def setup(self): a = np.arange(4) vec = self.declare_input('a', val=a) # Summation of all the entries of a vector self.register_output('einsum_summ1', ot.einsum_new_api(vec, operation=[(33, )]))
def setup(self): a = np.arange(4) vec = self.declare_input('a', val=a) self.register_output( 'einsum_summ1_sparse_derivs', ot.einsum_new_api(vec, operation=[(33, )], partial_format='sparse'))
def setup(self): shape2 = (5, 4) b = np.arange(20).reshape(shape2) mat = self.declare_input('b', val=b) # reorder of a matrix self.register_output( 'einsum_reorder1', ot.einsum_new_api(mat, operation=[(46, 99), (99, 46)]))
def setup(self): a = np.arange(4) vec = self.declare_input('a', val=a) # Inner Product of 2 vectors self.register_output( 'einsum_inner1', ot.einsum_new_api(vec, vec, operation=[(0, ), (0, )]))
def setup(self): a = np.arange(4) vec = self.declare_input('a', val=a) # Special operation: sum all the entries of the first and second # vector to a single scalar self.register_output( 'einsum_special2', ot.einsum_new_api(vec, vec, operation=[(1, ), (2, )]))
def setup(self): a = np.arange(4) vec = self.declare_input('a', val=a) self.register_output( 'einsum_outer1', ot.einsum_new_api( vec, vec, operation=[('rows', ), ('cols', ), ('rows', 'cols')], ))
def setup(self): shape2 = (5, 4) b = np.arange(20).reshape(shape2) mat = self.declare_input('b', val=b) self.register_output( 'einsum_reorder1_sparse_derivs', ot.einsum_new_api(mat, operation=[(46, 99), (99, 46)], partial_format='sparse'))
def setup(self): # Shape of Tensor shape3 = (2, 4, 3) c = np.arange(24).reshape(shape3) # Declaring tensor tens = self.declare_input('c', val=c) self.register_output( 'einsum_summ2_sparse_derivs', ot.einsum_new_api(tens, operation=[(33, 66, 99)], partial_format='sparse'))
def setup(self): # Shape of Tensor shape3 = (2, 4, 3) c = np.arange(24).reshape(shape3) # Declaring tensor tens = self.declare_input('c', val=c) # Summation of all the entries of a tensor self.register_output( 'einsum_summ2', ot.einsum_new_api( tens, operation=[(33, 66, 99)], ))
def setup(self): a = np.arange(4) vec = self.declare_input('a', val=a) # Special operation: summation of all the entries of first # vector and scalar multiply the second vector with the computed # sum self.register_output( 'einsum_special1', ot.einsum_new_api( vec, vec, operation=[(1, ), (2, ), (2, )], ))
def setup(self): # Shape of Tensor shape3 = (2, 4, 3) c = np.arange(24).reshape(shape3) # Declaring tensor tens = self.declare_input('c', val=c) # reorder of a tensor self.register_output( 'einsum_reorder2', ot.einsum_new_api( tens, operation=[(33, 66, 99), (99, 66, 33)], ))
def setup(self): a = np.arange(4) vec = self.declare_input('a', val=a) # Shape of Tensor shape3 = (2, 4, 3) c = np.arange(24).reshape(shape3) # Declaring tensor tens = self.declare_input('c', val=c) # Outer Product of a tensor and a vector self.register_output( 'einsum_outer2', ot.einsum_new_api( tens, vec, operation=[(0, 1, 30), (2, ), (0, 1, 30, 2)], ))
def setup(self): a = np.arange(4) vec = self.declare_input('a', val=a) # Shape of Tensor shape3 = (2, 4, 3) c = np.arange(24).reshape(shape3) # Declaring tensor tens = self.declare_input('c', val=c) self.register_output( 'einsum_outer2_sparse_derivs', ot.einsum_new_api(tens, vec, operation=[ (0, 1, 30), (2, ), (0, 1, 30, 2), ], partial_format='sparse'))