tensorflow入门

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tensorflow基本语法

#opencv tensorflow
#类比 语法 api 原理
#基础数据类型 运算符 流程 字典 数组
import tensorflow as tf
#常量
data1 = tf.constant(2,dtype=tf.int32)
#变量
data2 = tf.Variable(10,name='var')
print(data1)
print(data2)
'''
sess = tf.Session()
print(sess.run(data1))
init = tf.global_variables_initializer()
sess.run(init)
print(sess.run(data2))
sess.close()
# 本质 tf = tensor + 计算图
# tensor 数据
# op
# graphs 数据操作
# session
'''
init = tf.global_variables_initializer()
sess = tf.Session()
with sess:
    sess.run(init)
    print(sess.run(data2))

    Tensor("Const_2:0", shape=(), dtype=int32)
    <tf.Variable 'var_2:0' shape=() dtype=int32_ref>
    10

四则运算

import tensorflow as tf
data1 = tf.constant(6)
data2 = tf.constant(2)
dataAdd = tf.add(data1,data2)
dataMul = tf.multiply(data1,data2)
dataSub = tf.subtract(data1,data2)
dataDiv = tf.divide(data1,data2)
with tf.Session() as sess:
    print(sess.run(dataAdd))
    print(sess.run(dataMul))
    print(sess.run(dataSub))
    print(sess.run(dataDiv))
print('end!')

    8
    12
    4
    3.0
    end!

import tensorflow as tf
data1 = tf.constant(6)
data2 = tf.Variable(2)
dataAdd = tf.add(data1,data2)
dataCopy = tf.assign(data2,dataAdd)# dataAdd ->data2
dataMul = tf.multiply(data1,data2)
dataSub = tf.subtract(data1,data2)
dataDiv = tf.divide(data1,data2)
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    print(sess.run(dataAdd))
    print(sess.run(dataMul))
    print(sess.run(dataSub))
    print(sess.run(dataDiv))
    print('sess.run(dataCopy)',sess.run(dataCopy))#8->data2
    print('dataCopy.eval()',dataCopy.eval())#8+6->14->data = 14
    print('tf.get_default_session()',tf.get_default_session().run(dataCopy))
print('end!')

    8
    12
    4
    3.0
    sess.run(dataCopy) 8
    dataCopy.eval() 14
    tf.get_default_session() 20
    end!

矩阵基础

#placehold
import tensorflow as tf
data1 = tf.placeholder(tf.float32)
data2 = tf.placeholder(tf.float32)
dataAdd = tf.add(data1,data2)
with tf.Session() as sess:
    print(sess.run(dataAdd,feed_dict={data1:6,data2:2}))
    # 1 dataAdd 2 data (feed_dict = {1:6,2:2})
print('end!')

    8.0
    end!

#类比 数组 M行N列 []   内部[]  [里面 列数据]   [] 中括号整体 行数
#[[6,6]] [[6,6]]
import tensorflow as tf
data1 = tf.constant([[6,6]])
data2 = tf.constant([[2],
                     [2]])


data3 = tf.constant([[3,3]])
data4 = tf.constant([[1,2],
                     [3,4],
                     [5,6]])

print(data4.shape)# 维度
with tf.Session() as sess:
    print(sess.run(data4)) #打印整体
    print(sess.run(data4[0]))# 打印某一行
    print(sess.run(data4[:,0]))#MN 列
    print(sess.run(data4[0,1]))# 1 1  MN = 0 32 = M012 N01

    (3, 2)
    [[1 2]
     [3 4]
     [5 6]]
    [1 2]
    [1 3 5]
    2

矩阵运算

import tensorflow as tf
data1 = tf.constant([[6,6]])
data2 = tf.constant([[2],
                     [2]])
data3 = tf.constant([[3,3]])
data4 = tf.constant([[1,2],
                     [3,4],
                     [5,6]])
matMul = tf.matmul(data1,data2)
matMul2 = tf.multiply(data1,data2)
matAdd = tf.add(data1,data3)
with tf.Session() as sess:
    print(sess.run(matMul))#1 维 M=1 N2. 1X2(MK) 2X1(KN) = 1
    print(sess.run(matAdd))#1行2列
    print(sess.run(matMul2))# 1x2 2x1 = 2x2
    print(sess.run([matMul,matAdd]))

    [[24]]
    [[9 9]]
    [[12 12]
     [12 12]]
    [array([[24]]), array([[9, 9]])]

import tensorflow as tf
mat0 = tf.constant([[0,0,0],[0,0,0]])
mat1 = tf.zeros([2,3])
mat2 = tf.ones([3,2])
mat3 = tf.fill([2,3],15)
with tf.Session() as sess:
    #print(sess.run(mat0))
    #print(sess.run(mat1))
    #print(sess.run(mat2))
    print(sess.run(mat3))

    [[15 15 15]
     [15 15 15]]

import tensorflow as tf
mat1 = tf.constant([[2],[3],[4]])
mat2 = tf.zeros_like(mat1)
mat3 = tf.linspace(0.0,2.0,11)
mat4 = tf.random_uniform([2,3],-1,2)
with tf.Session() as sess:
    #print(sess.run(mat1))
    #print(sess.run(mat2))
    #print(sess.run(mat3))
    print(sess.run(mat4))

    [[ 1.01364231  0.03153861 -0.35802007]
     [ 1.68033934  1.30461025 -0.84316409]]

模块numpy的使用

#CURD
import numpy as np
data1 = np.array([1,2,3,4,5])
print(data1)
data2 = np.array([[1,2],
                  [3,4]])
print(data2)
#维度
print(data1.shape,data2.shape)
# zero ones
print(np.zeros([2,3]),np.ones([2,2]))
# 改查
data2[1,0] = 5
print(data2)
print(data2[1,1])
# 基本运算
data3 = np.ones([2,3])
print(data3*2)#对应相乘
print(data3/3)
print(data3+2)
# 矩阵+*
data4 = np.array([[1,2,3],[4,5,6]])
print(data3+data4)
print(data3*data4)

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