# X = Torch.randn(5 3)

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### “PyTorch - Basic operations”

x = torch.randn(5, 3).type(torch.FloatTensor) Identity matrices, Fill Tensor with 0, 1 or values eye = torch . eye ( 3 ) # Create an identity 3x3 tensor v = torch . ones ( 10 ) # A tensor of size 10 containing all ones v = torch . ones ( 2 , 1 , 2 , 1 ) # Size 2x1x2x1 v = torch . ones_like ( eye ) # A tensor with same shape as eye.

### Python - Pytorch randn() method - GeeksforGeeks

PyTorch torch.randn() returns a tensor defined by the variable argument size (sequence of integers defining the shape of the output tensor), containing random numbers from standard normal distribution.. Syntax: torch.randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) Parameters: size: sequence of integers defining the size of the output tensor.

### Python Examples of torch.randn - ProgramCreek.com

The following are 30 code examples for showing how to use torch.randn().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

### Introduction to PyTorch — PyTorch Tutorials 1.8.1+cu102 ...

# By default, it concatenates along the first axis (concatenates rows) x_1 = torch. randn (2, 5) y_1 = torch. randn (3, 5) z_1 = torch. cat ([x_1, y_1]) print (z_1) # Concatenate columns: x_2 = torch. randn (2, 3) y_2 = torch. randn (2, 5) # second arg specifies which axis to concat along z_2 = torch. cat ([x_2, y_2], 1) print (z_2) # If your ...

### torch.rand — PyTorch 1.8.1 documentation

torch.rand. The shape of the tensor is defined by the variable argument size. size ( int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. out ( Tensor, optional) – the output tensor. …

### PyTorch [Basics] — Tensors and Autograd | by Akshaj Verma ...

x3 = torch.randn(5, 2) x4 = torch.randn(5, 2) print(f"x3 + x4 = \n{x3 + x4}\n") print(f"x3 - x4 = \n{x3 - x4}\n") print(f"x3 * x4 (element wise)= \n{x3 * x4}\n") # You can also do [x3 @ x4.t()] for matrix multiplication. print(f"x3 @ x4 (matrix mul)= \n{torch.matmul(x3, x4.t())}\n") ##### OUTPUT ##### x3 + x4 = tensor([[-0.1989, 1.9295], [-0.1405, -0.8919], [-0.6190, -3

### Python Examples of torch.randn_like

The following are 30 code examples for showing how to use torch.randn_like().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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