YouTipPytorch Torch Randn
# PyTorch torch.randn Function
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This is commonly used in deep learning for initializing weights, generating random inputs, and other scenarios.
### Function Definition
torch.randn(*size, dtype=None, device=None, requires_grad=False)
**Parameters**:
* `*size` (int): The shape of the tensor.
* `dtype` (torch.dtype, optional): The data type, default is `torch.float32`.
* `device` (torch.device, optional): The device.
* `requires_grad` (bool, optional): Whether gradient computation is required.
**Return Value**:
* `torch.Tensor`: Returns a tensor containing random numbers.
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## Usage Examples
### Example 1: Creating a Random Tensor
## Example
import torch
# Create a 3x4 random tensor
x = torch.randn(3,4)
print(x)
Output:
tensor([[-0.2107, -0.6198, 0.2103, 0.4513], [-0.0124, -1.1746, 0.1844, -0.6199], [ 1.1729, -0.7669, 0.3034, -0.0808]])
### Example 2: Neural Network Weight Initialization
## Example
import torch
import torch.nn as nn
# Use randn to initialize neural network weights
linear = nn.Linear(10,5)
# Initialize weights with random values
nn.init.randn_(linear.weight)
nn.init.zeros_(linear.bias)
print("Weight shape:", linear.weight.shape)
print("Weight mean:", linear.weight.mean().item())
print("Weight std:", linear.weight.std().item())
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