HomeMachine LearningMachine Learning DIYCreating Two-Dimensional Tensors in Pytorch

# Creating Two-Dimensional Tensors in Pytorch

In PyTorch, a tensor is a multi-dimensional array that can be used for efficient computation on CPUs and GPUs. A two-dimensional tensor in PyTorch represents a matrix, where each element is identified by two indices: row and column.

To create a two-dimensional tensor in PyTorch, you can use the `torch.tensor()` function and provide a nested list or a NumPy array as the input. Here’s an example:

```import torch

# Create a two-dimensional tensor using a nested list
tensor_list = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Create a two-dimensional tensor using a NumPy array
import numpy as np
numpy_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
tensor_numpy = torch.tensor(numpy_array)```

You can also create an empty two-dimensional tensor and initialize it later. For that, you can use the `torch.empty()` function and specify the desired shape:

`empty_tensor = torch.empty(3, 3) # Creates a 3x3 tensor with uninitialized values`

Once you have created a two-dimensional tensor, you can perform various operations on it, such as element-wise operations, matrix multiplication, transposition, and more. PyTorch provides a wide range of functions and methods to manipulate tensors efficiently.

Here’s an example that demonstrates some common operations with two-dimensional tensors:

```import torch

# Create a two-dimensional tensor
tensor = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Accessing individual elements
print(tensor[0, 0]) # Output: 1
print(tensor[1, 2]) # Output: 6

# Slicing a tensor
print(tensor[:, 1]) # Output: tensor([2, 5, 8])
print(tensor[1:, :2]) # Output: tensor([[4, 5], [7, 8]])

# Matrix multiplication
other_tensor = torch.tensor([[2, 2, 2], [2, 2, 2], [2, 2, 2]])
result = tensor.matmul(other_tensor)
print(result)
# Output:
# tensor([[12, 12, 12],
# [30, 30, 30],
# [48, 48, 48]])

# Transposition
transposed_tensor = tensor.transpose(0, 1)
print(transposed_tensor)
# Output:
# tensor([[1, 4, 7],
# [2, 5, 8],
# [3, 6, 9]])```

These are just a few examples of what you can do with two-dimensional tensors in PyTorch. The library provides a rich set of operations and functions for working with tensors efficiently and effectively in deep learning applications.