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承接上次文章,这次是在观看python教程后,使用matplotlib可视化工具,对神经网络训练过程进行可视化,更直观了解机器学习学到了什么东西。
代码如下:
# -*- coding: utf-8 -*-
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
# Make up some real data
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
##plt.scatter(x_data, y_data)
##plt.show()
# 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=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 可视化数据集
fig = plt.figure()
ax = fig.add_subplot(1,1,1) ###ax表示连续作图 曲线
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]) ###抹除前一条曲线
except Exception:
pass
prediction_value = sess.run(prediction, feed_dict={xs: x_data}) ###value存储prediction数值
# plot the prediction
lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
plt.pause(2)
plt.pause(10)
在真实运算中,可以对最后的plt.pause选择时间间隔为0.1s,这样变化效果更快,小编为了截图,所以暂停时间稍长,实际运行效果如下:
第一个图是训练刚开始的时候,第二张图是最后的拟合效果图,还不错。