The torch.ones() method in PyTorch generates a tensor filled with the scalar value 1, with the specified shape, data type, device, and other optional parameters.
The above figure shows the basic workings of this method. You can see that it is helpful in creating masks, biases, or initializing weights in neural networks. You can use it to create 1D or multidimensional tensors.
The .ones() method helps create masks, biases, or initialize weights in neural networks. You can use it to create 1D or multidimensional tensors.
Syntax
torch.ones(*size,
out=None,
dtype=None,
layout=torch.strided,
device=None,
requires_grad=False)
Parameters
| Arguments | Description |
| *size (int) | It defines the shape of the output tensor. For example, (2, 3) means a 2×3 tensor. |
| out ((Tensor, optional) | If you have already pre-allocated a tensor, you can use the “out” argument to store the result in this pre-allocated tensor. |
| dtype (torch.dtype, optional) | By default, the data type is torch.float32, but using the dtype argument, you can set it to any acceptable data type. For example, torch.float63, torch.int64, torch.int32, etc. |
| layout (torch.layout, optional) | It determines the layout of the tensor. By default, it is torch.strided. |
| device (torch.device, optional) | It defines the current device. It can be either “CPU” or “CUDA”. |
| requires_grad (bool, optional) | It is a Boolean argument that defines whether an output tensor requires gradient computation. By default, it is False, but if True, it tracks gradients for autograd. |
Creating a Simple 1D Tensor
Let’s initialize a 1D tensor of ones.
import torch tensor = torch.ones(5) print(tensor) # Output: tensor([1., 1., 1., 1., 1.])We can check the shape of the above-generated tensor using the .shape attribute.
import torch tensor = torch.ones(5) print(tensor.shape) # Output: torch.Size([5])
You can see that the shape is one-dimensional.
Generating a multidimensional tensor
We need multidimensional tensors for matrix operations or neural network layers. For that usage, we can initialize a multidimensional tensor with 1s.
import torch tensor = torch.ones(2, 3) print(tensor) # Output: tensor([[1., 1., 1.], # [1., 1., 1.]])
Creating a Tensor on GPU
By passing a “device” argument to value “cuda”, we can create a tensor on the GPU. But make sure that you are using a GPU. If you are on CPU, it will throw an error.
I have already written a guide on how to check if you are connected to the GPU.
import torch
if torch.cuda.is_available():
tensor = torch.ones(2, 2, device='cuda')
print(tensor)
The screenshot above proves we generated a 2D tensor filled with 1s on a GPU device.
Specifying Data Type
Let’s generate an integer tensor by passing dtype=”torch.int64″.import torch int_tensor = torch.ones(2, 2, dtype=torch.int64) print(int_tensor) # Output: tensor([[1, 1], # [1, 1]])
Gradient tracking
We can enable gradient computation to train neural networks by passing require_grad=True.
import torch grad_tensor = torch.ones((3, 3), requires_grad=True) print(grad_tensor) # Output: # tensor([[1., 1., 1.], # [1., 1., 1.], # [1., 1., 1.]], requires_grad=True)
Pre-allocation
To pre-allocate a tensor, we can use torch.empty() or torch.zeros() function. It can be used to initialize a tensor, and then you can fill it with the result for further processing.
import torch pre_allocated_tensor = torch.empty(4, 4) torch.ones(4, 4, out=pre_allocated_tensor) print(pre_allocated_tensor) # Output: # tensor([[1., 1., 1., 1.], # [1., 1., 1., 1.], # [1., 1., 1., 1.], # [1., 1., 1., 1.]])
That’s all!
