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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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randn and randn_like in PyTorch

#python #pytorch #randn #randnlike

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*Memos:

randn() can create the 0D or more D tensor of the zero or more random floating-point numbers(Default) or complex numbers most of the time about between 2 and -2 from standard normal distribution as shown below:

*Memos:

  • randn() can be used with torch but not with a tensor.
  • The 1st or more arguments with torch are size(Required-Type:int, tuple of int, list of int or size()).
  • There is dtype argument with torch(Optional-Default:None-Type:dtype): *Memos:
  • There is device argument with torch(Optional-Default:None-Type:str, int or device()): *Memos:
  • There is requires_grad argument with torch(Optional-Default:False-Type:bool): *Memos:
    • requires_grad= must be used.
    • My post explains requires_grad argument.
  • There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument. 
import torch torch.randn(size=()) torch.randn(size=torch.tensor(8).size()) # tensor(0.3306)  torch.randn(size=(0,)) torch.randn(0) torch.randn(size=torch.tensor([]).size()) # tensor([])  torch.randn(size=(3,)) torch.randn(3) torch.randn(size=torch.tensor([8, 3, 6]).size()) # tensor([-0.6635, 1.6257, -2.0568])  torch.randn(size=(3, 2)) torch.randn(3, 2) torch.randn(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size()) # tensor([[-1.9382, 1.1300], [0.4138, 0.8232], [-0.9810, -0.1410]])  torch.randn(size=(3, 2, 4)) torch.randn(3, 2, 4) # tensor([[[-0.1585, 0.6327, -0.2487, -0.0963], # [-0.2869, 2.4429, 0.5098, -1.1569]], # [[ 0.2823, 1.0742, 1.0531, -0.2384], # [-0.2275, -2.6246, 0.3347, -1.6514]], # [[ 0.2934, -1.2568, 0.1798, 1.1989], # [ 0.5166, -1.2155, 0.5101, -0.7396]]])  torch.randn(size=(3, 2, 4), dtype=torch.complex64) torch.randn(3, 2, 4, dtype=torch.complex64) # tensor([[[0.5911+0.4230j, -0.1695-0.5487j, # -0.5253-1.3477j, 0.2331+1.9928j], # [1.1815+0.8113j, 0.1307+2.0229j, # 0.5588+0.6502j, 0.8352-1.1519j]], # [[1.5789+0.5048j, -1.2516-1.0842j, # 0.6233+0.7139j, 0.0393-0.4259j], # [0.1236+0.9666j, 0.2755+0.1572j, # -0.5766-0.0394j, -0.3715-0.2530j]], # [[0.6339-1.1397j, -0.1932-0.8051j, # 1.3219-0.4141j, 0.0786+0.1172j], # [-0.9245+0.9382j, -1.0984+0.4884j, # -0.3895-0.0102j, -0.0171+0.9639j]]])
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randn_like() can replace the zero or more floating-point numbers or complex numbers of a 0D or more D tensor with the zero or more random floating-point numbers or complex numbers most of the time about between 2 and -2 from standard normal distribution as shown below:

*Memos:

  • randn_like() can be used with torch but not with a tensor.
  • The 1st argument with torch is input(Required-Type:tensor of float or complex).
  • There is dtype argument with torch(Optional-Default:None-Type:dtype): *Memos:
    • If it's None, it's inferred from input.
    • dtype= must be used.
    • My post explains dtype argument.
  • There is device argument with torch(Optional-Default:None-Type:str, int or device()): *Memos:
    • If it's None, it's inferred from input.
    • device= must be used.
    • My post explains device argument.
  • There is requires_grad argument with torch(Optional-Default:False-Type:bool): *Memos:
    • requires_grad= must be used.
    • My post explains requires_grad argument. 
import torch my_tensor = torch.tensor(7.) torch.randn_like(input=my_tensor) # tensor(-2.3177)  my_tensor = torch.tensor([7., 4., 5.]) torch.randn_like(input=my_tensor) # tensor([-0.4706, -0.0940, 1.7397])  my_tensor = torch.tensor([[7., 4., 5.], [2., 8., 3.]]) torch.randn_like(input=my_tensor) # tensor([[-0.4152, 0.8599, -2.4599], # [-1.1088, -0.4828, 2.3003]])  my_tensor = torch.tensor([[[7., 4., 5.], [2., 8., 3.]], [[6., 0., 1.], [5., 9., 4.]]]) torch.randn_like(input=my_tensor) # tensor([[[-1.0316, 1.5852, -1.1012], [-2.3114, 0.5584, 2.3333]], # [[-0.5730, -0.3688, 0.0671], [1.2493, -0.0072, 0.1905]]])  my_tensor = torch.tensor([[[7.+4.j, 4.+2.j, 5.+3.j], [2.+5.j, 8.+1.j, 3.+9.j]], [[6.+9.j, 0.+3.j, 1.+8.j], [5.+3.j, 9.+4.j, 4.+6.j]]]) torch.randn_like(input=my_tensor) # tensor([[[-0.3193-0.2821j, -0.8942+0.7886j, 0.2968-0.0608j], # [-0.5996+0.8685j, 0.7314-0.5378j, 1.7339-0.8352j]], # [[-0.0787-0.1357j, -1.4519-0.0295j, 0.1233-0.1794j], # [ 0.4109-0.2422j, 0.3973+0.3363j, -1.0303-1.4719j]]])
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