# 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)

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## 代码设计

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

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])

)

# important step

init = tf.global_variables_initializer()

sess = tf.Session()

sess.run(init)

# plot the real data

fig = plt.figure()

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.pause(0.1)

# 0 相关源码 1 数据可视化的作用及常用方法 1.1 为什么要数据可视化 1.1.1 何为数据可视化? ◆ 将数据以图形图像的形式展现出来 ◆ 人类可以对三维及以下的数据产生直观的感受 1.1.
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