TensorFlow拟合曲线

本文是官方例子做了一点小小的改动，基于TensorFlow实现拟合曲线，对初学TensorFlow者有一定帮助。

训练500步，每50步输出一次，并显示在plt中。

开始训练：

训练200步后：

训练完成：

以下是完整代码：

#coding: utf-8
#author: 吴晶
#wechat: 18007148050



import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1,out_size])) + 0.1
    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
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) - 0.5 + noise

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

l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)

prediction = add_layer(l1,10,1,activation_function=None)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    fig = plt.figure()
    ax = fig.add_subplot(1,1,1)
    ax.scatter(x_data,y_data)
    plt.show(block = False)

    sess.run(init)

    for train in range(500):

        sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
        prediction_value = sess.run(prediction, feed_dict={xs: x_data})
        lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
        plt.pause(0.1)
        try:
            ax.lines.remove(lines[0])
        except Exception:
            pass
        if train % 50 == 0:

            print(train,sess.run(loss,feed_dict={xs:x_data,ys:y_data}))