环境
Mac os
python3.6
代码
# coding:utf-8"""
Author: roguesir
Date: 2017/8/30
GitHub: https://roguesir.github.com
Blog: http://blog.csdn.net/roguesir
"""from __future__ import print_function
import tensorflow as tf
import numpy as npdef add_layer(inputs, in_size, out_size, activation_function=None):# add one more layer and return the output of this layerWeights = 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) + biasesif activation_function is None:outputs = Wx_plus_belse: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# 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 prediction 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
if int((tf.__version__).split('.')[1]) < 12:init = tf.initialize_all_variables()
else:init = tf.global_variables_initializer()sess = tf.Session()
sess.run(init)for i in range(1000):# trainingsess.run(train_step, feed_dict={xs: x_data, ys: y_data})if i % 50 == 0:# to see the step improvementprint(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))