莫烦tensorflow系列教程学习

1.普通机器学习预测函数系数(y=0.1x+0.3)

# -*- coding:gbk -*-
import tensorflow as tf
import numpy as np

#生成数据,y=0.1x+0.3
x_data=np.random.rand(100).astype(np.float32)
y_data=x_data*0.1+0.3


###开始创建tensorflow结构###
weight=tf.Variable(tf.random_uniform([1],-1.0,1.0))
biases=tf.Variable(tf.zeros([1]))

y=weight*x_data+biases

#误差
loss=tf.reduce_mean(tf.square(y-y_data))
#优化器
optimizer=tf.train.GradientDescentOptimizer(0.5)
#用优化器减少误差
train=optimizer.minimize(loss)

#定义初始化变量
init=tf.initialize_all_variables()

###完成创建tensorflow结构###

sess=tf.Session()
sess.run(init)   #激活init

for step in range(201):
    sess.run(train)
    if step%20==0:
        print(step,sess.run(weight),sess.run(biases))

2.构建简单神经网络预测y=x^2,可视化界面显示

# -*- coding:gbk -*-
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs,in_size,out_size,activation_function=None):
    weight=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,weight)+biases
    if activation_function==None:
        outputs=wx_plus_b
    else:
        outputs=activation_function(wx_plus_b)
    return outputs


#numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)
#在指定的间隔内返回均匀间隔的数字
#[:,np.newaxis]给每个元素增加新维度,就相当于矩阵转置,一行多列变一列多行
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

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)

loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))

train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init=tf.initialize_all_variables()
sess=tf.Session()
sess.run(init)

#呼出画图窗口
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
#不暂停,持续更新状态
plt.ion()
plt.show()

for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i%50==0:
        #print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
        try:
            ax.lines.remove(lines[0])
        except Exception:
            pass
        prediction_value=sess.run(prediction,feed_dict={xs:x_data})
        lines=ax.plot(x_data,prediction_value,'r-',lw=5)
        plt.pause(0.1)

3.tensorflow board学习。。。好像就是添加几个命名。。。。。

# -*- coding:gbk -*-
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs,in_size,out_size,activation_function=None):
    with tf.name_scope('layer'):
        with tf.name_scope('weight'):
            weight=tf.Variable(tf.random_normal([in_size,out_size]),name='w')
        with tf.name_scope('biases'):
            biases=tf.Variable(tf.zeros([1,out_size])+0.1,name='b')
        with tf.name_scope('wx_plus_b'):
            wx_plus_b=tf.matmul(inputs,weight)+biases
        if activation_function==None:
            outputs=wx_plus_b
        else:
            outputs=activation_function(wx_plus_b)
        return outputs


#numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)
#在指定的间隔内返回均匀间隔的数字
#[:,np.newaxis]给每个元素增加新维度,就相当于矩阵转置,一行多列变一列多行
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

with tf.name_scope('inputs'):
    xs=tf.placeholder(tf.float32,[None,1],name='x_input')
    ys=tf.placeholder(tf.float32,[None,1],name='y_input')

l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activation_function=None)

with tf.name_scope('loss'):
    loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))

with tf.name_scope('train'):
    train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init=tf.initialize_all_variables()
sess=tf.Session()
writer=tf.summary.FileWriter("logs/",sess.graph)
sess.run(init)


for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i%50==0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

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