# -*- coding:utf-8 -*- import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # 线性回归 # 随机生成200个点 x_data = np.linspace(-0.5, 0.5, 200)[:, np.newaxis] # 在-0.5~0.5之间随机取200个点 并增加维度 200行1列 noise = np.random.normal(0, 0.02, x_data.shape) # 生成随机噪点 y_data = np.square(x_data) +noise # 创建占位符 x = tf.placeholder(tf.float32, [None, 1]) # 1列 不限行 y = tf.placeholder(tf.float32, [None, 1]) # 1列 不限行 # 定义神经网路中间层 Weights_L1 = tf.Variable(tf.random_normal([1, 10])) # 随机数变量 1行10列 biases_L1 = tf.Variable(tf.zeros([1, 10])) Wx_plus_b_L1 = tf.matmul(x, Weights_L1) + biases_L1 # 两个矩阵相乘再加一个 L1 = tf.nn.tanh(Wx_plus_b_L1) # 双曲正切函数 # 输出层 Weights_L2 = tf.Variable(tf.random_normal([10, 1])) biases_L2 = tf.Variable(tf.zeros([1, 1])) Wx_plus_b_L2 = tf.matmul(L1, Weights_L2) + biases_L2 prediction = tf.nn.tanh(Wx_plus_b_L2) # 二次代价函数 loss = tf.reduce_mean(tf.square(y-prediction)) train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for _ in range(2000): sess.run(train_step, feed_dict={x: x_data, y: y_data}) # 获得预测值 prediction_value = sess.run(prediction, feed_dict={x: x_data}) # 画图 plt.figure() plt.scatter(x_data, y_data) plt.plot(x_data, prediction_value, 'r-', lw=5) plt.show()
tensorflow入门学习笔记 6.非线性回归
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