TF之NN：利用神经网络系统自动学习散点(二次函数+noise+优化修正)输出结果可视化(matplotlib动态演示)

输出结果

代码设计

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       

# define placeholder for inputs to network
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)

# the error between prediciton and real data
loss = tf.reduce_mean(
    tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1])
    )
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)  
# important step
init = tf.global_variables_initializer()
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)
plt.ion()
plt.show()

for i in range(1000): 
    # training
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data}) 
    if i % 50 == 0:  
        # to visualize the result and improvement
        try:
            ax.lines.remove(lines[0])
        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.title('Matplotlib,NN,Efficient learning,Approach,Quadratic function --Jason Niu')
        plt.pause(0.1)