def setup(self):
# Creating the values for two tensors
val1 = np.array([[1, 5], [10, -3]])
val2 = np.array([[2, 6, 9], [-1, 2, 4]])
# Declaring the two input tensors
tensor1 = self.declare_input('tensor1', val=val1)
tensor2 = self.declare_input('tensor2', val=val2)
# Creating the output for matrix multiplication
self.register_output('ElementwiseMinWrongSize',
ot.min(tensor1, tensor2))
def setup(self):
m = 2
n = 3
# Shape of the three tensors is (2,3)
shape = (m, n)
# Creating the values for two tensors
val1 = np.array([[1, 5, -8], [10, -3, -5]])
val2 = np.array([[2, 6, 9], [-1, 2, 4]])
# Declaring the two input tensors
tensor1 = self.declare_input('tensor1', val=val1)
tensor2 = self.declare_input('tensor2', val=val2)
# Creating the output for matrix multiplication
self.register_output('ElementwiseMin', ot.min(tensor1, tensor2))
def setup(self):
m = 2
n = 3
o = 4
p = 5
q = 6
# Shape of a tensor
tensor_shape = (m, n, o, p, q)
num_of_elements = np.prod(tensor_shape)
# Creating the values of the tensor
val = np.arange(num_of_elements).reshape(tensor_shape)
# Declaring the tensor as an input
ten = self.declare_input('tensor', val=val)
# Computing the minimum across the entire tensor, returns single value
self.register_output('ScalarMin', ot.min(ten))
def setup(self):
m = 2
n = 3
o = 4
p = 5
q = 6
# Shape of a tensor
tensor_shape = (m, n, o, p, q)
num_of_elements = np.prod(tensor_shape)
# Creating the values of the tensor
val = np.arange(num_of_elements).reshape(tensor_shape)
# Declaring the tensor as an input
ten = self.declare_input('tensor', val=val)
# Computing the axiswise minimum on the tensor
axis = 1
self.register_output('AxiswiseMin', ot.min(ten, axis=axis))