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#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) 10 import tensorflow as tfdata1 = 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 tfdata1 = tf.constant(6)data2 = tf.Variable(2)dataAdd = tf.add(data1,data2)dataCopy = tf.assign(data2,dataAdd)# dataAdd ->data2dataMul = 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!
#placeholdimport tensorflow as tfdata1 = 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 tfdata1 = 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
import tensorflow as tfdata1 = 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 tfmat0 = 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 tfmat1 = 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]]
#CURDimport numpy as npdata1 = 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 onesprint(np.zeros([2,3]),np.ones([2,2]))# 改查data2[1,0] = 5print(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) 转载地址：http://mclnn.baihongyu.com/