版权声明：本样板的所有内容，包括文字、图片,均为原创。如有问题可以邮箱[email protected] https://blog.csdn.net/qq_29893385/article/details/82144776
首先说明一下TensorFlow的安装过程，可以参考下方链接进行，在Linux系统进行安装非常简单，
https://blog.csdn.net/y1250056491/article/details/78670710/
这次主要说明一下如何使用TensorFlow构建一个简单的神经网络去进行随机点集的拟合。
程序：
# -*- coding: utf-8 -*-
"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
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)
for i in range(1000):
# training
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 50 == 0:
# to see the step improvement
print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
基本说明一下，先导入模块然后def了一个网络层函数，使用random函数构造随机点集作为训练的数据集，然后调用层函数构造网络结构进行训练，期间使用可视化观看拟合程度。
训练结果如下：
最后输出的是LOSS，每隔50次进行一次打印，来确定学习的效果。下一次会添加可视化模块，来更直观的了解神经网络的学习过程。
参考博客：