用Batch Gradient Descent来拟合sinx

#author:victor
#import module
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import math

#define a add_layer function
def add_layer(inputs, in_size, out_size, activation_function=None):
    # add one more layer and return the output of this layer
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]))
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs
 
# Make up some real data
x_data = np.linspace(-np.pi,np.pi,300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.sin(x_data) + noise
 
# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
# add hidden layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, activation_function=tf.nn.tanh)
 
loss = tf.reduce_mean(tf.square(ys - prediction))
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(loss)
 
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
 
# plot the real data
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data, y_data)
# Interactive mode on
plt.ion()
plt.show()
 
for i in range(5000):
    # training
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    if i % 50 == 0:
        # to visualize the result and remove the previous line 
        try:
            #ax.lines.remove(lines[0])             
            #每次抹除线，先暂停0.1秒
             plt.pause(0.1)
             ax.lines.remove(lines[0])  #在图片中，去除掉第一个线段
        except Exception:
            pass
        prediction_value = sess.run(prediction, feed_dict={xs: x_data})
        # plot the prediction
        lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
        plt.pause(0.1)

运行效果