Inner Product between a Tensor and a Vector

This is an example of how to use the csdl inner function to compute the inner product between a tensor and a vector.

from csdl_om import Simulatorfrom csdl import Modelimport csdlimport numpy as np

class ExampleTensorVector(Model):
    def define(self):
        m = 3        n = 4        p = 5
        # Shape of the vectors        vec_shape = (m, )
        # Shape of the tensors        ten_shape = (m, n, p)
        # Values for the two vectors        vec1 = np.arange(m)
        # Number of elements in the tensors        num_ten_elements = np.prod(ten_shape)
        # Values for the two tensors        ten1 = np.arange(num_ten_elements).reshape(ten_shape)
        # Adding the vector and tensor to csdl        vec1 = self.declare_variable('vec1', val=vec1)        ten1 = self.declare_variable('ten1', val=ten1)
        # Tensor-Vector Inner Product specifying the first axis for        # Vector and Tensor        self.register_output(            'TenVecInner',            csdl.inner(ten1, vec1, axes=([0], [0])),        )

sim = Simulator(ExampleTensorVector())sim.run()
print('vec1', sim['vec1'].shape)print(sim['vec1'])print('ten1', sim['ten1'].shape)print(sim['ten1'])print('TenVecInner', sim['TenVecInner'].shape)print(sim['TenVecInner'])

[0. 1. 2.]ten1 (3, 4, 5)[[[ 0.  1.  2.  3.  4.]  [ 5.  6.  7.  8.  9.]  [10. 11. 12. 13. 14.]  [15. 16. 17. 18. 19.]]
 [[20. 21. 22. 23. 24.]  [25. 26. 27. 28. 29.]  [30. 31. 32. 33. 34.]  [35. 36. 37. 38. 39.]]
 [[40. 41. 42. 43. 44.]  [45. 46. 47. 48. 49.]  [50. 51. 52. 53. 54.]  [55. 56. 57. 58. 59.]]]TenVecInner (4, 5)[[100. 103. 106. 109. 112.] [115. 118. 121. 124. 127.] [130. 133. 136. 139. 142.] [145. 148. 151. 154. 157.]]