# -*- coding: utf-8 -*- """ Created on Mon Apr 22 21:02:02 2019 @author: a """ # -*- coding: utf-8 -*- """ Created on Sat Dec 1 16:53:26 2018 @author: a """ import tensorflow as tf ############创建三维矩阵 x = tf.placeholder(tf.int32,shape=[],name="input") y = tf.placeholder(tf.int32,shape=[],name="input2") z = tf.placeholder(tf.int32,shape=[],name="input3") matrix_element_num=x*y*z batch_sentence_nodes_vectors=tf.zeros(matrix_element_num,tf.float64) batch_sentence_nodes_vectors=tf.reshape(batch_sentence_nodes_vectors,[x,y,z]) sess = tf.Session() print (tf.shape(x)) xiaojie=sess.run([x,y,z],feed_dict={x:7,y:8,z:9}) print(xiaojie) xiaojie2=sess.run(batch_sentence_nodes_vectors,feed_dict={x:7,y:8,z:9}) ############创建三维矩阵 ############我们目前能够做的就是,指定第一维度的值,然后将一个二维矩阵,必须小于三维矩阵的第二维度和第三维度,替换掉一整个剖面。 #def modify_one_column(tensor,columnTensor,index,numlines,numcolunms):#index也是tensor def modify_one_profile(tensor,_2DmatrixTensor,index_firstDimension,size_firstDimension,size_secondDimension,size_thirdDimension): ##tensor为三维矩阵 ##首先,我们用index_firstDimenion取出整个tensor在第一维度取值index_firstDimenion的剖面,然后分为剖面左侧部分,剖面右侧部分,然后将取出的剖面替换成二维矩阵 _2DmatrixTensor=tf.expand_dims(_2DmatrixTensor,axis=0) #扩展成为三维 new_tensor_left=tf.slice(tensor, [0,0,0], [index_firstDimension,size_secondDimension,size_thirdDimension]) #剖面左侧部分 new_tensor_right=tf.slice(tensor, [index_firstDimension+1,0,0], [size_firstDimension-index_firstDimension-1,size_secondDimension,size_thirdDimension]) #剖面右侧部分 new_tensor=tf.concat([new_tensor_left,_2DmatrixTensor,new_tensor_right],0) return new_tensor_left,new_tensor_right,new_tensor #下面测试将一个不够维度的二维矩阵补齐按指定维度补齐 def buqi_2DmatrixTensor(_2DmatrixTensor,lines,columns,targetlines,targetcolumns): #首先在列上补齐 buqi_column=tf.zeros([lines,targetcolumns-columns],dtype=tf.float64) _2DmatrixTensor=tf.concat([_2DmatrixTensor,buqi_column],axis=1) buqi_line=tf.zeros(shape=[targetlines-lines,targetcolumns],dtype=tf.float64) _2DmatrixTensor=tf.concat([_2DmatrixTensor,buqi_line],axis=0) return _2DmatrixTensor #_2DmatrixTensor=tf.ones(y*z,tf.float64) #_2DmatrixTensor=tf.reshape(_2DmatrixTensor,[y,z]) size_firstDimension=tf.constant(7,tf.int32) size_secondDimension=tf.constant(8,tf.int32) size_thirdDimension=tf.constant(9,tf.int32) #_2DmatrixTensor=tf.ones(1*2,tf.float64) #_2DmatrixTensor=tf.reshape(_2DmatrixTensor,[1,2]) #lines=tf.constant(1,tf.int32) #columns=tf.constant(2,tf.int32) #_2DmatrixTensor=buqi_2DmatrixTensor(_2DmatrixTensor,lines,columns,size_secondDimension,size_thirdDimension) #_2DmatrixTensor=tf.ones(8*2,tf.float64) #_2DmatrixTensor=tf.reshape(_2DmatrixTensor,[8,2]) #lines=tf.constant(8,tf.int32) #columns=tf.constant(2,tf.int32) #_2DmatrixTensor=buqi_2DmatrixTensor(_2DmatrixTensor,lines,columns,size_secondDimension,size_thirdDimension) _2DmatrixTensor=tf.ones(1*9,tf.float64) _2DmatrixTensor=tf.reshape(_2DmatrixTensor,[1,9]) lines=tf.constant(1,tf.int32) columns=tf.constant(9,tf.int32) _2DmatrixTensor=buqi_2DmatrixTensor(_2DmatrixTensor,lines,columns,size_secondDimension,size_thirdDimension) ## for index in range(7): index_tensor=tf.constant(index,tf.int32) new_tensor_left,new_tensor_right,batch_sentence_nodes_vectors=modify_one_profile(batch_sentence_nodes_vectors,_2DmatrixTensor,index_tensor,size_firstDimension,size_secondDimension,size_thirdDimension) print (sess.run(batch_sentence_nodes_vectors,feed_dict={x:7,y:8,z:9}))
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