arange(), linspace(), logspace() and normal() in PyTorch

arange() can create the 1D tensor of zero or more integers or floating-point numbers as shown below:

*Memos:

• arange() can be used only from torch but not from a tensor.
• The 1st argument is start which is 0 by default.
• The 2nd argument is end.
• The 3rd argument is step which is 1 by default.
• Setting only one argument sets 0 to start.
• There is range() which is similar to arange() but range() is deprecated.

import torch

torch.arange(5)
# tensor([0, 1, 2, 3, 4])
torch.arange(5.)
# tensor([0., 1., 2., 3., 4.])

torch.arange(5, 15)
# tensor([5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
torch.arange(5., 15.)
# tensor([5., 6., 7., 8., 9., 10., 11., 12., 13., 14.])

torch.arange(5, 15, 3)
# tensor([5, 8, 11, 14])
torch.arange(5., 15., 3.)
# tensor([5., 8., 11., 14.])

torch.arange(-5, 5)
# tensor([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4])
torch.arange(-5., 5.)
# tensor([-5., -4., -3., -2., -1., 0., 1., 2., 3., 4.])

torch.arange(-5, 5, 3)
# tensor([-5, -2, 1, 4])
torch.arange(-5., 5., 3.)
# tensor([-5., -2., 1., 4.])

torch.arange(0)
torch.arange(0, 0)
torch.arange(0, 0, 3)
# tensor([], dtype=torch.int64)

torch.arange(0.)
torch.arange(0., 0.)
torch.arange(0., 0., 3.)
# tensor([])

linspace()
can create the 1D tensor of the zero or more floating-point numbers, integers or complex numbers which are evenly spaced from start to end as shown below:

*Memos:

• linspace() can be used only from torch but not from a tensor.
• The 1st argument(Required) is start.
• The 2nd argument(Required) is end.
• The 3rd argument(Required) is steps.
• Setting start and end of integer type is not enough to get the tensor of integer type so we must set integer type to dtype.
• The default type is float32.

import torch

torch.linspace(10, 20, 0)
torch.linspace(10., 20., 0)
# tensor([])

torch.linspace(10, 20, 1)
torch.linspace(10., 20., 1)
tensor([10.])

torch.linspace(10, 20, 2)
torch.linspace(10., 20., 2)
# tensor([10., 20.])

torch.linspace(10, 20, 3)
torch.linspace(10., 20., 3)
# tensor([10., 15., 20.])

torch.linspace(10, 20, 4)
torch.linspace(10., 20., 4)
# tensor([10.0000, 13.3333, 16.6667, 20.0000])

torch.linspace(10, 20, 4, dtype=torch.int64)
# tensor([10, 13, 16, 20], dtype=torch.int64)

torch.linspace(10+6j, 20+3j, 4)
# tensor([10.0000+6.j, 13.3333+5.j, 16.6667+4.j, 20.0000+3.j])

logspace()
can basically create the 1D tensor of the zero or more floating-point numbers, integers or complex numbers which are evenly spaced from basestart to baseend as shown below:

*Memos:

• logspace() can be used only from torch but not from a tensor.
• The 1st argument(Required) is start.
• The 2nd argument(Required) is end.
• The 3rd argument(Required) is steps.
• The 4th argument(Optional) is base which is 10.0 by default.
• The exponential parts ^start and ^end are evenly spaced.
• Setting start and end of integer type is not enough to get the tensor of integer type so we must set integer type to dtype.

import torch

torch.logspace(10, 20, 0)
torch.logspace(10., 20., 0)
# tensor([])
torch.logspace(10, 20, 1)
torch.logspace(10., 20., 1)
# tensor([1.0000e+10])
torch.logspace(10, 20, 2)
torch.logspace(10., 20., 2)
# tensor([1.0000e+10, 1.0000e+20])
torch.logspace(10, 20, 3)
torch.logspace(10., 20., 3)
# tensor([1.0000e+10, 1.0000e+15, 1.0000e+20])
torch.logspace(10, 20, 4)
torch.logspace(10., 20., 4)
# tensor([1.0000e+10, 2.1544e+13, 4.6416e+16, 1.0000e+20])
torch.logspace(10, 20, 4, 100)
torch.logspace(10., 20., 4, 100)
# tensor([1.0000e+20, 4.6416e+26, 2.1544e+33, inf])

torch.logspace(10, 20, 4, 100, dtype=torch.int64)
# tensor([-9223372036854775808,
#         -9223372036854775808, 
#         -9223372036854775808,
#         -9223372036854775808])

torch.logspace(10+6j, 20+3j, 4, 100)
# tensor([-8.0012e+19+5.9985e+19j,
#         -2.3708e+26-3.9904e+26j,
#         1.9593e+33-8.9592e+32j,
#         inf+infj])

normal() can create the 1D or more D tensor of zero or more random floating-point numbers(Default) or complex numbers from normal distribution as shown below:

*Memos:

• normal() can be used only from torch but not from a tensor.
• The default type is float32.
• The 1st argument is mean.
• The 2nd argument is std(Standard deviation).
• The 3rd argument is size.

import torch

torch.normal(0, 1, (0,))
# tensor([])
torch.normal(0, 1, (3,))
# tensor([2.4737, 1.0925, -0.2984])
torch.normal(0, 1, (3, 2))
# tensor([[ 1.5716,  0.2892], [-2.7665, -1.1040], [-0.8145, -0.0520]])
torch.normal(0, 1, (3, 2, 4))
# tensor([[[0.4032, -1.1011, -0.2356, -2.3010],
#          [0.3675, -0.1115, -0.9581, -1.3232]],
#         [[1.0273, 0.9534, 1.2640, -0.2971],
#          [2.5688, -0.0851, 0.7737, 1.2036]],
#         [[0.7446, -0.2630, -1.4919, -0.7096],
#          [-0.3706, 0.0073, 0.8246, 0.9313]]])
torch.normal(0, 1, (3, 2, 4), dtype=torch.complex64)
# tensor([[[0.1640-0.8105j, -0.0654+0.2241j,
#           0.1629-0.8095j, -0.2853+1.0529j],
#          [-0.4795-0.5646j, -0.6884-0.1562j,
#           -0.6333+0.7238j, 0.8710-1.1705j]],
#         [[-1.5067+1.2063j, 0.6296-0.0607j,
#           0.7197-0.4113j, 0.1742+0.3301j],
#          [-0.4388+1.0028j, 0.0297+0.2668j,
#           -0.3234+0.7390j,  0.0336+0.5532j]],
#         [[0.1296+0.9593j, -0.1632+0.3848j,
#           -0.6574-0.0247j, -0.6997-0.0548j],
#          [0.5176+1.0693j, 0.6728+1.2304j,
#           -0.6519+0.0970j, -0.5081-0.4026j]]])

torch.normal(5, 4, (0,))
# tensor([])
torch.normal(5, 4, (3,))
# tensor([7.2012, -1.6016, 5.2472])
torch.normal(5, 4, (3, 2))
# tensor([[9.6180, 12.7688], [-1.8151, 3.9245], [0.3551, 4.5507]])
torch.normal(5, 4, (3, 2, 4))
# tensor([[[9.7707+5.7214j, 4.7577+12.6819j,
#           7.8147+9.3258j, 4.5580+9.2139j],
#          [1.6257+3.8480j, 1.3294+4.7965j,
#           2.8914+4.5585j, 5.9573+4.1559j]],
#         [[8.0873+4.8930j, 2.9459+4.8999j,
#           6.6957+3.7738j, 1.4319+7.7348j],
#          [5.5551+5.8145j, 1.9599+8.7604j,
#           6.1416+5.7876j, 2.5204+3.0414j]],
#         [[4.5839+6.1299j, 3.1245+3.8965j,
#           9.1091+6.3294j, 2.8272+8.0571j],
#          [2.5482+3.1353j, 3.4173+2.0660j,
#           9.1401+5.8971j, 4.5078+4.3233j]]])