TensorFlow学习心得

初次尝试TensorFlow

import tensorflow as tf
import numpy as np
#create data
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1+0.3
#随机生成weight，biases为0
Weight = tf.Variable(tf.random_uniform([1],-1.0,1.0))
biases = tf.Variable(tf.zeros([1]))
#设置假设函数，loss函数，优化器，以及init初始化
y = Weight*x_data+biases
loss = tf.reduce_mean(tf.square(y-y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
init = tf.initialize_all_variables()
#激活初始化
sess = tf.Session()
sess.run(init)
#for循环训练
for step in range(201):
    sess.run(train)
    if step%20==0:
        print(step,sess.run(Weight),sess.run(biases))

矩阵相乘以及输出

import tensorflow as tf
import numpy as np

maxtrix1 =  tf.constant([[3,3]])
maxtrix2 = tf.constant([[2],[2]])
#maxtrix multipy np.dot(maxtrix1,maxtrix2)
# 这两行一样的这行是numpy的矩阵相乘，下面是TensorFlow的矩阵相乘
product = tf.matmul(maxtrix2,maxtrix1)
#方法一
sess = tf.Session()
result = sess.run(product)
print(result)
sess.close()
#方法二,不用close
with tf.Session() as sess:
    result2 = sess.run(product)
    print(result2)

定义变量，相加，更新

import tensorflow as tf
import numpy as np

state = tf.Variable(0,name="counter")
#print(state.name)
one = tf.constant(1)

new_value = tf.add(state,one)
update = tf.assign(state,new_value)

#一定要有这个init，如果定义了一个变量
init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))

placeholder

意思是运行的时候再给placeholder赋值,在sess.run时确定placeholder的值
它有几个参数，第一个参数是你要保存的数据的数据类型，大多数是tensorflow中的float32数据类型，后面的参数就是要保存数据的结构，比如要保存一个1×2的矩阵，则struct=[1 2]。

xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])
sess.run(train_step,feed_dict={xs:x_data,ys:y_data})

数据初始化

init = tf.initialize_all_variables()

建造神经网络

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(input,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(input,weight)+biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

#设置输入数据，加noise
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])
#设置hidden layer和output layer
l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction = add_layer(l1,10,1,activation_function=None)

#设置loss函数，使用gd优化
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
#激活初始化
init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)
#绘制散点图，连续变化
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data,s=10)
plt.ion()
plt.show()

#开始训练
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})
        #绘制预测曲线
        lines = ax.plot(x_data,prediction_value,"r-",lw=5)
        plt.pause(0.1)

tensorboard神经网络可视化工具

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(input,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.add(tf.matmul(input,weight),biases)
        if activation_function is None:
            output = Wx_plus_b
        else:
            output = activation_function(Wx_plus_b)
        return output


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')

h1 = add_layer(xs,1,10,activation_function = tf.nn.relu)
prediction = add_layer(h1,10,1,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.