Update values in tensor along deeper dimensions












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I have tensor A of shape MxNxC where M stands for number of examples, N is number of features and C is 3 euler rotation angles. Also i have a tensor B of similar shape, but instead of angles there are coordinates.
What is needed is to convert both of these tensors to one containing affine transformation matrices so its shape would be like MxNx4x4. I dont know how to iterate over these tensors together, I've looked for tf.map_fn and tf.scan but they iterate only with the first dimension. What im looking for is some method to apply function like the one below to all of the elements along the last axes.



def f(angles, vector): #dimensions 3 or 3x1
...
return matrix # dimension 4x4


Any help would be useful, thanks!










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    0














    I have tensor A of shape MxNxC where M stands for number of examples, N is number of features and C is 3 euler rotation angles. Also i have a tensor B of similar shape, but instead of angles there are coordinates.
    What is needed is to convert both of these tensors to one containing affine transformation matrices so its shape would be like MxNx4x4. I dont know how to iterate over these tensors together, I've looked for tf.map_fn and tf.scan but they iterate only with the first dimension. What im looking for is some method to apply function like the one below to all of the elements along the last axes.



    def f(angles, vector): #dimensions 3 or 3x1
    ...
    return matrix # dimension 4x4


    Any help would be useful, thanks!










    share|improve this question

























      0












      0








      0







      I have tensor A of shape MxNxC where M stands for number of examples, N is number of features and C is 3 euler rotation angles. Also i have a tensor B of similar shape, but instead of angles there are coordinates.
      What is needed is to convert both of these tensors to one containing affine transformation matrices so its shape would be like MxNx4x4. I dont know how to iterate over these tensors together, I've looked for tf.map_fn and tf.scan but they iterate only with the first dimension. What im looking for is some method to apply function like the one below to all of the elements along the last axes.



      def f(angles, vector): #dimensions 3 or 3x1
      ...
      return matrix # dimension 4x4


      Any help would be useful, thanks!










      share|improve this question













      I have tensor A of shape MxNxC where M stands for number of examples, N is number of features and C is 3 euler rotation angles. Also i have a tensor B of similar shape, but instead of angles there are coordinates.
      What is needed is to convert both of these tensors to one containing affine transformation matrices so its shape would be like MxNx4x4. I dont know how to iterate over these tensors together, I've looked for tf.map_fn and tf.scan but they iterate only with the first dimension. What im looking for is some method to apply function like the one below to all of the elements along the last axes.



      def f(angles, vector): #dimensions 3 or 3x1
      ...
      return matrix # dimension 4x4


      Any help would be useful, thanks!







      python tensorflow tensor






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      share|improve this question










      asked Nov 23 '18 at 12:13









      mcstarionimcstarioni

      166




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          you can try something like this:



          A_flattened = tf.reshape(A, [-1, 3])# flatten it out
          B_flattened = tf.reshape(B, [-1, 3])
          AB_flattened = tf.map_fn(convert_to_mat, (A_flattened, B_flattened))# convert_to_mat should return a 4x4 matrix
          AB = tf.reshape(AB_flattened, [M, N, 4, 4])


          this should do the trick!






          share|improve this answer





















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            you can try something like this:



            A_flattened = tf.reshape(A, [-1, 3])# flatten it out
            B_flattened = tf.reshape(B, [-1, 3])
            AB_flattened = tf.map_fn(convert_to_mat, (A_flattened, B_flattened))# convert_to_mat should return a 4x4 matrix
            AB = tf.reshape(AB_flattened, [M, N, 4, 4])


            this should do the trick!






            share|improve this answer


























              0














              you can try something like this:



              A_flattened = tf.reshape(A, [-1, 3])# flatten it out
              B_flattened = tf.reshape(B, [-1, 3])
              AB_flattened = tf.map_fn(convert_to_mat, (A_flattened, B_flattened))# convert_to_mat should return a 4x4 matrix
              AB = tf.reshape(AB_flattened, [M, N, 4, 4])


              this should do the trick!






              share|improve this answer
























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                you can try something like this:



                A_flattened = tf.reshape(A, [-1, 3])# flatten it out
                B_flattened = tf.reshape(B, [-1, 3])
                AB_flattened = tf.map_fn(convert_to_mat, (A_flattened, B_flattened))# convert_to_mat should return a 4x4 matrix
                AB = tf.reshape(AB_flattened, [M, N, 4, 4])


                this should do the trick!






                share|improve this answer












                you can try something like this:



                A_flattened = tf.reshape(A, [-1, 3])# flatten it out
                B_flattened = tf.reshape(B, [-1, 3])
                AB_flattened = tf.map_fn(convert_to_mat, (A_flattened, B_flattened))# convert_to_mat should return a 4x4 matrix
                AB = tf.reshape(AB_flattened, [M, N, 4, 4])


                this should do the trick!







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 23 '18 at 14:33









                hampihampi

                1164




                1164






























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