Tensorflow 初步——拟合二次函数曲线

Tensorflow基本使用

目标

拟合二次函数 y = x^2 + b的曲线图

import numpy as np
import  tensorflow as tf
import  matplotlib.pyplot as plt
def add_layer(inputs,insize,outsize,activation_func=None):
  Weights = tf.Variable(tf.random_normal([insize,outsize]))
  bias = tf.Variable(tf.zeros([1,outsize])+0.1)
  wx_plus_b = tf.matmul(inputs,Weights) + bias
  if activation_func:
      return activation_func(wx_plus_b)
  else:
      return wx_plus_b


x_data = np.linspace(-1,1,300)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data)  + noise

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
plt.ion()
plt.show()

xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])


l1 = add_layer(xs,1,10,activation_func=tf.nn.relu)
preds = add_layer(l1,10,1,activation_func=None)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - preds),reduction_indices=[1]))
train = tf.train.GradientDescentOptimizer(0.05).minimize(loss)

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  for i in range(2000):
      sess.run(train,feed_dict={xs:x_data,ys:y_data})
      if i %  50 == 0:
          preds_val = sess.run(preds,feed_dict={xs:x_data,ys:y_data})
          try:
              ax.lines.remove(lines[0])
          except:
              pass
          lines = ax.plot(x_data,preds_val,'r-',lw=5)
          plt.pause(0.5)

拟合结果