：
设置模型的原始数据，编写现行回归的训练模型代码，并使用梯度下降算法进行训练
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18W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W') b = tf.Variable(tf.zeros([1]), name='b') y = W * x_data + b loss = tf.reduce_mean(tf.square(y - y_data), name='loss') optimizer = tf.train.GradientDescentOptimizer(0.005) 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), "tb =", sess.run(b), "tloss =", sess.run(loss)) # 执行20次训练 for step in range(1000): sess.run(train) # 输出训练好的W和b if(step % 50 == 0): print ("W =", sess.run(W), "tb =", sess.run(b), "tloss =", sess.run(loss)) print ("最终的结果 W =", sess.run(W), "tb =", sess.run(b), "tloss =", sess.run(loss))
训练过程显示的参数结果如下：
W = [ 0.26262569] b = [ 0.] loss = 1366.89
W = [ 4.73117256] b = [ 0.23248257] loss = 386.389
W = [ 3.15473843] b = [ 1.66317821] loss = 32.05
W = [ 3.10857034] b = [ 2.7120173] loss = 27.6106
W = [ 3.07622719] b = [ 3.44677091] loss = 25.432
W = [ 3.05356979] b = [ 3.96149445] loss = 24.3628
W = [ 3.03769755] b = [ 4.32207775] loss = 23.8381
W = [ 3.02657843] b = [ 4.57468128] loss = 23.5806
W = [ 3.01878905] b = [ 4.7516408] loss = 23.4542
W = [ 3.01333213] b = [ 4.87560892] loss = 23.3922
W = [ 3.00950956] b = [ 4.96245146] loss = 23.3617
W = [ 3.00683141] b = [ 5.02328825] loss = 23.3468
W = [ 3.00495553] b = [ 5.06590843] loss = 23.3395
W = [ 3.00364113] b = [ 5.09576511] loss = 23.3359
W = [ 3.00272036] b = [ 5.11667919] loss = 23.3341
W = [ 3.00207567] b = [ 5.13133097] loss = 23.3332
W = [ 3.00162363] b = [ 5.14159679] loss = 23.3328
W = [ 3.00130725] b = [ 5.14878702] loss = 23.3326
W = [ 3.00108552] b = [ 5.15382385] loss = 23.3325
W = [ 3.00093007] b = [ 5.15735531] loss = 23.3324
W = [ 3.00082135] b = [ 5.1598258] loss = 23.3324
最终的结果 W = [ 3.00074625] b = [ 5.16152811] loss = 23.3324
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3plt.scatter(x_data,y_data,c='r') plt.plot(x_data,sess.run(W)*x_data+sess.run(b)) plt.show()
最后
以上就是糊涂未来最近收集整理的关于使用TensorFlow实现简单的线性回归模型的全部内容，更多相关使用TensorFlow实现简单内容请搜索靠谱客的其他文章。
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